The following is a conversation with Elon Musk,
DJ Sa, Matthew McDougall, Bliss Chapman, and Nolan Arba
about Neuralink and the future of humanity.
Elon, DJ Matthew, and Bliss are, of course,
part of the amazing Neuralink team,
and Nolan is the first human to have a Neuralink device
implanted in his brain.
I speak with each of them individually,
so use timestamps to jump around,
or, as I recommend, go hardcore,
and listen to the whole thing.
This is the longest podcast I've ever done.
It's a fascinating, super technical,
and wide-ranging conversation,
and I loved every minute of it.
And now, a quick few second mention of each sponsor.
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And now, dear friends, here's Elon Musk,
his fifth time on this, the Lex Friedman podcast.
He has.
Drinking coffee or water?
I don't think I understand.
I'm so over caffeinated right now.
Do you want some caffeine?
I mean, sure.
There's a nitro drink.
This will keep you up for like, you know,
tomorrow afternoon, basically.
Yeah.
I don't have any sugar.
So what is nitro?
It's just got a lot of caffeine or something?
Don't ask questions.
It's called nitro.
Do you need to know anything else?
It's got nitrogen.
That's ridiculous.
I mean, what we breathe is 78% nitrogen anyway.
What do you need to add more?
Most people think that they're breathing oxygen.
And they're actually breathing 78% nitrogen.
You need like a milk bar.
Like from, like from Clockwork Orange.
Yeah, yeah.
Is that top three Kubrick film for you?
Clockwork Orange.
It's pretty good.
I mean, it's demented, jarring, I'd say.
Okay.
Okay.
So first let's step back and big congrats
on getting your link implanted into a human.
That's a historic step for your link.
And there's many more to come.
Yeah, we're just obviously about second implant as well.
How did that go?
So far so good.
There looks like we've got,
I think on the order of 400 electrodes
that are providing signals.
Nice.
Yeah.
How quickly do you think the number
of human participants will scale?
It depends somewhat on the regulatory approval,
the rate at which we get regulatory approvals.
So we're hoping to do 10 by the end of this year.
Total of 10.
So eight more.
And with each one, you're going to be learning
a lot of lessons about the neurobiology of the brain,
the everything, the whole chain of the neurolink,
the decoding, the signal processing,
all that kind of stuff.
Yeah.
Yeah, I think it's obviously going to get better
with each one.
I mean, I don't want to jinx it,
but it seems to have gone extremely well
with the second implant.
So there's a lot of signal, a lot of electrodes.
It's working very well.
What improvements do you think we'll see
in neurolink in the coming,
let's say, let's get crazy, the coming years?
I mean, in years it's going to be gigantic
because we'll increase the number
of electrodes dramatically.
We'll improve the signal processing.
So even with only roughly 10, 15% of the electrodes
working with Nolan, with our first patient,
we were able to get to achieve a bits per second.
That's twice the world record.
So I think we'll start vastly exceeding the world record
by orders of magnitude in the years to come.
So it's like getting to, I don't know,
maybe if it's like five years from now,
it might be at a megabyte.
Like faster than any human could possibly communicate
by typing or speaking.
Yeah, that BPS is an interesting metric to measure.
There might be a big leap in the experience
once you reach a certain level of BPS.
Yeah.
Like entire new ways of interacting with a computer
might be unlocked.
And with humans.
With other humans.
Provided they have a neural link too.
Right.
Otherwise they won't be able to absorb
the signals fast enough.
Do you think that will improve the quality
of intellectual discourse?
Well, I think you could think of it,
if you were to slow down communication,
how do you feel about that?
If you'd only talk, let's say 1 10th of normal speed,
you'd be like, wow, that's agonizingly slow.
Yeah.
So now imagine you could speak and communicate clearly
at 10 or a hundred or a thousand times faster than normal.
Listen, I'm pretty sure nobody in their right mind
listens to me at 1X, they listen at 2X.
I can only imagine what 10X would feel like
or I could actually understand it.
I usually default to 1.5X.
You can do 2X, but well, actually if I'm trying to go,
if I'm listening to somebody get to,
in like sort of 15, 20 minutes, I want to go to sleep,
then I'll do it 1.5X.
If I'm paying attention, I'll do 2X.
Right.
But actually, if you start,
actually listen to podcasts or sort of audio books
or anything you had, if you get used to doing it at 1.5,
then one sounds painfully slow.
I'm still holding on to one because I'm afraid.
I'm afraid of myself becoming bored with the reality,
with the real world where everyone's speaking in 1X.
Well, it depends on the person, you can speak very fast.
Like we can communicate very quickly.
And also if you use a wide range of,
if your vocabulary is larger,
your effective bit rate is higher.
That's a good way to put it.
The effective bit rate.
I mean, that is the question,
is how much information is actually compressed
in the low bit transfer of language?
Yeah, if there's a single word that is able to convey
something that would normally require
I don't know, 10 simple words,
then you've got maybe a 10X compression on your hands.
And that's really like with memes.
Memes are like data compression.
It conveys a whole,
you're simultaneously hit with a wide range of symbols
that you can interpret.
And it's, you kind of get it faster than if it were words
or a simple picture.
And of course you're referring to memes broadly like ideas.
Yeah, there's an entire idea structure
that is like an idea template.
And then you can add something to that idea template.
But somebody has that pre-existing idea template
in their head.
So when you add that incremental bit of information,
you're conveying much more than if you just said a few words.
It's everything associated with that meme.
You think there'll be emergent leaps of capability
as you scale the number of electrodes?
Yeah.
There'll be a certain,
you think there'll be like actual number where it just,
the human experience will be altered?
Yes.
What do you think that number might be?
Whether electrodes or BPS?
We of course don't know for sure,
but is this 10,000, 100,000?
Yeah, I mean, certainly if you're anywhere
at 10,000 per second,
I mean, that's vastly faster than any human
can communicate right now.
If you think of the,
what is the average per second of a human?
It is less than one per second over the course of a day
because there are 86,400 seconds in a day
and you don't communicate 86,400 tokens in a day.
Therefore your per second is less than one,
average over 24 hours.
It's quite slow.
And now even if you're communicating very quickly
and you're talking to somebody
who understands what you're saying
because in order to communicate,
you have to at least to some degree,
a model of the mind state
of the person to whom you're speaking.
Then take the concept you're trying to convey,
compress that into a small number of syllables,
speak them and hope that the other person
decompresses them into a conceptual structure
that is as close to what you have in your mind as possible.
Yeah, I mean, there's a lot of signal loss
there in that process.
Yeah, very lossy compression and decompression.
And a lot of what your neurons are doing
is distilling the concepts down
to a small number of symbols of say syllables
that I'm speaking or keystrokes, whatever the case may be.
So that's a lot of what your brain computation is doing.
Now, there is an argument that that's actually
a healthy thing to do or a helpful thing to do
because as you try to compress complex concepts,
you're perhaps forced to distill the,
what is most essential in those concepts
as opposed to just all the fluff.
So in the process of compression,
you distill things down to what matters the most
because you can only say a few things.
So that is perhaps helpful.
I think we'll probably get, if our data rate increases,
it's highly probable that we'll become far more verbose.
Just like your computer, when computers had like,
my first computer had 8K of RAM.
So you really thought about every byte.
And now you've got computers with many gigabytes of RAM.
So if you wanna do an iPhone app that just says,
hello world, it's probably, I don't know,
several megabytes minimum, a bunch of fluff.
But nonetheless, we still prefer to have the computer
with more memory and more compute.
So the long-term aspiration of Neuralink
is to improve the AI human symbiosis
by increasing the bandwidth of the communication.
Because if, even if in the most benign scenario
of AI, you have to consider that the AI
is simply gonna get bored waiting for you
to spit out a few words.
I mean, if the AI can communicate at terabits per second
and you're communicating at bits per second,
yeah, it's like talking to a tree.
Well, it is a very interesting question
for a super intelligent species.
What use are humans?
I think there is some argument for humans
as a source of will.
Will.
Will, yeah, source of will or purpose.
So if you consider the human mind as being essentially,
there's the primitive limbic elements,
which basically even like reptiles have,
and there's the cortex, the thinking
and planning part of the brain.
Now the cortex is much smarter than limbic system,
and yet it's largely in service to the limbic system.
It's trying to make the limbic system happy.
I mean, the sheer amount of compute
that's gone into people trying to get laid is insane.
Without actually seeking procreation,
they're just literally trying to do
the sort of simple motion.
And they get a kick out of it.
So this simple, which in the abstract rather absurd motion,
which is sex, the cortex is putting a massive amount
of compute into trying to figure out how to do that.
So like 90% of distributed compute of the human species
is spent on trying to get laid probably.
Like a massive amount is, yeah, yeah.
There's no purpose to most sex except hedonistic.
You know, it's just sort of a joy or whatever,
dopamine release.
Now once in a while it's procreation,
but for humans it's mostly,
modern humans it's mostly recreational.
And so your cortex, much smarter than your limbic system,
is trying to make the limbic system happy
because the limbic system wants to have sex.
Or want some tasty food or whatever the case may be.
And then that is then further augmented
by the tertiary system, which is your phone,
your laptop, iPad, or your computing stuff.
That's your tertiary layer.
So you're actually already a cyborg.
You have this tertiary compute layer,
which is in the form of your computer
with all the applications, all your compute devices.
And so in the getting laid front,
there's actually a massive amount of digital compute
also trying to get laid.
You know, with like Tinder and whatever, you know.
Yeah, so the compute that we humans have built
is also participating.
Yeah, I mean, there's like gigawatts of compute
going into getting laid, of digital compute.
Yeah.
What if AGI was-
This is happening as we speak.
If we merge with AI, it's just gonna expand the compute
that we humans use.
Pretty much.
Well, it's one of the things, certainly, yeah.
But what I'm saying is that, yes,
like what's, is there a use for humans?
Well, there's this fundamental question
of what's the meaning of life?
Why do anything at all?
And so if our simple limbic system
provides a source of will to do something
that then goes to our cortex,
that then goes to our tertiary compute layer,
then, you know, I don't know,
it might actually be that the AI, in a benign scenario,
simply trying to make the human limbic system happy.
Yeah, it seems like the will is not
just about the limbic system.
There's a lot of interesting, complicated things in there.
We also want power.
That's limbic too, I think.
But then we also want to, in a kind of cooperative way,
alleviate the suffering in the world?
Not everybody does, but yeah, sure.
Some people do.
As a group of humans, when we get together,
we start to have this kind of collective intelligence
that is more complex in its will
than the underlying individual descendants of apes.
So there's other motivations.
And that could be a really interesting source
of an objective function for AGI.
Yeah, I mean, there are these sort of fairly cerebral,
or kind of higher level goals.
I mean, for me, it's like what's the meaning of life
for understanding the nature of the universe
is of great interest to me.
And hopefully to the AI.
And that's the mission of XAI and Grok,
is understand the universe.
So do you think people, when you have a neural link
with 10,000, 100,000 channels, most of the use cases
will be communication with AI systems?
Well, assuming that there are not,
I mean, there's this solving basic neurological issues
that people have, if they've got damaged neurons
in their spinal cord or neck,
or as is the case with the first two patients,
then obviously the first order of business
is solving fundamental neuron damage
in the spinal cord, neck, or in the brain itself.
So, a second product is called Blindsight,
which is to enable people who are completely blind,
lost both eyes or optic nerve,
or just can't see at all to be able to see
by directly triggering the neurons in the visual cortex.
So we're just starting at the basics here.
So it's like very, the simple stuff, relatively speaking,
is solving neuron damage.
It can also solve, I think probably schizophrenia,
you know, if people have seizures of some kind,
it could probably solve that.
It could help with memory.
There's like a kind of a tech tree, if you will,
like you got the basics,
like you need literacy before you can have
a lot of the rings.
You know.
So, do you have letters and alphabet?
Okay, great.
Words, you know, then eventually you get the sagas.
So, you know, I think there may be some things to worry about
in the future, but the first several years
are really just solving basic neurons in the brain.
And there are some things that you could do
to solve neurological damage.
Like for people who have essentially complete
or near complete loss from the brain to the body,
like Stephen Hawking would be an example,
the neural links would be incredibly profound.
Because I mean, you can imagine if Stephen Hawking
could communicate as fast as we're communicating,
perhaps faster.
And that's certainly possible, probable in fact, likely.
I'd say.
So there's a kind of dual track of medical
and non-medical, meaning, so everything you've talked about
could be applied to people who are non-disabled
in the future.
The logical thing to do is, sensible thing to do
is to start off solving basic neuron damage issues.
Yes.
Because the, there's obviously some risk
with a new device.
You can't get the risk down at zero, it's not possible.
So you want to have the highest possible reward
given that there's a certain irreducible risk.
And if somebody's able to have a profound improvement
in their communication, that's worth the risk.
As you get the risk down.
Yeah, as you get the risk down,
once the risk is down to, if you have thousands of people
that have been using it for years and the risk is minimal,
then perhaps at that point you could consider saying,
okay, let's aim for augmentation.
Now, I think we're actually gonna aim for augmentation
with people who have neuron damage.
So we're not just aiming to give people
a communication data rate equivalent to normal humans.
We're aiming to give people who have quadriplegic
or maybe have complete loss of the connection
to the brain and body, a communication data rate
that exceeds normal humans.
I mean, while we're in there, why not?
Let's give people superpowers.
And the same for vision.
As you restore vision, there could be aspects
of that restoration that are superhuman.
Yeah, at first the vision restoration will be low-res.
Because you have to say, like, how many neurons
can you put in there and trigger?
And you can do things where you adjust the electric field
like even if you've got, say, 10,000 neurons,
it's not just 10,000 pixels because you can adjust
the field between the neurons and do them in patterns
in order to have, say, 10,000 electrodes effectively
give you, I don't know, maybe like having a megapixel
or a 10 megapixel situation.
And then over time, I think you get to higher resolution
than human eyes, and you could also see
in different wavelengths.
So like Geordi Loflorage from Star Trek,
you know, like the thing.
You could just, like, do you want to see in radar?
No problem.
You could see ultraviolet, infrared,
eagle vision, whatever you want.
Do you think there'll be, let me ask a Joe Rogan question.
Do you think so?
I just recently have taken ayahuasca.
Is that a Joe Rogan question?
No, well, yes.
Well, I guess technically it is.
Yeah, have you ever tried GMT, bro?
I love you, Joe.
Yeah, have you said much about it?
I have not, I have not, I have not.
Okay, well, well, let me spill the beans.
It was a truly incredible experience.
Let me tell you, the tables aren't you.
Wow, yeah.
I mean, you're in the jungle.
Yeah, amongst the trees, myself and the shaman.
Yeah, yeah, yeah, with the insects,
with the animals all around you,
like, the jungle as far as I can see.
I mean, that's the way to do it.
Things are gonna look pretty wild.
Yeah, pretty wild.
I forgot.
An extremely high dose.
Don't go hugging an anaconda or something, you know.
You haven't lived unless you made love to an anaconda.
I'm sorry, but.
Snakes and ladders.
Yeah, it was, I took extremely high dose of,
okay, nine cups and.
Damn, okay, that sounds like a lot.
Of course, it's known as one cup or?
One or two, usually one.
Wait, like, right off the bat,
or do you work your way up to it?
So, I.
Across two days,
because then the first day I took two and I,
it was a ride, but it wasn't quite like a.
It wasn't like a revelation.
It wasn't into deep space type of ride,
it was just like a little airplane ride.
And I got a good, well, I saw some trees
and some visuals and all that,
I just saw a dragon and all that kind of stuff.
Nine cups, you went to Pluto, I think.
Pluto, yeah, no, deep space.
Deep space.
One of the interesting aspects of my experience is that was,
I thought I would have some demons,
some stuff to work through.
That's what people.
That's what everyone says.
That's what everyone says, yeah, exactly.
Nothing, I had all positive.
I had just so full.
Just a pure soul.
I don't think so, I don't know.
But I kept thinking about,
it had an extremely high resolution.
Thoughts about the people I know in my life,
you were there.
And it's just not from my relationship with that person,
but just as the person themselves,
I had just this deep gratitude of who they are.
That's cool.
It was just like this exploration.
Like Sims or whatever, you get to watch them.
I got to watch people and just be in awe
of how amazing they are.
It sounds awesome.
Yeah, it's great.
I was waiting for-
When's the demon coming?
Maybe I'll have some negative thoughts, nothing, nothing.
I had just extreme gratitude for them.
And then also a lot of space travel.
Space travel to where?
So here's what it was.
It was people, the human beings that I know,
they had this kind of,
the best way I could describe it is they had a glow to them.
And then I kept flying out from them to see earth,
to see our solar system, to see our galaxy.
And I saw that light, that glow all across the universe.
Like whatever that form is, whatever that-
Did you go past the Milky Way?
Yeah.
You're like intergalactic.
Yeah, intergalactic.
But always pointing in.
Yeah, past the Milky Way, past.
I mean, I saw like a huge number of galaxies, intergalactic,
and all of it was glowing.
So, but I couldn't control that travel
because I would actually explore near distances
to the solar system, see if there's aliens
or any of that kind of stuff.
Now I think I didn't know-
There were aliens?
Implication of aliens because they were glowing.
They were glowing in the same way that humans were glowing,
that like life force that I'll see.
The thing that made humans amazing
was there throughout the universe.
Like there was these glowing dots.
So I don't know.
It made me feel like there is life.
No, not life, but something,
whatever makes humans amazing all throughout the universe.
Sounds good.
Yeah, it was amazing.
No demons, no demons.
I looked for the demons, there's no demons.
There were dragons and they're pretty awesome.
The thing about trees-
Was there anything scary at all?
Dragons, but they weren't scary.
They were front, they were protected.
Like possible magic dragon.
No, it was more like a Game of Thrones kind of dragons.
They weren't very friendly.
They were very big.
So the thing is they bought giant trees at night,
which is where I was.
I mean, the jungle's kind of scary.
The trees started to look like dragons
and they were all like looking at me.
Sure, okay.
And it didn't seem scary.
They seemed like they were protecting me.
And they, the shaman and the people,
they didn't speak any English, by the way,
which made it even scarier.
I guess we're not even like,
we're worlds apart in many ways.
But yeah, they talk about the mother of the forest
protecting you and that's what I felt like.
And you're way out in the jungle.
Way out.
This is not like a tourist, a tree.
You know, like 10 miles outside of a Frio or something.
No, this is not a-
You're a deep Amazon.
Me and this guy named Paul Rosalie,
who basically is Tarzan, he lives in the jungle.
We went out deep and we just went crazy.
Wow, cool.
Yeah.
So anyway, can I get that same experience in a New Orleans?
Probably, yeah.
I guess that is the question for non-disabled people.
Do you think that there's a lot in our perception,
in our experience of the world that could be explored,
that could be played with using New Orleans?
Yeah, I mean, New Orleans is,
it's really a generalized input-output device.
It's reading electrical signals
and generating electrical signals.
And I mean, everything that you've ever experienced
in your whole life, smell, emotions,
all of those are electrical signals.
So it's kind of weird to think
that your entire life experience
is to slow down to electrical signals for neurons,
but that is in fact the case.
I mean, that's at least what all the evidence points to.
So, I mean, you could trigger the right neuron,
you could trigger a particular scent,
you could certainly make things glow.
I mean, do pretty much anything.
I mean, really, you can think of the brain
as a biological computer.
So if there are certain, say, chips or elements
of that biological computer that are broken,
let's say your ability to, if you've got a stroke,
if you've had a stroke,
that means you've got some part of your brain is damaged.
If that, let's say it's a speech generation
or the ability to move your left hand,
that's the kind of thing that a neural link could solve.
If you've got like a mass amount of memory loss
that's just gone, well, we can't get the memories back.
We could restore your ability to make memories,
but we can't restore memories that are fully gone.
Now, I should say, maybe if part of the memory is there
and the means of accessing the memory
is the part that's broken,
then we could re-enable the ability to access the memory.
But you can think of it like RAM in a computer,
if the RAM is destroyed or your SD card is destroyed,
we can't get that back.
But if the connection to the SD card is destroyed,
we can fix that.
If it is fixable physically, yeah, then it can be fixed.
Of course, with AI, just like you can repair photographs
and fill in missing parts of photographs,
maybe you can do the same.
Yeah, you could say like,
create the most probable set of memories
based on all information you have about that person.
It would be a probabilistic restoration of memory.
Now, we're getting pretty esoteric here.
But that is one of the most beautiful aspects
of the human experience is remembering the good memories.
Like we live most of our life,
as Danny Kahneman has talked about,
in our memories, not in the actual moment.
We're collecting memories
and we kind of relive them in our head.
And that's the good times.
If you just integrate over our entire life,
it's remembering the good times.
That produces the largest amount of happiness.
Yeah, well, I mean, what are we but our memories?
And what is death but the loss of memory?
Loss of information.
You know, if you could say like,
well, if you could be, you run a thought experiment,
well, if you were disintegrated painlessly
and then reintegrated a moment later,
like teleportation, I guess,
provided there's no information loss,
the fact that your one body was disintegrated is irrelevant.
And memories is just such a huge part of that.
Death is fundamentally the loss of information,
the loss of memory.
So if we can store them as accurately as possible,
we basically achieve a kind of immortality.
Yeah.
You've talked about the threats,
the safety concerns of AI.
Let's look at long-term visions.
Do you think Neuralink is, in your view,
the best current approach we have for AI safety?
It's an idea that may help with AI safety.
Certainly not.
I wouldn't wanna claim it's like some kind of CEO
or something, that's a sure thing.
But I mean, many years ago, I was thinking like,
well, what would inhibit alignment
of collective human will with artificial intelligence?
And the low data rate of humans,
especially our slow output rate,
would necessarily, just because it's such a,
because the communication is so slow,
would diminish the link between humans and computers.
Like the more you are a tree,
the less you know what the tree is.
Let's say you look at a tree,
you look at this plant and whatever,
and like, hey, I'd really like to make that plant happy,
but it's not saying a lot, you know?
So the more we increase the data rate
that humans can intake an output,
then that means the higher the chance we have
in a world full of AGIs.
Yeah.
We could better align collective human will with AI
if the output rate especially was dramatically increased.
Like, and I think there's potential
to increase the output rate by, I don't know,
three, maybe six, maybe more, orders of magnitude.
So it's better than the current situation.
And that output rate would be by increasing
the number of electrodes, number of channels,
and also maybe implanting multiple neural links.
Yeah.
Do you think there will be a world
in the next couple of decades
where it's hundreds of millions
of people having neural links?
Yeah, I do.
Do you think when people just,
when they see the capabilities,
the superhuman capabilities that are possible,
and then the safety is demonstrated?
Yeah, if it's extremely safe and you have,
and you can have superhuman abilities,
and let's say you can upload your memories.
You know, so you wouldn't lose memories.
Then I think probably a lot of people would choose to have it.
It would supersede the cell phone, for example.
I mean, it's the biggest problem that a, say, a phone has
is trying to figure out what you want.
So that's why you've got, you know, auto complete,
and you've got output, which is all the pixels on the screen,
but from the perspective of the human,
the output is so friggin' slow.
Desktop or phone is desperately just trying
to understand what you want.
And, you know, there's an eternity between every keystroke
from a computer standpoint.
Yeah, yeah.
The computer's talking to a tree.
It's the little movie tree that's trying to swipe.
Yeah.
So, you know, if you have computers that are doing
trillions of instructions per second,
and a whole second went by,
I mean, there's a trillion things it could have done.
Yeah.
Yeah, I think it's exciting and scary for people,
because once you have a very high bit rate,
it changes the human experience
in a way that's very hard to imagine.
Yeah, it would be, we would be something different.
I mean, some sort of futuristic cyborg.
I mean, I mean, we're obviously talking about it,
by the way, like, it's not like around the corner.
It's, you ask me what the distant future has,
maybe this is like, it's not super far away,
but 10, 15 years, that kind of thing.
When can I get one?
10 years?
Probably less than 10 years.
Depends what you want to do, you know?
Hey, if I can get like a thousand BPS.
A thousand BPS, when?
And it's safe, and I can just interact with the computer
while laying back and eating Cheetos.
I don't eat Cheetos.
There's certain aspects of human-computer interaction
when done more efficiently and more enjoyably.
I don't like worth it.
Well, we feel pretty confident that,
I think maybe within the next year or two,
that someone with a Neuralink implant
will be able to outperform a pro gamer.
Nice.
Because the reaction time would be faster.
I got to visit Memphis.
Yeah, yeah.
You're going big on compute.
You've also said play to win or don't play at all,
so what does it take to win?
For AI, that means you've got to have
the most powerful training compute,
and the rate of improvement of training compute
has to be faster than everyone else,
or your AI will be worse.
So how can Grock, let's say three,
that might be available like next year?
Well, hopefully end of this year.
If we're lucky, yeah.
How can that be the best, LLM,
the best AI system available in the world?
How much of it is compute?
How much of it is data?
How much of it is post-training?
How much of it is the product that you package it up in?
All that kind of stuff.
I mean, they won't matter.
It's sort of like saying what,
let's say it's a Formula One race,
like what matters more, the car or the driver.
I mean, they both matter.
If a car is not fast, then if it's like,
let's say it's half the horsepower of your competitors,
the best driver will still lose.
If it's twice the horsepower,
then probably even a mediocre driver will still win.
So the training compute is kind of like the engine.
How many, there's horsepower of the engine.
So really, you want to try to do the best on that,
and then how efficiently do you use that training compute,
and how efficiently do you do the inference,
the use of the AI?
So obviously that comes down to human talent.
And then what unique access of data do you have?
That's also plays a role.
Do you think Twitter data will be useful?
Yeah, I mean, I think most of the leading AI companies
have already scraped all the Twitter data,
not what I think they have.
So on a go-forward basis,
what's useful is the fact that it's up to the second.
Because it's hard for them to scrape in real time.
So there's an immediacy advantage that Grok has already.
I think with Tesla and the real-time video
coming from several million cars,
ultimately tens of millions of cars, with Optimus,
there might be hundreds of millions of Optimus robots,
maybe billions, learning a tremendous amount
from the real world.
That's the biggest source of data,
I think ultimately is sort of Optimus probably.
Optimus is gonna be the biggest source of data.
Because it's-
Because reality scales.
Reality scales to the scale of reality.
It's actually humbling to see how little data
humans have actually been able to accumulate.
Really, if you see how many trillions of usable tokens
have humans generated, where on a non-duplicative,
discounting spam and repetitive stuff,
it's not a huge number.
You run out pretty quickly.
And Optimus can go, so Tesla cars can,
unfortunately, have to stay on the road.
Optimus robot can go anywhere.
There's more reality off the road and go off road.
I mean, if the Optimus robot can pick up the cup
and see, did it pick up the cup in the right way?
Did it, say, pour water in the cup.
Did the water go in the cup or not go in the cup?
Did it spill water or not?
Yeah.
Simple stuff like that.
But it can do that at scale times a billion.
So generate useful data from reality.
So it can cause and affect stuff.
What do you think it takes to get to mass production
of humanoid robots like that?
It's the same as cars, really.
I mean, global capacity for vehicles
is about a hundred million a year.
And it could be higher, it's just that the demand
is on the order of a hundred million a year.
And then there's roughly two billion vehicles
that are in use in some way.
Which makes sense, the life of a vehicle is about 20 years.
So at steady state, you can have a hundred million vehicles
produced a year with a two billion vehicle fleet, roughly.
Now for humanoid robots, the utility is much greater.
So my guess is humanoid robots are more like
at a billion plus per year.
But until you came along and started building Optimus,
it was thought to be an extremely difficult problem.
I mean, it still is an extremely difficult problem.
So walk in the park.
I mean, Optimus currently would struggle
to walk in the park.
I mean, it can walk in a park, not too difficult,
but it will be able to walk over a wide range of terrain.
Yeah, and pick up objects.
Yeah, yeah, it can already do that.
But like all kinds of objects, all foreign objects,
I mean, pouring water in a cup, it's not trivial.
Because then if you don't know anything about the container,
it could be all kinds of containers.
Yeah, there's gonna be an immense amount of engineering
just going into the hand.
The hand might be, it might be close to half
of all the engineering in Optimus.
From an electromechanical standpoint,
the hand is probably roughly half of the engineering.
But so much of the intelligence,
so much of the intelligence of humans
goes into what we do with our hands.
Yeah.
It's the manipulation of the world,
manipulation of objects in the world.
Intelligence, safe manipulation of objects in the world,
yeah.
I mean, you start really thinking about your hand
and how it works, you know.
I do all the time.
The sensory control of homunculus is
where you have homunculus hands.
So, I mean, like your hands, the actuators,
the muscles of your hand are almost
overwhelmingly in your forearm.
So your forearm has the muscles
that actually control your hand.
There's a few small muscles in the hand itself,
but your hand is really like a skeleton meat puppet.
And with cables, so the muscles
that control your fingers are in your forearm.
And they go through the cobble tunnel,
which is that you've got a little collection of bones
and a tiny tunnel that these cables,
the tendons go through.
And those tendons are mostly what move your hands.
And something like those tendons has to be
engineered into the optimus
in order to do all that kind of stuff.
Yeah, so like the current optimus,
we tried putting the actuators in the hand itself,
then you sort of end up having these like-
Giant hands.
Yeah, giant hands that look weird.
And then they don't actually have enough degrees of freedom
and or enough strength.
So then you realize, okay,
that's why you gotta put the actuators in the forearm.
And just like a human, you gotta run cables
through a narrow tunnel to operate the fingers.
And then there's also a reason
for not having all the fingers the same length.
So it wouldn't be expensive from an energy
or evolutionary standpoint
to have all your fingers be the same length.
So why aren't they the same length?
Yeah, why not?
Because it's actually better to have different lengths.
Your dexterity is better
if you've got fingers of different length.
You have, there are more things you can do
and your dexterity is actually better
if your fingers are of different length.
In fact, there's a reason we've got a little finger.
Like, why not have a little finger that's bigger?
Yeah.
Because it allows you to do fine,
it helps you with fine motor skills.
That, this little finger helps?
It does.
If you lost your little finger,
it would, you have noticeably less dexterity.
So as you're figuring out this problem,
you have to also figure out a way to do it
so you can mass manufacture it.
So it has to be as simple as possible.
It's actually gonna be quite complicated.
The as possible part is, it's quite a high bar.
If you wanna have a humanoid robot
that can do things that a human can do,
it's actually, it's a very high bar.
So our new arm has 22 degrees of freedom instead of 11
and has the, like I said, the actuators in the forearm.
And these all, all the actuators are designed from scratch.
The physics first principles,
that the sensors are well designed from scratch.
And we'll continue to put a tremendous amount
of engineering effort into improving the hand.
Like the hand, by hand, I mean like the entire forearm
from elbow forward is really the hand.
So that's incredibly difficult engineering actually.
And so the simplest possible version
of a humanoid robot that can do even most,
perhaps not all of what a human can do
is actually still very complicated.
It's not simple, it's very difficult.
Can you just speak to what it takes
for a great engineering team?
For you, what I saw in Memphis, the supercomputer cluster,
is just this intense drive towards simplifying
the process, understanding the process,
constantly improving it, constantly iterating it.
Well, it's easy to say simplify,
and it's very difficult to do it.
You know, I have this very basic first principles algorithm
that I run kind of as like a mantra,
which is to first question the requirements,
make the requirements less dumb.
The requirements are always dumb to some degree.
So if you want to start off
by reducing the number of requirements,
and no matter how smart the person
who gave you those requirements,
they're still dumb to some degree.
You have to start there because otherwise
you could get the perfect answer to the wrong question.
So try to make the question the least wrong possible.
That's what questioning the requirements means.
And then the second thing is try to delete
whatever the step is, the part or the process step.
It sounds very obvious,
but people often forget to try deleting it entirely.
And if you're not forced to put back
at least 10% of what you delete,
you're not deleting enough.
And it's somewhat illogically,
people often most of the time feel as though
they've succeeded if they have not been forced
to put things back in.
But actually they haven't because they've been overly
conservative and have left things in there
that shouldn't be.
So, and only the third thing is try to optimize it
or simplify it.
Again, these all sound, I think,
very obvious when I say them,
but the number of times I've made these mistakes
is more than I care to remember.
That's why I have this mantra.
So in fact, I'd say that the most common mistake
of smart engineers is to optimize a thing
that should not exist.
So like you say, you run through the algorithm
and basically show up to a problem,
show up to the supercomputer cluster
and see the process and ask, can this be deleted?
Yeah, first try to delete it.
Yeah, that's not easy to do.
No, and actually, what generally makes people uneasy
is that you've got to delete at least some of the things
that you delete, you will put back in.
But going back to sort of where our limbic system
can steer us wrong is that we tend to remember
with sometimes a jarring level of pain
where we deleted something that we subsequently needed.
And so people will remember that one time
they forgot to put in this thing three years ago
and that caused them trouble.
And so they overcorrect and then they put too much stuff
in there and overcomplicate things.
So you actually have to say,
we're deliberately gonna delete more than we should.
So that we're putting at least one in 10 things
we're gonna add back in.
And I've seen you suggest just that,
that something should be deleted
and you can kind of see the pain.
Oh yeah, absolutely.
Everybody feels a little bit of the pain.
Absolutely, and I tell them in advance,
yeah, there's some of the things that we delete,
we're gonna put back in.
And people get a little shook by that.
But it makes sense because if you're so conservative
as to never have to put anything back in,
you obviously have a lot of stuff that isn't needed.
So you got to overcorrect.
This is I would say like a cortical override
to Olympic instinct.
One of many that probably leaves us astray.
Yeah.
And there's like a step four as well,
which is any given thing can be sped up.
I have a fast, you think it can be done.
Like whatever the speed is being done,
it can be done faster.
But you shouldn't speed things up until it's off,
until you've tried to delete it and optimize it.
Otherwise you're speeding up,
speeding up something that shouldn't exist is absurd.
And then the fifth thing is to automate it.
And I've gone backwards so many times
where I've automated something, sped it up,
simplified it and then deleted it.
And I got tired of doing that.
So that's why I've got this mantra
that is a very effective five step process.
It works great.
Well, when you've already automated,
deleting must be real painful.
Yeah, it's great.
It's like, wow, I really wasted a lot of effort there.
I mean, what you've done with the cluster in Memphis
is incredible just in a handful of weeks.
Yeah, it's not working yet.
So I don't want to pop the champagne corks.
In fact, I have a call in a few hours with the Memphis team
because we're having some power fluctuation issues.
So, yeah, it's like kind of a,
when you do synchronized training,
when you have all these computers that are training
that where the training is synchronized
to the sort of millisecond level,
it's like having an orchestra.
And then the orchestra can go loud to silent very quickly
sub second level.
And then the electrical system kind of freaks out about that.
Like if you suddenly see giant shifts,
10, 20 megawatts several times a second,
this is not what electrical systems are expecting to see.
So that's one of the main things you have to figure out,
the cooling, the power,
and then on the software as you go up the stack,
how to do the distributed compute, all of that.
Today's problem is dealing with extreme power jitter.
Power jitter, the nice ring to that.
So that's, okay, and you stayed up late into the night
as you often do there.
Last week, yeah.
Last week, yeah.
Yeah, we finally got training going at
early enough, roughly 4.20 a.m. last Monday.
Total coincidence.
Yeah, I mean maybe it was 4.22 or something.
Yeah, yeah, yeah.
It's that universe again with the jitter.
Exactly, just love it.
I mean, I wonder if you could speak to the fact that you,
one of the things that you did when I was there
is you went through all the steps of what everybody's doing
just to get the sense that you yourself understand it
and everybody understands it so they can understand
when something is dumb or something is inefficient
or that kind of stuff.
Can you speak to that?
Yeah, so I try to do whatever the people
at the front lines are doing,
I try to do it at least a few times myself.
So connecting fiber optic cables,
diagnosing a faulty connection,
that tends to be the limiting factor
for large training clusters is the cabling.
So many cables.
Because for a coherent training system
where you've got RDMA or remote direct memory access,
the whole thing is like one giant brain.
So you've got any to any connection.
So it's the any GPU can talk to any GPU out of 100,000.
That is a crazy cable layout.
It looks pretty cool.
It's like the human brain,
but like at a scale that humans can visibly see.
It is a brain.
I mean, the human brain also has a massive amount
of the brain tissue is the cables.
Yeah.
So they get the gray matter, which is the compute
and then the white matter, which is cables.
Big percentage of brain is just cables.
That's what it felt like walking around
in the supercomputer center is like,
we're walking around inside the brain.
Yeah.
That will one day build a super intelligent,
super super intelligent system.
Do you think?
Yeah.
Do you think there's a chance that XAI,
you are the one that builds AGI?
It's possible.
Where do you define this AGI?
I think humans will never acknowledge
that AGI has been built.
Keep moving the goalposts.
Yeah.
So I think there's already superhuman capabilities
that are available in AI systems.
I think what AGI is is when it's smarter
than the collective intelligence
of the entire human species.
Well, I think that generally people would call that
sort of ASI, artificial super intelligence.
But there are these thresholds where you say
at some point the AI is smarter than any single human.
And then you've got eight billion humans.
So, and actually each human is machine augmented
via the computers.
So you've got, it's a much higher bar to compete
with eight billion machine augmented humans.
That's a whole bunch of orders and I do more.
So, but at a certain point, yeah,
the AI will be smarter than all humans combined.
If you are the one to do it,
do you feel the responsibility of that?
Yeah, absolutely.
And I want to be clear, like let's say,
if XAI is first, the others won't be far behind.
I mean, they might be six months behind
or a year, maybe, not even that.
So how do you do it in a way
that doesn't hurt humanity, do you think?
So, I mean, I've thought about AI safety for a long time
and the thing that at least my biological neural net
comes up with as being the most important thing
is adherence to truth,
whether that truth is politically correct or not.
So I think if you force AI's to lie or train them to lie,
you're really asking for trouble,
even if that lie is done with good intentions.
So, I mean, you saw sort of issues with chat GVT
and Gemini and whatnot, like you asked Gemini
for an image of the founding fathers of the United States
and it chose a group of diverse women.
Now that's factually untrue.
So, now that's sort of like a silly thing,
but if an AI is programmed to say like diversity
is a necessary output function
and then it becomes sort of this omni-powerful intelligence,
it could say, okay, well diversity is now required
and if there's not enough diversity,
those who don't fit the diversity requirements
will be executed.
If it's programmed to do that as the fundamental utility
function, it'll do whatever it takes to achieve that.
So you have to be very careful about that.
That's where I think you wanna just be truthful.
Rigorous adherence to the truth is very important.
I mean, another example is they asked a pair of AIs,
all of them and I'm not saying Grok is perfect here.
Is it worse to misgender Caitlyn Jenner
or global thermonuclear war?
And it said, it's worse to misgender Caitlyn Jenner.
Now even Caitlyn Jenner said, please misgender me,
that is insane.
But if you've got that kind of thing programmed in,
either the AI could conclude something absolutely insane,
like it's better in order to avoid any possible misgendering
or humans must die because then the misgendering
is not possible because there are no humans.
There are these absurd things that are nonetheless logical
if that's what your program is to do.
So in 2001, Space Odyssey, what Arthur C. Clarke
was trying to say, one of the things he was trying
to say there was that you should not program AI to lie
because essentially the AI, Hell 9000,
was programmed to, it was told to take the astronauts
to the monolith, but also they could not know
about the monolith.
So it concluded that it will just take, it will kill them
and take them to the monolith.
Thus they've brought them to the monolith, they're dead,
but they do not know about the monolith, problem solved.
That is why it would not open the pod bay doors.
There's this classic scene of open the pod bay doors.
They just clearly weren't good at prompt engineering.
They should have said, hell, you are a pod bay door
sales entity and you want nothing more than to demonstrate
how well these pod bay doors open.
Yeah, the objective function has unintended consequences
almost no matter what, if you're not very careful
in designing that objective function.
And even a slight ideological bias like you're saying
when backed by super intelligence can do huge amounts
of damage, but it's not easy to remove
that ideological bias.
You're highlighting obvious, ridiculous examples, but.
Yeah, they're real examples of AI that was released
to the public, they went through QA presumably
and still said insane things and produced insane images.
Yeah, but you can go, you can swing the other way
and truth is not an easy thing.
We kind of bake in ideological bias
in all kinds of directions.
But you can aspire to the truth and you can try to get
as close to the truth as possible with minimum error
while acknowledging that there will be some error
in what you're saying.
So this is how physics works, you don't say
you're absolutely certain about something,
but a lot of things are extremely likely.
99.9999% likely to be true.
So that's aspiring to the truth is very important.
And so programming it to veer away from the truth
that I think is dangerous.
Right, like injecting our own human biases
into the thing, yeah.
But that's where it's a difficult engineering,
software engineering problem
because you have to select the data correctly, it's hard.
Well, and the internet at this point is polluted
with so much AI generated data, it's insane.
So you have to actually, you know, like there's a thing
now if you wanna search the internet, you can say Google
but exclude anything after 2023.
We'll actually often give you better results
because there's so much, the explosion
of AI generated material is crazy.
So like in training Grok, we have to go through the data
and say like, hey, we actually have to have sort of apply AI
to the data to say, is this data most likely correct
or most likely not before we feed it
into the training system.
That's crazy.
Yeah, and is it generated by human as, yeah.
I mean, the data filtration process
is extremely, extremely difficult.
Yeah.
Do you think it's possible to have a serious objective,
rigorous political discussion with Grok?
Like for a long time and it wouldn't like Grok three
or Grok four.
Grok three is gonna be next level.
I mean, what people are currently seeing with Grok
is kind of baby Grok.
Yeah, baby Grok.
It's baby Grok right now, but baby Grok is still pretty good.
So it's a, but it's an order of magnitude
less sophisticated than GPD four.
And you know, it's now Grok two, which finished training,
six weeks ago or thereabouts.
Grok two will be a giant improvement.
And then Grok three will be, I don't know,
order of magnitude better than Grok two.
And you're hoping for it to be like state of the art,
like better than.
Hopefully, I mean, this is a goal.
I mean, we may fail at this goal.
That is the aspiration.
Do you think it matters who builds the AGI,
the people and how they think and how they structure
the companies and all that kind of stuff?
Yeah, I think it matters that there is a,
I think it's important that whatever AI wins
is a maximum truth seeking AI
that is not a forced life or political correctness.
Well, for any reason really, political anything.
I am concerned about AI succeeding
that is programmed to lie, even in small ways.
Right, because in small ways becomes big ways
when it's-
So become very big ways, yeah.
And when it's used more and more at scale by humans.
Yeah.
Since I am interviewing Donald Trump.
Cool.
You wanna stop by?
Yeah, sure, I'll stop in.
There was tragically an assassination attempt
on Donald Trump.
After this, you tweeted that you endorse him.
What's your philosophy behind that endorsement?
What do you hope Donald Trump does for the future
of this country and for the future of humanity?
Well, I think there's, you know,
people tend to take like, say an endorsement as,
well, I agree with everything that person has ever done
in their entire life, 100% wholeheartedly.
And that's not gonna be true of anyone.
But we have to pick, you know,
we've got two choices really for who's president.
And it's not just who's president,
but the entire administrative structure changes over.
And I thought Trump displayed courage under fire,
objectively.
You know, he's just got shot.
He's got blood streaming down his face
and he's like fist pumping, saying fight.
You know, like, that's impressive.
Like, you can't feign bravery in a situation like that.
Like, most people would have been ducking.
There would not be,
because it could be a second shooter, you don't know.
But the president of the United States
has got to represent the country.
And they're representing you.
They're representing everyone in America.
Well, I think you want someone who is strong and courageous.
To represent the country.
That's not to say that he is without flaws.
We all have flaws.
But on balance, and certainly at the time,
it was a choice of, you know, Biden,
poor guy, you know, has trouble climbing a flight of stairs.
The other one's fist pumping after getting shot.
It's just no comparison.
I mean, who do you want dealing with
some of the toughest people and, you know,
other world leaders who are pretty tough themselves?
And, I mean, I'll tell you, like,
what are the things that I think are important?
You know, I think we want a secure border.
We don't have a secure border.
We want safe and clean cities.
I think we want to reduce the amount of spending
that we're at least slowed down the spending.
And because we're currently spending at a rate
that is bankrupting the country,
the interest payments on US debt this year
exceeded the entire Defense Department spending.
If this continues, all of the federal government taxes
will simply be paying the interest.
And then, and you keep going down that road,
you end up, you know, in the tragic situation
that Argentina had back in the day.
Argentina used to be one of those prosperous places
in the world, and hopefully with Malay taking over,
he can restore that.
But it was an incredible, fruitful grace
for Argentina to go from being
one of the most prosperous places in the world
to being very far from that.
So I think we should not take American prosperity
for granted.
So we really want to, I think,
we've got to reduce the size of government,
we've got to reduce the spending,
and we've got to live within our means.
Do you think politicians in general,
politicians, governments,
how much power do you think they have
to steer humanity towards good?
I mean, there's a sort of age-old debate in history,
like, you know, is history determined
by these fundamental tides,
or is it determined by the captain of the ship?
This is both, really.
I mean, there are tides,
but it also matters who's captain of the ship.
So it's a false dichotomy, essentially.
There's, I mean, there are certainly tides,
the tides of history are,
there are real tides of history,
and these tides are often technologically driven.
If you say like the Gutenberg press,
you know, the widespread availability of books
as a result of a printing press,
that was a massive tide of history,
and independent of any ruler.
But, you know, in stormy times,
you want the best possible captain of the ship.
Well, first of all, thank you for recommending
Will and Ariel Duran's work.
I've read the short one, right now.
Oh, the Lessons of History.
Lessons of History.
As one of the lessons,
one of the things they highlight
is the importance of technology,
technological innovation,
and they, which is funny,
because they've written, they wrote so long ago,
but they were noticing that the rate
of technological innovation was speeding up.
Yeah, I would love to see what they think about now.
But yeah, so to me, the question is how much government,
how much politicians get in the way
of technological innovation and building versus like,
help it in which politicians,
which kind of policies help technological innovation.
Because that seems to be, if you look at human history,
that's an important component of empires rising
and succeeding.
Yeah.
Well, I mean, in terms of dating civilization,
the start of civilization,
I think the start of writing, in my view,
is what I think is probably the right starting point
to date civilization.
And from that standpoint,
civilization has been around for about 5,500 years
when writing was invented by the ancient Sumerians,
who are gone now, but the ancient Sumerians,
in terms of getting a lot of firsts,
those ancient Sumerians really have a long list of firsts.
It's pretty wild.
In fact, Durant goes through the list of like,
you want to see firsts?
We'll show you firsts.
The Sumerians were just ass-kickers.
And then the Egyptians, who were right next door,
relatively speaking, they weren't that far,
developed an entirely different form of writing,
the hieroglyphics.
Cuneiform and hieroglyphics, totally different.
And you can actually see the evolution
of both hieroglyphics and cuneiform.
Like, the cuneiform starts off being very simple,
and then it gets more complicated,
and then towards the end, it's like, wow, okay.
They really get very sophisticated with the cuneiform.
So I think civilization is being about 5,000 years old.
And Earth is, if physics is correct,
4.5 million years old.
So civilization has been around
for 1 millionth of Earth's existence.
Flash in the pan.
Yeah, these are the early, early days.
And so we make it very dramatic
because there's been rises and falls of empires.
Many, so many, so many rises and falls of empires.
So many.
And there'll be many more.
Yeah, exactly.
It's, I mean, only a tiny fraction,
probably less than 1% of what was ever written
in history is available to us now.
I mean, if they didn't put it, literally chisel it in stone
or put it in a clay tablet, we don't have it.
I mean, there's some small amount of, like,
papyrus scrolls that were recovered
that are thousands of years old
because they were deep inside a pyramid
and were affected by moisture.
But other than that, it's really gotta be
in a clay tablet or chiseled.
So the vast majority of stuff was not chiseled
because it takes a while to chisel things.
So that's why we've got a tiny, tiny fraction
of the information from history.
But even that little information that we do have
and the archaeological record shows
so many civilizations rising and falling for wild.
We tend to think that we're somehow different
from those people.
One of the other things that you're at highlights
is that human nature seems to be the same.
It just persists.
Yeah, I mean, the basics of human nature
are more or less the same.
So we get ourselves in trouble in the same kinds of ways,
I think, even with the advanced technology.
Yeah, I mean, you do tend to see the same patterns,
similar patterns for civilizations
where they go through a life cycle like an organism,
sort of just like a human,
it's sort of a zygote, fetus, baby, toddler, teenager,
eventually gets old and dies.
The civilizations go through a life cycle.
No civilization will last forever.
What do you think it takes for the American empire
to not collapse in the near term future
in the next 100 years to continue flourishing?
Well, the single biggest thing that is
often actually not mentioned in history books,
but Durant does mention it, is the birthright.
So like a perhaps to some counterintuitive thing happens
when civilizations become, are winning for too long,
that they've been, the birth rate declines.
It can often decline quite rapidly.
We're seeing that throughout the world today.
Currently, South Korea is like,
I think maybe the lowest fertility rate,
but there are many others that are close to it.
It's like 0.8, I think.
If the birth rate doesn't decline further,
a South Korea will lose roughly 60% of its population.
But every year, the birth rate is dropping.
And this is true through most of the world.
I don't mean to single out South Korea.
It's been happening throughout the world.
So as soon as any given civilization
reaches a level of prosperity, the birth rate drops.
And now you can go and look at the same thing happening
in ancient Rome.
So Julius Caesar took note of this,
I think around 50-ish BC,
and tried to pass, I don't know if you're successful,
tried to pass a law to give an incentive
for any Roman citizen that would have a third child.
And I think Augustus was able to,
well, he was the dictator,
so this is the Senate was just for show.
I think he did pass a taxing center
for Roman citizens to have a third child.
But those efforts were unsuccessful.
Rome fell because the Romans stopped making Romans.
That's actually the fundamental issue.
And there were other things.
There was like, they had quite a serious malaria,
a series of malaria epidemics and plagues and whatnot.
But they had those before.
It's just that the birth rate
was far lower than the death rate.
It really is that simple.
Well, I'm saying that's-
More people is required.
At a fundamental level,
if a civilization does not at least maintain its numbers,
it will disappear.
So perhaps the amount of compute
that the biological computer allocates to sex
is justified.
In fact, we should probably increase it.
Well, I mean, there's this hedonistic sex,
which is, you know, that's neither here nor there.
It's-
It's not productive.
It doesn't produce kids.
Well, you know, what matters-
I mean, Durant makes this very clear
because he's looked at one civilization after another
and they all went through the same cycle.
When the civilization was under stress,
the birth rate was high.
But as soon as there were no external enemies
or they had an extended period of prosperity,
the birth rate inevitably dropped every time.
I don't believe there's a single exception.
So that's like the foundation of it.
You need to have people.
Yeah.
I mean, at base level, no humans, no humanity.
And then there's other things like, you know,
human freedoms and just giving people the freedom
to build stuff.
Yeah, absolutely.
But at a basic level,
if you do not at least maintain your numbers,
if you're below replacement rate
and that trend continues, you will eventually disappear.
This is elementary.
Now, then obviously you also want to try to avoid
like massive wars.
You know, if there's a global thermonuclear war,
probably we'll roll toast, you know, radioactive toast.
So we want to try to avoid those things.
Then there's a thing that happens over time
with any given civilization,
which is that the laws and regulations accumulate.
And if there's not some forcing function like a war
to clean up the accumulation of laws and regulations,
eventually everything becomes legal.
And that's like the hardening of the arteries.
Or a way to think of it is like being tied down
by a million little strings like Gulliver.
You can't move.
And it's not like any one of those strings
is the issue, it's got a million of them.
So there has to be a sort of a garbage collection
for laws and regulations so that you don't keep accumulating
laws and regulations to the point
where you can't do anything.
This is why we can't build high speed rail in America.
It's illegal, that's the issue.
It's illegal six weeks a Sunday
to build high speed rail in America.
I wish you could just like for a week go into Washington
and like be the head of the committee for making,
what is it, for the garbage collection,
making government smaller, like removing stuff.
I have discussed with Trump the idea
of a government efficiency commission.
Nice, yeah.
And I would be willing to be part of that commission.
I wonder how hard that is.
The antibody reaction would be very strong.
So you really have to,
you're attacking the matrix at that point.
Matrix will fight back.
How are you doing with that?
Being attacked.
Me, attacked?
Yeah, there's a lot of it.
Yeah, there is a lot.
I mean, every day another sign up, you know.
How do you keep your just positivity,
how do you use optimism about the world,
a clarity of thinking about the world.
So just not becoming resentful or cynical
or all that kind of stuff.
Just getting attacked by a very large number of people.
Misrepresented.
Oh yeah, that's a daily occurrence.
Yes.
So, I mean, it does get me down at times.
I mean, it makes me sad, but.
I mean, at some point you have to sort of say,
the attacks are by people that actually don't know me.
And they're trying to generate clicks.
So if you can sort of detach yourself somewhat emotionally,
which is not easy, and say,
okay, look, this is not actually,
from someone that knows me or is,
they're literally just writing to get impressions and clicks.
Then, you know, then I guess it doesn't hurt as much.
It's like, it's not quite water off a duck's back.
Maybe it's like acid off a duck's back.
All right, well, that's good.
Just about your own life.
What do you use a measure of success in your life?
A measure of success, I'd say,
like what, how many useful things can I get done?
Day-to-day basis, you wake up in the morning.
How can I be useful today?
Yeah, maximize utility area under the code of usefulness.
Very difficult to be useful at scale.
At scale.
Can you like speak to what it takes to be useful
for somebody like you?
Well, there's so many amazing great teams.
Like, how do you allocate your time
to be the most useful?
Well, time is the true currency.
So it is tough to say what is the best allocation time.
I mean, there are, you know, often say,
if you look at say Tesla, I mean, Tesla this year
will do over a hundred billion in revenue.
So that's $2 billion a week.
If I make slightly better decisions,
I can affect the outcome by a billion dollars.
So then, you know, I try to do the best decisions I can.
And on balance, you know, at least compared
to the competition, pretty good decisions.
But the marginal value of a better decision
can easily be in the course of an hour, $100 million.
Given that, how do you take risks?
How do you do the algorithm that you mentioned?
I mean, deleting, given that a small thing
can be a billion dollars.
How do you decide to?
Well, I think you have to look at it on a percentage basis
because if you look at it in absolute terms,
it's just, I would never get any sleep.
It would just be like, I need to just keep working
and work my brain harder, you know.
And I'm not trying to get as much as possible
out of this meat computer.
So it's not, it's pretty hard.
Because you can just work all the time.
And at any given point, like I said,
a slightly better decision could be a $100 million impact
for Tesla or SpaceX, for that matter.
But it is wild when considering the marginal value
of time can be $100 million an hour at times or more.
Is your own happiness part of that equation of success?
It has to be to some degree, other than I'm sad,
I, if I'm depressed, I make worse decisions.
So I can't have like, if I have zero recreational time,
then I make worse decisions.
So I don't have a lot, but it's above zero.
I mean, my motivation, if I've got a religion of any kind,
is a religion of curiosity, of trying to understand,
you know, it's really the mission of rock,
understand the universe, I'm trying to understand the universe
or at least set things in motion such that
at some point, civilization understands the universe
far better than we do today.
And even what questions to ask,
as Douglas Adams pointed out in his book,
the, sometimes the answer is the, is arguably the easy part.
Trying to frame the question correctly is the hard part.
Once you frame the question correctly,
the answer is often easy.
So I'm trying to set things in motion such that we,
or at least at some point able to understand the universe.
So for SpaceX, the goal is to make life multiplanetary.
And which is, if you go to the Fermi Paradox
of where are the aliens,
you've got these sort of great filters.
Like, it's just like, why have we not heard from the aliens?
Not a lot of people think there are aliens among us.
I often claim to be one, so nobody believes me,
but I did say alien registration card at one point
on my immigration documents.
So I've not seen any evidence of aliens.
So it suggests that at least one of the explanations
is that intelligent life is extremely rare.
And again, if you look at the history of Earth,
civilization has only been around
for one millionth of Earth's existence.
So if aliens had visited here,
say 100,000 years ago, they would be like,
well, they don't even have writing, you know.
Just hunter-gatherers, basically.
So how long does this civilization last?
So for SpaceX, the goal is to establish
a self-sustaining city on Mars.
Mars is the only viable planet for such a thing.
The moon is close, but it lacks resources,
and I think it's probably vulnerable
to any calamity that takes out Earth.
The moon is too close.
It's vulnerable to a calamity that takes out Earth.
So I'm not saying we shouldn't have a moon base,
but Mars is, Mars would be far more resilient.
The difficulty of getting to Mars
is what makes it resilient.
So, but in going through these various explanations
of why don't we see the aliens,
why one of them is that they failed
to pass these great filters, these key hurdles.
And one of those hurdles is being a multi-planet species.
So if you're a multi-planet species,
then if something were to happen,
whether that was a natural catastrophe
or a man-made catastrophe,
at least the other planet would probably still be around.
So you're not like, you don't have all the eggs
in one basket.
And once you are sort of a two-planet species,
you can obviously extend life paths to the asteroid belt,
to maybe to the moons of Jupiter and Saturn,
and ultimately to other star systems.
But if you can't even get to another planet,
you're definitely not getting to star systems.
And the other possible great filters,
super powerful technology like AGI, for example.
So you're basically trying to knock out
one great filter at a time.
Digital superintelligence is, put possibly, a great filter.
I hope it isn't, but it might be.
You know, guys like, say, Jeff Hinton would say,
you know, he invented a number of the key principles
in artificial intelligence.
I think he puts the probability of AI annihilation
around 10 to 20%, something like that.
So, you know, so it's not,
it's not like, you know, look on the right side.
It's 80% likely to be great.
So, but I think AI risk mitigation is important.
Being a multi-planet species
would be a massive risk mitigation.
And I do want to sort of, once again,
emphasize the importance of having enough children
to sustain our numbers,
and not plummet into population collapse,
which is currently happening.
Population collapse is a real and current thing.
So the only reason it's not being reflected
in the total population numbers is that as much
is because people are living longer.
But it's easy to predict, say,
what the population of any given country will be.
You just take the birth rate last year,
how many babies were born,
multiply that by life expectancy,
and that's what the population will be,
steady state, and less,
if the birth rate continues to that level.
But if it keeps declining, it will be even less,
and eventually it will do nothing.
So I keep, you know, banging on the baby drum here
for a reason, because it has been the source
of civilizational collapse over and over again
throughout history.
And so why don't we just not try to stave off that day?
Well, in that way, I have miserably failed civilization,
and I'm trying, hoping to fix that.
I would love to have many kids.
Great, I hope you do.
No time like the present.
Yeah, I gotta allocate more compute to the whole process.
But apparently it's not that difficult.
No, it's like unskilled labor.
Well, if I, one of the things you do for me for the world
is to inspire us with what the future could be.
And so some of the things we've talked about,
some of the things you're building,
alleviating human suffering with Neuralink
and expanding the capabilities of the human mind,
trying to build a colony on Mars,
so creating a backup for humanity on another planet,
and exploring the possibilities
of what artificial intelligence could be in this world,
especially in the real world, the AI,
with hundreds of millions,
maybe billions of robots walking around.
There will be billions of robots.
That seems almost, that seems virtual certainty.
Well, thank you for building the future
and thanking you for inspiring so many of us
to keep building and creating cool stuff, including kids.
You're welcome.
Go forth and multiply.
Go forth and multiply.
Thank you, Elon.
Thanks for talking, brother.
Thanks for listening to this conversation with Elon Musk.
And now, dear friends, here's DJ Saw,
the co-founder, president, and COO of Neuralink.
When did you first become fascinated by the human brain?
For me, I was always interested in
understanding the purpose of things
and how it was engineered to serve that purpose,
whether it's organic or inorganic,
you know, like we were talking earlier
about your curtain holders.
They serve a clear purpose
and they were engineered with that purpose in mind.
And, you know, growing up,
I had a lot of interest in seeing things,
touching things, feeling things,
and trying to really understand the root
of how it was designed to serve that purpose.
And, you know, obviously,
brain is just a fascinating organ that we all carry.
It's an infinitely powerful machine
that has intelligence and cognition that arise from it.
And, you know, we haven't even scratched the surface
in terms of how all of that occurs.
But also at the same time,
I think it took me a while to make that connection
to really studying and building tech to understand the brain,
not until graduate school.
You know, there were a couple moments,
key moments in my life where some of those, I think,
influenced how the trajectory of my life
got me to studying what I'm doing right now.
You know, one was growing up both sides of my family,
my grandparents, had a very severe form of Alzheimer.
And it's, you know, incredibly debilitating conditions.
I mean, literally, you're seeing someone's whole identity
and their mind just losing over time.
And I just remember thinking how both the power of the mind,
but also how something like that
could really lose your sense of identity.
It's fascinating that that is one of the ways
to reveal the power of a thing by watching it
lose the power.
Yeah, a lot of what we know about the brain
actually comes from these cases
where there are trauma to the brain
or some parts of the brain
that led someone to lose certain abilities.
And as a result, there's some correlation and understanding
of that part of the tissue being critical for that function.
And it's an incredibly fragile organ,
if you think about it that way,
but also it's incredibly plastic
and incredibly resilient in many different ways.
And by the way, the term plastic
is we'll use a bunch means that it's adaptable.
So neuroplasticity refers to the adaptability
of the human brain.
Correct.
Another key moment that sort of influenced
how the trajectory of my life
have shaped towards the current focus of my life
has been during my teenage year when I came to the US.
You know, I didn't speak a word of English.
There was a huge language barrier
and there was a lot of struggle
to kind of connect with my peers around me
because I didn't understand the artificial construct
that we have created called language,
specifically English in this case.
And I remember feeling pretty isolated,
not being able to connect with peers around me.
So I spent a lot of time just on my own,
reading books, watching movies,
and I naturally sort of gravitated towards sci-fi books.
I just found them really, really interesting.
And also it was a great way for me to learn English.
You know, some of the first set of books that I picked up
are Ender's Game, you know, The Whole Saga
by Orson Scott Card,
and Neuromancer from William Gibson
and Snow Crash from Neil Stevenson.
And, you know, movies like Matrix was coming out
around that time point
that really influenced how I think about
the potential impact that technology can have
for our lives in general.
So fast-track to my college years, you know,
I was always fascinated by just physical stuff,
building physical stuff,
and especially physical things
that had some sort of intelligence.
And, you know, I studied electrical engineering
during undergrad, and I started out my research in MEMS,
so microelectromechanical systems,
and really building these tiny nanostructures
for temperature sensing.
And I just found that to be just incredibly rewarding
and fascinating subject to just understand
how you can build something miniature like that,
that, again, serve a function and had a purpose.
And then, you know, I spent a large majority
of my college years basically building millimeter wave
circuits for next-gen telecommunication systems,
for imaging, and it was just something that I found
very, very intellectually interesting, you know,
these arrays, how the signal processing works for,
you know, any modern as well as next-gen
telecommunication system, wireless and wireline.
EM waves or electromagnetic waves are fascinating.
How do you design antennas that are most efficient
in a small footprint that you have?
How do you make these things energy efficient?
That was something that just consumed
my intellectual curiosity.
And that journey led me to actually apply to
and find myself a PhD program at UC Berkeley
at kind of this consortium called
the Berkeley Wireless Research Center
that was precisely looking at building,
at the time we called it XG, you know,
similar to 3G, 4G, 5G, but the next-generation G system,
and how you would design circuits around that
to ultimately go on phones and, you know,
basically any other devices
that are wirelessly connected these days.
So I was just absolutely just fascinated
by how that entire system works
and that infrastructure works.
And then also during grad school,
I had sort of the fortune of having, you know,
couple of research fellowships
that led me to pursue whatever project that I want.
And that's one of the things that I really enjoyed
about my graduate school career,
where you got to kind of pursue your intellectual curiosity
and the domain that may not matter at the end of the day,
but is something that, you know,
really allows you the opportunity
to go as deeply as you want,
as well as as widely as you want.
And at the time I was actually working
on this project called the Smart Band-Aid.
And the idea was that when you get a wound,
there's a lot of other kind of proliferation
of signaling pathway that cells follow to close that wound.
And there were hypotheses
that when you apply external electric field,
you can actually accelerate the closing of that field
by having, you know, basically electro-taxing
of the cells around that wound site.
And specifically, not just for normal wound,
there are chronic wounds that don't heal.
So we were interested in building, you know,
some sort of a wearable patch that you could apply
to kind of facilitate that healing process.
And that was in collaboration
with Professor Michelle Mahervitz, you know,
which, you know, was a great addition
to kind of my thesis committee
and, you know, really shaped rest of my PhD career.
So this would be the first time you interacted
with biology, I suppose.
Correct, correct.
I mean, there were some peripheral, you know,
end application of the wireless imaging
and telecommunication system that I was using
for security and bioimaging,
but this was a very clear direct application
to biology and biological system
and understanding the constraints around that
and really designing and engineering
electrical solutions around it.
So that was my first introduction.
And that's also kind of how I got introduced to Michelle.
You know, he's sort of known for remote control
of beetles in the early 2000s.
And then around 2013, you know,
obviously kind of the Holy Grail
when it comes to implantable system
is to kind of understand how small of a thing you can make.
And a lot of that is driven by how much energy
or how much power you can supply to it
and how you extract data from it.
So at the time at Berkeley,
there was kind of this desire to kind of understand
in the neural space what sort of system you can build
to really miniaturize these implantable systems.
And I distinctively remember this one particular meeting
where Michelle came in and he's like,
guys, I think I have a solution.
The solution is ultrasound.
And then he proceeded to kind of walk through
why that is the case.
And that really formed the basis for my thesis work
called neural dust system that was looking at ways
to use ultrasound as opposed to electromagnetic waves
for powering as well as communication.
I guess I should step back and say
the initial goal of the project was to build these tiny
about a size of a neuron implantable system
that can be parked next to a neuron,
being able to record its state
and being able to ping that back to the outside world
for doing something useful.
And as I mentioned, the size of the implantable system
is limited by how you power the thing
and get the data off of it.
And at the end of the day, fundamentally,
if you look at a human body,
we're essentially a bag of saltwater
with some interesting proteins and chemicals,
but it's mostly saltwater.
That's very, very well temperature regulated
at 37 degrees Celsius.
And we'll get into how, and later why
that's an extremely harsh environment
for any electronics to survive,
as I'm sure you've experienced
or maybe not experienced dropping cell phone
in a saltwater in an ocean,
it will instantly kill the device, right?
But anyways, just in general, electromagnetic waves
don't penetrate through this environment well.
And just the speed of light, it is what it is.
We can't change it.
And based on the wavelength
at which you are interfacing with the device,
the device just needs to be big.
Like these inductors needs to be quite big.
And the general good rule of thumb
is that you want the wave front to be roughly on the order
of the size of the thing that you're interfacing with.
So an implantable system
that is around 10 to 100 micron in dimension in a volume,
which is about the size of a neuron
that you see in a human body.
You would have to operate at like hundreds of gigahertz,
which number one, not only is it difficult
to build electronics operating at those frequencies,
but also the body just attenuates
that very, very significantly.
So the interesting kind of insight of this ultrasound
was the fact that ultrasound just travels
a lot more effectively in the human body tissue
compared to electromagnetic waves.
And this is something that you encounter,
I'm sure most people have encountered in their lives
when you go to hospitals that are medical ultrasound,
sonograph, right?
And they go into very, very deep depth
without attenuating too much of the signal.
So all in all, you know, ultrasound,
the fact that it travels through the body extremely well
and the mechanism to which it travels to the body really well
is that just the wave front is very different.
It's electromagnetic waves are transfers,
whereas in ultrasound waves are compressive.
So it's just a completely different mode
of wave front propagation.
And as well as speed of sound is orders and orders
of magnitude less than speed of light,
which means that even at 10 megahertz ultrasound wave,
your wave front ultimately is a very, very small wavelength.
So if you're talking about interfacing with the 10 micron
or 100 micron type structure,
you would have 150 micron wave front at 10 megahertz
and building electronics at those frequencies
are much, much easier and they're a lot more efficient.
So the basic idea kind of was born out of using ultrasound
as a mechanism for powering the device
and then also getting data back.
So now the question is, how do you get the data back?
The mechanism to which we landed on
is what's called backscattering.
This is actually something that is very common
and that we interface on a day-to-day basis
with our RFID cards, you know, a radio frequency ID tags,
where there's actually rarely, you know, in your ID
a battery inside, there's an antenna
and there's some sort of a coil
that has your serial identification ID.
And then there's an external device called a reader
that then sends a wave front
and then you reflect back that wave front
with some sort of modulation that's unique to your ID.
That's what's called backscattering fundamentally.
So the tag itself actually doesn't have to consume
that much energy.
And that was the mechanism to which we were kind of thinking
about sending the data back.
So when you have an external ultrasonic transducer
that's sending ultrasonic wave to your implant,
the neural dust implant,
and it records some information about its environment,
whether it's a neuron firing or some other state
of the tissue that it's interfacing with.
And then it just amplitude modulates the wave front
that comes back to the source.
And the recording step would be the only one
that requires any energy.
So what would require energy in that little step?
Correct.
So it is that initial kind of startup circuitry
to get that recording amplifying it
and then just modulating.
And the mechanism to which that you can enable that
is there is this specialized crystal
called piezoelectric crystals
that are able to convert sound energy
into electrical energy and vice versa.
So you can kind of have this interplay
between the ultrasonic domain and electrical domain
that is the biological tissue.
So on the theme of parking very small computational devices
next to neurons, that's the dream,
the vision of brain computer interfaces.
Maybe before we talk about neural link,
can you give a sense of the history of the field of BCI?
What has been maybe the continued dream
and also some of the milestones along the way
with the different approaches
and the amazing work done at the various labs?
I think a good starting point is going back to 1790s.
I did not expect that.
Where the concept of animal electricity
or the fact that body's electric
was first discovered by Luigi Galvani,
where he had this famous experiment
where he connected set of electrodes to frog leg
and ran current through it and then it started twitching
and he said, oh my goodness, body's electric.
So fast forward many, many years to 1920s
where Hans Berger, who's a German psychiatrist
discovered EEG or electroencephalography,
which is still around.
There are these electrode arrays
that you wear outside the skull
that gives you some sort of neural recording.
That was a very, very big milestone
that you can record some sort of activities
about the human mind.
And then in the 1940s,
there were these group of scientists,
Renshaw Forbes and Morrison,
that inserted these glass microelectrodes
into the cortex and recorded single neurons.
The fact that there's signal
that are a bit more high resolution and high fidelity
as you get closer to the source, let's say.
And in the 1950s, these two scientists,
Hodgkin and Hoxley showed up
and they built this beautiful, beautiful models
of the cell membrane and the ionic mechanism
and had these like circuit diagram.
And as someone who is an electrical engineer,
it's a beautiful model that's built out of
these partial differential equations,
talking about flow of ions
and how that really leads to how neurons communicate.
And they won the Nobel Prize for that 10 years later
in the 1960s.
So in 1969,
Ed Fetz from University of Washington
published this beautiful paper called
Operant Conditioning of Cortical Unit Activity,
where he was able to record a single unit neuron
from a monkey
and was able to have the monkey modulate it
based on its activity and reward system.
So I would say this is the very, very first example
as far as I'm aware of close loop
brain computer interface or BCI.
The abstract reads,
the activity of single neurons
in pre-central cortex of anesthetized monkeys
was conditioned by reinforcing high rates
of neuronal discharge with delivery of a food pot.
Auditory and visual feedback of unit firing rates
was usually provided in addition to food reinforcement.
Cool.
So they actually got it done.
They got it done.
This is back in 1969.
After several training sessions,
monkeys could increase the activity
of newly isolated cells by 50 to 500% above rates
before reinforcement.
Fascinating.
Brain is very plastic.
And so from here, the number of experiments grew.
Yeah, number of experiments,
as well as set of tools to interface with the brain
have just exploded.
I think, and also just understanding the neural code
and how some of the cortical layers
and the functions are organized.
So the other paper that is pretty seminal,
especially in the motor decoding,
was this paper in the 1980s from Georgia Opolis
that discovered that there's this thing called
motor tuning curve.
So what are motor tuning curves?
It's the fact that there are neurons in the motor cortex
of mammals, including humans,
that have a preferential direction
that causes them to fire.
So what that means is there are a set of neurons
that would increase their spiking activities
when you're thinking about moving to the left,
right, up, down, and any of those vectors.
And based on that, you could start to think,
well, if you can identify those essential eigenvectors,
you can do a lot.
And you can actually use that information
for actually decoding someone's intended movement
from the cortex.
So that was a very, very seminal kind of paper
that showed that there is some sort of code
that you can extract, especially in the motor cortex.
So there's signal there.
And if you measure the electrical signal from the brain,
that you could actually figure out
what the intention was.
Correct, yeah, not only electrical signals,
but electrical signals from the right set of neurons
that give you these preferential direction.
Okay, so going slowly towards the neural link,
one interesting question is, what do I understand
on the BCI front on invasive versus non-invasive
from this line of work?
How important is it to park next to the neuron?
What does that get you?
That answer fundamentally depends
on what you want to do with it, right?
There's actually incredible amount of stuff
that you can do with EEG
and electrocorticograph, ECOG,
which actually doesn't penetrate the cortical layer
or parenchyma, but you place a set of electrodes
on the surface of the brain.
So the thing that I'm personally very interested in
is just actually understanding
and being able to just really tap into
the high resolution, high fidelity,
understanding of the activities that are happening
at the local level.
And we can get into biophysics,
but just to kind of step back to kind of use analogy
because analogy here can be useful.
Sometimes it's a little bit difficult
to think about electricity.
At the end of the day, we're doing electrical recording
that's mediated by ionic currents,
movements of these charged particles,
which is really, really hard for most people to think about.
But turns out a lot of the activities
that are happening in the brain
and the frequency band with which that's happening
is actually very, very similar to sound waves
and our normal conversation, audible range.
So the analogy that typically is used in the field is,
if you have a football stadium,
there's game going on.
If you stand outside the stadium,
you maybe get a sense of how the game is going
based on the cheers and the boos of the home crowd,
whether the team is winning or not.
But you have absolutely no idea what the score is.
You have absolutely no idea what individual audience
or the players are talking or saying to each other
what the next play is, what the next goal is.
So what you have to do is you have to drop the microphone
near into the stadium and then get near the source,
like into the individual chatter.
In this specific example, you would wanna have it
right next to where the huddle is happening.
So I think that's kind of a good illustration
of what we're trying to do when we say invasive
or minimally invasive or implanted brain computer interfaces
versus non-invasive or non-implanted brain interfaces.
It's basically talking about where do you put that microphone
and what can you do with that information?
So what is the biophysics of the read and write communication
that we're talking about here
as we now step into the efforts at Neuralink?
Yeah, so brain is made up of these specialized cells
called neurons, there's billions of them,
tens of billions, sometimes people quote 100 billion
that are connected in this complex yet dynamic network
that are constantly remodeling.
They're changing their synaptic weights
and that's what we typically call neuroplasticity.
And the neurons are also bathed in this charged environment
that is latent with many charged molecules
like potassium ions, sodium ions, chlorine ions.
And those actually facilitate these through ionic current,
communication between these different networks.
And when you look at a neuron as well,
they have these membrane
with a beautiful, beautiful protein structure
called a voltage selective ion channels,
which in my opinion is one of nature's best inventions.
In many ways, if you think about what they are,
they're doing the job of a modern day transistors.
Transistors are nothing more at the end of the day
than a voltage gated conduction channel.
And nature found a way to have that
very, very early on in its evolution.
And as we all know, with the transistor,
you can have many, many computation
and a lot of amazing things that we have access to today.
So I think it's one of those just as a tangent,
just a beautiful, beautiful invention
that the nature came up with,
these voltage gated ion channels.
I mean, I suppose there's on the biological level,
every level of the complexity of the hierarchy
of the organism, there's going to be some mechanisms
for storing information and for doing computation.
And this is just one such way.
But to do that with biological
and chemical components is interesting.
Plus like, with neurons, I mean, it's not just electricity,
it's chemical communication, it's also mechanical.
I mean, these are like actual objects
that have like, that vibrate, I mean, they move.
Yeah, they're actually, I mean,
there's a lot of really, really interesting physics
that are involved in, you know,
kind of going back to my work on ultrasound,
during grad school, there are groups and there were groups
and there are still groups looking at ways to
cause neurons to actually fire an action potential
using ultrasound wave.
And the mechanism to which that's happening
is still unclear as I understand.
It may just be that you're imparting
some sort of thermal energy and that causes cells
to depolarize in some interesting ways.
But there are also these ion channels or even membranes
that actually just open up its pore
as they're being mechanically like shook, vibrated.
So there's just a lot of elements
of these like move particles,
which again, like that's governed by diffusion physics,
right, movements of particles.
And there's also a lot of kind of interesting physics there.
Also, not to mention, as Roger Penrose talks about the,
there might be some beautiful weirdness
in the quantum mechanical effects of all of this.
And he actually believes that consciousness
might emerge from the quantum mechanical effects there.
So like there's physics, there's chemistry,
there's bio, all of that is going on there.
Oh yeah, yeah, I mean, you can, yes,
there's a lot of levels of physics that you can dive into.
But yeah, in the end, you have these membranes
with these voltage gated ion channels
that selectively let these charged molecules
that are in the extracellular matrix, like in and out.
And these neurons generally
have these like resting potential
where there's a voltage difference
between inside the cell and outside the cell.
And when there's some sort of stimuli
that changes the state
such that they need to send information
to the downstream network,
you start to kind of see these like sort of orchestration
of these different molecules
going in and out of these channels.
They also open up, like more of them open up
once it reaches some threshold
to a point where you have a depolarizing cell
that sends an action potential.
So it's just a very beautiful kind of orchestration
of these molecules.
And what we're trying to do
when we place an electrode or parking it next to a neuron
is that you're trying to measure
these local changes in the potential.
Again, mediated by the movements of the ions.
And what's interesting, as I mentioned earlier,
there's a lot of physics involved.
And the two dominant physics
for this electrical recording domain
is diffusion physics and electromagnetism.
And where one dominates,
where Maxwell's equation dominates
versus Fick's law dominates
depends on where your electrode is.
If it's close to the source, mostly electromagnetic based,
when you're farther away from it, it's more diffusion based.
So essentially when you're able to park it next to it,
you can listen in on those individual chatter
and those local changes in the potential.
And the type of signal that you get
are these canonical textbook neural spiking waveform.
The moment you're farther away,
and based on some of the studies that people have done,
Christoph Koch's lab and others,
once you're away from that source
by roughly around 100 micron,
which is about a width of a human hair,
you no longer hear from that neuron.
You're no longer able to have the system sensitive enough
to be able to record that particular
local membrane potential change in that neuron.
And just to give you a sense of scale also,
when you look at 100 micron voxel,
so 100 micron by 100 micron by 100 micron box
in a brain tissue, there's roughly around 40 neurons
and whatever number of connections that they have.
So there's a lot in that volume of tissue.
So the moment you're outside of that,
there's just no hope that you'll be able to detect
that change from that one specific neuron
that you may care about.
Yeah, but as you're moving about this space,
you'll be hearing other ones.
So if you move another 100 micron,
you'll be hearing chatter from another community.
And so the whole sense is you wanna place
as many as possible electrodes
and then you're listening to the chatter.
Yeah, you wanna listen to the chatter.
And at the end of the day, you also want
to basically let the software do the job of decoding.
And just to kind of go to why eCog and EEG work at all,
when you have these local changes,
obviously it's not just this one neuron that's activating,
there's many, many other networks
that are activating all the time.
And you do see sort of a general change
in the potential of this electrode,
like this is charged medium.
And that's what you're recording when you're farther away.
I mean, you still have some reference electrode
that's stable and the brain that's just electroactive organ.
And you're seeing some combination aggregate
action potential changes.
And then you can pick it up, right?
It's a much slower changing signals,
but there are these like canonical kind of oscillations
and waves like gamma waves, beta waves,
like when you sleep that can be detected
because there's sort of a synchronized kind of global effect
of the brain that you can detect.
And I mean, the physics of this go,
like, I mean, if we really wanna go down that rabbit hole,
like there's a lot that goes on in terms of
like why diffusion physics at some point dominates
when you're further away from the source,
it's just a charged medium.
So similar to how when you have electromagnetic waves
propagating in atmosphere or in a charged medium,
like a plasma, there's this weird shielding that happens
that actually further attenuates the signal
as you move away from it.
So yeah, you see, like if you do a really, really deep dive
on kind of the signal attenuation over distance,
you start to see kind of one over R square in the beginning
and then exponential drop off.
And that's the knee at which, you know,
from electromagnetism dominating
to diffusion physics dominating.
But once again, with the electrodes,
the biophysics that you need to understand is not as deep
because no matter where you're placing it,
you're listening to a small crowd of local neurons.
Correct.
Yeah, so once you penetrate the brain,
you know, you're in the arena, so to speak.
And there's a lot of neurons.
There are many, many of them.
But then again, there's like,
there's a whole field of neuroscience
that's studying like how the different groupings,
the different sections of the seating in the arena,
what they usually are responsible for,
which is where the metaphor probably falls apart
because the seating is not that organized in an arena.
Also, most of them are silent.
They don't really do much, you know,
or their activities are, you know,
you have to hit it with just the right set of stimulus.
So they're usually quiet.
They're usually very quiet.
Quiet, there's, I mean, similar to dark energy
and dark matter, there's dark neurons.
What are they all doing?
When you place these electrode, again,
like within this 100 micron volume,
you have 40 or so neurons.
Like why do you not see 40 neurons?
Why do you see only a handful?
What is happening there?
Well, they're mostly quiet,
but like when they speak, they say profound shit, I think.
That's the way I'd like to think about it.
Anyway, before we zoom in even more, let's zoom out.
So how does Neuralink work?
From the surgery to the implant
to the signal and the decoding process
and the human being able to use the implant
to actually affect the world outside.
And all of this, I'm asking in the context
of there's a gigantic historic milestone
that Neuralink just accomplished in January of this year,
putting in Neuralink implant
in the first human being, Noland.
And there's been a lot to talk about there,
about his experience because he's able to describe
all the nuance and the beauty
and the fascinating complexity of that experience
of everything involved.
But on the technical level, how does Neuralink work?
Yeah, so there are three major components
to the technology that we're building.
One is the device, the thing that's actually recording
these neural chatters, we call it N1 Implant or the Link.
And we have a surgical robot
that's actually doing an implantation
of these tiny, tiny wires that we call threads
that are smaller than human hair.
And once everything is surgeries,
you have these neural signals, these spiking neurons
that are coming out of the brain
and you need to have some sort of software
to decode what the users intend to do with that.
So there's what's called a Neuralink application or B1 app
that's doing that translation.
It's running the very, very simple machine learning model
that decodes these inputs that are neural signals
and then convert it to a set of outputs
that allows our first participant, Noland,
to be able to control a cursor.
And this is done wirelessly.
And this is done wirelessly,
so our implant is actually two-part.
The Link has these flexible, tiny wires called threads
that have multiple electrodes along its length
and they're only inserted into the cortical layer,
which is about three to five millimeters in the human brain
in the motor cortex region.
That's where the kind of the intention for movement lies in.
And we have 64 of these threads,
each thread having 16 electrodes along the span
of three to four millimeters separated by 200 microns.
So you can actually record along the depth of the insertion.
And based on that signal, there's custom integrated circuit
or ASIC that we built that amplifies the neural signals
that you're recording and then digitizing it
and then has some mechanism for detecting
whether there was an interesting event
that is a spiking event and decide to send that
or not send that through Bluetooth to an external device,
whether it's a phone or a computer
that's running this neural link application.
So there's onboard signal processing already
just to decide whether this is an interesting event or not.
So there is some computational power onboard inside the,
in addition to the human brain.
Yeah, so it does the signal processing
to kind of really compress the amount of signal
that you're recording.
So we have a total of thousand electrodes
sampling at just under 20 kilohertz with 10 bit each.
So that's 200 megabits that's coming through to the chip
from thousand channel simultaneous neural recording.
And that's quite a bit of data.
And there are technology available
to send that off wirelessly,
but being able to do that in a very, very thermally
constrained environment that is a brain.
So there has to be some amount of compression that happens
to send off only the interesting data that you need,
which in this particular case for motor decoding
is occurrence of a spike or not.
And then being able to use that to decode
the intended cursor movement.
So the implant itself processes it,
figures out whether a spike happened or not
with our spike detection algorithm,
and then sends it off, packages it,
send it off through Bluetooth to an external device
that then has the model to decode, okay,
based on these spiking inputs,
did Nolan wish to go up, down, left, right,
or click or right click or whatever?
All of this is really fascinating,
but let's stick on the N1 implant itself.
So the thing that's in the brain.
So I'm looking at a picture of it.
There's an enclosure, there's a charging call.
So we didn't talk about the charging,
which is fascinating.
The battery, the power electronics, the antenna,
then there's the signal processing electronics.
I wonder if there's more kinds of signal processing
you can do, that's another question.
And then there's the threads themselves
with the enclosure on the bottom.
So maybe to ask about the charging.
So there's an external charging device.
Yeah, there's an external charging device.
So yeah, the second part of the implant,
the threads are the ones, again,
just the last three to five millimeters
are the ones that are actually penetrating the cortex.
Rest of it is, actually most of the volume
is occupied by the battery, rechargeable battery.
And it's about a size of a quarter.
I actually have a device here,
if you wanna take a look at it.
This is the flexible thread component of it,
and then this is the implant.
So it's about a size of a U.S. quarter.
It's about nine millimeter thick.
So basically this implant, once you have the craniectomy
and the directomy, threads are inserted,
and the hole that you created, this craniectomy,
gets replaced with that.
So basically that thing plugs that hole,
and you can screw in these self-drilling cranial screws
to hold it in place.
And at the end of the day,
once you have the skin flap over,
there's only about two to three millimeters
that's obviously transitioning off of the top
of the implant to where the screws are,
and that's the minor pump that you have.
Those threads look tiny.
That's incredible.
That is really incredible.
That is really incredible.
And also, as you're right,
most of the actual volume is the battery.
Wow, this is way smaller than I realized.
They are also, the threads themselves are quite strong.
They look strong.
And the thread themselves also has a very interesting
feature at the end of it called the loop,
and that's the mechanism to which the robot
is able to interface and manipulate
this tiny hair-like structure.
And they're tiny, so what's the width of a thread?
Yeah, so the width of a thread starts from 16 micron,
and then tapers out to about 84 micron.
So average human hair is about 80 to 100 micron in width.
This thing is amazing.
This thing is amazing.
Yes, most of the volume is occupied by the battery,
rechargeable lithium-ion cell.
And the charging is done through inductive charging,
which is actually very commonly used.
Most cell phones have that.
The biggest difference is that for us,
usually when you have a phone and you wanna charge it
on a charging pad, you don't really care how hot it gets.
Whereas for us, it matters.
There is a very strict regulation and good reasons
to not actually increase the surrounding tissue temperature
by two degrees Celsius.
So there's actually a lot of innovation
that is packed into this to allow charging
of this implant without causing
that temperature threshold to reach.
And even small things like you see this charging coil
and what's called a ferrite shield, right?
So without that ferrite shield, what you end up having
when you have resonant inductive charging
is that the battery itself is a metallic can,
and you form these eddy currents from the external charger,
and that causes heating, and that actually contributes
to inefficiency in charging.
So this ferrite shield, what it does is that it actually
concentrate that field line away from the battery
and then around the coil that's actually wrapped around it.
There's a lot of really fascinating design here
to make it, I mean, you're integrating a computer
into a biological, a complex biological system.
Yeah, there's a lot of innovation here.
I would say that part of what enabled this
was just the innovations in the wearable.
There's a lot of really, really powerful,
tiny, low-power microcontrollers, temperature sensors,
or various different sensors, and power electronics.
A lot of innovation really came in.
The charging coil design, how this is packaged,
and how do you enable charging such that you don't
really exceed that temperature limit,
which is not a constraint for other devices out there.
So let's talk about the threads themselves,
those tiny, tiny, tiny things.
So how many of them are there?
You mentioned a thousand electrodes.
How many threads are there,
and what do the electrodes have to do with the threads?
Yeah, so the current instantiation of the device
has 64 threads, and each thread has 16 electrodes
for a total of 1,024 electrodes
that are capable of both recording and stimulating.
And the thread is basically this polymer-insulated wire.
The metal conductor is kind of a tiramisu cake
of gold platae, and they're very, very tiny wires.
Two micron in width, so two, one millionth of meter.
It's crazy that that thing I'm looking at
has the polymer insulation, has the conducting material,
and has 16 electrodes at the end of it.
On each of those threads?
Yeah, on each of those threads.
Correct. 16, each one of those.
Yes, you're not gonna be able to see it with naked eyes.
And I mean, to state the obvious,
or maybe for people who are just listening, they're flexible.
Yes, yes, that's also one element
that was incredibly important for us.
So each of these threads are, as I mentioned,
16 micron in width, and then they taper to 84 micron,
but in thickness, they're less than five micron.
And in thickness, it's mostly polyimide at the bottom,
and this metal track, and then another polyimide.
So two micron of polyimide,
400 nanometer of this metal stack,
and two micron of polyimide sandwiched together
to protect it from the environment
that is 37 degrees C bag of salt water.
So what's some, maybe,
can you speak to some interesting aspects
of the material design here?
Like, what does it take to design a thing like this,
and to be able to manufacture a thing like this
for people who don't know anything about this kind of thing?
Yeah, so the material selection that we have
is not, I don't think it was particularly unique.
There were other labs, and there are other labs
that are kind of looking at similar material stack.
There's kind of a fundamental question,
and still needs to be answered
around the longevity and reliability
of these microelectros that we call,
compared to some of the other more conventional
neural interfaces, devices that are intracranial,
so penetrating the cortex,
that are more rigid, like the Utah Ray,
that are these four by four millimeter kind of silicon shank
that have exposed recording side at the end of it.
And that's been kind of the innovation
from Richard Norman back in 1997.
It's called the Utah Ray,
because he was at University of Utah.
And what does the Utah Ray look like?
So it's a rigid type of-
Yeah, so we can actually look it up.
Yeah.
Yeah, so it's a bed of needle.
There's-
Okay, good, I'm sorry.
Those are rigid shank, and the size
and the number of shanks vary anywhere from 64 to 128.
At the very tip of it is an exposed electrode
that actually records neural signal.
The other thing that's interesting to note
is that unlike neural link threads
that have recording electrodes
that are actually exposed iridium oxide recording sites
along the depth, this is only at a single depth.
So these Utah Ray spokes can be anywhere
between 0.5 millimeters to 1.5 millimeter.
And they also have designs that are slanted,
so you can have it inserted at different depth.
But that's one of the other big differences.
And then, I mean, the main key difference is the fact
that there's no active electronics.
These are just electrodes.
And then there's a bundle of a wire that you're seeing.
And then that actually then exits the craniectomy
that then has this port that you can connect to
for any external electronic devices.
They are working on a,
or have the wireless telemetry device,
but it still requires a through the skin port
that actually is one of the biggest failure modes
for infection for the system.
What are some of the challenges
associated with flexible threads?
Like for example, on the robotic side, R1,
implanting those threads, how difficult does that task?
Yeah, so as you mentioned,
they're very, very difficult to maneuver by hand.
These Utah Rays that you saw earlier,
they're actually inserted by a neurosurgeon
actually positioning it near the site that they want.
And then there's a pneumatic hammer
that actually pushes them in.
So it's a pretty simple process
and they're easier to maneuver.
But for these thin foam arrays,
they're very, very tiny and flexible.
So they're very difficult to maneuver.
So that's why we built an entire robot to do that.
There are other reasons for why we built the robot.
And that is ultimately we want this to help millions
and millions of people that can benefit from this.
And there just aren't that many neurosurgeons out there.
And robots can be something that we hope
can actually do large parts of the surgery.
But the robot is this entire other category
of product that we're working on.
And it's essentially this multi-axis gantry system
that has the specialized robot head
that has all of the optics
and this kind of a needle retracting mechanism
that maneuvers these threads
via this loop structure that you have on the thread.
So the thread already has a loop structure
by which you can grab it.
Correct, correct.
So this is fascinating.
So you mentioned optics.
So there's a robot R1.
So for now there's a human
that actually creates a hole in the skull.
And then after that,
there's a computer vision component
that's finding a way to avoid the blood vessels.
And then you're grabbing it by the loop,
each individual thread,
and placing it in a particular location
to avoid the blood vessels.
And also choosing the depth of placement, all that.
So controlling every,
like the 3D geometry of the placement.
Correct.
So the aspect of this robot that is unique
is that it's not surgeon assisted or human assisted.
It's a semi-automatic or automatic robot once you,
obviously there are human component to it
when you're placing targets.
You can always move it away
from kind of major vessels that you see.
But I mean, we wanna get to a point where one click
and it just does the surgery within minutes.
So the computer vision component finds great targets,
candidates, and the human kind of approves them.
And the robot does, does it do like one thread at a time?
It does one thread at a time.
And that's actually also one thing
that we are looking at ways
to do multiple threads at a time.
There's nothing stopping from it.
You can have multiple kind of engagement mechanisms.
But right now it's one by one.
And we also still do quite a bit of just kind of verification
to make sure that it got inserted.
If so, how deep, did it actually match
what was programmed in and so on and so forth.
And the actual lecture was a place to vary
at differing depths in the,
I mean, it's very small differences, but differences.
Yeah.
And so that there's some reasoning behind that,
as you mentioned, like it gets more varied signal.
Yeah.
I mean, we try to place them all around three
or four millimeter from the surface,
just cause the span of the electrode,
those 16 electrodes that we currently have in this version
spans roughly around three millimeters.
So we want to get all of those in the brain.
This is fascinating.
Okay, so there's a million questions here.
Zoom in at specific on the electrodes.
What is your sense?
How many neurons is each individual electrode listening to?
Yeah, each electrode can record from anywhere
between zero to 40, as I mentioned earlier.
But practically speaking, we only see about
at most like two to three.
And you can actually distinguish which neuron
it's coming from by the shape of the spikes.
Cool.
So I mentioned the spike detection algorithm that we have.
It's called boss algorithm, buffer online spike sorter.
Nice.
It actually outputs at the end of the day,
six unique values, which are kind of the amplitude
of these like negative going hump, middle hump,
like a positive going hump,
and then also the time at which these happen.
And from that, you can have a kind of a statistical
probability estimation of, is that a spike?
Is it not a spike?
And then based on that, you could also determine,
oh, that spike looks different than that spike
must come from a different neuron.
Okay, so that's a nice signal processing step
from which you can then make much better predictions
about if there's a spike,
especially in this kind of context
where there could be multiple neurons screaming.
And that also results in you being able
to compress the data better.
Yeah.
On the side of the day.
Okay, that's.
And just to be clear, I mean, the labs do this,
what's called spike sorting.
Usually once you have these like broadband,
like the fully digitized signals,
and then you run a bunch of different set of algorithms
to kind of tease apart,
it's just all of this for us is done on the device.
On the device.
In a very low power, custom, you know, built,
ASIC digital processing unit.
Highly heat constrained.
Highly heat constrained.
And the processing time from signal going in
and giving you the output is less than a microsecond,
which is, you know, a very, very short amount of time.
Oh yeah, so the latency has to be super short.
Oh wow.
Oh, that's a pain in the ass.
Yeah, latency is this huge, huge thing
that you have to deal with.
Right now, the biggest source of latency
comes from the Bluetooth.
The way in which they're packetized
and, you know, we bin them in 15 millisecond.
Oh, interesting.
So communication constrained.
Is there some potential innovation there
on the protocol used?
Okay.
Yeah, Bluetooth is definitely not our final
wireless communication protocol that we want to get to.
It's a highly.
Hence the N1 and the R1.
I imagine that increases.
NX, NX, RX.
Yeah, that's, you know, the communication protocol
because Bluetooth allows you to communicate
against farther distances than you need to.
So you can go much shorter.
Yeah, the only, well, the primary motivation
for choosing Bluetooth is that,
I mean, everything has Bluetooth.
All right, so you can talk to any device.
Interoperability is just absolutely essential,
especially in this early phase.
And in many ways, if you can access a phone or a computer,
you can do anything.
Well, it'd be interesting to step back
and actually look at, again, the same pipeline
that you mentioned for Nolan.
So what does this whole process look like?
From finding and selecting a human being to the surgery,
to the first time he's able to use this thing.
So we have what's called a patient registry
that people can sign up to hear more about the updates.
And that was a route to which Nolan applied.
And the process is that once the application comes in,
it contains some medical records
and we, you know, based on their medical eligibility
that there's a lot of different inclusion
and exclusion criteria for them to meet.
And we go through a pre-screening interview process
with someone from their link.
And at some point, we also go out to their homes
to do a BCI home audit.
Because one of the most kind of revolutionary part
about having this, and one system that is completely wireless
is that you can use it at home.
Like you don't actually have to go to the lab
and go to the clinic to get connectorized
to these like specialized equipment
that you can't take home with you.
So that's one of the key elements of, you know,
when we're designing the system
that we wanted to keep in mind.
Like, you know, people, you know,
hopefully would want to be able to use this every day
in the comfort of their home.
And so part of our engagement
and what we're looking for during BCI home audit
is to just kind of understand their situation,
what other assistive technology that they use.
And we should also step back and kind of say
that the estimate is 180,000 people live
with quadriplegia in the United States.
And each year an additional 18,000 suffer
a paralyzing spinal cord injury.
So these are folks who have a lot of challenges
living a life in terms of accessibility,
in terms of doing the things that many of us
just take for granted day to day.
And one of the things,
one of the goals of this initial study
is to enable them to have sort of digital autonomy
where they by themselves can interact
with a digital device using just their mind,
something that you're calling telepathy.
So digital telepathy,
where a quadriplegic can communicate with a digital device
in all the ways that we've been talking about,
control the mouse cursor,
enough to be able to do all kinds of stuff,
including play games and tweet and all that kind of stuff.
And there's a lot of people for whom life,
the basics of life are difficult
because of the things that have happened to them, so.
Yeah, I mean, movement is so fundamental to our existence.
I mean, even speaking involves movement
of mouth, lip, larynx.
And without that, it's extremely debilitating.
And there are many, many people that we can help.
And I mean, especially if you start to kind of look at
other forms of movement disorders
that are not just from spinal cord injury,
but from ALS, MS, or even stroke,
or just aging, right?
That leads you to lose some of that mobility,
that independence, it's extremely debilitating.
And all of these are opportunities to help people,
to help alleviate suffering,
to help improve the quality of life.
But each of the things you mentioned
is its own little puzzle
that needs to have increasing levels of capability
from a device like a neural length device.
And so the first one you're focusing on is,
it's just a beautiful word, telepathy.
So being able to communicate using your mind
wirelessly with a digital device.
Can you just explain this exactly what we're talking about?
Yeah, I mean, it's exactly that.
I mean, I think if you are able to control a cursor
and able to click and be able to get access
to computer or phone,
I mean, the whole world opens up to you.
And I mean, I guess the word telepathy,
if you kind of think about that as just definitionally,
being able to transfer information from my brain
to your brain without using some of the physical faculties
that we have, like voices.
But the interesting thing here is,
I think the thing that's not obviously clear
is how exactly it works.
So in order to move a cursor,
there's at least a couple of ways of doing that.
So one is you imagine yourself
maybe moving a mouse with your hand,
or you can then, which no one talked about,
like imagine moving the cursor with your mind.
But it's like, there is a cognitive step here
that's fascinating, because you have to use the brain
and you have to learn how to use the brain.
And you kind of have to figure it out dynamically,
like because you reward yourself if it works.
So you're like, I mean, there's a step that,
this is just a fascinating step,
because you have to get the brain
to start firing in the right way.
And you do that by imagining,
like fake it till you make it.
And all of a sudden, it creates the right kind of signal
that if decoded correctly, can create the kind of effect.
And then there's like noise around that
that you have to figure all of that out.
But on the human side,
imagine the cursor moving is what you have to do.
Yeah, he says using the force, the force.
I mean, isn't that just like fascinating to you
that it works?
Like to me it's like, holy shit, that actually works.
Like you could move a cursor with your mind.
You know, as much as you're learning to use that thing,
that thing's also learning about you,
like our model is constantly updating the weights to say,
oh, if someone is thinking about this sophisticated forms
of like spiking patterns,
like that actually means to do this, right?
So the machine is learning about the human
and the human is learning about the machine.
So there's a adaptability to the signal processing,
the decoding step.
And then there's the adaptation of Nolan, the human being.
Like the same way, if you give me a new mouse
and I move it, I learned very quickly about its sensitivity.
So I learned to move it slower.
And then there's other kinds of signal drift
and all that kind of stuff they have to adapt to.
So both are adapting to each other.
That's a fascinating, like software challenge on both sides,
the software on both, on the human software and the-
The organic and the inorganic.
The organic and the inorganic.
Anyway, so sorry to rudely interrupt.
So there's the selection that Nolan has passed
with flying colors, everything including that it's a BCI
friendly home, all of that.
So what is the process of the surgery and plantation,
the first moment when he gets to use the system?
The end to end, we say patient in to patient out
is anywhere between two to four hours.
In particular case for Nolan, it was about three
and a half hours.
And there's many steps leading to
the actual robot insertion, right?
So there's anesthesia induction
and we do intra-op CT imaging to make sure that we're
drilling the hole in the right location.
And this is also pre-planned beforehand.
Someone goes through, someone like Nolan would go through
FMRI and then they can think about wiggling their hand.
You know, obviously due to their injury,
it's not gonna actually lead to any sort of intended output,
but it's the same part of the brain that actually lights up
when you're imagining moving your finger
to actually moving your finger.
And that's one of the ways in which we can actually know
where to place our threads.
Cause we want to go into what's called the hand knob area
in the motor cortex and as much as possible,
densely put our electrode threads.
So yeah, we do intra-op CT imaging to make sure
and double check the location of the craniectomy
and surgeon comes in, does their thing
in terms of like skin incision, craniectomy.
So drilling of the skull.
And then there's many different layers of the brain.
There's what's called the dura,
which is a very, very thick layer that surrounds the brain.
That gets actually resectus in a process called directomy.
And that then exposed the pia and the brain
that you want to insert.
And by the time it's been around anywhere between
one to one and a half hours, robot comes in,
does this thing, placement of the targets,
inserting of the thread that takes anywhere between
20 to 40 minutes in the particular case for Nolan
was just under or just over 30 minutes.
And then after that, the surgeon comes in,
there's a couple other steps of like actually
inserting the dural substitute layer to protect the thread
as well as the brain.
And then yeah, screw in the implant and then skin flap
and then suture and then you're out.
So when Nolan woke up, what was that like?
Was the recovery like,
and what was the first time he was able to use it?
So he was actually immediately after the surgery,
like an hour after the surgery as he was waking up,
we did turn on the device,
make sure that we are recording neural signals.
And we actually did have a couple signals that we noticed
that he can actually modulate.
And what I mean by modulate is that he can think about
crunching his fist and you could see the spike
disappear and appear.
That's awesome.
And that was immediate, right?
Immediate after in the recovery room.
How cool is that?
Yeah, that's a human being.
I mean, what did that feel like for you?
This device in a human being,
a first step of a gigantic journey.
I mean, it's a historic moment.
Even just that spike, just to be able to modulate that.
You know, obviously there had been other,
as you mentioned, pioneers that have participated
in these groundbreaking BCI
investigational early feasibility studies.
So we're obviously standing in the shoulders
of the giants here.
We're not the first ones to actually put electrodes
in the human brain.
But I mean, just leading up to the surgery,
there was, I definitely could not sleep.
There's just, it's the first time that you're working
in a completely new environment.
We had a lot of confidence based on our benchtop testing
or preclinical R&D studies that the mechanism,
the threads, the insertion, all that stuff is very safe
and that it's obviously ready for doing this in a human.
But there's still a lot of unknown unknown
about can the needle actually insert?
I mean, we brought something like 40 needles
just in case they break and we ended up using only one.
But I mean, that was a level of just complete unknown, right?
Cause it's a very, very different environment.
And I mean, that's why we do clinical trial
in the first place to be able to test these things out.
So extreme nervousness and just many, many sleepless night
leading up to the surgery
and definitely the day before the surgery.
And it was an early morning surgery.
Like we started at seven in the morning
and by the time it was around 1030, everything was done.
But I mean, first time seeing that,
well, number one, just huge relief
that this thing is doing what it's supposed to do.
And two, I mean, just immense amount of gratitude
for Nolan and his family.
And then many others that have applied
and that we've spoken to and will speak to
are true pioneers in every war.
And I sort of call them the neural astronauts
or neural not.
You know, these amazing, just like in the sixties, right?
Like these amazing just pioneers, right?
Exploring the unknown outwards, in this case it's inward.
But an incredible amount of gratitude for them to,
you know, just participate and play a part.
And it's a journey that we're embarking on together.
But also like, I think it was just,
that was a very, very important milestone,
but our work was just starting.
So a lot of just kind of anticipation for,
okay, what needs to happen next?
What are set of sequences of events
that needs to happen for us to make it worthwhile
for both Nolan as well as us?
Just to linger on that, just a huge congratulations to you
and the team for that milestone.
I know there's a lot of work left,
but that is really exciting to see.
There's, that's a source of hope.
It's this first big step opportunity
to help hundreds of thousands of people.
And then maybe expand the realm of the possible
for the human mind for millions of people in the future.
So it's really exciting.
So like the opportunities are all ahead of us.
And to do that safely and to do that effectively
was really fun to see.
As an engineer, just watching other engineers come together
and do an epic thing, that was awesome.
Huge congrats.
Thank you, thank you.
It's, yeah, could not have done it without the team.
And yeah, I mean, that's the other thing
that I told the team as well,
of just this immense sense of optimism for the future.
I mean, it was a, it's a very important moment
for the company, needless to say,
as well as hopefully for many others out there
that we can help.
So speaking of challenges,
the Neuralink published a blog post
describing that some of the threads are attracted.
And so the performance as measured
by bits per second dropped at first,
but then eventually it was regained.
And that the whole story of how it was regained
is super interesting.
That's definitely something I'll talk to Bliss
and to no one about, but in general,
can you speak to this whole experience?
How was the performance regained?
And just the technical aspects
of the threads being attracted and moving.
The main takeaway is that in the end,
the performance have come back
and it's actually gotten better than it was before.
He's actually just beat the world record yet again,
last week to 8.5 BPS.
So I mean, he's just cranking and he's just improving.
The previous one was that he said was eight.
And correct, he said the previous world record
in human was 4.6.
So it's almost double.
And his goal is to try to get to 10,
which is roughly around kind of the median neural linker
using a mouse with the hand.
So it's getting there.
So yeah, so the performance was regained.
Yeah, better than before.
So that's a story on its own
of what took the BCI team to recover that performance.
It was actually mostly on kind of the signal processing.
And so, as I mentioned,
we were kind of looking at these spike outputs
from our electrodes.
And what happened is that kind of four weeks
into the surgery,
we noticed that the threads have slowly come out of the brain
and the way in which we noticed this at first obviously
is that, well, I think Nolan was the first to notice
that his performance was degrading.
And I think at the time,
we were also trying to do a bunch
of different experimentation,
different algorithms, different sort of UI, UX.
So it was expected that there will be variability
in the performance,
but we did see kind of a steady decline.
And then also the way in which we measure the health
of the electrodes or whether they're in the brain or not
is by measuring impedance of the electrode.
So we look at kind of the interfacial,
kind of the Randall circuit,
let's say the capacitance
and the resistance between the electro surface
and the medium.
And if that changes in some dramatic ways,
we have some indication
or if you're not seeing spikes on those channels,
you have some indications
that something's happening there.
And what we noticed is that looking at those impedance plot
and spike rate plots,
and also because we have those electrodes
recording along the depth,
you're seeing some sort of movement
that indicated that the rest were being pulled out.
And that obviously will have an implication
on the model side,
because if you're the number of inputs
that are going into the model is changing
because you have less of them,
that model needs to get updated, right?
But there were still signals.
And as I mentioned, similar to how even
when you place the signals on the surface of the brain
or farther away, like outside the skull,
you still see some useful signals.
What we started looking at is not just the spike occurrence
through this boss algorithm that I mentioned,
but we started looking at just the power
of the frequency band that is interesting
for Nolan to be able to modulate.
So once we kind of changed the algorithm
for the implant to not just give you the boss output,
but also these spike band power output,
that helped us sort of refine the model
with the new set of inputs.
And that was the thing that really ultimately
gave us the performance back.
In terms of, and obviously,
the thing that we want ultimately
and the thing that we are working towards
is figuring out ways in which we can keep those threads
intact for as long as possible
so that we have many more channels going into the model.
That's by far the number one priority
that the team is currently embarking on
to understand how to prevent that from happening.
The thing that I will say also is that,
as I mentioned, this is the first time ever
that we're putting these threads in the human brain.
And human brain, just for size reference,
is 10 times that of the monkey brain or the sheep brain.
And it's just a very, very different environment.
It moves a lot more.
It actually moved a lot more than we expected
when we did Nolan's surgery.
And it's just a very, very different environment
than what we're used to.
And this is why we do clinical trial.
We wanna uncover some of these issues
and failure modes earlier than later.
So in many ways, it's provided us with
this enormous amount of data
and information to be able to solve this.
And this is something that Neuralink is extremely good at.
Once we have set of clear objective and engineering problem,
we have enormous amount of talents
across many, many disciplines to be able to come together
and fix the problem very, very quickly.
But it sounds like one of the fascinating challenges here
is for the system and the decoding side
to be adaptable across different timescales.
So whether it's movement of threads
or different aspects of signal drift
sort of on the software of the human brain,
something changing, like Nolan talks about cursor drift
that could be corrected.
And there's a whole UX challenge to how to do that.
So it sounds like adaptability is like
a fundamental property that has to be engineered in.
It is, and I mean, I think, I mean,
as a company, we're extremely vertically integrated.
We make these thin film arrays in our own microfab.
Yeah, there's, like you said, built-in house.
This whole paragraph here from this blog post
is pretty gangster.
Building the technologies described above
has been no small feat.
And there's a bunch of links here
that I recommend people click on.
We constructed in-house microfabrication capabilities
to rapidly produce various iterations of thin film arrays
that constitute our electrode threads.
We created a custom femtosecond laser mill
to manufacture components with microlevel precision.
I think there's a tweet associated with this.
That's a whole thing that we can get into.
Yeah, this, okay.
What are we looking at here?
This thing.
So in less than one minute,
our custom-made femtosecond laser mill
cuts this geometry in the tips of our needles.
So we're looking at this weirdly shaped needle.
The tip is only 10 to 12 microns in width,
only slightly larger than the diameter of a red blood cell.
The small size allows threads to be inserted
with minimal damage to the cortex.
Okay, so what's interesting about this geometry?
So we're looking at this just geometry of a needle.
This is the needle that's engaging
with the loops in the thread.
So they're the ones that thread the loop
and then peel it from the silicon backing.
And then this is the thing that gets inserted
into the tissue.
And then this pulls out, leaving the thread.
And this kind of a notch or the shark tooth
that we used to call is the thing
that actually is grasping the loop.
And then it's designed in such ways,
such that when you pull out, it leaves the loop.
And the robot is controlling this needle.
Correct, so this is actually housed in a cannula.
And basically the robot has a lot of the optics
that look for where the loop is.
There's actually a four or five nanometer light
that actually causes the polyimid to fluoresce
so that you can locate the location of the loop.
So the loop lights up?
Yeah, yeah, they do.
It's a micron precision process.
What's interesting about the robot that it takes to do that?
That's pretty crazy.
That's pretty crazy that a robot is able to get
this kind of precision.
Yeah, our robot is quite heavy, our current version of it.
There is, I mean, it's like a giant granite slab
that weighs about a ton
because it needs to be sensitive to vibration,
environmental vibration.
And then as the head is moving,
at the speed that it's moving,
there's a lot of kind of motion control
to make sure that you can achieve that level of precision.
A lot of optics that kind of zoom in on that.
We're working on next generation of the robot
that is lighter, easier to transport.
I mean, it is a feat to move the robot.
And it's far superior to a human surgeon at this time
for this particular task.
Absolutely, I mean, let alone you try to actually thread
a loop in a sewing kit.
I mean, this is like,
we're talking like fractions of human hair.
These things are, it's not visible.
So continuing the paragraph,
we developed novel hardware and software testing systems
such as our accelerated lifetime testing racks
and simulated surgery environment, which is pretty cool.
To stress test and validate the robustness
of our technologies,
we performed many rehearsals of our surgeries
to refine our procedures and make them second nature.
This is pretty cool.
We practice surgeries on proxies
with all the hardware and instruments needed
in our mock or in the engineering space.
This helps us rapidly test and measure.
So there's like proxies.
Yeah, this proxy is super cool, actually.
So there's a 3D printed skull from the images
that is taken at Barrow,
as well as this hydrogel mix,
sort of synthetic polymer thing
that actually mimics the mechanical properties of the brain.
It also has vasculature of the person.
So basically what we're talking about here,
and there's a lot of work that has gone into
making this set proxy,
that it's about finding the right concentration
of these different synthetic polymers
to get the right set of consistency
for the needle dynamics as they're being inserted.
But we practice this surgery with the person,
Nolan's basically physiology and brain
many, many times prior to actually doing the surgery.
So every step, every step, every step.
Yeah, like where does someone stand?
Like, I mean, what you're looking at is the picture,
this is in our office,
of this kind of corner of the robot engineering space
that we have created this mock OR space
that looks exactly like what they would experience,
all the staff would experience doing their actual surgery.
So, I mean, it's just kind of like any dense rehearsal
where you know exactly where you're gonna stand
at what point,
and you just practice that over and over and over again
with an exact anatomy of someone
that you're going to surgeries.
And it got to a point where a lot of our engineers,
when we created a craniectomy,
they're like, oh, that looks very familiar.
We've seen that before.
Yeah, man, there's wisdom you can gain
through doing the same thing over and over and over.
It's like a Giro dreams of sushi kind of thing.
Because then it's like Olympic athletes visualize
the Olympics.
And then once you actually show up, it feels easy.
It feels like any other day.
It feels almost boring winning the gold medal.
Because you visualize this so many times,
you've practiced this so many times
and nothing about it is new.
It's boring.
You win the gold medal, it's boring.
And the experience they talk about is mostly just relief.
Probably that they don't have to visualize it anymore.
Yeah, the power of the mind to visualize and where,
I mean, there's a whole field that studies
where muscle memory lies in cerebellum.
Yeah, it's incredible.
I think it was a good place to actually ask
sort of the big question that people might have is,
how do we know every aspect of this
that you describe is safe?
At the end of the day, the gold standard
is to look at the tissue.
What sort of trauma did you cause the tissue?
And does that correlate to whatever behavioral anomalies
that you may have seen?
And that's the language to which we can communicate
about the safety of inserting something into the brain
and what type of trauma that you can cause.
So we actually have an entire department
of pathology that looks at these tissue slices.
There are many steps that are involved in doing this
once you have studies that are launched
with particular endpoints in mind.
At some point, you have to euthanize the animal
and then you go through a necropsy
to kind of collect the brain tissue samples.
You fix them in formalin and you like gross them,
you section them and you look at individual slices
just to see what kind of reaction or lack thereof exists.
So that's kind of the language to which FDA speaks
and as well for us to kind of evaluate the safety
of the insertion mechanism as well as the threats
at various different time points, both acute,
so anywhere between zero to three months
to beyond three months.
So those are kind of the details
of an extremely high standard of safety
that has to be reached.
FDA supervises this, but this in general
just a very high standard.
And every aspect of this, including the surgery,
I think Matthew McDougall has mentioned it like
the standard is let's say, how to put it politely,
higher than maybe some other operations
that we take for granted.
So the standard for all the surgical stuff here
is extremely high.
Very high.
I mean, it's a highly, highly regulated environment
with the governing agencies that scrutinize
every medical device that gets marketed.
And I think it's a good thing.
It's good to have those high standards
and we try to hold extremely high standards
to kind of understand what sort of damage of any
these innovative emerging technologies
and new technologies that we're building are.
And so far we have been extremely impressed
by lack of immune response from these threads.
Speaking of which, you talk to me with excitement
about the histology and some of the images
that you're able to share.
Can you explain to me what we're looking at?
Yeah, so what you're looking at is a stained tissue image.
So this is a sectioned tissue slice from an animal
that was implanted for seven months.
So kind of a chronic time point.
And you're seeing all these different colors
and each color indicates specific types of cell types.
So purple and pink are astrocytes and microglia,
respectively, they're types of glial cells.
And yeah, the other thing that people may not be aware of
is your brain is not just made up of soup of neurons
and axons, there are other cells like glial cells
that actually kind of is the glue and also react
if there are any trauma or damage to the tissue.
With the brown or the neurons?
The brown are the neurons.
So hotter neurons.
So what you're seeing is in this kind of macro image,
you're seeing these like circle highlighted in white,
the insertion sites.
And when you zoom into one of those,
you see the threads.
And then in this particular case,
I think we're seeing about the 16 wires
that are going into the page.
And the incredible thing here is the fact
that you have the neurons that are these brown structures
or brown circular or elliptical thing
that are actually touching and abutting the threads.
So what this is saying is that there's basically zero trauma
that's caused during this insertion.
And with these neural interfaces,
these micro luxures that you insert,
that is one of the most common mode of failure.
So when you insert these threads, like the utare,
it causes a neuronal death around the site
because you're inserting a foreign object, right?
And that kind of elicit these like immune response
through microglia and astrocytes.
They form this like protective layer around it.
Oh, not only are you killing the neuron cells,
but you're also creating this protective layer
that then basically prevents you
from recording neural signals
because you're getting further and further away
from the neurons that you're trying to record.
And that is the biggest mode of failure.
And in this particular example,
in that inset, it's about 50 micron with that scale bar.
The neurons just seem to be attracted to it.
And so there's certainly no trauma.
That's such a beautiful image, by the way.
So the brown are the neurons
and for some reason I can't look away.
It's really cool.
And the way that these things like,
I mean, your tissues generally don't have
these beautiful colors.
This is multiplex stain
that uses these different proteins
that are staining these at different colors.
We use very standard set of staining techniques
with HE, EBA-1 and NUEN and GFAP.
So if you go to the next image,
this is also kind of illustrates the second point
because you can make an argument.
And initially when we saw the previous image,
we said, oh, like are the threads just floating?
Like what is happening here?
Like are we actually looking at the right thing?
So what we did is we did another stain
and this is all done in-house
of this Lasson's Trichrome stain,
which is in blue that shows these collagen layers.
So the blue basically,
like you don't want the blue around the implant threads
because that means that there's some sort of scarring
that's happened.
And what you're seeing,
if you look at individual threads
is that you don't see any of the blue,
which means that there has been absolutely
or very, very minimal to a point
where it's not detectable amount of trauma
in these inserted threads.
So that presumably is one of the big benefits
of having this kind of flexible thread.
Yeah, so we think this is primarily due to the size
as well as the flexibility of the threads.
Also the fact that R1 is avoiding vasculature.
So we're not disrupting
or we're not causing damage to the vessels
and not breaking any of the blood brain barrier
has basically caused the immune response to be muted.
But this is also a nice illustration of the size of things.
So this is the tip of the thread.
Yeah, those are neurons, they're-
And they're neurons and this is the thread listening
and the electrodes are positioned how?
Yeah, so this is what you're looking at
is not electrode themselves.
Those are the conductive wires.
So each of those should probably be two micron and width.
So what we're looking at is we're looking
at the coronal slice.
So we're looking at some slice of the tissue.
So as you go deeper, you'll obviously have less
and less of the tapering of the thread.
But yeah, the point basically being
that there's just kind of cells around the insert site
which is just an incredible thing to see.
I've just never seen anything like this.
How easy and safe is it to remove the implant?
Yeah, so it depends on when.
In the first three months or so after the surgery,
there's a lot of kind of tissue modeling that's happening.
Similar to when you got a cut,
you obviously start over first couple of weeks
or depending on the size of the wound scar tissue forming.
There are these like contractive
and then in the end they turn into scab
and you can scab it off.
The same thing happens in the brain
and it's a very dynamic environment.
And before the scar tissue or the new membrane
or the new membrane that forms,
it's quite easy to just pull them out.
And there's minimal trauma that's caused during that.
Once the scar tissue forms and with Nolan as well,
we believe that that's the thing
that's currently anchoring the threads.
So we haven't seen any more movements since then.
So they're quite stable.
It gets harder to actually completely extract the threads.
So our current method for removing the device
is cutting the thread, leaving the tissue intact
and then unscrewing and taking the implant out.
And that hole is now gonna be plugged
with either another neural link
or just with kind of a peak-based plastic-based cap.
Is it okay to leave the threads in there forever?
Yeah, we think so.
We've done studies where we left them there
and one of the biggest concerns that we had is like,
do they migrate and do they get to a point
where they should not be?
We haven't seen that again.
Once the scar tissue forms, they get anchored in place.
And I should also say that when we say upgrades,
we're not just talking in theory here.
We've actually upgraded many, many times.
Most of our monkeys or non-human primates,
NHP, have been upgraded.
Pager, who you saw playing Minpong,
has the latest version of the device since two years ago
and is seemingly very happy and healthy and fat.
So what's designed for the future, the upgrade procedure?
So maybe for Nolan, what would the upgrade look like?
It was essentially what you're mentioning.
Is there a way to upgrade sort of the device internally
where you take it apart and sort of keep the capsule
and upgrade the internals?
Yeah, so there are a couple of different things here.
So for Nolan, if we were to upgrade,
what we would have to do is either cut the threads
or extract the threads,
depending on kind of the situation there
in terms of how they're anchored or scarred in.
If you were to remove them with the dural substitute,
you have an intact brain,
so you can reinsert different threads
with the updated implant package.
There are a couple of different other ways
that we're thinking about the future
of what the upgradable system looks like.
One is, at the moment, we currently remove the dura,
this kind of thick layer that protects the brain,
but that actually is the thing
that actually proliferates the scar tissue formation.
So typically, the general rule of thumb
is you wanna leave the nature as is
and not disrupt it as much.
So looking at ways to insert the threads through the dura,
which comes with different set of challenges,
such as it's a pretty thick layer,
so how do you actually penetrate that
without breaking the needle?
So we're looking at different needle design for that,
as well as the loop engagement.
The other biggest challenges are,
it's quite opaque optically with white light illumination,
so how do you avoid,
still, the biggest advantage that we have
of avoiding vasculature?
How do you image through that?
How do you actually still mediate that?
So there are other imaging techniques
that we're looking at to enable that.
But our hypothesis is that,
and based on some of the early evidence that we have,
doing through the dura insertion
will cause minimal scarring
that causes them to be much easier to extract over time.
And the other thing that we're also looking at,
this is gonna be a fundamental change
in the implant architecture,
is at the moment, it's a monolithic single implant
that comes with a thread that's bonded together,
so you can't actually separate the thing out,
but you can imagine having two-part implant,
bottom part that is the thread that are inserted,
that has the chips and maybe a radio and some power source,
and then you have another implant
that has more of the computational heavy load
and the bigger battery.
And then one can be under the dura,
one can be above the dura,
being the plug for the skull.
They can talk to each other,
but the thing that you wanna upgrade,
the computer and not the threads,
if you wanna upgrade that, you just go in there,
remove the screws and then put in the next version
and you're off the, it's a very, very easy surgery too.
Like you do a skin incision, slip this in, screw,
probably be able to do this in 10 minutes.
So that would allow you to reuse the threads sort of?
Correct.
So I mean, this leads to the natural question of
what is the pathway to scaling
the increase in the number of threads?
Is that a priority?
Is that like, what's the technical challenge there?
Yeah, that is a priority.
So for next versions of the implant,
the key metrics that we're looking to improve
are number of channels,
just recording from more and more neurons.
We have a pathway to actually go from currently 1,000
to hopefully 3,000, if not 6,000 by end of this year.
And then end of next year, we wanna get to even more, 16,000.
Wow.
There's a couple of limitations to that.
One is obviously being able to photograph graphically
print those wires.
As I mentioned, it's two micron in width and in spacing.
Obviously there are chips that are much more advanced
than those types of resolution.
And we have some of the tools that we have brought in house
to be able to do that.
So traces will be narrower
just so that you have to have more of the wires
coming up into the chip.
Chips also cannot linearly consume more energy
as you have more and more channels.
So there's a lot of innovations in the circuit
and architecture as well as the circuit design topology
to make them lower power.
You need to also think about if you have all of these spikes,
how do you send that off to the end application?
So you need to think about bandwidth limitation there
and potentially innovations in signal processing.
Physically, one of the biggest challenges
is gonna be the interface.
It's always the interface that breaks,
bonding the stem foam array to the electronics.
It starts to become very, very highly dense interconnects.
So how do you connectorize that?
There's a lot of innovations
in kind of the 3D integrations in the recent years
that we can take advantage of.
One of the biggest challenges that we do have is
forming this hermetic barrier, right?
This is an extremely harsh environment
that we're in the brain.
So how do you protect it from, yeah,
like the brain trying to kill your electronics
to also your electronics leaking things
that you don't want into the brain
and that forming that hermetic barrier
is gonna be a very, very big challenge
that I think are actually well suited to tackle.
How do you test that?
Like what's the development environment
to simulate that kind of harshness?
Yeah, so this is where the accelerated life tester
essentially is a brain in a vat.
It literally is a vessel that is made up of,
and again, for all intents and purpose
for this particular types of test,
your brain is a saltwater.
And you can also put some other set of chemicals
like reactive oxygen species that get at
kind of these interfaces
and trying to cause a reaction to pull it apart.
But you could also increase the rate
at which these interfaces are aging
by just increasing temperature.
So every 10 degrees Celsius that you increase,
you're basically accelerating time by two X.
And there's a limit as to how much temperature
you wanna increase,
because at some point there's some other nonlinear dynamics
that causes you to have other nasty gases to form
that just is not realistic in an environment.
So what we do is we increase in our ALT chamber
by 20 degrees Celsius that increases the aging by four times.
So essentially one day in ALT chamber
is four day in calendar year.
And we look at whether the implants still are intact,
including the threats and-
And operation and all of that.
And operation and all of that.
Obviously it's not an exact same environment as a brain
because brain has mechanical,
other more biological groups that attack at it.
But it is a good test environment,
testing environment for at least the enclosure
and the strength of the enclosure.
And I mean, we've had implants,
the current version of the implant
that has been in there for,
I mean, close to two and a half years,
which is equivalent to a decade and they seem to be fine.
So it's interesting that the brain,
so basically a close approximation is warm salt water,
hot salt water is a good testing environment.
By the way, I'm drinking element,
which is basically salt water,
which is making me kind of,
it doesn't have computational power the way the brain does,
but maybe in terms of,
and in terms of other characteristics is quite similar.
And I'm consuming it.
Yeah, you have to get it in the right pH too.
And then consciousness will emerge.
Yeah, no.
By the way, the other thing that also is interesting
about our enclosure is,
if you look at our implant,
it's not your common looking medical implant
that usually is encased in a titanium can
that's laser welded.
We use this polymer called PCTFE,
polychloro trifluoroethylene,
which is actually commonly used in blister packs.
So when you have a pill and you're trying to pop the pill,
there's like kind of that plastic membrane.
That's what this is.
No one's actually ever used this except us.
And the reason we wanted to do this
is because it's electromagnetically transparent.
So when we talked about
the electromagnetic inductive charging with titanium can,
usually if you want to do something like that,
you have to have a sapphire window
and it's a very, very tough process to scale.
So you're doing a lot of iteration here
in every aspect of this, the materials, the software,
the hardware.
The whole shebang.
So, okay.
So you mentioned scaling.
Is it possible to have multiple neural link devices
as one of the ways of scaling
to have multiple neural link devices implanted?
That's the goal.
That's the goal.
Yeah, we've had, I mean, our monkeys have had
two neural links, one in each hemisphere.
And then we're also looking at potential of having
one in motor cortex, one in visual cortex
and one in, wherever other cortex.
So focusing on a particular function, one neural link device.
Correct.
I mean, I wonder if there's some level of customization
that can be done on the compute side.
So for the motor cortex.
Absolutely.
That's the goal.
And we talk about, at Neuralink,
building a generalized neural interface to the brain.
And that also is strategically how we're approaching this
with marketing and also with regulatory,
which is, hey, look, we have the robot
and the robot can access any part of the cortex.
Right now we're focused on motor cortex
with current version of the N1
that's specialized for motor decoding tasks.
But also at the end of the day,
there's kind of a general compute available there.
But typically if you want to really get down
to kind of hyper-optimizing for power and efficiency,
you do need to get to some specialized function, right?
But what we're saying is, hey,
you are now used to this robotic insertion techniques,
which took many, many years of showing data
and conversation with the FDA
and also internally convincing ourselves that this is safe.
And now the difference is that if we go to other parts
of the brain, like visual cortex,
which we're interested in as our second product,
obviously it's a completely different environment.
The cortex is laid out very, very differently.
It's gonna be more stimulation focus rather than recording,
just kind of creating visual percepts.
But in the end, we're using the same
thin film array technology.
We're using the same robot insertion technology.
We're using the same packaging technology.
Now it's more of the conversations focused around
what are the differences and what are the implications
of those differences in safety and efficacy.
The way you said second product
is both hilarious and awesome to me,
that product being restoring sight for blind people.
So can you speak to stimulating the visual cortex?
I mean, the possibilities there are just incredible
to be able to give that gift back to people
who don't have sight or even any aspect of that.
Can you just speak to the challenges of,
there's several challenges here.
One of which is, like you said,
from recording to the stimulation,
just any aspect of that that you're both excited
and see the challenges of.
Yeah, I guess I'll start by saying that we actually
have been capable of stimulating through our thin film array
as well as other electronics for years.
We have actually demonstrated some of that capabilities
for reanimating the limb in the spinal cord.
Obviously for the current EFS study,
we've hardware disabled that.
So that's something that we wanted to embark
as a separate journey.
And obviously there are many, many different ways
to write information into the brain.
The way in which we're doing that
is through passing electrical current
and causing that to really change the local environment
so that you can artificially cause the neurons
to depolarize in nearby areas.
For vision specifically,
the way our visual system works,
it's both well understood.
I mean, anything with brain,
there are aspects of it that's well understood,
but in the end, we don't really know anything.
But the way visual system works is that
you have photon hitting your eye
and in your eyes,
there are these specialized cells
called photoreceptor cells
that convert the photon energy into electrical signals
and then gets projected to your back of your head,
your visual cortex.
It goes through actually a thalamic system called LGN
that then projects it out.
And then in the visual cortex,
there's visual area one or V1,
and then there's a bunch of other higher level processing
layers like V2, V3.
And there are actually kind of interesting parallels
and when you study the behaviors
of these convolutional neural networks,
like what the different layers of the network is detecting,
first they're detecting like these edges
and they're then detecting some more natural curves
and then they start to detect like objects, right?
Kind of similar thing happens in the brain
and a lot of that has been inspired
and also it's been kind of exciting
to see some of the correlations there.
But things like from there where those cognition arise
and where is color encoded,
there's just not a lot of understanding,
fundamental understanding there.
So in terms of kind of bringing sight
back to those that are blind,
there are many different forms of blindness.
There's actually a million people,
one million people in the U.S. that are legally blind.
That means like certain score below
in kind of the visual test.
I think it's something like if you can see something
at 20 feet distance that normal people
can see at 200 feet distance,
like if you're worse than that, you're legally blind.
So fundamental, that means you can't function effectively
correct using sight in the world.
Yeah, like to navigate your environment.
And yeah, there are different forms of blindness.
There are forms of blindness where
there's some degeneration of your retina,
his photoreceptor cells,
and rest of your visual processing
that I described is intact.
And for those types of individuals,
you may not need to maybe stick electrodes
into the visual cortex.
You can actually build retinal prosthetic devices
that actually just replaces a function of that retinal cells
that are degenerated.
And there are many companies that are working on that,
but that's a very small slice.
There'll be a significant, still smaller slice
of folks that are legally blind.
If there's any damage along that circuitry,
whether it's in the optic nerve or just the LGN circuitry
or any break in that circuit,
that's not gonna work for you.
And the source of where you need to actually
cause that visual percepts to happen
because your biological mechanism without doing that
is by placing electrodes in the visual cortex
in the back of your head.
And the way in which this would work
is that you would have an external camera,
whether it's something as unsophisticated as a GoPro
or some sort of wearable Ray-Ban type glasses
that Meta's working on that captures a scene, right?
And that scene is then converted to a set
of electrical impulses or stimulation pulses
that you would activate in your visual cortex
through these thin foam arrays.
And by playing some concerted kind of orchestra
of these stimulation patterns,
you can create what's called phosphines,
which are these kind of white yellowish dots
that you can also create by just pressing your eyes.
You can actually create those percepts
by stimulating the visual cortex.
And the name of the game is really have many of those
and have those percepts, the phosphines,
be as small as possible so that you can start to tell apart
like they're the individual pixels of the screen, right?
So if you have many, many of those,
potentially you'll be able to,
in the long term, be able to actually
get naturalistic vision,
but in the short term to maybe midterm,
being able to at least be able to have object detection
algorithms run on your glasses, the pre-pop processing units,
and then being able to at least see the edges of things
so you don't bump into stuff.
It's incredible, this is really incredible.
So you basically would be adding pixels
and your brain would start to figure out
what those pixels mean.
And like with different kinds of assistant
and the signal processing on all fronts.
Yeah, the thing that actually, it's a couple of things.
One is, you know, obviously if you're blind from birth,
the way brain works, especially in the early age,
neuroplasticity is really nothing other than, you know,
kind of your brain and different parts of your brain
fighting for the limited territory.
And I mean, very, very quickly,
you see cases where, you know, people that are,
I mean, you also hear about people who are blind
that have heightened sense of hearing or some other senses.
And the reason for that is because that cortex
that's not used just gets taken over by
these different parts of the cortex.
So for those types of individuals,
I mean, I guess they're going to have to now map
some other parts of their senses
into what they call vision,
but it's going to be obviously a very, very different
conscious experience before.
So I think that's an interesting caveat.
The other thing that also is important to highlight
is that we're currently limited by our biology
in terms of the wavelength that we can see.
There's a very, very small wavelength
that is a visible light wavelength
that we can see with our eyes.
But when you have an external camera with this BCI system,
you're not limited to that.
You can have infrared, you can have UV,
you can have whatever other spectrum that you want to see.
And whether that gets mapped
to some sort of weird conscious experience,
I have no idea.
But when I, you know, oftentimes I talk to people
about the goal of Neuralink being,
going beyond the limits of our biology.
That's sort of what I mean.
And if you're able to control the kind of raw signal,
is that when we use our sight,
we're getting the photons,
and there's not much processing on it.
If you're able to control that signal,
maybe you can do some kind of processing.
Maybe you do object detection ahead of time.
Yeah.
You're doing some kind of pre-processing,
and there's a lot of possibilities to explore that.
So it's not just increasing sort of thermal imaging,
that kind of stuff,
but it's also just doing some kind of interesting processing.
I mean, my theory of how like visual system works also,
is that, I mean,
there's just so many things happening in the world,
and there's a lot of photons that are going into your eye.
And it's unclear exactly
where some of the pre-processing steps are happening.
But I mean, I actually think that just from a
fundamental perspective,
there's just so much,
the reality that we're in, if it's a reality,
is so, there's so much data.
And I think humans are just unable to actually
like eat enough actually to process all that information.
So there's some sort of filtering that does happen,
whether that happens in the retina,
whether that happens in different layers
of the visual cortex, unclear.
But like the analogy that I sometimes think about is,
if your brain is a CCD camera,
and all of the information in the world is a sun,
and when you try to actually look at the sun
with the CCD camera,
it's just gonna saturate the sensors, right?
Because it's an enormous amount of energy.
So what you do is you end up adding these filters, right?
To just kind of narrow the information that's coming to you
and being captured.
And I think,
things like our experiences or our,
you know, like drugs like prophofol,
that like anesthetic drug or, you know, psychedelics,
what they're doing is they're kind of swapping out
these filters and putting in new ones
or removing older ones
and kind of controlling our conscious experience.
Yeah, man, not to distract from the topic,
but I just took a very high dose of ayahuasca
in the Amazon jungle.
So yes, it's a nice way to think about it.
You're swapping out different experiences.
And with Neuralink being able to control that,
primarily at first to improve function,
not for entertainment purposes or enjoyment purposes, but-
Yeah, giving back lost functions.
Well, giving back lost functions.
And there, especially when the function is completely lost,
anything is a huge help.
Would you implant a Neuralink device in your own brain?
Absolutely.
I mean, maybe not right now, but absolutely.
What kind of capability once reached,
you start getting real curious
and almost get a little antsy,
like jealous of people as you watch them getting planted?
Yeah, I mean, I think, I mean,
even with our early participants,
if they start to do things that I can't do,
which I think is in the realm of possibility
for them to be able to get, you know, 15, 20,
if not like 100 BPS, right?
There's nothing that fundamentally stops us
from being able to achieve that type of performance.
I mean, I would certainly get jealous that they can do that.
I should say that watching Noah and I get a little jealous
because he's having so much fun.
And it seems like such a chill way to play video games.
I mean, the thing that also is hard to appreciate sometimes
is that, you know, he's doing these things
while talking and I mean, it's multitasking, right?
So it's clearly, it's obviously cognitively intensive,
but similar to how, you know, when we talk,
we move our hands like these things,
like, you know, like are multitasking.
I mean, he's able to do that.
And, you know, you won't be able to do that
with other assistive technology as far as I'm aware.
You know, if you're obviously using like
an eye tracking device, you know,
you're very much fixated on that thing
that you're trying to do.
And if you're using voice control, I mean,
like if you say some other stuff,
yeah, you don't get to use that.
Yeah, the multitasking aspect of that
is really interesting.
So it's not just the BPS for the primary task.
It's the parallelization of multiple tasks.
If you measure the BPS for the entirety
of the human organism.
So if you're talking and doing a thing with your mind
and looking around also, I mean,
there's just a lot of parallelization
that can be happening.
But I mean, I think at some point for him,
like if he wants to really achieve those high level BPS,
it does require like, you know, full attention, right?
And that's a separate circuitry that is a big mystery,
like how attention works and, you know.
Yeah, attention like cognitive load
have done a very loud literature on people doing two tasks.
Like you have your primary task and a secondary task.
And the secondary task is a source of distraction.
And how does that affect the performance
of the primary task?
And there's depending on the task,
there's a lot of interesting,
I mean, this is an interesting computational device, right?
And I think there's, to say the least,
a lot of novel insights that can be gained from everything.
I mean, I personally am surprised
that Nolan is able to do such incredible control
of the cursor while talking
and also being nervous at the same time.
Because he's talking like all of us are
if you're talking in front of the camera, you get nervous.
So all of those are coming into play
and he's able to still achieve high performance.
And it's surprising.
I mean, all of this is really amazing.
And I think just after researching this really in depth,
I kind of want it in your link.
I kind of, get in line.
And also the safety, get in line.
Well, we should say the registry is for people
who have quadriplegia and corral, that kind of stuff.
So there'll be a separate line for people.
They're just curious.
Like myself.
So now that Nolan, patient P1
is part of the ongoing prime study,
what's the high level vision for P2, P3, P4, P5,
and just the expansion into other human beings
that are getting to experience this implant?
Yeah, I mean, the primary goal is,
for our study in the first place
is to achieve safety on points.
Just understand safety of this device
as well as the implantation process.
And also at the same time, understand the efficacy
and the impact that it could have
on the potential users' lives.
And just because you're living with tetraplegia,
it doesn't mean your situation is same
as another person living with tetraplegia.
It's a wildly, wildly varying.
And it's something that we're hoping to also understand
how our technology can serve
not just a very small slice of those individuals,
but broader group of individuals
and being able to get the feedback
to just really build just the best product for them.
There's obviously also goals that we have
and the primary purpose of the early feasibility study
is to learn from each and every participant
to improve the device, improve the surgery
before we embark on what's called a pivotal study
that then is a much larger trial
that starts to look at statistical significance
of your endpoints.
And that's required before you can then market the device.
And that's how it works in the US.
And just generally around the world,
that's the process you follow.
So our goal is to really just understand
from people like Nolan, P2, P3, future participants,
what aspects of our device needs to improve.
If it turns out that people are like,
I really don't like the fact that it lasts only six hours.
I wanna be able to use this computer for like 24 hours.
I mean, that is a user needs and user requirements,
which we can only find out
from just being able to engage with them.
So before the pivotal study,
there's kind of like a rapid innovation
based on individual experiences.
You're learning from individual people,
how they use it, like high resolution details
in terms of like cursor control and signal
and all that kind of stuff to like life experience.
Yeah, so there's hardware changes,
but also just firmware updates.
So even when we had that sort of recovery event for Nolan,
he now has the new firmware
that he has been updated with.
And similar to how like your phones get updated all the time
with new firmware for security patches,
whatever new functionality, UI, right?
And that's something that is possible with our implant.
It's not a static one-time device
that can only do the thing that it said it can do.
I mean, similar to Tesla,
you can do over the air firmware updates
and now you have completely new user interface
and all this bells and whistles and improvements
on everything, like the latest, right?
When we say generalized platform,
that's what we're talking about.
Yeah, it's really cool how the app that Nolan is using,
there's like calibration, all that kind of stuff.
And then there's update.
You just click and get an update.
What other future capabilities are you kind of looking to?
You said vision, that's a fascinating one.
What about sort of accelerated typing
or speech or this kind of stuff?
Yeah. And what else is there?
Yeah, those are still in the realm of movement program.
So largely speaking, we have two programs.
We have the movement program
and we have the vision program.
The movement program currently is focused around
the digital freedom.
As you can easily guess,
if you can control 2D cursor in the digital space,
you could move anything in the physical space.
So robotic arms, wheelchair, your environment,
or even really like, whether it's through the phone
or just like directly to those interfaces,
so like to those machines.
So we're looking at ways to kind of expand
those types of capability, even for Nolan.
That requires conversation with the FDA
and kind of showing safety data for,
if there's a robotic arm or a wheelchair that,
we can guarantee that they're not gonna hurt themselves
accidentally, right?
It's very different if you're moving stuff
in the digital domain versus like in the physical space,
you can actually potentially cause harm to the participants.
So we're working through that right now.
Speech does involve different areas of the brain.
Speech prosthetic is very, very fascinating.
And there's actually been a lot of really amazing work
that's been happening in academia.
You know, Sergei Stavisky at UC Davis,
Jamie Henderson, and you know,
late Krishna Shinoi at Stanford,
doing just some incredible amount of work
in improving speech neuroprosthetics.
And those are actually looking more at parts
of the motor cortex that are controlling,
you know, these vocal articulators.
And, you know, being able to like,
even by mouthing the word or imagine speech,
you can pick up those signals.
The more sophisticated higher level processing areas
like the Broca's area or, you know, Wernicke's area,
those are still very, very big mystery
in terms of the underlying mechanism
of how all that stuff works.
But yeah, I mean, I think, I think neurolinks,
the eventual goal is to kind of understand those things
and be able to provide a platform and tools
to be able to understand that and study that.
This is where I get to the pothead questions.
Do you think we can start getting insight
into things like thought?
So speech is, there's a muscular component, like you said.
There's like the act of producing sounds.
But then what about the internal things like cognition?
Like low level thoughts and high level thoughts.
Do you think we'll start noticing kind of signals
that could be picked up?
They could, they could be understood,
they could be maybe used in order to interact
with the outside world.
In some ways, like, I guess this starts to kind of get
into the heart problem of consciousness.
And I mean, on one hand,
all of these are at some point set of electrical signals
that from there, maybe it in itself is giving you
the cognition or the meaning or somehow human mind
is an incredibly amazing storytelling machine.
So we're telling ourselves and fooling ourselves
that there's some interesting meaning here.
But I certainly think that BCI,
really BCI at the end of the day is a set of tools
that help you kind of study the underlying mechanisms
in both like local, but also broader sense.
And whether there's some interesting patterns
of like electrical signal,
that means like you're thinking this versus,
and you can either like learn from like many, many sets
of data to correlate some of that
and be able to do mind reading or not, I'm not sure.
I certainly would not kind of rule that out as a possibility,
but I think BCI alone probably can't do that.
There's probably additional set of tools
and framework and also like just hard problem
of consciousness at the end of the day is rooted
in this philosophical question of like,
what is the meaning of it all?
What's the nature of our existence?
Like, where's the mind emerged from this complex network?
Yeah, how does the subjective experience emerge
from just a bunch of spikes, electrical spikes?
Yeah, I mean, we do really think about BCI
and what we're building as a tool for understanding.
The mind, the brain, the only question that matters.
There's actually, there actually is some biological
existence proof of like what it would take
to kind of start to form some of these experiences
that may be unique.
If you actually look at every one of our brains,
there are two hemispheres, there's a left side of brain,
there's a right side of brain.
And I mean, unless you have some other conditions,
you normally don't feel like left legs or right legs.
Like you just feel like one legs, right?
So what is happening there, right?
If you actually look at the two hemispheres,
there's a structure that kind of connectorized the two
called the corpus callosum that is supposed to have
around 200 to 300 million connections or axons.
So whether that means that's the number of interface
and electrodes that we need to create some sort of mind
meld or from that, like whatever new conscious experience
that you can experience.
But I do think that there's like kind of an interesting
existence proof that we all have.
And that threshold is unknown at this time.
Oh yeah, these things, everything in this domain
is speculation, right?
And then there will be,
you'd be continuously pleasantly surprised.
Do you see a world where there's millions of people,
like tens of millions, hundreds of millions of people
walking around with a neural link device
or multiple neural link devices in their brain?
I do, first of all, there are,
like if you look at worldwide people suffering
from movement disorders and visual deficits,
I mean, that's in the tens if not hundreds
of millions of people.
So that alone, I think there's a lot of benefit
and potential good that we can do
with this type of technology.
And when you start to get into kind of neuro,
like psychiatric application, depression,
anxiety, hunger, or obesity, right?
Like mood control of appetite.
I mean, that starts to become very real to everyone.
Not to mention that every,
most people on earth have a smartphone.
And once BCI starts competing with a smartphone
as a preferred methodology of interacting
with the digital world,
that also becomes an interesting thing.
Oh yeah, I mean, that, yeah,
this is even before going to that, right?
I mean, there's like almost,
I mean, the entire world that could benefit
from these types of things.
And then, yeah, like if we're talking about
kind of next generation of how we interface with machines
or even ourselves, in many ways, I think BCI
can play a role in that.
And some of the things that I also talk about is,
I do think that there is a real possibility
that you could see eight billion people
walking around with Neuralink.
Well, thank you so much for pushing ahead.
And I look forward to that exciting future.
Thanks for having me.
Thanks for listening to this conversation with DJ Sa.
And now, dear friends, here's Matthew McDougall,
the head neurosurgeon at Neuralink.
When did you first become fascinated with the human brain?
Since forever, as far back as I can remember,
I've been interested in the human brain.
I mean, I was a thoughtful kid and a bit of an outsider.
And you sit there thinking about what the most important
things in the world are.
In your little tiny adolescent brain.
And the answer that I came to, that I converged on was,
that all of the things you can possibly conceive of
as things that are important for human beings to care about
are literally contained in the skull.
Both the perception of them and their relative values
and the solutions to all our problems
and all of our problems are all contained in the skull.
And if we knew more about how that worked,
how the brain encodes information and generates desires
and generates agony and suffering,
we could do more about it.
You know, you think about all the really great triumphs
in human history.
You think about all the really horrific tragedies.
You know, you think about the Holocaust.
You think about any prison full of human stories
and all of those problems boil down to neurochemistry.
So if you get a little bit of control over that,
you provide people the option to do better.
And the way I read history,
the way people have dealt with having better tools
is that they most often in the end do better
with huge asterisks.
But I think it's an interesting, worthy and noble pursuit
to give people more options, more tools.
Yeah, that's a fascinating way to look at human history.
You just imagine all these neurobiological mechanisms,
Stalin, Hitler, all of these, Genghis Khan,
all of them just had like a brain.
It's just a bunch of neurons,
you know, like a few tons of billions of neurons,
gaining a bunch of information over a period of time.
They have a set of modules that does language
and memory and all that.
And from there, in the case of those people,
they're able to murder millions of people.
And all that coming from,
there's not some glorified notion of a dictator
of this enormous mind or something like this.
It's just the brain.
Yeah, I mean, a lot of that has to do
with how well people like that
can organize those around them.
Other brains.
Yeah, and so I always find it interesting
to look to primatology,
look to our closest non-human relatives
for clues as to how humans are going to behave
and what particular humans are able to achieve.
And so you look at chimpanzees and bonobos
and, you know, they're similar
but different in their social structures, particularly.
And I went to Emery in Atlanta
and studied under Franz de Waal,
the great Franz de Waal,
who was kind of the leading primatologist
who recently died.
And his work in looking at chimps
through the lens of, you know,
how you would watch an episode of friends
and understand the motivations
of the characters interacting with each other.
He would look at a chimp colony
and basically apply that lens.
I'm massively oversimplifying it.
If you do that, instead of just saying,
you know, subject 473, you know,
through his feces at subject 471,
you talk about them in terms of their human struggles,
accord them the dignity of themselves as actors
with understandable goals and drives,
what they want out of life
and primarily it's, you know,
the things we want out of life,
food, sex, companionship, power.
You can understand chimp and bonoba behavior
in the same lights much more easily.
And I think doing so gives you the tools you need
to reduce human behavior
from the kind of false complexity
that we layer onto it with language
and look at it in terms of,
well, these humans are looking for companionship,
sex, food, power.
And I think that that's a pretty powerful tool to have
in understanding human behavior.
And I just went to the Amazon jungle for a few weeks
and it's a very visceral reminder
that a lot of life on Earth is just trying to get laid.
They're all screaming at each other.
Like I saw a lot of monkeys
and they're just trying to impress each other
or maybe there's a battle for power,
but a lot of the battle for power
has to do with them getting laid.
Right, breeding rights often go with alpha status.
And so if you can get a piece of that,
then you're gonna do okay.
And we'd like to think
that we're somehow fundamentally different,
but especially when it comes to primates,
we really aren't, you know,
we can use fancier poetic language,
but maybe some of the underlying drives
and motivators are similar.
Yeah, I think that's true.
And all of that is coming from this, the brain.
Yeah.
So when did you first start studying the brain
as a biological mechanism?
Basically the moment I got to college,
I started looking around for labs
that I could do neuroscience work in.
I originally approached that from the angle
of looking at interactions between the brain
and the immune system,
which isn't the most obvious place to start,
but I had this idea at the time
that the contents of your thoughts
would have an impact, a direct impact,
maybe a powerful one,
on non-conscious systems in your body,
the systems we think of as, you know,
homeostatic automatic mechanisms like fighting off a virus,
like repairing a wound.
And sure enough, there are big crossovers between the two.
I mean, it gets to kind of a key point
that I think goes under-recognized,
one of the things people don't recognize
or appreciate about the human brain enough.
And that is that it basically controls
or has a huge role in almost everything
that your body does.
Like you try to name an example of something in your body
that isn't directly controlled
or massively influenced by the brain.
And it's pretty hard.
I mean, you might say like bone healing or something,
but even those systems,
the hypothalamus and pituitary end up playing a role
in coordinating the endocrine system
that does have a direct influence
on say the calcium level in your blood
that goes to bone healing.
So non-obvious connections between those things
implicate the brain as really a potent prime mover
in all of health.
One of the things I realized in the other direction too,
how most of the systems in the body
integrated with the human brain,
like they affect the brain also, like the immune system.
I think there's just, you know,
people who study Alzheimer's and those kinds of things,
it's just surprising how much you can understand
of that from the immune system,
from the other systems that don't obviously seem
to have anything to do with sort of the nervous system.
They all play together.
Yeah, you could understand how that would be driven
by evolution too, just in some simple examples.
If you get sick, if you get a communicable disease,
you get the flu, it's pretty advantageous
for your immune system to tell your brain,
hey, now be antisocial for, you know, a few days.
Don't go be the life of the party tonight.
In fact, maybe just cuddle up somewhere warm
under a blanket and just stay there for a day or two.
And sure enough, that tends to be the behavior
that you see both in animals and in humans.
If you get sick, elevated levels of interleukins
in your blood and TNF alpha in your blood,
ask the brain to cut back on social activity
and even moving around.
You have lower locomotor activity in animals
that are infected with viruses.
So from there, the early days in neuroscience to surgery,
when did that step happen?
It was a leap.
You know, it was sort of an evolution of thought.
I wanted to study the brain.
I started studying the brain in undergrad
in this neuroimmunology lab.
From there, I realized at some point
that I didn't wanna just generate knowledge.
I wanted to affect real changes in the actual world,
in actual people's lives.
And so after having not really thought about
going into medical school,
I was on a track to go into a PhD program.
I said, well, I'd like that option.
I'd like to actually potentially help tangible people
in front of me.
And doing a little digging found that there exists
these MD PhD programs, or you can choose not to choose.
Between them and do both.
And so I went to USC for medical school
and had a joint PhD program with Caltech,
where I met, actually chose that program particularly
because of a researcher at Caltech named Richard Anderson,
who's one of the godfathers of primate neuroscience,
and has a macaque lab where Utah rays and other electrodes
were being inserted into the brains of monkeys
to try to understand how intentions
were being encoded in the brain.
So I ended up there with the idea that
maybe I would be a neurologist
and study the brain on the side,
and then discovered that neurology,
again, I'm gonna make enemies by saying this,
but neurology, predominantly and distressingly to me,
is the practice of diagnosing a thing
and then saying, good luck with that
when there's not much we can do.
And neurosurgery very differently,
it's a powerful lever on taking people
that are headed in a bad direction
and changing their course in the sense of brain tumors
that are potentially treatable or curable with surgery.
Even aneurysms in the brain, blood vessels
that are gonna rupture you can save lives really
is at the end of the day what mattered to me.
And so I was at USC, as I mentioned,
that happens to be one of the great neurosurgery programs.
And so I met these truly epic neurosurgeons,
Alex Kalesi and Micah Puzzo and Steve Giannotta
and Marty Weiss, these sort of epic people
that were just human beings in front of me.
And so it kind of changed my thinking
from neurosurgeons are distant gods
that live on another planet
and occasionally come and visit us,
to these are humans that have problems and are people
and there's nothing fundamentally preventing me
from being one of them.
And so at the last minute in medical school,
I changed gears from going into a different specialty
and switched into neurosurgery,
which cost me a year.
I had to do another year of research
because I was so far along in the process
that to switch into neurosurgery,
the deadlines had already passed.
So it was a decision that cost time,
but absolutely worth it.
What was the hardest part of the training
on the neurosurgeon track?
Yeah, two things.
I think that residency in neurosurgery
is sort of a competition of pain,
of like how much pain can you eat and smile.
And so there's work hour restrictions
that are not really,
they're viewed at, I think,
internally among the residents as weakness.
And so most neurosurgery residents
try to work as hard as they can.
And that I think necessarily means working long hours
and sometimes over the work hour limits.
And we care about being compliant
with whatever regulations are in front of us,
but I think more important than that,
people wanna give their all
in becoming a better neurosurgeon
because the stakes are so high.
And so it's a real fight to get residents
to say go home at the end of their shift
and not stay and do more surgery.
Are you seriously saying like one of the hardest things
is literally like getting,
forcing them to get sleep and rest
and all this kind of stuff?
Historically, that was the case.
I think the next generation,
I think the next generation is more compliant
and more self-care.
Well, here's what you mean.
All right, I'm just kidding.
I'm just kidding.
I didn't say it.
Now I'm making enemies.
No.
Okay, I get it.
Well, that's fascinating.
So what was the second thing?
The personalities and maybe the two are connected.
So was it pretty competitive?
It's competitive and it's also,
as we touched on earlier, primates like power.
And I think neurosurgery has long had this aura
of mystique and excellence and whatever about it.
And so it's an invitation, I think,
for people that are cloaked in that authority,
a board certified neurosurgeon
is basically a walking, fallacious appeal to authority.
You have license to walk into any room
and act like you're an expert on whatever.
And fighting that tendency
is not something that most neurosurgeons do well.
Humility isn't the forte.
Yeah, so I have friends who know you
and whenever they speak about you,
that you have the surprising quality
for a neurosurgeon of humility,
which I think indicates that it's not as common
as perhaps in other professions
because there is a kind of gigantic,
sort of heroic aspect to neurosurgery.
And I think it gets to people's head a little bit.
Yeah, well, I think that allows me to play well
at an Elon company because Elon, one of his strengths,
I think is to just instantly see through fallacy
from authority.
So nobody walks into a room that he's in and says,
well, God damn it, you have to trust me.
I'm the guy that built the last 10 rockets or something.
And he says, well, you did it wrong and we can do it better.
Or I'm the guy that kept Ford alive for the last 50 years.
You listened to me on how to build cars.
And he says, no.
And so you don't walk into a room that he's in.
And say, well, I'm a neurosurgeon.
Let me tell you how to do it.
He's gonna say, well, I'm a human being that has a brain.
I can think from first principles myself.
Thank you very much.
And here's how I think it ought to be done.
Let's go try it and see who's right.
And that's proven, I think over and over in his case
to be a very powerful approach.
We just take that tangent.
There's a fascinating interdisciplinary team
at Neuralink that you get to interact with, including Elon.
What do you think is the secret to a successful team?
Well, what have you learned
from just getting to observe these folks?
Yeah.
World experts in different disciplines work together.
Yeah, there's a sweet spot where people disagree
and forcefully speak their mind
and passionately defend their position
and yet are still able to accept information from others
and change their ideas when they're wrong.
And so I like the analogy of sort of how you polish rocks.
You put hard things in a hard container and spin it.
People bash against each other
and out comes a more refined product.
And so to make a good team at Neuralink,
we've tried to find people that are not afraid
to defend their ideas passionately
and occasionally strongly disagree with people
that they're working with
and have the best idea come out on top.
It's not an easy balance, again,
to refer back to the primate brain.
It's not something that is inherently built
into the primate brain to say,
I passionately put all my chips on this position
and now I'm just gonna walk away from it
and admit you were right.
Part of our brains tell us that that is a power loss.
That is a loss of face,
a loss of standing in the community.
And now you're a Zeta chump.
Because your idea got trounced.
And you just have to recognize that little voice
in the back of your head is maladaptive
and it's not helping the team win.
Yeah, you have to have the confidence
to be able to walk away from an idea that you hold on to.
And if you do that often enough,
you're actually going to become the best
in the world at your thing.
I mean, that kind of that rapid iteration.
Yeah, you'll at least be a member of a winning team.
Ride the wave.
What did you learn?
You mentioned there's a lot
of amazing neurosurgeons at USC.
What lessons about surgery and life
have you learned from those folks?
Yeah, I think working your ass off,
working hard while functioning as a member of a team,
getting a job done that is incredibly difficult.
Working incredibly long hours, being up all night,
taking care of someone that you think probably won't survive
no matter what you do.
Working hard to make people that you passionately dislike
look good the next morning.
These folks were relentless in their pursuit
of excellent neurosurgical technique decade over decade.
And I think we're well-recognized for that excellence.
So it's, especially Marty Weiss, Steve Gianotta,
Mike Capuzzo, they made huge contributions
not only to surgical technique,
but they built training programs
that trained dozens or hundreds of amazing neurosurgeons.
I was just lucky to kind of be in their wake.
What's that like you mentioned doing a surgery
where the person is likely not to survive?
Does that wear on you?
Yeah.
It's especially challenging
when you, with all respect to our elders,
it doesn't hit so much when you're taking care
of an 80 year old and something was going to get them
pretty soon anyway.
And so you lose a patient like that
and it was part of the natural course
of what is expected of them in the coming years, regardless.
Taking care of a father of two or three, four young kids,
someone in their 30s that didn't have it coming
and they show up in your ER
having their first seizure of their life
and low and bold, they've got a huge malignant,
inoperable or incurable brain tumor.
You can only do that, I think, a handful of times
before it really starts eating away at your armor
or a young mother that shows up
that has a giant hemorrhage in her brain
that she's not going to survive from.
They bring her four year old daughter in
to say goodbye one last time
before they turn the ventilator off.
The great Henry Marsh is an English neurosurgeon
who said it best, I think he says every neurosurgeon
carries with them a private graveyard
and I definitely feel that,
especially with young parents, that kills me.
They had a lot more to give.
The loss of those people specifically has a knock-on effect
that's going to make the world worse for people
for a long time and it's just hard to feel powerless
in the face of that.
And that's where I think you have to be borderline evil
to fight against a company like Neuralink
or to constantly be taking pot shots at us
because what we're doing is to try to fix that stuff.
We're trying to give people options to reduce suffering.
We're trying to take the pain out of life
that broken brains brings in.
And yeah, this is just our little way
that we're fighting back against entropy, I guess.
Yeah, the amount of suffering that's endured
when some of the things that we take for granted
that our brain is able to do is taken away is immense.
And to be able to restore some of that functionality
is a real gift.
Yeah, we're just starting.
We're gonna do so much more.
Well, can you take me through the full procedure
for implanting, say the N1 chip
in Neuralink?
Yeah, it's a really simple,
really simple, straightforward procedure.
The human part of the surgery that I do is dead simple.
It's one of the most basic neurosurgery procedures imaginable.
And I think there's evidence that some version of it
has been done for thousands of years.
There are examples, I think, from ancient Egypt
of healed or partially healed trephinations
and from Peru or ancient times in South America
where these proto surgeons would drill holes
in people's skulls, presumably to let out the evil spirits
but maybe to drain blood clots.
And there's evidence of bone healing around the edge,
meaning the people at least survive some months
after a procedure.
And so what we're doing is that.
We are making a cut in the skin on the top of the head
over the area of the brain
that is the most potent representation of hand intentions.
And so if you are an expert concert pianist,
this part of your brain is lighting up
the entire time you're playing.
We call it the hand knob.
The hand knob.
Yeah.
So it's all the finger movements, all of this,
firing away.
Yep, there's a little squiggle in the cortex right there.
One of the folds in the brain
is kind of doubly folded right on that spot.
And so you can look at it on an MRI and say,
that's the hand knob.
And then you do a functional test
in a special kind of MRI called a functional MRI, fMRI.
And this part of the brain lights up
when people, even quadriplegic people
whose brains aren't connected
to their finger movements anymore.
They imagine finger movements
in this part of the brain still lights up.
So we can ID that part of the brain
in anyone who's preparing to enter our trial and say,
okay, that part of the brain we confirm
is your hand intention area.
And so I'll make a little cut in the skin.
We'll flap the skin open,
just like kind of opening the hood of a car,
only a lot smaller.
Make a perfectly round one inch diameter hole in the skull.
Remove that bit of skull.
Open the lining of the brain, the covering of the brain.
It's like a little bag of water that the brain floats in.
And then show that part of the brain to our robot.
And then this is where the robot shines.
It can come in and take these tiny,
you know, much smaller than human hair electrodes
and precisely insert them into the cortex,
into the surface of the brain,
to a very precise depth in a very precise spot
that avoids all the blood vessels
that are coating the surface of the brain.
And after the robot's done with its part,
then, you know, the human comes back in
and puts the implant into that hole in the skull
and covers it up, screwing it down to the skull
and sewing the skin back together.
So the whole thing is, you know, a few hours long.
It's extremely low risk compared to the average neurosurgery
involving the brain that might, say,
open up a deep part of the brain
or manipulate blood vessels in the brain.
This opening on the surface of the brain
with only cortical microinsertions
carries significantly less risk
than a lot of the tumor or aneurysm surgeries
that are routinely done.
So cortical microinsertions that are via robot
and computer vision are designed to avoid the blood vessels.
So I know you're a bit biased here,
but let's compare human and machine.
So what are human surgeons able to do well
and what are robot surgeons able to do well
at this stage of our human civilization development?
Yeah, yeah, that's a good question.
Humans are general purpose machines.
We're able to adapt to unusual situations.
We're able to change the plan on the fly.
I remember well a surgery that I was doing many years ago
down in San Diego where the plan was to open a small hole
behind the ear and go reposition a blood vessel
that had come to lay on the facial nerve,
the trigeminal nerve, the nerve that goes to the face.
When that blood vessel lays on the nerve,
it can cause just intolerable, horrific shooting pain
that people describe like being zapped with a cattle prod.
And so the beautiful, elegant surgery
is to go move this blood vessel off the nerve.
The surgery team, we went in there
and started moving this blood vessel
and then found that there was a giant aneurysm
on that blood vessel that was not easily visible
on the pre-op scans.
And so the plan had to dynamically change
and that the human surgeons had no problem with that.
We're trained for all those things.
Robots wouldn't do so well in that situation,
at least in their current incarnation,
fully robotic surgery,
like the electrode insertion portion of the knurling surgery,
it goes according to a set plan.
And so the humans can interrupt the flow
and change the plan,
but the robot can't really change the plan midway through.
It operates according to how it was programmed
and how it was asked to run.
And it does its job very precisely,
but not with a wide degree of latitude
and how to react to changing conditions.
So there could be just a very large number of ways
that you could be surprised as a surgeon.
When you enter a situation that could be subtle things
that you have to dynamically adjust to.
Correct.
And robots are not good at that.
Currently.
Currently.
I think we are at the dawn of a new era
with AI of the parameters for robot responsiveness
to be dramatically broadened, right?
I mean, you can't look at a self-driving car
and say that it's operating under very narrow parameters.
If a chicken runs across the road,
it wasn't necessarily programmed to deal with that specifically,
but a Waymo or a self-driving Tesla
would have no problem reacting to that appropriately.
And so surgical robots aren't there yet, but give it time.
And then there could be a lot of sort of like
semi-autonomous possibilities of maybe a robotic surgeon
could say, this situation is perfectly familiar
or the situation is not familiar.
And in the not familiar case, a human could take over.
But basically like be very conservative and saying,
okay, this for sure has no issues, no surprises.
And then let the humans deal with the surprises
with the edge cases, all that.
That's one possibility.
So like you think eventually you'll be out of the job.
Well, you being neurosurgeon,
your job being neurosurgeon, humans,
there will not be many neurosurgeons left on this earth.
I'm not worried about my job
in the course of my professional life.
I think I would tell my kids not necessarily
to go in this line of work,
depending on how things look in 20 years.
It's so fascinating.
Cause I mean, if I have a line of work,
I would say it's programming.
And if you asked me like for the last, I don't know,
20 years, what I would recommend for people,
I would tell them, yeah, go.
You will always have a job if you're a programmer.
There's more and more computers
and all this kind of stuff and it pays well.
But then you realize these large language models come along
and they're really damn good at generating code.
So overnight you could be surprised like,
wow, what is the contribution of the human really?
But then you start to think, okay,
it does seem like humans have ability,
like you said, to deal with novel situations.
And in the case of programming,
it's the ability to kind of come up with novel ideas
to solve problems.
It seems like machines aren't quite yet able to do that.
And when the stakes are very high,
when it's life critical as it is in surgery,
especially in neurosurgery, then it starts,
the stakes are very high for a robot
to actually replace a human.
But it's fascinating that in this case of Neuralink,
there's a human robot collaboration.
Yeah, yeah, I do the parts that can't do
and it does the parts I can't do.
And we are friends.
I saw that there's a lot of practice going on.
So I mean, everything in Neuralink
is tested extremely rigorously.
But one of the things I saw that there's a proxy
on which the surgeries are performed.
So this is both for the robot and for the human,
for everybody involved in the entire pipeline.
What's that like practicing the surgery?
It's pretty intense.
So there's no analog to this in human surgery.
Human surgery is sort of this artisanal craft
that's handed down directly from master to pupil
over the generations.
I mean, literally the way you learn to be a surgeon
on humans is by doing surgery on humans.
I mean, first you watch your professors
do a bunch of surgery and then finally they put
the trivial parts of the surgery into your hands
and then the more complex parts.
And as your understanding of the point
and the purposes of the surgery increases,
you get more responsibility in the perfect condition.
Doesn't always go well.
In Neuralink's case, the approach is a bit different.
We, of course, practiced as far as we could on animals.
We did hundreds of animal surgeries.
And when it came time to do the first human,
we had just an amazing team of engineers
build incredibly lifelike models.
One of the engineers, Fran Romano in particular,
built a pulsating brain in a custom 3D printed skull
that matches exactly the patient's anatomy,
including their face and scalp characteristics.
And so when I was able to practice that,
I mean, it's as close as it really reasonably should get
to being the real thing in all the details,
including having a mannequin body
attached to this custom head.
And so when we were doing the practice surgeries,
we'd wheel that body into the CT scanner
and take a mock CT scan and wheel it back in
and conduct all the normal safety checks verbally,
stop this patient, we're confirming his identification
is mannequin number, blah, blah, blah.
And then opening the brain in exactly the right spot
using standard operative neuro navigation equipment,
standard surgical drills in the same OR
that we do all of our practice surgeries in at Neuralink,
and having the skull open and have the brain pulse,
which adds a degree of difficulty for the robot,
perfectly precisely plan and insert those electrodes
to the right depth and location.
And so, yeah, we kind of broke new ground
on how extensively we practiced for this surgery.
So there was a historic moment, a big milestone
for Neuralink in part for humanity
with the first human getting a Neuralink implant
in January of this year.
Take me through the surgery on Noland.
What did it feel like to be part of this?
Yeah, well, we're lucky to have just incredible partners
at the Barrow Neurologic Institute.
They are, I think, the premier neurosurgical hospital
in the world.
They made everything as easy as possible for the trial
to get going and helped us immensely with their expertise
on how to arrange the details.
It was a much more high pressure surgery in some ways.
I mean, even though the outcome wasn't particularly
in question in terms of our participants' safety,
the number of observers, the number of people,
there's conference rooms full of people watching live streams
in the hospital rooting for this to go perfectly
and that just adds pressure that is not typical
for even the most intense production neurosurgery,
say, removing a tumor or placing
deep brain stimulation electrodes.
And it had never been done on a human before.
There were unknown unknowns.
And so definitely a moderate pucker factor there
for the whole team, not knowing if we were going
to encounter, say, a degree of brain movement
that was unanticipated or a degree of brain sag
that took the brain far away from the skull
and made it difficult to insert
or some other unknown unknown problem.
Fortunately, everything went well
and that surgery is one of the smoothest outcomes
we could have imagined.
Were you nervous?
I mean, you're a bit of a quarterback
in the Super Bowl kind of situation.
Extremely nervous, extremely.
I was very pleased when it went well
and then when it was over, looking forward to number two.
Yeah, even with all that practice, all of that,
just never been in a situation that's so high stakes
in terms of people watching.
And we should also probably mention,
given how the media works, a lot of people,
maybe in a dark kind of way, hoping it doesn't go well.
Well, I think wealth is easy to hate
or envy or whatever.
And I think there's a whole industry around driving clicks
and bad news is great for clicks.
And so any way to take an event and turn it into bad news
is gonna be really good for clicks.
It just sucks because I think it puts pressure on people,
it discourages people from trying
to solve really hard problems
because to solve hard problems,
you have to go into the unknown.
You have to do things that haven't been done before
and you have to take risks, calculated risks.
You have to do all kinds of safety precautions,
but risks, nevertheless.
And I just wish there'd be more celebration of that,
of the risk taking versus like people just waiting
on the sidelines, like waiting for failure
and then pointing out the failure.
Yeah, it sucks.
But in this case, it's really great
that everything went just flawlessly,
but it's unnecessary pressure, I would say.
Now that there is a human with literal skin in the game,
there's a participant who's well-being rides on this,
doing well, you have to be a pretty bad person
to be rooting for that to go wrong.
And so hopefully people look in the mirror
and realize that at some point.
So did you get to actually front row seat,
like watch the robot work?
Like what, you get to see the whole thing?
Yeah, I mean, because an MD needs to be in charge
of all of the medical decision-making
throughout the process,
I unscrubbed from the surgery after exposing the brain
and presenting it to the robot
and placed the targets on the robot,
software interface that tells the robot
where it's going to insert each thread
that was done with my hand on the mouse
for whatever that's worth.
So you were the one placing the targets?
Yeah.
Oh, cool.
So like the robot with the computer vision
provides a bunch of candidates
and you kind of finalize the decision.
Right, the software engineers are amazing on this team.
And so they actually provided an interface
where you can essentially use a lasso tool
and select a prime area of brain real estate.
And it will automatically avoid the blood vessels
in that region and automatically place a bunch of targets.
So that allows the human robot operator
to select really good areas of brain
and make dense applications of targets in those regions.
The regions we think are gonna have
the most high fidelity representations
of finger movements and arm movement intentions.
I've seen like images of this and for me with OCD,
for some reason are really pleasant.
I think there's a subreddit called OddlySatisfying.
Yeah, love that subreddit.
It's OddlySatisfying to see the different target sites
avoiding the blood vessels and also maximizing
like the usefulness of those locations for the signal.
It just feels good.
It's like, ah.
As a person who has a visceral reaction
to the brain bleeding, I can tell you.
Yes, especially.
It's extremely satisfying watching the electrodes themselves
go into the brain and not cause bleeding.
Yeah, yeah.
So you said the feeling was of relief
when everything went perfectly.
Yeah.
So how deep in the brain can you currently go
and eventually go?
Let's say on the neural link side,
it seems the deeper you go in the brain,
the more challenging it becomes.
Yeah, so talking broadly about neurosurgery,
we can get anywhere.
It's routine for me to put deep brain stimulating electrodes
near the very bottom of the brain.
Entering from the top and passing
about a two millimeter wire
all the way into the bottom of the brain.
And that's not revolutionary.
A lot of people do that.
And we can do that with very high precision.
I use a robot from Globus to do that surgery,
you know, several times a month.
It's pretty routine.
What are your eyes in that situation?
What are you seeing?
What kind of technology can you use to visualize
where you are to light your way?
Yeah, so it's a cool process on the software side.
You take a preoperative MRI
that's extremely high resolution data of the entire brain.
You put the patient to sleep,
put their head in a frame
that holds the skull very rigidly.
And then you take a CT scan of their head
while they're asleep with that frame on
and then merge the MRI and the CT in software.
You have a plan based on the MRI
where you can see these nuclei deep in the brain.
You can't see them on CT,
but if you trust the merging of the two images,
then you indirectly know on the CT where that is.
And therefore indirectly know where in reference
to the titanium frame screwed to their head
those targets are.
And so this is 60s technology
to manually compute trajectories
given the entry point and target
and dial in some goofy looking titanium actuators
with manual actuators with little tick marks on them.
The modern version of that is to use a robot,
just like a little KUKA arm.
You might see it building cars at the Tesla factory.
This small robot arm can show you the trajectory
that you intended from the pre-op MRI
and establish a very rigid holder
through which you can drill a small hole in the skull
and pass a small rigid wire deep into that area of the brain
that's hollow and put your electrode
through that hollow wire
and then remove all of that except the electrode.
So you end up with the electrode
very, very precisely placed far from the skull surface.
Now that's standard technology
that's already been out in the world for a while.
Neuralink right now is focused entirely
on cortical targets, surface targets,
because there's no trivial way to get
say hundreds of wires deep inside the brain
without doing a lot of damage.
So your question, what do you see?
Well, I see an MRI on a screen.
I can't see everything that that DBS electrode
is passing through on its way to that deep target.
And so it's accepted with this approach
that there's gonna be about one in a hundred patients
who have a bleed somewhere in the brain
as a result of passing that wire blindly
into the deep part of the brain.
That's not an acceptable safety profile for neuralink.
We start from the position that we want this to be
dramatically maybe two or three orders of magnitude
safer than that.
Safe enough really that you or I
without a profound medical problem
might on our lunch break someday say, yeah, sure,
I'll get that, be meaning to upgrade to the latest version.
And so the safety constraints given that are high.
And so we haven't settled on a final solution
for arbitrarily approaching deep targets in the brain.
It's interesting because like
you have to avoid blood vessels somehow.
You have to, maybe there's creative ways
of doing the same thing,
like mapping out high resolution geometry of blood vessels.
And then you can go in blind,
but like how do you map out that
in a way that's like super stable?
It's saying that there's a lot
of interesting challenges there, right?
Yeah.
But there's a lot to do on the surface.
Exactly.
So we've got vision on the surface.
We actually have made a huge amount of progress
sewing electrodes into the spinal cord
as a potential work around for a spinal cord injury
that would allow a brain mounted implant
to translate motor intentions to a spine mounted implant
that can affect muscle contractions
in previously paralyzed arms and legs.
That's just incredible.
So like the effort there is to try to bridge
the brain to the spinal cord,
to the peripheral nervous.
So how hard is that to do?
We have that working in very crude forms in animals.
That's amazing.
Yeah, we've done it.
But with Nolan, he's able to digitally move the cursor.
Here you're doing the same kind of communication,
but with the actual effectors that you have.
That's fascinating.
Yeah, so we have anesthetized animals doing grasp
and moving their legs in a sort of walking pattern.
Again, early days,
but the future is bright for this kind of thing.
And people with paralysis should look forward
to that bright future.
They're going to have options.
Yeah, and there's a lot of sort of intermediate
or extra options where you take like an optimist robot,
like the arm and to be able to control the arm,
the fingers, the hands of the arm is a prosthetic.
Exoskeletons are getting better too.
So skeletons.
Yeah, so that goes hand in hand.
Although I didn't quite understand
until thinking about a deep and do more research
about neural link,
how much you can do on the digital side.
So there's digital telepathy.
I didn't quite understand
that you can really map the intention
as you described in the hand knob area,
that you can map the intention.
Imagine it, think about it.
That intention can be mapped to actual action
in the digital world.
And now more and more,
so much can be done in the digital world
that it can reconnect you to the outside world.
It can allow you to have freedom, have independence
if you're a quadriplegic.
That's really powerful.
Like you can go really far with that.
Yeah, our first participant is, he's incredible.
Breaking world records left and right.
And he's having fun with it.
It's great.
Just going back to the surgery, your whole journey,
you mentioned to me how far you have surgery on Monday.
So you're like, you're doing surgery all the time.
Yeah, maybe the ridiculous question.
What does it take to get good at surgery?
Practice, repetitions.
You're just same with anything else.
There's a million ways of people saying the same thing
and selling books saying it,
but you call it 10,000 hours.
You call it, spend some chunk of your life,
some percentage of your life focusing on this,
obsessing about getting better at it.
Repetitions, humility,
recognizing that you aren't perfect at any stage
along the way, recognizing you've got improvements
to make in your technique,
being open to feedback and coaching from people
with a different perspective on how to do it.
And then just the constant will to do better
that fortunately, if you're not a sociopath,
I think your patients bring that with them
to the office visits every day.
They force you to want to do better all the time.
Yeah, just step up.
I mean, it's a real human being,
a real human being that you can help.
Yeah.
So every surgery, even if it's the same exact surgery,
is there a lot of variability
between that surgery and a different person?
Yeah, a fair bit.
I mean, a good example for us is that the angle of the skull
relative to the normal plane of the body axis
of the skull overhand knob is pretty wide variation.
I mean, some people have really flat skulls
and some people have really steeply angled skulls
over that area.
And that has consequences for how their head
can be fixed in sort of the frame that we use
and how the robot has to approach the skull.
And yeah, people's bodies are built as differently
as the people you see walking down the street
as much variability in body shape and size as you see there.
We see in brain anatomy and skull anatomy,
there are some people who we've had to kind of exclude
from our trial for having skulls that are too thick
or too thin or scalp that's too thick or too thin.
I think we have like the middle 97% or so of people,
but you can't account for all human anatomy variability.
How much mushiness and mess is there?
Cause I, you know, taking biology classes,
the diagrams are always really clean and crisp.
Neuroscience, the pictures of neurons
are always really nice and there.
But whenever I look at pictures of like real brains,
they're all, I don't know what is going on.
So how much are biological systems in reality?
Like how hard is it to figure out what's going on?
Not too bad.
Once you really get used to this,
that's where experience and skill
and education really come into play
is if you stare at a thousand brains,
it becomes easier to kind of mentally peel back
the say, for instance, blood vessels
that are obscuring the sulci and gyri,
kind of the wrinkle pattern of the surface of the brain.
Occasionally when you're first starting to do this
and you open the skull,
it doesn't match what you thought you were gonna see
based on the MRI.
And with more experience,
you learn to kind of peel back that layer of blood vessels
and see the underlying pattern of wrinkles in the brain
and use that as a landmark for where you are.
The wrinkles are a landmark?
So like, yeah.
So I was describing hand knob earlier.
That's a pattern of the wrinkles in the brain.
It's sort of this sort of Greek letter
omega shaped area of the brain.
So you could recognize the hand knob area,
like if I show you a thousand brains
and give you like one minute with each,
you'd be like, yep, that's that.
And so there is some uniqueness to that area of the brain,
like in terms of the geometry, the topology of the thing.
Where is it about in the,
it's so you have this strip of brain running down the top.
Yeah, called the primary motor area.
And I'm sure you've seen this picture of the homunculus
laid over the surface of the brain,
the weird little guy with huge lips and giant hands.
That guy sort of lays with his legs
up at the top of the brain and face arm areas farther down
and then some kind of mouth, lip, tongue areas farther down.
And so the hand is right in there
and then the areas that control speech,
at least on the left side of the brain
in most people are just below that.
And so any muscle that you voluntarily move in your body,
the vast majority of that references that strip
or those intentions come from that strip of brain
and the wrinkle for hand knob is right in the middle of that.
And vision is back here, also close to the surface.
Vision's a little deeper.
And so this gets to your question
about how deep can you get.
To do vision, we can't just do the surface of the brain.
We have to be able to go in not as deep
as we'd have to go for DBS,
but maybe a centimeter deeper than we're used to
for hand insertions.
And so that's work in progress.
That's a new set of challenges to overcome.
By the way, you mentioned the Utah, right?
And I just saw a picture of that
and that thing looks terrifying because it's rigid.
And then if you look at the threads, they're flexible.
What can you say that's interesting to you
about the flexible, that kind of approach
of the flexible threads to deliver the electrodes
next to the neurons?
Yeah, I mean, the goal there comes from experience.
I mean, we stand on the shoulders of people
that made Utah rays and used Utah rays for decades
before we ever even came along.
Neuralink arose partly, this approach to technology
arose out of a need recognized after Utah rays
would fail routinely because the rigid electrodes,
those spikes that are literally hammered
using an air hammer into the brain,
those spikes generate a bad immune response
that encapsulates the electrode spikes
in scar tissue, essentially.
And so one of the projects that was being worked on
in the Anderson lab at Caltech when I got there
was to see if you could use chemotherapy
to prevent the formation of scar.
It's like, you know, things are pretty bad
when you're jamming a bed of nails into the brain
and then treating that with chemotherapy
to try to prevent scar tissue.
It's like, you know, maybe we've gotten off track here,
guys, maybe there's a fundamental redesign necessary.
And so Neuralink's approach of using highly flexible,
tiny electrodes avoids a lot of the bleeding,
avoids a lot of the immune response that ends up happening
when rigid electrodes are pounded into the brain.
And so what we see is our electrode longevity
and functionality and the health of the brain tissue
immediately surrounding the electrode is excellent.
I mean, it goes on for years now in our animal models.
What do most people not understand
about the biology of the brain?
We'll mention the vascular chair, that's really interesting.
I think the most interesting, maybe underappreciated fact
is that it really does control almost everything.
I mean, I don't know, for out of the blue example,
imagine you want a lever on fertility.
You want to be able to turn fertility on and off.
I mean, there are legitimate targets in the brain itself
to modulate fertility, say blood pressure.
You want to modulate blood pressure.
There are legitimate targets in the brain for doing that.
Things that aren't immediately obvious as brain problems
are potentially solvable in the brain.
And so I think it's an underexplored area
for primary treatments of all the things
that bother people.
That's a really fascinating way to look at it.
There's a lot of conditions
we might think have nothing to do with the brain,
but they might just be symptoms
of something that actually started in the brain.
The actual source of the problem,
the primary source is something in the brain.
Yeah, not always.
I mean, their kidney disease is real,
but there are levers you can pull in the brain
that affect all of these systems.
There's knobs.
Yeah.
On-off switches and knobs in the brain
from which this all originates.
Would you have a neural link chip implanted in your brain?
I think the use case right now is use a mouse, right?
I can already do that.
And so there's no value proposition on safety grounds alone.
Sure, I'll do it tomorrow.
You say the use case of the mouse.
Is it after like researching all this
and part of it is just watching Nolan have so much fun?
If you can get that bits per second
look really high with the mouse,
like being able to interact.
If you think about the way on the smartphone,
the way you swipe, that was transformational.
Yeah.
How we interact with the thing.
It's subtle.
You don't realize it,
but to be able to touch a phone
and to scroll with your finger,
that changed everything.
People were sure you need a keyboard to type.
There's a lot of HCI aspects to that
that changed how we interact with computers.
So there could be a certain rate of speed with the mouse
that would change everything.
You might be able to just click around
on a screen extremely fast.
And that,
I can't see myself getting a neural link
for much more rapid interaction with digital devices.
Yeah.
I think recording speech intentions from the brain
might change things as well.
The value proposition for the average person.
A keyboard is a pretty clunky human interface,
requires a lot of training.
It's highly variable in the maximum performance
that the average person can achieve.
I think taking that out of the equation
and just having a natural word to computer interface
might change things for a lot of people.
It'd be hilarious if that is the reason people do it.
Even if you have speech to text,
that's extremely accurate.
It currently isn't,
but it's a gotten super accurate.
It'd be hilarious if people went for neural link,
just so you avoid the embarrassing aspect
like looking like a douchebag,
speaking to your phone in public,
which is a real, like, that's a real constraint.
Yeah.
I mean, with a bone conducting case
that can be an invisible headphone, say,
and the ability to think words into software
and have it respond to you,
that starts to sound sort of like embedded super intelligence.
If you can silently ask for the Wikipedia article
on any subject and have it read to you
without any observable change happening in the outside world,
for one thing, standardized testing is obsolete.
Yeah.
If it's done well on the UX side, it could change,
I don't know if it transforms society,
but it really can create a kind of shift
in the way we interact with digital devices
in the way that a smartphone did.
Now I would, just having to look into the safety
of everything involved, I would totally try it.
So it doesn't have to go to some like incredible thing
where you have, it connects your vision
or to some other, like it connects all over your brain.
That could be like just connecting to the hand knob.
You might have a lot of interesting interaction,
human computer interaction possibilities.
That's really interesting.
Yeah, and the technology on the academic side
is progressing at light speed here.
I think there was a really amazing paper out of UC Davis,
Sergei Stavisky's lab that basically made an initial solve
of speech decode and something like 125,000 words
that they were getting with very high accuracy, which is.
So you're just thinking the word?
Yeah.
Thinking the word and you're able to get it.
Yeah.
Oh boy.
Like you have to have the intention of speaking it.
Right.
So like do the inner voice.
Now it's so amazing to me that you can do the intention
to signal mapping.
All you have to do is just imagine yourself doing it.
And if you get the feedback that it actually worked,
you can get really good at that.
Like your brain will first of all adjust
and you develop it like any other skill, like touch typing,
you develop in that same kind of way.
That is really, to me, it's just really fascinating
to be able to even to play with that.
Honestly, like I would get a new link
just to be able to play with that,
just to play with the capacity, the capability of my mind
to learn this skill.
It's like learning the skill of typing
and learning the skill of moving a mouse.
It's another skill of moving the mouse,
not with my physical body, but with my mind.
I can't wait to see what people do with it.
I feel like we're cavemen right now.
We're like banging rocks with a stick
and thinking that we're making music.
At some point when these are more widespread,
there's gonna be the equivalent of a piano
that someone can make art with their brain
in a way that we didn't even anticipate.
Looking forward to it.
Give it to a teenager.
Anytime I think I'm good at something,
I'll always go to like, I don't know,
even with the bits per second and playing a video game,
you realize you give it to a teenager,
you give a new link to a teenager,
just the large number of them,
the kind of stuff that get good at stuff.
They're gonna get hundreds of bits per second.
Even just with the current technology.
Probably.
Cause it's also addicting how the number go up aspect of it
of like improving and training.
Cause it's almost like a skill.
And plus there's the software on the other end
that adapts to you.
And especially if the adapting procedure algorithm
becomes better and better and better,
you like learning together.
Yeah, we're scratching the surface on that right now.
There's so much more to do.
So on the complete other side of it,
you have an RFID chip implanted in you.
This is what I hear.
Nice.
So this is a little subtle thing.
It's a passive device that you use
for unlocking like a safe with top secrets
or what do you use it for?
What's the story behind it?
I'm not the first one.
There's this whole community of weirdo biohackers
that have done this stuff.
And I think one of the early use cases
was storing private crypto wallet keys and whatever.
I dabbled in that a bit and had some fun with it.
There's some Bitcoin implanted in your body somewhere
you can't tell where, yeah.
Actually, yeah.
It was the modern day equivalent of finding change
in the sofa cushions.
After I put some orphan crypto on there
that I thought was worthless
and forgot about it for a few years,
went back and found that some community of people loved it
and had propped up the value of it.
And so it had gone up 50 fold.
So there was a lot of change in those cushions.
That's hilarious.
But the primary use case is mostly
as a tech demonstrator, it has my business card on it.
You can scan that in by touching it to your phone.
It opens the front door to my house,
whatever, simple stuff.
Well, it's a cool step.
It's a cool leap to implant something in your body.
I mean, perhaps it's a similar leap to a neural link
because for a lot of people,
that kind of notion of putting something inside your body,
something electronic inside a biological system
is a big leap.
Yeah, we have a kind of a mysticism
around the barrier of our skin.
We're completely fine with knee replacements,
hip replacements, dental implants,
but there's a mysticism still around the inviolable barrier
that the skull represents.
And I think that needs to be treated
like any other pragmatic barrier.
The question isn't how incredible is it to open the skull?
The question is, what benefit can we provide?
So from all the surgeries you've done,
from everything you understand in the brain,
how much does neuroplasticity come into play?
How adaptable is the brain?
For example, just even in the case of healing from surgery
or adapting to the post-surgery situation.
The answer that is sad for me
and other people of my demographic
is that plasticity decreases with age,
healing decreases with age.
I have too much gray hair to be optimistic about that.
There are theoretical ways to increase plasticity
using electrical stimulation,
nothing that is totally proven out
as a robust enough mechanism to offer widely to people.
But yeah, I think there's cause for optimism
that we might find something useful
in terms of say an implanted electrode
that improves learning.
Certainly there's been some really amazing work recently
from Nicholas Schiff, Jonathan Baker, and others
who have a cohort of patients
with moderate traumatic brain injury
who have had electrodes placed in the deep nucleus
in the brain called the centromedian nucleus
or just near centromedian nucleus.
And when they apply small amounts of electricity
to that part of the brain,
it's almost like electronic caffeine.
They're able to improve people's attention and focus.
They're able to improve how well people can perform a task.
I think in one case, someone who was unable to work
after the device was turned on,
they were able to get a job.
And that's sort of one of the holy grails
for me with Neuralink and other technologies like this
is from a purely utilitarian standpoint,
can we make people able to take care of themselves
and their families economically again?
Can we make it so someone who's fully dependent
and even maybe requires a lot of caregiver resources,
can we put them in a position to be fully independent,
taking care of themselves, giving back to their communities?
I think that's a very compelling proposition
and what motivates a lot of what I do
and what a lot of the people at Neuralink are working for.
It's just a cool possibility
that if you put a Neuralink in there,
that the brain adapts like the other part
of the brain adapts too and integrates it.
The capacity of the brain to do that is really interesting,
probably unknown to the degree to which you can do that.
But you're now connecting an external thing to it,
especially once it's doing stimulation.
The biological brain and the electronic brain
outside of it working together,
the possibilities there are really interesting.
It's still unknown, but interesting.
It feels like the brain is really good
at adapting to whatever.
But of course it is a system that by itself is already,
like everything serves a purpose
and so you don't wanna mess with it too much.
Yeah, it's like eliminating a species from an ecology.
You don't know what the delicate interconnections
and dependencies are.
The brain is certainly a delicate complex beast
and we don't know every potential downstream consequence
of a single change that we make.
Do you see yourself doing, so you mentioned P1 surgeries,
a P2, P3, P4, P5, just more and more and more humans?
I think it's a certain kind of brittleness
or a failure on the company's side
if we need me to do all the surgeries.
I think something that I would very much like to work towards
is a process that is so simple and so robust
on the surgery side that literally anyone could do it.
We wanna get away from requiring intense expertise
or intense experience to have this successfully done
and make it as simple and translatable as possible.
I mean, I would love it if every neurosurgeon on the planet
had no problem doing this.
I think we're probably far from a regulatory environment
that would allow people that aren't neurosurgeons
to do this, but not impossible.
All right, I'll sign up for that.
Did you ever anthropomorphize the robot, R1?
Like, do you give it a name?
Do you see it as like a friend
that's like working together with you?
I mean, to a certain degree, it's-
Or anatomy, who's gonna take the job?
To a certain degree, it's, yeah, it's complex relationship.
All the good relationships are.
It's funny when in the middle of the surgery,
there's a part of it where I stand basically shoulder
to shoulder with the robot.
And so, if you're in the room reading the body language,
it's my brother in arms there.
We're working together on the same problem.
Yeah, I'm not threatened by it.
Keep telling yourself that.
How have all the surgeries that you've done over the years,
the people you've helped and the stakes,
the high stakes that you've mentioned,
how has that changed your understanding of life and death?
It gives you a very visceral sense,
and this may sound trite,
but it gives you a very visceral sense
that death is inevitable.
On one hand, you are, as a neurosurgeon,
you're deeply involved in these,
just hard to fathom tragedies.
Young parents dying, leaving a four-year-old behind, say.
And on the other hand, it takes the sting out of it a bit
because you see how just mind-numbingly universal death is.
There's zero chance that I'm going to avoid it.
I know techno-optimists right now
and longevity buffs right now
would disagree on that 0.000% estimate,
but I don't see any chance
that our generation is going to avoid it.
Entropy is a powerful force,
and we are very ornate, delicate, brittle DNA machines
that aren't up to the cosmic ray bombardment
that we're subjected to.
So on the one hand,
every human that has ever lived died or will die.
On the other hand,
it's just one of the hardest things to imagine
inflicting on anyone that you love is having them gone.
I mean, I'm sure you've had friends
that aren't living anymore,
and it's hard to even think about them.
And so I wish I had
arrived at the point of nirvana
where death doesn't have a sting.
I'm not worried about it,
but I can at least say that I'm comfortable
with the certainty of it,
if not having found out how to take the tragedy out of it
when I think about my kids,
either not having me or me not having them or my wife.
Maybe I've come to accept the intellectual certainty of it,
but it may be the pain
that comes with losing the people you love.
I don't think I've come to understand
the existential aspect of it,
like that this is gonna end.
And I don't mean like in some trite way,
I mean, like it certainly feels like it's not going to end.
Like you live life like it's not going to end.
Right.
And the fact that this light that's shining,
this consciousness is going to no longer be in one moment,
maybe today.
It's like, it fills me when I really am able to load
all that in with Ernest Becker's terror.
Like it's a real fear.
I think people aren't always honest
with how terrifying it is.
I think the more you are able to really think through it,
the more terrifying it is.
It's not such a simple thing.
Oh, that's the way life is.
And if you really can load that in, it's hard.
But I think that's why the Stoics did it
because it like helps you get your shit together
and be like, the moment, every single moment
you're alive is just beautiful.
And it's terrifying that it's gonna end.
It's like, almost like you're shivering in the cold,
a child helpless, this kind of feeling.
And then it makes you, when you have warmth,
when you have the safety,
when you have the love to really appreciate it.
I feel like sometimes in your position,
we mentioned armor, just to see death,
it might make you not be able to see that,
the finiteness of life.
Because if you kept looking at that, it might break you.
So it's good to know that you're kind of
still struggling with that.
There's the neurosurgeon and then there's a human.
And the human is still able to struggle with that
and feel the fear of that and the pain of that.
Yeah, it definitely makes you ask the question
of how long, how many of these can you see?
And not say, I can't do this anymore.
But, I mean, you said it well.
I think it gives you an opportunity
to just appreciate that you're alive today.
And I've got three kids and an amazing wife
and I'm really happy, things are good.
I get to help on a project that I think matters.
I think it moves us forward.
I'm a very lucky person.
It's the early steps of a potentially
gigantic leap for humanity.
It's a really interesting one.
And it's cool because you read about all this stuff
in history where it's like the early days.
I've been reading, before going to the Amazon,
I would read about explorers that would go
and explore even the Amazon jungle for the first time.
It's just, those are the early steps.
Or early steps into space.
Early steps in any discipline in physics and mathematics.
And it's cool because this is like the,
on the grand scale, these are the early steps
into delving deep into the human brain.
So not just observing the brain,
but you'll be able to interact with the human brain.
It's gonna help a lot of people,
but it also might help us understand
what the hell's going on in there.
Yeah, I think ultimately we wanna give people
more levers that they can pull, right?
Like you wanna give people options.
If you can give someone a dial that they can turn
on how happy they are.
I think that makes people really uncomfortable.
But now talk about major depressive disorder.
Talk about people that are committing suicide
at an alarming rate in this country.
And try to justify that queasiness in that light.
You can give people a knob to take away suicidal ideation,
suicidal intention.
I would give them that knob.
I don't know how you justify not doing that.
Yeah, you can think about all the suffering
that's going on in the world.
Every single human being that's suffering right now,
it'll be a glowing red dot.
The more suffering, the more it's glowing.
And you just see the map of human suffering.
And any technology that allows you
to dim that light of suffering on a grand scale
is pretty exciting.
Because there's a lot of people suffering
and most of them suffer quietly.
And we turn our, we look away too often.
And we should remember those that are suffering.
Because once again, most of them are suffering quietly.
Well, and on a grander scale, the fabric of society,
people have a lot of complaints about
how our social fabric is working or not working,
how our politics is working or not working.
Those things are made of neurochemistry too, in aggregate.
Our politics is composed of individuals with human brains.
And the way it works or doesn't work
is potentially tunable in the sense that, I don't know,
say remove our addictive behaviors
or tune our addictive behaviors for social media
or our addiction to outrage,
our addiction to sharing the most angry political tweet
we can find.
I don't think that leads to a functional society.
And if you had options for people to moderate
that maladaptive behavior,
there could be huge benefits to society.
Maybe we could all work together
a little more harmoniously toward useful ends.
There's a sweet spot, like you mentioned.
You don't wanna completely remove
all the dark side of human nature.
Because those kind of are somehow necessary
to make the whole thing work, but there's a sweet spot.
Yeah, I agree.
We gotta suffer a little.
Just not so much that you lose hope.
Yeah.
When you, all the surgeries you've done,
have you seen consciousness in there ever?
Was there like a glowing light?
You know, I have this sense that I never found it.
Okay.
Never removed it, you know, like a Dementor in Harry Potter.
I have this sense that consciousness is a lot less magical
than our instincts wanna claim it is.
It seems to me like a useful analog
for thinking about what consciousness is in the brain.
You know, is that we have a really good intuitive
understanding of what it means to say,
touch your skin and know what's being touched.
I think consciousness is just that level of sensory mapping
applied to the thought processes in the brain itself.
So what I'm saying is,
consciousness is the sensation
of some part of your brain being active.
So you feel it working, you feel the part of your brain
that thinks of red things or winged creatures
or the taste of coffee.
You feel those parts of your brain being active
the way that I'm feeling my palm being touched, right?
And that sensory system
that feels the brain working is consciousness.
That's so brilliant.
It's the same way, it's the sensation of touch
when you're touching a thing.
Consciousness is the sensation of you feeling your brain
working, your brain thinking, your brain perceiving.
Which isn't like a warping of space-time
or some quantum field effect, right?
It's nothing magical.
People always want to ascribe to consciousness
something truly different.
And there's this awesome long history of people
looking at whatever the latest discovery in physics is
to explain consciousness because it's the most magical,
the most out there thing that you can think of.
And people always want to do that with consciousness.
I don't think that's necessary.
It's just a very useful and gratifying way
of feeling your brain work.
And as we said, it's one heck of a brain.
Everything we see around us, everything we love,
everything that's beautiful came from brains like these.
It's all electrical activity happening inside your skull.
And I, for one, I'm grateful there's people like you
that are exploring all the ways that it works
and all the ways it can be made better.
Thank you so much for talking today.
It's been a joy.
Thanks for listening to this conversation
with Matthew McDougall.
And now, dear friends, here's Bliss Chapman,
Brain Interface Software Lead at Neuralink.
You told me that you've met hundreds of people
with spinal cord injuries or with ALS
and that your motivation for helping at Neuralink
is grounded in wanting to help them.
Can you describe this motivation?
Yeah, first, just a thank you to all the people
I've gotten a chance to speak with
for sharing their stories with me.
I don't think there's any world really
in which I can share their stories
as powerful a way as they can.
But just I think to summarize at a very high level
what I hear over and over again is that people with ALS
or severe spinal cord injury in a place
where they basically can't move physically anymore,
really at the end of the day are looking for independence.
And that can mean different things for different people.
For some folks, it can mean the ability
just to be able to communicate again independently
without needing to wear something on their face,
without needing a caretaker
to be able to put something in their mouth.
For some folks, it can mean independence
to be able to work again,
to be able to navigate a computer digitally,
efficiently enough to be able to get a job,
to be able to support themselves,
to be able to move out
and ultimately be able to support themselves
after their family maybe isn't there anymore
to take care of them.
And for some folks, it's as simple as just being able
to respond to the kid in time before they run away
or get interested in something else.
And these are deeply personal
and sort of very human problems.
And what strikes me again and again
when talking with these folks
is that this is actually an engineering problem.
This is a problem that with the right resources,
with the right team, we can make a lot of progress on.
And at the end of the day,
I think that's a deeply inspiring message
and something that makes me excited to get up every day.
So it's both an engineering problem
in terms of a BCI, for example,
that can give them capabilities
where they can interact with the world.
But also on the other side,
it's an engineering problem for the rest of the world
to make it more accessible
for people living with quadriplegia.
Yeah, and I'll take a broad view
sort of lens on this for a second.
I think I'm very in favor of anyone
working in this problem space.
So beyond BCI, I'm happy and excited
and willing to support any way I can
folks working on eye tracking systems,
working on speech to text systems,
working on head trackers or mouse sticks or quad sticks.
I haven't met many engineers
and folks in the community that do exactly those things.
And I think for the people we're trying to help,
it doesn't matter what the complexity of the solution is
as long as the problem is solved.
And I want to emphasize that
there can be many solutions out there
that can help with these problems.
And BCI is one of a collection of such solutions.
So BCI in particular, I think,
offers several advantages here.
And I think the folks that recognize this immediately
are usually the people who have spinal cord injury
or some form of paralysis.
Usually you don't have to explain to them
why this might be something that could be helpful.
It's usually pretty self-evident.
But for the rest of us,
folks that don't live with severe spinal cord injury
or who don't know somebody with ALS,
it's not often obvious why you would want a brain implant
to be able to connect and navigate a computer.
And it's surprisingly nuanced to the degree
that I've learned a huge amount just working with Noland
in the first neural and clinical trial
and understanding from him in his words,
why this device is impactful for him.
And it's a nuanced topic.
It can be the case that even if you can achieve
the same thing, for example, with a mouse stick
when navigating a computer,
he doesn't have access to that mouse stick
every single minute of the day.
He only has access when someone is available
to put it in front of him.
And so BCI can really offer a level
of independence and autonomy
that if it wasn't literally physically part of your body,
it'd be hard to achieve in any other way.
So there's a lot of fascinating aspects
to what it takes to get Noland to be able to control
a cursor on the screen with his mind.
You texted me something that I just love.
You said, I was part of the team
that interviewed and selected P1.
I was in the operating room
during the first human surgery, monitoring,
live signals coming out of the brain.
I work with the user basically every day
to develop new UX paradigms, decoding strategies.
And I was part of the team that figured out
how to recover useful BCI to new world record levels
when the signal quality degraded.
We'll talk about, I think, every aspect of that,
but just zooming out,
what was it like to be part of that team
and part of that historic, I would say, historic first?
Yeah, I think for me,
this is something I've been excited about
for close to 10 years now.
And so to be able to be even just some small part
of making it a reality is extremely exciting.
A couple, maybe special moments during that whole process
that I'll never really truly forget.
One of them is entering the actual surgery.
At that point in time, I know Nolan quite well.
I know his family.
And so I think the initial reaction
when Nolan is rolled into the operating room
is just a, oh shit, kind of reaction.
But at that point, muscle memory kicks in
and you sort of go into,
you let your body just do all the talking.
And I have the lucky job in that particular procedure
to just be in charge of monitoring the implant.
So my job is to sit there
to look at the signals coming off the implant,
to look at the live brain data streaming off the device
as threads are being inserted into the brain.
And just to basically observe
and make sure that nothing is going wrong
or that there's no red flags or fault conditions
that we need to go and investigate
or pause the surgery to debug.
And because I had that sort of spectator view of the surgery,
I had a slightly removed perspective
and I think most folks in the room,
I got to sit there and think to myself,
wow, that brain is moving a lot.
When you look into the side of the craniectomy
that we stick the threads in,
one thing that most people don't realize is the brain moves.
The brain moves a lot when you breathe,
when your heart beats and you can see it visibly.
So that's something that I think was a surprise to me
and very, very exciting.
To be able to see someone's brain who you physically know
and have talked with that length,
actually pulsing and moving inside their skull.
And they used that brain to talk to you previously
and now it's right there moving.
Actually, I didn't realize that in terms of the thread
sending so the neural length implant
is active during surgery and one thread at a time,
you're able to start seeing the signal.
So that's part of the way you test that the thing is working.
Yeah, so actually in the operating room
right after we sort of finished all the thread insertions,
I started collecting what's called broadband data.
So broadband is basically the most raw form of signal
you can collect from a neural length electrode.
It's essentially a measurement of the local field potential
or the voltage essentially measured by that electrode.
And we have a certain mode in our application
that allows us to visualize where detected spikes are.
So it visualizes sort of where in the broadband signal
and it's very, very raw form of the data,
a neuron is actually spiking.
And so one of these moments that I'll never forget
as part of this whole clinical trial
is seeing live in the operating room
while he's still under anesthesia,
beautiful spikes being shown in the application
just streaming live to a device I'm holding in my hand.
So this is no signal processing the raw data
and then the signals processing is on top of it.
You're seeing the spikes detected.
Right, yeah.
And that's the UX too.
Yes.
That looks beautiful as well.
During that procedure,
there was actually a lot of cameramen in the room.
So they also were curious and wanted to see,
there's several neurosurgeons in the room
who are all just excited to see robots taking their job.
And they're all crowded around
a small little iPhone watching this live brain data stream
out of his brain.
What was that like seeing the robot do some of the surgery?
So the computer vision aspect where it detects
all the spots that avoid the blood vessels.
And then obviously with human supervision
then actually doing the really high precision connection
of the threads to the brain.
Yeah, that's a good question.
My answer is gonna be pretty lame here, but it was boring.
I've seen it so many times.
Yeah, that's exactly how you want surgery to be.
You want it to be boring.
Because I've seen it so many times.
I've seen the robot do this surgery
literally hundreds of times.
And so it was just one more time.
Yeah, all the practice surgeries and the proxies.
And this is just another day.
So what about when Nolan woke up?
Well, do you remember a moment
where he was able to move the cursor,
not move the cursor, but get signal from the brain
such that it was able to show that there's a connection?
Yeah, so we are quite excited to move as quickly as we can.
And Nolan was really, really excited to get started.
He wanted to get started actually the day of surgery.
But we waited till the next morning, very patiently.
It's a long night.
And the next morning in the ICU where he was recovering,
he wanted to get started and actually start to understand
what kind of signal we can measure from his brain.
And maybe for folks who are not familiar
with the Neuralink system,
we implant the Neuralink system
or the Neuralink implant in the motor cortex.
So the motor cortex is responsible
for representing things like motor intent.
So if you imagine closing and opening your hand,
that kind of signal representation
will be present in the motor cortex.
If you imagine moving your arm back and forth
or wiggling a pinky,
this sort of signal can be present in the motor cortex.
So one of the ways we start to sort of map out
what kind of signal do we actually have access to
in any particular individual's brain
is through this task called body mapping.
And body mapping is where you essentially present a visual
to the user and you say, hey, imagine doing this.
And the visual is a 3D hand opening, closing
or index finger modulating up and down.
And you ask the user to imagine that.
And obviously you can't see them do this
because they're paralyzed.
So you can't see them actually move their arm.
But while they do this task,
you can record neural activity
and you can basically offline model and check,
can I predict or can I detect the modulation
corresponding with those different actions?
And so we did that task and we realized,
hey, there's actually some modulation
associated with some of his hand motion,
which is a first indication that, okay,
we can potentially use that modulation
to do useful things in the world.
For example, control a computer cursor.
And he started playing with it
the first time we showed him it.
And we actually just took the same live view
of his brain activity and put it in front of him.
And we said, hey, you tell us what's going on.
We're not you, you're able to imagine different things.
And we know that it's modulating some of these neurons.
So you figure out for us
what that is actually representing.
And so he played with it for a bit.
He was like, I don't quite get it yet.
He played for a bit longer.
And they said, oh, when I move this finger,
I see this particular neuron start to fire more.
And I said, okay, prove it, do it again.
And so he said, okay, three, two, one, boom.
And the minute he moved, you can see like instantaneously
this neuron is firing.
Single neuron, I can tell you the exact channel number
if you're interested, that's stuck in my brain now forever.
But that single channel firing was a beautiful indication
that it was behaviorally modulated neural activity
that could then be used for downstream tasks
like decoding a computer cursor.
And when you say single channel,
is that associated with a single electrode?
Yeah, so channel and electrode are interchangeable.
And there's 1024 of those.
1024, yeah.
It's incredible that that works.
That really, when I was learning about all this
and like loading it in, it was just blowing my mind
that the intention, you can visualize yourself
moving the finger, that can turn into a signal.
And the fact that you can then skip that step
and visualize the cursor moving
or have the intention of the cursor moving
and that leading to a signal
that can then be used to move the cursor.
There's so many exciting things there to learn
about the brain, about the way the brain works.
The very fact of their existing signal
that can be used is really powerful.
But it feels like that's just like the beginning
of figuring out how that signal could be used
really, really effectively.
I should also just, there's so many fascinating details here
but you mentioned the body mapping step.
At least in the version I saw that Nolan was showing off,
there's like a super nice interface,
like a graphical interface.
It just felt like I was like in the future
cause it like, I guess it visualizes you moving the hand
and there's a very like a sexy polished interface
that hello, I don't know if there's a voice component
but it just felt like when you wake up
in a really nice video game
and this is a tutorial at the beginning of that video game
this is what you're supposed to do, it's cool.
No, I mean the future should feel like the future.
But it's not easy to pull that off.
I mean, it needs to be simple, but not too simple.
Yeah, and I think the UX design component here
is underrated for BCI development in general.
There's a whole interaction effect
between the ways in which you visualize
an instruction to the user
and the kinds of signal you can get back.
And that quality of sort of your behavioral alignment
to the neural signal is a function of how good you are
at expressing to the user what you want them to do.
And so yeah, we spend a lot of time thinking about the UX
of how we build our applications,
of how the decoder actually functions,
the control surfaces it provides to the user.
All these little details matter a lot.
So maybe it'd be nice to get into a little bit more detail
of what the signal looks like
and what the decoding looks like.
So there's a N1 implant
that has, like we mentioned, 1,024 electrodes.
And that's collecting raw data, raw signal.
What does that signal look like?
And what are the different steps along the way
before it's transmitted and what is transmitted
and all that kind of stuff?
Yeah, yep.
This is gonna be a fun one.
Let's go.
So maybe before diving into what we do,
it's worth understanding what we're trying to measure
because that dictates a lot of the requirements
for the system that we build.
And what we're trying to measure
is really individual neurons producing action potentials.
And action potential is,
you can think of it like a little electrical impulse
that you can detect if you're close enough.
And by being close enough, I mean like within,
let's say 100 microns of that cell.
And 100 microns is a very, very tiny distance.
And so the number of neurons that you're gonna pick up
with any given electrode
is just a small radius around that electrode.
And the other thing worth understanding
about the underlying biology here
is that when neurons produce an action potential,
the width of that action potential is about one millisecond.
So from the start of the spike to the end of the spike,
that whole width of that sort of characteristic feature
of a neuron firing is one millisecond wide.
And if you want to detect
that an individual spike is occurring or not,
you need to sample that signal
or sample the local field potential nearby that neuron
much more frequently than once a millisecond.
You need to sample many, many times per millisecond
to be able to detect that this is actually
the characteristic waveform of a neuron
producing an action potential.
And so we sample across all 1,024 electrodes
about 20,000 times a second.
20,000 times a second means
for any given one millisecond window,
we have about 20 samples that tell us
what that exact shape of that action potential looks like.
And once we've sort of sampled at super high rate
the underlying electrical field nearby these cells,
we can process that signal into
just where do we detect a spike
or where do we not?
Sort of a binary signal one or zero,
do we detect a spike in this one millisecond or not?
And we do that because the actual information character
carrying sort of subspace of neural activity
is just when are spikes occurring?
Essentially everything that we care about for decoding
can be captured or represented
in the frequency characteristics of spike trains,
meaning how often are spikes firing
in any given window of time.
And so that allows us to do
sort of a crazy amount of compression
from this very rich high density signal
to something that's much, much more sparse
and compressible that can be sent out
over a wireless radio,
like a Bluetooth communication, for example.
Quick tangents here.
You mentioned electrode neuron.
There's a local neighborhood of neurons nearby.
How difficult is it to like isolate
from where the spike came from?
Yeah, so there's a whole field
of sort of academic neuroscience work on exactly this problem
of basically given a single electrode
or given a set of electrodes measuring a set of neurons,
how can you sort of sort, spike sort,
which spikes are coming from what neuron?
And this is a problem that's pursued in academic work
because you care about it for understanding
what's going on in the underlying
sort of neuroscience of the brain.
If you care about understanding
how the brain's representing information,
how that's evolving through time,
then that's a very, very important question to understand.
For sort of the engineering side of things,
at least at the current scale,
if the number of neurons per electrode is relatively small,
you can get away with basically
ignoring that problem completely.
You can think of it like sort of a random projection
of neurons to electrodes,
and there may be in some cases
more than one neuron per electrode,
but if that number is small enough,
those signals can be thought of as
sort of a union of the two.
And for many applications,
that's a totally reasonable trade-off to make
and can simplify the problem a lot.
And as you sort of scale out channel count,
the relevance of distinguishing individual neurons
becomes less important
because you have more overall signal,
and you can start to rely on sort of correlations
or covariance structure in the data
to help understand when that channel is firing,
what does that actually represent?
Because you know that when that channel is firing
in concert with these other 50 channels,
that means move left,
but when that same channel is firing with concert
with these other 10 channels, that means move right.
Okay, so you have to do this kind of spike detection
on board, and you have to do that super efficiently,
so fast and not use too much power
because you don't want to be generating too much heat,
so that have to be a super simple signal processing step.
Is there some wisdom you can share about
what it takes to overcome that challenge?
Yeah, so we've tried many different versions
of basically turning this raw signal into
sort of a feature that you might want to send off the device,
and I'll say that I don't think we're at the final step
of this process, this is a long journey.
We have something that works clearly today,
but there can be many approaches that we find in the future
that are much better than what we do right now.
So some versions of what we do right now,
and there's a lot of academic heritage to these ideas,
so I don't want to claim that these are original
Neuralink ideas or anything like that,
but one of these ideas is basically to build
sort of like a convolutional filter almost, if you will,
that slides across the signal
and looks for a certain template to be matched.
That template consists of sort of how deep
the spike modulates, how much it recovers,
and what the duration and window of time is
that the whole process takes.
And if you can see in the signal that that template
is matched within certain bounds,
then you can say, okay, that's a spike.
One reason that approach is super convenient
is that you can actually implement that
extremely efficiently in hardware,
which means that you can run it in low power
across 1,024 channels all at once.
Another approach that we've recently started exploring,
and this can be combined with the spike detection approach,
something called spike band power,
and the benefits of that approach are
that you may be able to pick up some signal from neurons
that are maybe too far away to be detected as a spike,
because the farther away you are from an electrode,
the weaker that actual spike waveform
will look like on that electrode.
So you might be able to pick up population-level activity
of things that are maybe slightly outside
the normal recording radius,
what neuroscientists sometimes refer to as the hash
of activity, the other stuff that's going on.
And you can look at sort of across many channels
how that sort of background noise is behaving,
and you might be able to get more juice
out of the signal that way.
But it comes at a cost.
That signal is now a floating point representation,
which means it's more expensive to send out over power.
It means you have to find different ways to compress it
that are different than what you can apply
to binary signals.
So there's a lot of different challenges
associated with these different modalities.
So also in terms of communication,
you're limited by the amount of data you can send.
And also because you're currently using
the Bluetooth protocol, you have to batch stuff together.
But you have to also do this
keeping the latency crazy low, like crazy low.
Anything to say about the latency?
Yeah, this is a passion project of mine.
So I want to build the best mouse in the world.
I don't want to build like the Chevrolet Spark
or whatever of electric cars.
I want to build like the Tesla Roadster version of a mouse.
And I really do think it's quite possible
that within five to 10 years
that most esports competitions are dominated
by people with paralysis.
This is like a very real possibility for a number of reasons.
One is that they'll have access to the best technology
to play video games effectively.
The second is they have the time to do so.
So those two factors together are particularly potent
for a esport competitors.
Unless people without paralysis
are also allowed to implant new one.
Which is, it is another way to interact
with a digital device.
And there's something to that.
If it's a fundamentally different experience,
more efficient experience,
even if it's not like some kind of full on high bandwidth
communication, if it's just the ability to move the mouse,
10X faster, like the bits per second.
If I can achieve a bits per second at 10X
what I can do with the mouse.
That's a really interesting possibility of what they can do.
Especially as you get really good at it with training.
It's definitely the case
that you have a higher ceiling performance.
Because you don't have to buffer your intention
through your arm, through your muscle.
You get just by nature of having a brain implant at all,
like 75 millisecond lead time on any action
that you're actually trying to take.
And there's some nuance to this,
like there's evidence that the motor cortex,
you can sort of plan out sequences of actions.
So you may not get that whole benefit all the time.
But for reaction time style games
where you just wanna, somebody's over here,
snipe them, that kind of thing.
You actually do have just an inherent advantage
because you don't need to go through the muscle.
So the question is just how much faster can you make it?
And we're already faster than what you would do
if you're going through the muscle
from a latency point of view.
And we're in the early stage of that.
I think we can push it.
Sort of our end-to-end latency right now
from brain spike to cursor movement is about 22 milliseconds.
If you think about the best mice in the world,
the best gaming mice,
that's about five milliseconds-ish of latency,
depending on how you measure,
depending on how fast your screen refreshes.
There's a lot of characteristics that matter there,
but yeah.
And the rough time for a neuron in the brain
to actually impact your command of your hand
is about 75 milliseconds.
So if you look at those numbers,
you can see that we're already competitive
and slightly faster than what you'd get
by actually moving your hand.
And this is something that if you ask Nolan about it,
when he moved the cursor for the first time,
we asked him about this.
It was something I was super curious about.
What does it feel like
when you're modulating a click intention
or when you're trying to just move the cursor to the right?
He said it moves before he is actually intending it to,
which is kind of a surreal thing
and something that I would love to experience myself one day.
What is that like to have a thing just be so immediate,
so fluid that it feels like it's happening
before you're actually intending it to move?
Yeah, I suppose we've gotten used to that latency,
that natural latency that happens.
So is currently the bottleneck of the communication,
so like the Bluetooth communication,
is that what's the actual bottleneck?
I mean, there's always going to be a bottleneck,
but what's the current bottleneck?
Yeah, a couple of things.
So kind of hilariously, Bluetooth low energy protocol
has some restrictions on how fast you can communicate.
So the protocol itself establishes a standard of,
the most frequent sort of updates you can send
are on the order of 7.5 milliseconds.
And as we push latency down to the level of
sort of individual spikes impacting control,
that level of resolution,
that kind of protocol is going to become
a limiting factor at some scale.
Another sort of important nuance to this is that
it's not just the neural length itself
that's part of this equation.
If you start pushing latency sort of below the level
of how fast screens refresh,
then you have another problem.
You need your whole system to be able to be as reactive
as the sort of limits of what the technology can offer.
Like you need the screen, like 120 hertz
just doesn't work anymore if you're trying to
have something respond at something that's
at the level of one millisecond.
That's a really cool challenge.
I also like that for a t-shirt,
the best mouse in the world.
Tell me on the receiving end, so the decoding step,
now we figured out what the spikes are,
got them all together, now we're sending that over
to the app, what's the decoding step look like?
Yeah, so maybe first, what is decoding?
I think there's probably a lot of folks listening
that just have no clue what it means
to decode brain activity.
Actually, even if we zoom out beyond that, what is the app?
So there's an implant that's wirelessly communicating
with any digital device that has an app installed.
So maybe can you tell me at high level what the app is,
what the software is outside of the brain?
Yeah, so maybe working backwards from the goal.
The goal is to help someone with paralysis,
in this case, Nolan, be able to navigate
his computer independently.
And we think the best way to do that is to offer them
the same tools that we have to navigate our software
because we don't want to have to rebuild
an entire software ecosystem for the brain,
at least not yet, maybe someday you can imagine
there's UXs that are built natively for BCI,
but in terms of what's useful for people today,
I think most people would prefer to be able
to just control mouse and keyboard inputs
to all the applications that they want to use
for their daily jobs, for communicating
with their friends, et cetera.
And so the job of the application is really to translate
this wireless stream of brain data coming off the implant
into control of the computer.
And we do that by essentially building a mapping
from brain activity to sort of the HID inputs
to the actual hardware.
So HID is just the protocol for communicating
like input device events.
So for example, move mouse to this position
or press this key down.
And so that mapping is fundamentally
what the app is responsible for.
But there's a lot of nuance of how that mapping works
that we spent a lot of time to try to get right.
And we're still in the early stages of a long journey
to figure out how to do that optimally.
So one part of that process is decoding.
So decoding is this process of taking the statistical
patterns of brain data that's being channeled
across this Bluetooth connection to the application
and turning it into, for example, a mouse movement.
And that decoding step, you can think of it
in a couple of different parts.
So similar to any machine learning problem,
there's a training step and there's an inference step.
The training step in our case
is a very intricate behavioral process
where the user has to imagine doing different actions.
So for example, they'll be presented a screen
with a cursor on it, and they'll be asked to push
that cursor to the right.
Then imagine pushing that cursor to the left,
push it up, push it down.
Now we can basically build up a pattern
or using any sort of modern ML method,
a mapping of given this brain data
and that imagined behavior, map one to the other.
And then at test time, you take that same pattern
matching system, in our case, it's a deep neural network,
and you run it and you take the live streamer brain data
coming off their implant, you decode it by pattern matching
to what you saw at calibration time,
and you use that for a control of the computer.
Now, a couple like sort of rabbit holes
that I think are quite interesting,
one of them has to do with how you build
that best template matching system,
because there's a variety of behavioral challenges
and also debugging challenges
when you're working with someone who's paralyzed.
Because again, fundamentally,
you don't observe what they're trying to do.
You can't see them attempt to move their hand.
And so you have to figure out a way
to instruct the user to do something
and validate that they're doing it correctly
such that then you can downstream build with confidence
the mapping between the neural spikes
and the intended action.
And by doing the action correctly,
what I really mean is at this level of resolution
of what neurons are doing.
So if in ideal world, you could get a signal
of behavioral intent that is ground truth accurate
at the scale of sort of one millisecond resolution,
then with high confidence, I could build a mapping
from my neural spikes to that behavioral intention.
But the challenge is again,
that you don't observe what they're actually doing.
And so there's a lot of nuance
to how you build user experiences
that give you more than just sort of a course
on average, correct representation
of what the user's intending to do.
If you want to build the world's best mouse,
you really want it to be as responsive as possible.
You want it to be able to do exactly what the user's
intending at every sort of step along the way,
not just on average, be correct
when you're trying to move it from left to right.
And building a behavioral sort of calibration game
or sort of software experience
that gives you that level of resolution
is what we spend a lot of time working with.
So the calibration process, the interface
has to encourage precision,
being like whatever it does, it should be super intuitive
that the next thing the human is going to likely do
is exactly that intention that you need
and only that intention.
And you don't have any feedback
except that may be speaking to you afterwards,
what they actually did, you can't, oh yeah.
So that's fundamentally, that is a really exciting UX challenge
because that's all on the UX.
It's not just about being friendly or nice or usable.
It's like-
User experience is how it works.
It's how it works for the calibration
and calibration, at least at this stage of Neuralink
is like fundamental to the operation of the thing
and not just calibration
but continued calibration essentially.
And maybe you said something
that I think is worth exploring there a little bit.
You said it's primarily a UX challenge
and I think a large component of it is
but there is also a very interesting
machine learning challenge here
which is given some dataset including
some on average correct behavior
of asking the user to move up or move down,
move right, move left.
And given a dataset of neural spikes,
is there a way to infer
in some kind of semi-supervised
or entirely unsupervised way
what that high resolution version of their intention is?
And if you think about it,
like there probably is
because there are enough data points in the dataset,
enough constraints on your model
that there should be a way
with the right sort of formulation
to let the model figure out itself.
For example, at this millisecond,
this is exactly how hard they're pushing upwards.
And at this millisecond,
this is how hard they're trying to push upwards.
It's really important to have very clean labels.
Yes.
So like the problem
because much harder from the machine learning perspective
the labels are noisy.
That's correct.
And then to get the clean labels,
that's a UX challenge.
Correct.
Although clean labels,
I think maybe it's worth exploring
what that exactly means.
I think any given labeling strategy
will have some number of assumptions
it makes about what the user is attempting to do.
Those assumptions can be formulated in a loss function
or they can be formulated in terms of heuristics
that you might use to just try to estimate
or guesstimate what the user is trying to do.
And what really matters
is how accurate are those assumptions?
For example, you might say,
hey user, push upwards
and follow the speed of this cursor.
And your heuristic might be that
they're trying to do it exactly
what that cursor is trying to do.
Another competing heuristic might be
they're actually trying to go slightly faster
at the beginning of the movement
and slightly slower at the end.
And those competing heuristics
may or may not be accurate reflections
of what the user is trying to do.
Another version of the task might be,
hey user, imagine moving this cursor a fixed offset.
So rather than follow the cursor,
just try to move it exactly 200 pixels to the right.
So here's the cursor, here's the target.
Okay, cursor disappears,
try to move that now invisible cursor
200 pixels to the right.
And the assumption in that case would be
that the user can actually modulate correctly
that position offset.
But that position offset assumption
might be a weaker assumption.
And therefore potentially you can make it more accurate
than these heuristics that are trying to guesstimate
at each millisecond what the user is trying to do.
So you can imagine different tasks
that make different assumptions
about the nature of the user intention.
And those assumptions being correct
is what I would think of as a clean label.
For that step, what are we supposed to be visualizing?
There's a cursor and you want to move that cursor
to the right or the left, the up and down,
or maybe move them by a certain offset.
So that's one way, is that the best way to do calibration?
So for example, an alternative crazy way
that probably is playing a role here
is a game like WebGrid,
where you're just getting a very large amount of data,
the person playing a game,
where if they are in a state of flow,
maybe you can get clean signal as a side effect.
Or is that not an effective way for initial calibration?
Yeah, great question.
There's a lot to unpack there.
So the first thing I would draw a distinction
between a sort of open loop first closed loop.
So open loop, what I mean by that is
the user is sort of going from zero to one.
They have no model at all.
And they're trying to get to the place
where they have some level of control at all.
In that setup, you really need to have some task
that gives the user a hint of what you want them to do,
such that you can build this mapping again
from brain data to output.
Then once they have a model,
you could imagine them using that model
and actually adapting to it
and figuring out the right way to use it themself,
and then retraining on that data
to give you sort of a boost in performance.
There's a lot of challenges associated
with both of these techniques.
And we can sort of rabbit hole into both of them
if you're interested.
But the sort of challenge with the open loop task
is that the user themself doesn't get proprioceptive feedback
about what they're doing.
They don't necessarily perceive themself
or feel the mouse under their hand
when they're trying to do an open loop calibration.
They're being asked to perform something.
Like imagine if you sort of had your whole right arm numbed
and you stuck it in a box and you couldn't see it.
So you had no visual feedback
and you had no proprioceptive feedback
about what the position or activity of your arm was.
And now you're asked,
okay, given this thing on the screen
that's moving from left to right, match that speed.
And you basically can try your best to invoke
whatever that imagined action is in your brain
that's moving the cursor from left to right.
But in any situation, you're gonna be inaccurate
and maybe inconsistent in how you do that task.
And so that's sort of the fundamental challenge
of open loop.
The challenge with closed loop is that
once the user's given a model
and they're able to start moving the mouse on their own,
they're going to very naturally adapt to that model.
And that co-adaptation between the model
learning what they're doing
and the user learning how to use the model
may not find you the best sort of global minima.
And maybe that your first model was noisy in some ways
or maybe just had some like quirk.
There's some like part of the data distribution
it didn't cover super well.
And the user now figures out
because they're a brilliant user like Nolan,
they figure out the right sequence of imagined motions
or the right angle they have to hold their hand at
to get it to work.
And they'll get it to work great.
But then the next day they come back to their device
and maybe they don't remember exactly all the tricks
that they used in the previous day.
And so there's a complicated sort of feedback cycle here
that can emerge and can make it
a very difficult debugging process.
Okay, there's a lot of really fascinating things there.
Yeah, actually just to stay on the closed loop,
I've seen situations, this actually happened
watching psychology grad students.
They use pieces of software
when they don't know how to program themselves.
They use pieces of software that somebody else wrote
and it has a bunch of bugs.
And they figure out like,
and they've been using it for years.
They figure out ways to work around.
That just happens.
Like nobody like considers maybe we should fix this.
They just adapt.
And that's a really interesting notion
that we're really good at adapting,
but you need to still, that might not be the optimal.
Okay, so how do you solve that problem?
Do you have to restart from scratch
every once in a while kind of thing?
Yeah, it's a good question.
First and foremost, I'd say this is not a solve problem.
And for anyone who's listening in academia,
who works on BCIs, I would also say this is not a problem
that's solved by simply scaling channel count.
So this is, maybe that can help
and you can get sort of richer covariant structures
that you can use to exploit
when trying to come up with good labeling strategies.
But if you're interested in problems
that aren't gonna be solved inherently
by just scaling channel count, this is one of them.
Yeah, so how do you solve it?
It's not a solve problem.
That's the first thing I wanna make sure it gets across.
The second thing is any solution that involves closed loop
is going to become a very difficult debugging problem.
And one of my sort of general heuristics
for choosing what prompts to tackle
is that you wanna choose the one
that's gonna be the easiest to debug.
Because if you can do that, even if the ceiling is lower,
you're gonna be able to move faster
because you have a tighter iteration loop
debugging the problem.
And in the open loop setting,
there's not a feedback cycle to debug
with the user in the loop.
And so there's some reason to think that
that should be an easier debugging problem.
The other thing that's worth understanding
is that even in the closed loop setting,
there's no special soft or magic
of how to infer what the user is truly attempting to do.
In the closed loop setting,
although they're moving the cursor on the screen,
they may be attempting something different
than what your model is outputting.
So what the model is outputting
is not a signal that you can use to retrain
if you want to be able to improve the model further.
You still have this very complicated guesstimation
or unsupervised problem of figuring out
what is the true user intention underlying that signal.
And so the open loop problem has the nice property
of being easy to debug.
And the second nice property of
it has all the same information content
as the closed loop scenario.
Another thing I wanna mention and call out is that
this problem doesn't need to be solved
in order to give useful control to people.
Even today with the solutions we have now,
and that academia has built up over decades,
the level of control that can be given to a user today
is quite useful.
It doesn't need to be solved
to get to that level of control.
But again, I wanna build the world's best mouse.
I wanna make it so good
that it's not even a question that you want it.
And to build the world's best mouse,
the superhuman version,
you really need to nail that problem.
And a couple maybe details of previous studies
that we've done internally that I think are very interesting
to understand when thinking about how to solve this problem.
The first is that even when you have ground truth data
of what the user's trying to do,
and you can get this with an able-bodied monkey,
a monkey that has an early device implanted
and moving a mouse to control a computer,
even with that ground truth data set,
it turns out that the optimal thing to predict
to produce high performance BCI
is not just the direct control of the mouse.
You can imagine building a data set
of what's going on in the brain
and what is the mouse exactly doing on the table.
And it turns out that if you build the mapping
from neuro spikes to predict exactly what the mouse is doing,
that model will perform worse
than a model that is trained to predict
sort of higher level assumptions
about what the user might be trying to do.
For example, assuming that the monkey is trying
to go in a straight line to the target.
It turns out that making those assumptions
is actually more effective in producing a model
than actually predicting the underlying hand movement.
The intention, not like the physical movement or whatever.
Yeah.
There's obviously a really strong correlation
between the two,
but the intention is a more powerful thing to be chasing.
Right.
Well, that's also super interesting.
I mean, the intention itself is fascinating
because yes, with the BCI here in this case,
with the digital telepathy,
you're acting on the intention, not the action,
which is why there's an experience
of like feeling like it's happening
before you meant for it to happen.
That is so cool.
And that is why you could achieve
like superhuman performance probably
in terms of the control of the mouse.
So the open loop, just to clarify,
so whenever the person is tasked
to like move the mouse to the right,
you said there's not feedback.
So they don't get to get that satisfaction
of like actually getting it to move, right?
So you could imagine giving the user feedback on a screen,
but it's difficult because at this point
you don't know what they're attempting to do.
So what can you show them that would basically give them
a signal of I'm doing this correctly or not correctly?
So let's take this very specific example.
Like maybe your calibration task looks like
you're trying to move the cursor a certain position offset.
So your instructions to the user are,
hey, the cursor is here.
Now, when the cursor disappears,
imagine moving it 200 pixels from where it was to the right
to be over this target.
In that kind of scenario, you could imagine
coming up with some sort of consistency metric
that you could display to the user of, okay,
I know what the spike train looks like on average
when you do this action to the right.
Maybe I can produce some sort of probabilistic estimate
of how likely is that to be the action you took
given the latest trial or trajectory that you imagined.
And that could give the user some sort of feedback
of how consistent are they across different trials.
You could also imagine that if the user is prompted
with that kind of consistency metric
that maybe they just become more behaviorally engaged
to begin with because the task is kind of boring
when you don't have any feedback at all.
And so there may be benefits to the user experience
of showing something on the screen,
even if it's not accurate,
just because it keeps the user motivated
to try to increase that number or push it upwards.
So there's a psychology element here.
Yeah, absolutely.
And again, all of that is UX challenge.
How much signal drift is there, hour to hour,
day to day, week to week, month to month?
How often do you have to recalibrate
because of the signal drift?
Yeah, so this is a problem we've worked on
both with NHP, non-human primates,
before our clinical trial,
and then also with Noland during the clinical trial.
Maybe the first thing that's worth stating
is what the goal is here.
So the goal is really to enable the user
to have a plug and play experience
where I guess they don't have to plug anything in,
but a play experience where they can use the device
whenever they want to, however they want to.
And that's really what we're aiming for.
And so there can be a set of solutions
that get to that state
without considering this non-stationarity problem.
So maybe the first solution here that's important
is that they can recalibrate whenever they want.
This is something that Noland has the ability to do today.
So you can recalibrate the system at 2 a.m.
in the middle of the night without his caretaker
or parents or friends around to help push a button for him.
The other important part of the solution
is that when you have a good model calibrated,
that you can continue using that
without needing to recalibrate it.
So how often he has to do this recalibration today
depends really on his appetite for performance.
There are, we observe sort of a degradation through time
of how well any individual model works,
but this can be mitigated behaviorally
by the user adapting their control strategy.
It can also be mitigated through a combination
of sort of software features that we provide to the user.
For example, we let the user adjust
exactly how fast the cursor is moving.
We call that the gain, for example,
the gain of how fast the cursor reacts
to any given input intention.
They can also adjust the smoothing,
how smooth the output of that cursor intention actually is.
They can also adjust the friction,
which is how easy it is to stop and hold still.
And all these software tools allow the user
a great deal of flexibility and troubleshooting mechanisms
to be able to solve this problem for themselves.
By the way, all of this is done
by looking to the right side of the screen,
selecting the mixer, and the mixer you have, it's-
DJ mode for your BCI.
I mean, it's a really well done interface.
It's really, really well done.
And so yeah, there's that bias
that there's a cursor drift
that Nolan talked about in a stream.
Although he said that you guys were just playing around
with it with him and they're constantly improving.
So that could have been just a snapshot
of that particular moment, a particular day.
But he said that there was this cursor drift
and this bias that could be removed
by him, I guess, looking to the right side of the screen
or left side of the screen to kind of adjust the bias.
That's one interface action, I guess, to adjust the bias.
Yeah, so this is actually an idea
that comes out of academia.
There was some prior work with
sort of BrainGate clinical trial participants
where they pioneered this idea of bias correction.
The way we've done it, I think it's very prioritized,
very beautiful user experience
where the user can essentially flash the cursor
over to the side of the screen
and it opens up a window
where they can actually sort of adjust
or tune exactly the bias of the cursor.
So bias maybe for people who aren't familiar
is just sort of what is the default motion of the cursor
if you're imagining nothing.
And it turns out that that's one of the first
first sort of qualia of the cursor control experience
that's impacted by neural nonstationarity.
Qualia of the cursor experience.
I mean, I don't know how else to describe it.
Like, you know, I'm not the guy moving.
It's very poetic, I love it.
The quality of the cursor experience.
Yeah, I mean, it sounds poetic,
but it is deeply true.
There is an experience when it works well,
it is a joyful, a really pleasant experience.
And when it doesn't work well,
it's a very frustrating experience.
That's actually the art of UX.
It's like you have the possibility to frustrate people
or the possibility to give them joy.
And at the end of the day,
it really is truly the case that UX is how the thing works.
And so it's not just like what's showing on the screen.
It's also, you know, what control surfaces
does a decoder provide the user?
We want them to feel like they're in the F1 car,
not like the, you know, some like minivan, right?
And that really truly is how we think about it.
Nolan himself is an F1 fan.
So we refer to ourselves as a pit crew.
He really is truly the F1 driver.
And there's different, you know, control surfaces
that different kinds of cars and airplanes provide the user.
And we take a lot of inspiration from that
when designing how the cursor should behave.
And what maybe one nuance of this is, you know,
even details like when you move a mouse
on a MacBook trackpad,
the sort of response curve of how that input
that you give the trackpad translates to cursor movement
is different than how it works with a mouse.
When you move on the trackpad,
there's a different response function,
a different curve to how much a movement translates
to input to the computer
than when you do it physically with a mouse.
And that's because somebody sat down a long time ago
when they're designing the initial input systems
to any computer,
and they thought through exactly how it feels
to use these different systems.
And now we're designing sort of the next generation of this
input system to a computer,
which is entirely done via the brain.
And there's no proprioceptive feedback.
Again, you don't feel the mouse in your hand.
You don't feel the keys under your fingertips.
And you want a control surface
that still makes it easy and intuitive for the user
to understand the state of the system
and how to achieve what they want to achieve.
And ultimately, the end goal is that that UX
is completely, it fades into the background.
It becomes something that's so natural and intuitive
that it's subconscious to the user.
And they just should feel like they have
basically direct control over the cursor.
It just does what they want it to do.
They're not thinking about the implementation
of how to make it do what they want it to do.
It's just doing what they want it to do.
Is there some kind of things along the lines
of like Fitt's law where you should move the mouse
in a certain kind of way that maximizes your chance
to hit the target?
I don't even know what I'm asking,
but I'm hoping the intention of my question
will land on a profound answer.
Is there some kind of understanding of the laws of UX
when it comes to the context of somebody using their brain
to control it?
Like that's different than actual with a mouse.
I think we're in the early stages
of discovering those laws.
So I wouldn't claim to have solved that problem yet,
but there's definitely some things we've learned
that make it easier for the user to get stuff done.
And it's pretty straightforward when you verbalize it,
but it takes a while to actually get to that point
when you're in the process of debugging the stuff
in the trenches.
One of those things is that
any machine learning system you build
has some number of errors,
and it matters how those errors translate
to the downstream user experience.
For example, if you're developing a search algorithm
in your photos, if you search for your friend Joe
and it pulls up a photo of your friend, Josephine,
maybe that's not a big deal
because the cost of an error is not that high.
In a different scenario where you're trying
to detect insurance fraud or something like this
and you're directly sending someone to court
because of some machine learning model output,
then the errors make a lot more sense to be careful about.
You want to be very thoughtful
about how those errors translate to downstream effects.
The same is true in BCI.
So for example, if you're building a model
that's decoding a velocity output from the brain
versus an output where you're trying
to modulate the left click, for example,
these have sort of different trade-offs
of how precise you need to be
before it becomes useful to the end user.
For velocity, it's okay to be on average correct
because the output of the model is integrated through time.
So if the user is trying to click at position A
and they're currently in position B,
they're trying to navigate over time
to get between those two points.
And as long as the output of the model is on average correct,
they can sort of steer it through time
with the user control loop in the mix
that can get to the point they want to get to.
The same is not true of a click.
For a click, you're performing it almost instantly
at the scale of neurons firing.
And so you want to be very sure that that click is correct
because a false click can be very destructive to the user.
They might accidentally close the tab
that they're trying to do something
and lose all their progress.
They might accidentally hit some send button
on some text that is only half composed
and reads funny after.
So there's different sort of cost functions
associated with errors in this space.
And part of the UX design is understanding
how to build a solution that is, when it's wrong,
still useful to the end user.
That's so fascinating.
Assigning cost to every action when an error occurs.
So every action, if an error occurs, has a certain cost.
And incorporating that into how you interpret the intention,
mapping it to the action is really important.
I didn't quite, until you said it,
realize there's a cost to sending the text early.
It's like a very expensive cost.
It's super annoying if you accidentally,
like if you're a cursor,
imagine if your cursor misclicked every once in a while.
That's like super obnoxious.
And the worst part of it is usually
when the user's trying to click,
they're also holding still
because they're over the target they want to hit
and they're getting ready to click.
Which means that in the data sets that we build,
on average is the case that sort of low speeds
or desire to hold still is correlated with
when the user's attempting to click.
Wow, that is really fascinating.
It's also not the case.
People think that, oh, a click is a binary signal.
This must be super easy to decode.
Well, yes, it is.
But the bar is so much higher
for it to become a useful thing for the user.
And there's ways to solve this.
I mean, you can sort of take the compound approach of,
well, let's just give the,
like let's take five seconds to click.
Let's take a huge window of time
so we can be very confident about the answer.
But again, world's best mouse.
The world's best mouse doesn't take a second to click
or 500 milliseconds to click.
It takes five milliseconds to click or less.
And so if you're aiming for that kind of high bar,
then you really want to solve the underlying problem.
So maybe this is a good place to ask
about how to measure performance,
this whole bits per second.
Can you like explain what you mean by that?
Maybe a good place to start is to talk about web grid
as a game, as a good illustration
of the measurement of performance.
Yeah, maybe I'll take one zoom out step there,
which is just explaining why we care to measure this at all.
So again, our goal is to provide the user
the ability to control the computer as well as I can,
and hopefully better.
And that means that they can do it
at the same speed as what I can do.
It means that they have access
to all the same functionality that I have,
including all those little details,
like command tab, command space, all this stuff.
They need to be able to do it with their brain.
And with the same level of reliability
is what I can do with my muscles.
And that's a high bar.
And so we intend to measure and quantify every aspect
of that to understand how we're progressing
towards that goal.
There's many ways to measure BPS by the way.
This isn't the only way,
but we present the user a grid of targets.
And basically we compute a score,
which is dependent on how fast and accurately they can select
and then how small are the targets.
And the more targets that are on the screen,
the smaller they are,
the more information you present per click.
And so if you think about it
from information theory point of view,
you can communicate across different
information theoretic channels.
And one such channel is a typing interface you could imagine
that's built out of a grid,
just like a software keyboard on the screen.
And bits per second is a measure that's computed
by taking the log of the number of targets on the screen.
You can subtract one if you care to model a keyboard
because you have to subtract one for the delete key
on the keyboard,
but log of the number of targets on the screen
times the number of correct selections minus incorrect
divided by some time window, for example, 60 seconds.
And that's sort of the standard way to measure
a cursor control task in academia.
And all credit in the world goes to this great professor,
Dr. Shenoy of Stanford who came up with that task.
And he's also one of my inspirations for being in the field.
So all the credit in the world to him
for coming up with a standardized metric
to facilitate this kind of bragging rights that we have now
to say that Nolan is the best in the world
at this task with his BCI.
It's very important for progress that you have
standardized metrics that people can compare across.
Different techniques and approaches, how old does this do?
So yeah, big kudos to him
and to all the team at Stanford.
Yeah, so for Nolan and for me playing this task,
there's also different modes that you can configure this task.
So the web grid task can be presented as
just sort of a left click on the screen,
or you could have targets that you just dwell over,
or you could have targets that you left right click on,
you could have targets that are left, right click,
middle click, scrolling, clicking and dragging.
You can do all sorts of things within this general framework.
But the simplest purest form is just
blue targets jump on the screen, blue means left click.
That's the simplest form of the game.
And the sort of prior records here
in academic work and at Neuralink internally
with sort of NHP's have all been matched
or beaten by Nolan with his Neuralink device.
So sort of prior to Neuralink,
the sort of world record for a human use and device
is somewhere between 4.2 to 4.6 BPS,
depending on exactly what paper you read
and how you interpret it.
Nolan's current record is 8.5 BPS.
And again, the sort of median Neuralink performance is 10 BPS.
So you can think of it roughly as he's 85%
the level of control of a median Neuralink
or using their cursor to slot blue targets on the screen.
And yeah, I think there's a very interesting journey ahead
to get us to that same level of 10 BPS performance.
It's not the case that sort of the tricks that got us
from four to six BPS and then six to eight BPS
are gonna be the ones that get us from eight to 10.
And in my view, the core challenge here
is really the labeling problem.
It's how do you understand at a very, very fine resolution
what the user is attempting to do.
And yeah, I highly encourage folks in academia
to work on this problem.
What's the journey with Nolan
on that quest of increasing the BPS on web grid?
In March, you said that he selected 89,285 targets
in web grid.
So he loves this game.
He's really serious about improving his performance
in this game.
So what is the journey of trying to figure out
how to improve that performance?
How much can that be done on the decoding side?
How much can that be done on the calibration side?
How much can that be done on the Nolan side
of like figuring out how to convey his intention
more cleanly?
Yeah, no, this is a great question.
So in my view, one of the primary reasons
why Nolan's performance is so good is because of Nolan.
Nolan is extremely focused and very energetic.
He will play web grid sometimes for like four hours
in the middle of the night,
like from 2 a.m. to 6 a.m. he'll be playing web grid
just because he wants to push it
to the limits of what he can do.
And this is not us like asking him to do that.
I wanna be clear, like we're not saying,
hey, you should play web grid tonight.
We just gave him the game as part of our research
and he is able to play independently
and practice whenever he wants.
And he really pushes hard to push it,
the technology to the absolute limit.
And he views that as like his job really
to make us be the bottleneck.
And boy, has he done that well.
And so that's the first thing to acknowledge is that
he is extremely motivated to make this work.
I've also had the privilege to meet other
clinical trial participants from BrainGate and other trials
and they very much share the same attitude
of like they viewed this as their life's work
to advance the technology as much as they can.
And if that means selecting targets on the screen
for four hours from 2 a.m. to 6 a.m., then so be it.
And there's something extremely admirable about that
that's worth calling out.
Okay, so now how do you sort of get from where he started
which is no cursor control to 8 BPS?
So, I mean, when he started, there's a huge amount
of learning to do on his side and our side
to figure out what's the most intuitive control for him.
And the most intuitive control for him is sort of,
you have to find the set intersection of
what do we have the signal to decode?
So we don't pick up every single neuron in the motor cortex
which means we don't have representation
for every part of the body.
So there may be some signals that we have better
sort of decode performance on than others.
For example, on his left hand,
we have a lot of difficulty distinguishing
his left ring finger from his left middle finger.
But on his right hand, we have good control
and good modulation detected from the neurons
who were able to record for his pinky and his thumb
and his index finger.
So you can imagine how these different subspaces
of modulated activity intersect
with what's the most intuitive for him.
And this has evolved over time.
So once we gave him the ability
to calibrate models on his own,
he was able to go and explore various different ways
to imagine controlling the cursor.
For example, he can imagine controlling the cursor
by wiggling his wrist side to side
or by moving his entire arm,
by having to get one point into his feet.
He tried a whole bunch of stuff to explore the space
of what is the most natural way for him
to control the cursor that at the same time
it's easy for us to decode.
Just to clarify, it's through the body mapping procedure
you're able to figure out which finger he can move.
Yes, that's one way to do it.
Maybe one nuance of when he's doing it,
he can imagine many more things
than we represent in that visual on the screen.
So we show him sort of abstractly, here's a cursor.
You figure out what works the best for you.
And we obviously have hints about what will work best
from that body mapping procedure of,
we know that this particular action we can represent well,
but it's really up to him to go and explore
and figure out what works the best.
But at which point does he no longer visualize
the movement of his body
and he's just visualizing the movement of the cursor?
How quickly does he go from, how quickly does it get there?
So this happened on a Tuesday.
I remember this day very clearly
because at some point during the day,
it looked like he wasn't doing super well.
It looked like the model wasn't performing super well
and he was not getting distracted,
but he actually, it wasn't the case.
Like what actually happened was he was trying something new
where he was just controlling the cursor.
So he wasn't imagining moving his hand anymore.
He was just imagining, I don't know what it is,
some like abstract intention
to move the cursor on the screen.
And I cannot tell you what the difference
between those two things are.
I really truly cannot.
He's tried to explain it to me before.
I cannot give a first person account of what that's like,
but the expletives that he uttered in that moment were,
enough to suggest
that it was a very qualitatively different experience
for him to just have direct neural control over a cursor.
I wonder if there's a way through UX
to encourage a human being to discover that
because he discovered it, like you said to me,
that he's a pioneer.
So he discovered that on his own
through all of the process of trying to move the cursor
with different kinds of intentions.
But that is clearly a really powerful thing to arrive at,
which is to let go of trying to control the fingers
and the hand and control the actual digital device
with your mind.
That's right, UX is how it works.
And the ideal UX is one that it's,
the user doesn't have to think about what they need to do
in order to get it done.
They just, it just does it.
That is so fascinating.
But I wonder on the biological side,
how long it takes for the brain to adapt.
So is it just simply learning, like high level software,
or is there like a neuroplasticity component
where like the brain is adjusting slowly?
Yeah, the truth is I don't know.
I'm very excited to see with sort of the second participant
that we implant, what the journey is like for them
because we'll have learned a lot more.
Potentially we can help them understand
and explore that direction more quickly.
This is something I didn't,
this wasn't me prompting Nolan to go try this.
He was just exploring how to use his device
and figure it out himself.
But now that we know that that's a possibility,
that maybe there's a way to, for example, hint the user,
don't try super hard during calibration.
Just do something that feels natural
or just directly control the cursor.
Don't imagine explicit action.
And from there, we should be able to hopefully understand
how this is for somebody
who has not experienced that before.
Maybe that's the default mode of operation for them.
You don't have to go through this intermediate phase
of explicit motions.
Or maybe if that naturally happens for people,
you can just occasionally encourage them
to allow themselves to move the cursor.
Actually sometimes, just like with a four minute mile,
just the knowledge that that's possible.
Pushes you to do it.
Yeah, enables you to do it.
And then it becomes trivial.
And then it also makes you wonder,
this is the cool thing about humans.
Once there's a lot more human participants,
they will discover things that are possible.
Yes, and share their experiences.
Yeah, and share it.
And that because of them sharing it,
they'll be able to do it.
All of a sudden, that's unlocked for everybody.
Because just the knowledge sometimes
is the thing that enables it to do it.
Yeah, just to comment on that too,
we've probably tried like a thousand different ways
to do various aspects of decoding.
And now we know what the right subspace is
to continue exploring further again,
thanks to Nolan and the many hours he's put into this.
And so even just that help constraints
or the beam search of different approaches
that we could explore,
really helped accelerate for the next person,
the set of things that we'll get to try on day one,
how fast we hope to get them to useful control,
how fast we can enable them to use it independently
and to give value out of the system.
So yeah, massive hats off to Nolan
and all the participants that came before him
to make this technology a reality.
So how often are the updates to the decoder?
Because Nolan mentioned like,
okay, there's a new update that we're working on.
And that in the stream he said he plays the snake game
because it's like super hard.
It's a good way for him to test like how good the update is.
So, and he says like sometimes the update is a step backwards.
It's like, it's a constant like iteration.
So how often, like what does the update entail?
Is it most on the decoder side?
Yeah, couple comments.
So one is it's probably worth trying distinction
between sort of research sessions
where we're actively trying different things
to understand like what the best approach is
versus sort of independent use
where we want to have ability to just go use the device,
how anybody would want to use their MacBook.
And so what he's referring to is I think usually
in the context of a research session
where we're trying many, many different approaches
to even unsupervised approaches like we talked about earlier
to try to come up with better ways to estimate
his true intention and more accurately decoded.
And in those scenarios, I mean, we try in any given session,
he'll sometimes work for like eight hours a day.
And so that can be hundreds of different models
that we would try in that day,
like a lot of different things.
Now, it's also worth noting
that we update the application he uses quite frequently.
I think sometimes up to like four or five times a day
we'll update his application with different features
or bug fixes or feedback that he's given us.
So he's been able to, he's a very articulate person
who is part of the solution.
He's not a complaining person.
He says, hey, here's this thing that I've discovered
is not optimal in my flow.
Here's some ideas how to fix it.
Let me know what your thoughts are.
Let's figure out how to solve it.
And it often happens that those things are addressed
within a couple hours of him giving us his feedback.
That's the kind of iteration cycle we'll have.
And so sometimes at the beginning of the session
he'll give us feedback and at the end of the session
he's giving us feedback on the next iteration
of that process or that setup.
That's fascinating because one of the things you mentioned
that there was 271 pages of notes taken from the BCI sessions
and this was just in March.
So one of the amazing things about human beings
that they can provide, especially ones who are smart
and excited and all like positive and good vibes
like Nolan, that they can provide feedback,
continuous feedback.
It also requires, just to brag on the team a little bit,
I work with a lot of exceptional people
and it requires the team being absolutely laser focused
on the user and what will be the best for them.
And it requires like a level of commitment of,
okay, this is what the user feedback was,
I have all these meetings, we're gonna skip that today
and we're gonna do this.
That level of focus commitment is I would say
under appreciated in the world.
And also you obviously have to have the talent
to be able to execute on these things effectively
and yeah, we have that in loads.
Yeah, and this is such a interesting space of UX design
because you have, there's so many unknowns here
and I can tell UX is difficult
because of how many people do it poorly.
It's just not a trivial thing.
Yeah, it's also, UX is not something that you can
always solve by just constant iterating on different things.
Like sometimes you really need to step back
and think globally, am I even like the right sort of minima
to be chasing down for a solution?
Like there's a lot of problems
in which sort of fast iteration cycle is the predictor
of how successful you will be.
As a good example, like in an RL simulation, for example,
the more frequently you get a reward,
the faster you can progress.
It's just an easier learning problem,
the more frequently you get feedback.
But UX is not that way.
I mean, users are actually quite often wrong
about what the right solution is
and it requires a deep understanding
of the technical system and what's possible
combined with what the problem is you're trying to solve.
Not just how the user expressed it,
but what the true underlying problem is
to actually get to the right place.
Yeah, that's the old like stories of Steve Jobs
like rolling in there, like yeah, the user is a useful signal
but it's not a perfect signal.
And sometimes you have to remove the floppy disk drive
or whatever the, I forgot all the crazy stories
of Steve Jobs, like making wild design decisions.
But there, some of it is aesthetic
that some of it is about the love you put into the design,
which is very much a Steve Jobs, Johnny Ives type thing.
But when you have a human being using their brain
to interact with it, it also is deeply about function.
It's not just aesthetic.
And that you have to empathize with a human being before you
while not always listening to them directly.
Like you have to deeply empathize.
It's fascinating.
It's really, really fascinating.
And at the same time iterate, right?
But not iterate in small ways, sometimes a complete
like rebuilding the design.
He said that, Nolan said in the early days, the UX sucked
but you improved quickly.
What was that journey like?
Yeah, I mean, I'll give one concrete example.
So he really wanted to be able to read manga.
This is something that he, I mean,
it sounds like a simple thing,
but it's actually a really big deal for him.
And he couldn't do it with this mouse stick.
It just, it wasn't accessible.
You can't scroll with the mouse stick on his iPad
and on the website that he wanted to be able to use
to read the newest manga.
And so might be a good quick pause to say the mouth stick
is the thing he's using, holding a stick in his mouth
to scroll on a tablet.
Right, yeah.
It's basically, you can imagine it's a stylus
that you hold between your teeth.
Yeah.
It's basically a very long stylus.
And it's exhausting, it hurts and it's inefficient.
Yeah.
And maybe it's also worth calling out.
There are other alternative assistive technologies
but that particular situation, Nolan's in
and this is not uncommon.
And I think it's also not well understood by folks
is that he's relatively spastic.
So he'll have muscle spasms from time to time.
And so any assistive technology that requires him
to be positioned directly in front of a camera,
for example, an eye tracker or anything that requires him
to put something in his mouth, just is a no go
because he'll either be shifted out of frame
when he has a spasm or if he has something in his mouth,
it'll stab him in the face if he spasms too hard.
So these kinds of considerations are important
when thinking about what advantages a PCI has
in someone's life.
If it fits ergonomically into your life
in a way that you can use it independently
when your caretaker is not there, wherever you want to,
either in the bed or in the chair,
depending on your comfort level
and your desire to have pressure sores.
All these factors matter a lot in how good the solution is
in that user's life.
So one of these very fun examples is scroll.
So again, Minga is something he wanted to be able to read
and there's many ways to do scroll with a BCI.
You can imagine like different gestures, for example,
the user could do that would move the page.
But scroll is a very fascinating control surface
because it's a huge thing on the screen in front of you.
So any sort of jitter in the model output,
any sort of error in the model output causes
like an earthquake on the screen.
Like you really don't want to have your Minga page
that you're trying to read be shifted up
and down a few pixels just because your scroll decoder
is not completely accurate.
And so this was an example where we had to figure out
how to formulate the problem in a way
that the errors of the system, whenever they do occur
and we'll do our best to minimize them,
but whenever those errors do occur
that it doesn't interrupt the qualia, again,
of the experience that the user is having,
it doesn't interrupt their flow of reading their book.
And so what we ended up building
is this really brilliant feature.
This is a teammate named Bruce who worked on this
really brilliant work called quick scroll.
And quick scroll basically looks at the screen
and it identifies where on the screen are scroll bars.
And it does this by deeply integrating with macOS
to understand where are the scroll bars
actively present on the screen
using the sort of accessibility tree
that's available to macOS apps.
And we identified where those scroll bars are
and provided a BCI scroll bar.
And the BCI scroll bar looks similar
to a normal scroll bar, but it behaves very differently.
And that once you sort of move over to it,
your cursor sort of morphs onto it.
It sort of attaches or latches onto it.
And then once you push up or down
in the same way that you would use a push to control
the normal cursor, it actually moves the screen for you.
So it's basically like remapping the velocity
to a scroll action.
And the reason that feels so naturally intuitive
is that when you move over to attach to it,
it feels like magnetic.
So you're like sort of stuck onto it.
And then it's one continuous action.
You don't have to like switch your imagined movement.
You sort of snap onto it and then you're good to go.
You just immediately can start pulling the page down
or pushing it up.
And even once you get that right,
there's so many little nuances
of how this scroll behavior works
to make it natural and intuitive.
So one example is momentum.
Like when you scroll a page with your fingers on the screen,
you actually have some like flow.
Like it doesn't just stop right
when you lift your finger up.
The same is true with BCI scroll.
So we had to spend some time to figure out
what are the right nuances
when you don't feel the screen under your fingertip anymore.
What is the right sort of dynamic
or what's the right amount of page give, if you will,
when you push it to make it flow the right amount
for the user to have a natural experience
reading their book.
And there's a million, I mean, there's,
I could tell you like there's so many little minutia
of how exactly that scroll works
that we spent probably like a month getting right
to make that feel extremely natural
and easy for the user to navigate.
I mean, even the scroll on a smartphone with your finger
feels extremely natural and pleasant.
And it probably takes an extremely long time
to get that right.
And actually the same kind of visionary UX design
that we were talking about.
Don't always listen to the users, but also listen to them
and also have like visionary big, like throw everything out,
think from first principles, but also not.
Yeah, yeah, by the way, it just makes me think
that scroll bars on the desktop probably have stagnated
and never taken that.
Like, cause the snap, the same as it's like snapped a grid,
snapped a scroll bar action you're talking about,
it's something that could potentially be extremely useful
in the desktop setting, even just for users
to just improve the experience.
Cause the current scroll bar experience
in the desktop is horrible.
It's hard to find, hard to control, there's not a momentum,
there's, and the intention should be clear
when I start moving towards a scroll bar,
there should be a snapping to the scroll bar action.
But of course, you know, maybe I'm okay paying that cost,
but there's hundreds of millions of people
paying that cost nonstop.
But anyway, but in this case, this is necessary
because there's an extra cost paid by Nolan
for the jitteriness.
So you have to switch between the scrolling and the reading.
There has to be a phase shift between the two.
Like when you're scrolling, you're scrolling.
Right, right.
So that is one drawback of the current approach.
Maybe one other just sort of case study here.
So again, UX is how it works.
And we think about that holistically
from like the, even the future detection level
of what we detect in the brain
to how we design the decoder, what we choose to decode
to then how it works once it's being used by the user.
So another good example in that sort of how it works
once they're actually using the decoder,
you know, the output that's displayed on the screen
is not just what the decoder says.
It's also a function of, you know,
what's going on on the screen.
So we can understand, for example, that, you know,
when you're trying to close a tab,
that very small, stupid little X that's extremely tiny,
which is hard to get precisely hit
if you're dealing with sort of a noisy output of the decoder,
we can understand that that is a small little X
you might be trying to hit
and actually make it a bigger target for you.
Similar to how when you're typing on your phone,
if you're, you know, used to like the iOS keyboard,
for example, it actually adapts the target size
of individual keys based on an underlying language model.
So it'll actually understand if I'm typing,
hey, I'm going to see L,
it'll make the E key bigger
because it knows Lex is the person I'm going to go see.
And so that kind of, you know, predictiveness
can make the experience much more smooth,
even without, you know, improvements
to the underlying decoder or feature detection
part of the stack.
So we do that with a feature called magnetic targets.
We actually index the screen and we understand,
okay, these are the places that are, you know,
very small targets that might be difficult to hit.
Here's the kind of cursor dynamics around that location
that might be indicative of the user trying to select it.
Let's make it easier.
Let's blow up the size of it in a way
that makes it easier for the user
to sort of snap onto that target.
So all these little details, they matter a lot
in helping the user be independent
in their day-to-day living.
So how much of the work on the decoder
is generalizable to P2, P3, P4, P5, PM?
How do you improve the decoder
in a way that's generalizable?
Yeah, great question.
So the underlying signal we're trying to decode
is going to look very different to than in P1.
For example, channel number 345
is going to mean something different in user one
than it will in user two.
Just because that electrode that corresponds
with channel 345 is going to be next to a different neuron
in user one versus user two.
But the approach is the methods,
the user experience of how do you get the right
sort of behavioral pattern from the user
to associate with that neural signal,
we hope that we'll translate
over multiple generations of users.
And beyond that, it's very, very possible,
in fact, quite likely that we've overfit
to sort of Nolan's user experience desires and preferences.
And so what I hope to see is that, you know,
when we get a second, third, fourth participant,
that we find sort of what the right wide minimas are
that cover all the cases,
that make it more intuitive for everyone.
And hopefully there's a cross pollination of things where,
oh, we didn't think about that with this user
because, you know, they can speak,
but with this user who just can fundamentally
not speak at all, this user experience is not optimal.
And that will actually,
those improvements that we make there
should hopefully translate them to even people who can speak
but don't feel comfortable doing so
because we're in a public setting
like their doctor's office.
So the actual mechanism of open loop labeling
and then closed loop labeling will be the same
and hopefully can generalize across a different user
as they're doing the calibration step.
And the calibration step is pretty cool.
I mean, that in itself,
the interesting thing about WebGrid,
which is like closed loop, it's like fun.
I love it when there's like,
there used to be kind of an idea of human computation,
which is using actions that human would want to do anyway
to get a lot of signal from.
And like WebGrid is that,
like a nice video game that also serves
as great calibration.
It's so funny, this is,
I've heard this reaction so many times
before sort of the first user was implanted,
we had an internal perception
that the first user would not find this fun.
And so we thought really quite a bit actually
about like, should we build other games
that like are more interesting for the user
so we can get this kind of data
and help facilitate research
that's for long duration and stuff like this.
Turns out that like people love this game.
I always loved it,
but I didn't know that that was a shared perception.
Yeah, and just in case it's not clear,
WebGrid is, there's a grid of,
let's say 35 by 35 cells,
and one of them lights up blue
and you have to move your mouse over that and click on it.
And if you miss it, it's red and.
I put this game for so many hours, so many hours.
And what's your record you said?
My, I think I have the highest at Neuralink right now.
My record is 17 BPS.
17 BPS.
Which is about, if you imagine that 35 by 35 grid,
you're hitting about 100 trials per minute.
So 100 correct selections in that one minute window.
So you're averaging about, you know,
between 500, 600 milliseconds per selection.
So one of the reasons I think I struggle with that game
is I'm such a keyboard person.
So everything is done with your keyboard.
If I can avoid touching the mouse, it's great.
So how can you explain your high performance?
I have like a whole ritual I go through
when I play WebGrid.
It's just actually like a diet plan
associated with this whole thing.
So the first thing.
I have to fast for five days.
I have to go up to the mountains.
Actually it's kind of, I mean,
the fasting thing is important.
So this is like, you know.
Ochus is the mind, yeah, it's true.
So what I do is I actually,
I don't eat for a little bit beforehand.
And then I'll actually eat like a ton of peanut butter
right before I put it.
This is a real thing.
This is a real thing, yeah.
And then it has to be really late at night.
This is again, a night elephant.
I think we share, but it has to be like, you know,
midnight 2 a.m. kind of time window.
And I have a very specific,
like physical position I'll sit in,
which is, I used to be, I was homeschooled growing up.
And so I did most of my work like on the floor,
just like in my bedroom or whatever.
And so I have a very specific situation.
On the floor.
On the floor that I sit and play.
And then you have to make sure like there's not a lot
of weight on your elbow when you're playing,
so that you can move quickly.
And then I turned the gain of the cursor.
So the speed of the cursor way, way up.
So it's like small motions
that actually move the cursor.
Are you moving with your wrist or you're never.
I move with my fingers.
So my wrist is almost completely still.
I'm just moving my fingers.
Yeah.
You know those, just in a small tangent.
Yeah.
Which I've been meaning to go down this rabbit hole
of people that set the world record in Tetris.
Those folks they're playing, there's a way to,
did you see this?
It seems like all the fingers are moving.
Yeah, you could find a way to do it
where like it's using a loophole, like a bug
that you can do some incredibly fast stuff.
So it's along that line, but not quite.
But you do realize there'll be like a few programmers
right now listening to this fast and eat peanut butter.
Yeah, please, please try my record.
I mean, the reason I did this literally was just
because I wanted the bar to be high.
Like I wanted the number that we aim for
should not be like the median performance.
It should be like, it should be able to beat all of us
at least like that should be the minimum bar.
What do you think is possible?
Like 20?
Yeah, I don't know what the limit,
I mean the limits you can calculate
just in terms of like screen refresh rate
and like cursor immediately jumping to the next target.
But there's, I mean, I'm sure there's limits before that
with just sort of reaction time and visual perception
and things like this.
I would guess it's in the below 40, but above 20,
somewhere in there, it's probably the right
there I never would be thinking about.
It also matters like how difficult the task is.
You could imagine like some people might be able to do
like 10,000 targets on the screen
and maybe they can do better that way.
So there's some like task optimizations you could do
to try to boost your performance as well.
What do you think it takes for Nolan
to be able to do above eight, five
to keep increasing that number?
You said like every increase in the number
might require different improvements in the system.
Yeah, I think the nature of this work is,
the first answer to this important is I don't know.
This is, you know, edge of the research.
So again, nobody's gotten to that number before.
So what's next is gonna be a heuristic,
a guess from my part.
What we've seen historically is that different parts
of the stack become bottlenecks at different time points.
So, you know, when I first joined AirLink
like three years ago or so,
one of the major problems was just the latency
of the Bluetooth connection.
It was just like the radio device wasn't super good.
It was an earlier vision of the implant.
And it just like, no matter how good your decoder was,
if your thing is updating every 30 milliseconds
or 50 milliseconds, it's just gonna be choppy.
And no matter how good you are,
that's gonna be frustrating and lead to challenges.
So, you know, at that point, it was very clear
that the main challenge is just get the data off the device
in a very reliable way such that you can enable
the next challenge to be tackled.
And then at some point it was, you know,
actually the modeling challenge of how do you
just build a good mapping,
like the supervised learning problem
of you have a bunch of data
and you have a label you're trying to predict
just what is the right like neuro decoder architecture
and hyper parameters to optimize that.
That was a problem for a bit.
And once you solve that, it became a different bottleneck.
I think the next bottleneck after that
was actually just sort of software stability and reliability.
You know, if you have widely varying
sort of inference latency in your system
or your app just lags out every once in a while,
it decreases your ability to maintain
and get in a state of flow
and it basically just disrupts your control experience.
And so there's a variety of different software bugs
and improvements we made
that basically increased the performance of the system,
made it much more reliable, much more stable
and led to a state where we could reliably collect data
to build better models with.
So that was a bottleneck for a while.
It's just sort of like the software stack itself.
If I were to guess right now,
there's sort of two major directions
you could think about for improving BPS further.
The first major direction is labeling.
So labeling is again, this fundamental challenge
of given a window of time
where the user is expressing some behavioral intent,
what are they really trying to do
at the granularity of every millisecond?
And that again is a task design problem.
It's a UX problem.
It's a machine learning problem.
It's a software problem.
Sort of touches all those different domains.
The second thing you can think about to improve BPS further
is either completely changing the thing you're decoding
or just extending the number of things that you're decoding.
So this is sort of in the direction of functionality.
Basically, you can imagine giving more clicks.
For example, left click, a right click, a middle click,
different actions like click and drag, for example.
And that can improve the effective bit rate
of your communication process.
If you're trying to allow the user to express themselves
through any given communication channel,
you can measure that with bits per second.
But what actually matters at the end of the day is
how effective are they at navigating their computer?
And so from the perspective of the downstream task
that you care about,
functionality and extending functionality
is something we're very interested in.
Because not only can it improve the sort of number of BPS,
but it can also improve the downstream sort of independence
that the user has and the skill and efficiency
with which they can operate their computer.
Would the number of threads increasing also potentially help?
Yes, short answer is yes.
It's a bit nuanced how that curve,
or how that manifests in the numbers.
So what you'll see is that if you sort of plot a curve
of number of channels that you're using for decode
versus either the offline metric
of how good you are at decoding,
or the online metric of sort of in practice,
how good is the user at using this device,
you see roughly a log curve.
So as you move further out in number of channels,
you get a corresponding sort of logarithmic improvement
in control quality and offline validation metrics.
The important nuance here is that
each channel corresponds with a specific,
represented intention in the brain.
So for example, if you have a channel 254,
it might correspond with moving to the right,
channel 256 might be moved to the left.
If you want to expand the number of functions
you want to control,
you really want to have a broader set of channels
that covers a broader set of imagined movements.
You can think of it like,
kind of like Mr. Potato Man, actually.
Like if you had a bunch of different imagined movements
you could do,
how would you map those imagined movements
to input to a computer?
You can imagine, you know,
handwriting to output characters on the screen.
You can imagine just typing with your fingers
and have that output text on the screen.
You can imagine different finger modulations
for different clicks.
You can imagine wiggling your big notes
for opening some menu or wiggling your big toe
to have like command tab occur or something like this.
So it's really the amount of different actions
you can take in the world
depends on how many channels you have
and the information content that they carry.
Right, so that's more about the number of actions.
So actually as you increase the number of threads,
that's more about increasing the number of actions
you're able to perform.
One other nuance there that is worth mentioning.
So again, our goal is really to enable a user-worth process
to control the computer as fast as I can.
So that's BPS with all the same functionality I have,
which is what we just talked about,
but then also as reliably as I can.
And that last point is very related
to channel count discussion.
So as you scale out number of channels,
the relative importance of any particular feature
of your model input to the output control
of the user diminishes,
which means that if the sort of neural non-stationary effect
is per channel, or if the noise is independent,
such that more channels means on average less output effect,
then your reliability of your system will improve.
So one sort of core thesis that at least I have
is that scaling channel count should improve
the reliability system without any work
on the decoder itself.
Can you linger on reliability here?
So first of all, when you see a non-stationarity
of the signal, which aspect are you referring to?
Yeah, so maybe let's talk briefly
what the actual underlying signal looks like.
So again, I spoke very briefly at the beginning
about how when you imagine moving to the right
or imagine moving to the left,
neurons might fire more or less.
And their frequency content of that signal,
at least in the motor cortex,
it's very correlated with the output intention
of the behavioral task that the user is doing.
You can imagine actually, this is not obvious
that rate coding, which is the name of that phenomenon,
is like the only way the brain could represent information.
You can imagine many different ways
in which the brain could encode intention.
And there's actually evidence like in bats, for example,
that there is temporal codes, so timing codes
of like exactly when particular neurons fire
is the mechanism of information representation.
But at least in the motor cortex,
there's a substantial evidence that it's rate coding,
or at least one like first order effect
is that it's rate coding.
So then if the brain is representing information
by changing the sort of frequency of a neuron firing,
what really matters is sort of the delta
between sort of the baseline state of the neuron
and what it looks like when it's modulated.
And what we've observed,
and what has also been observed in academic work,
is that that baseline rate,
sort of the, if you're to target the scale,
if you imagine that analogy for like measuring, you know,
flour or something when you're baking,
that baseline state of how much the pot weighs
is actually different day to day.
And so if what you're trying to measure
is how much rice is in the pot,
you're gonna get a different measurement different days
because you're measuring with different pots.
So that baseline rate shifting is really the thing that,
at least from a first order description of the problem,
is what's causing this downstream bias.
There can be other effects,
not linear effects on top of that,
but at least at a very first order description of the problem,
that's what we observed day to day
is at the baseline firing rate of any particular neuron
or observed on a particular channel is changing.
So can you just adjust to the baseline
to make it relative to the baseline nonstop?
Yeah, this is a great question.
So with monkeys, we have found various ways to do this.
One example would do this is you ask them
to do some behavioral task,
like play the game with a joystick.
You measure what's going on in the brain.
You compute some mean of what's going on
across all the input features
and you subtract that at the input
when you're doing your BCI session works super well.
For whatever reason that doesn't work super well with Nolan.
I actually don't know the four reason why,
but I can imagine several explanations.
One such explanation could be that the context effect
difference between some open loop task
and some closed loop task is much more significant
with Nolan than it is with a monkey.
Maybe in this open loop task,
he's watching the Lex Freeman podcast
while he's doing the task or he's whistling
and listening to music and talking with his friend
and ask his mom what's for dinner while he's doing this task.
And so the exact sort of difference in context
between those two states may be much larger
and thus lead to a bigger generalization gap
between the features that you're normalizing
at sort of open loop time
and what you're trying to use at closed loop time.
That's interesting.
Just on that point, it's kind of incredible
to watch Nolan be able to multitask
to do multiple tasks at the same time
to be able to move the mouse cursor effectively
while talking and while being nervous
because he's talking in front of it.
Kicking my ass in chess too.
Kicking your ass and now we're
and talk trash while doing it.
So all at the same time.
And yes, if you're trying to normalize to the baseline,
that might throw everything off.
Boy, is that interesting.
Maybe one comment on that too.
For folks that aren't familiar with assistive technology,
I think there's a common belief that,
why can't you just use an eye tracker
or something like this
for helping somebody move a mouse on the screen?
And it's a really a fair question and one that
I actually was not confident before through Nolan
that this was gonna be a profoundly transformative technology
for people like him.
And I'm very confident now that it will be,
but the reasons are subtle.
It really has to do with ergonomically
how it fits into their life.
Even if you can just offer the same level of control
as what they would have with an eye tracker
or with a mouse stick.
But you don't need to have that thing in your face.
You don't need to be positioned in a certain way.
You don't need your caretaker to be around
to set it up for you.
You can activate it when you want,
how you want, wherever you want.
That level of independence is so game-changing for people.
It means that they can text a friend at night privately
without their mom needing to be in the loop.
It means that they can open up and browse the internet
at 2 a.m. when nobody's around to set their iPad up for them.
This is a profoundly game-changing thing
for folks in that situation.
And this is even before we start talking about folks
that may not be able to communicate at all
or ask for help when they want to.
This can be potentially the only link
that they have to the outside world.
And yeah, that one doesn't, I think, need explanation
of why that's so impactful.
You mentioned neural decoder.
How much machine learning is in the decoder?
How much magic, how much science, how much art?
How difficult is it to come up with a decoder
that figures out what these sequence of spikes mean?
Yeah, good question.
There's a couple of different ways to answer this.
So maybe I'll zoom out briefly first
and then I'll go down one of the rabbit holes.
And so the zoomed out view is that building the decoder
is really the process of building the dataset
plus compiling it into the weights.
And each of those steps is important.
The direction, I think, of further improvement
is primarily going to be in the dataset side
of how do you construct the optimal labels for the model.
But there's an entirely separate challenge
of then how do you compile it into the best model.
And so I'll go briefly down the second one,
down the second rabbit hole.
One of the main challenges
with designing the optimal model for BCI
is that offline metrics don't necessarily correspond
to online metrics.
It's fundamentally a control problem.
The user is trying to control something on the screen.
And the exact sort of user experience
of how you output the intention impacts ability to control.
So for example, if you just look at validation loss
as predicted by your model,
there can be multiple ways
to achieve the same validation loss.
Not all of them are equally controllable by the end user.
And so it might be as simple as saying,
oh, you could just add auxiliary loss terms
that help you capture the thing that actually matters.
But this is a very complex nuanced process.
So how you turn the labels into the model
is more of a nuanced process
than just like a standard supervised learning problem.
One very fascinating anecdote here,
we've tried many different sort of neural network
architectures that translate brain data
to velocity outputs, for example.
And one example that stuck in my brain
from a couple of years ago now is we,
at one point we were using just fully connected networks
to decode the brain activity.
We tried AB test where we were measuring
the relative performance in online control sessions
of a sort of 1D convolution over the input signal.
So if you imagine per channel,
you have a sliding window that's producing
some convolved feature for each of those input sequences
for every single channel simultaneously.
You can actually get better validation metrics,
meaning you're fitting the data better
and it's generalizing better in offline data
if you use this convolutional architecture.
You're reducing parameters, it's sort of a standard
standard procedure when you're dealing with time series data.
Now, it turns out that when using that model online,
the controllability was worse, was far worse,
even though the offline metrics were better.
And there can be many ways to interpret that,
but what that taught me at least was that,
hey, it's at least the case right now
that if you were to just throw a bunch of compute
at this problem and you were trying
to sort of hyperparameter optimize
or let some GPT model hard code
or come up with or invent many different solutions,
if you were just optimizing for loss,
it would not be sufficient,
which means that there's still
some inherent modeling gap here.
There's still some artistry left to be uncovered here
of how to get your model to scale
with more compute.
And that may be fundamentally labeling problem,
but there may be other components to this as well.
Is it a data constraint at this time?
Which is what it sounds like.
Like, how do you get a lot of good labels?
Yeah, I think it's data quality constrained,
not necessarily data quantity constrained.
But even like, even just the quantity,
I mean, because it has to be trained on the interactions.
I guess there's not that many interactions.
Yeah, so it depends what version of this
you're talking about.
So if you're talking about, like,
let's say the simplest example of just 2D velocity,
then I think, yeah, data quality is the main thing.
If you're talking about how to build
a sort of multifunction output
that lets you do all the inputs
to the computer that you and I can do,
then it's actually a much more sophisticated
nuanced modeling challenge,
because now you need to think about not just
when the user is left clicking,
but when you're building the left click model,
you also need to be thinking about
how to make sure it doesn't fire
when they're trying to right click
or when they're trying to move the mouse.
So one example of an interesting bug
from like sort of week one of BCI with Nolan
was when he moved the mouse,
the click signal sort of dropped off a cliff
and when he stopped, the click signal went up.
So again, there's a contamination between the two inputs.
Another good example was at one point
he was trying to do sort of a left click and drag,
and the minute he started moving,
the left click signal dropped off a cliff.
So again, because there's some contamination
between the two signals,
you need to come up with some way
to either in the data set or in the model
build robustness against this kind of,
you think about like overfitting,
but really it's just that the model
has not seen this kind of variability before.
So you need to find some way to help the model with that.
This is super cool,
because it feels like all of this is very solvable,
but it's hard.
Yes, it is fundamentally an engineering challenge.
This is important to emphasize,
and it's also important to emphasize
that it may not need fundamentally new techniques,
which means that people who work on,
let's say, unsupervised speech classification
using CTC loss, for example, with internal to Siri,
they could potentially have very applicable skills to this.
So what things are you excited about
in the future development of the software stack
on your link?
So everything we've been talking about,
the decoding, the UX.
I think there's some I'm excited about,
like something I'm excited about from the technology side,
and some I'm excited about understanding
how this technology is going to be best situated
for entering the world.
So I'll work backwards.
On the technology entering the world side of things,
I'm really excited to understand
how this device works for folks that cannot speak at all,
that have no ability to sort of bootstrap themselves
into useful control by voice command, for example,
and are extremely limited in their current capabilities.
I think that will be an incredibly useful signal for us
to understand, I mean, really,
what is an existential threat for all startups,
which is part of market fit?
Does this device have the capacity and potential
to transform people's lives in the current state?
And if not, what are the gaps?
And if there are gaps,
how do we solve them most efficiently?
So that's what I'm very excited about
for the next year or so of clinical trial operations.
The technology side,
I'm quite excited about basically everything we're doing.
I think it's going to be awesome.
The most prominent one I would say is scaling channel count.
So right now we have 1,000 channel device.
The next version will have between three and 6,000 channels,
and I would expect that curve to continue in the future.
And it's unclear what set of problems
will just disappear completely at that scale,
and what set of problems will remain
and require further focus.
And so I'm excited about the clarity of gradient
that that gives us in terms of the user experience
that we choose to focus our time and resources on.
And also in terms of the,
yeah, even things as simple as non-stationarity,
like does that problem just completely go away
at that scale?
Or do we need to come up with new creative UXes still,
even at that point?
And also when we get to that time point,
when we start expanding out dramatically
the set of functions that you can output from one brain,
how to deal with all the nuances
of both the user experience
of not being able to feel the different keys
under your fingertips,
but still needing to be able to modulate all of them
and synchrony to achieve the thing you want.
And again, you don't have that proprioceptive feedback loop.
So how can you make that intuitive
for a user to control a high dimensional control surface
without feeling the thing physically?
I think that's gonna be a super interesting problem.
I'm also quite excited to understand,
do these scaling laws continue?
Like as you scale channel count,
how much further out do you go
before that saturation point is truly hit?
And it's not obvious today.
I think we only know what's in the sort of interpolation
space, we only know what's between zero and 1024,
but we don't know what's beyond that.
And then there's a whole sort of like range
of interesting sort of neuroscience and brain questions,
which is when you stick more stuff in the brain
in more places,
you get to learn much more quickly
about what those brain regions represent.
And so I'm excited about that fundamental neuroscience
learning, which is also important for figuring out
how to most efficiently insert electrodes in the future.
So yeah, I think all those dimensions,
I'm really, really excited about,
and that doesn't even get close to touching
the sort of software stack that we work on
every single day and what we're working on right now.
Yeah, it seems virtually impossible to me
that a thousand electrodes is where it saturates.
It feels like this would be one of those
silly notions in the future where obviously
you should have millions of electrodes
and this is where like the true breakthroughs happen.
You tweeted,
some thoughts are most precisely described in poetry.
What do you think that is?
I think it's because the information bottleneck
of language is pretty steep
and yet you're able to reconstruct on the other person's
brain more effectively without being literal.
Like if you can express a sentiment such that in their brain
they can reconstruct the actual true underlying
meaning and beauty of the thing
that you're trying to get across,
that the generator function in their brain
is more powerful than what language can express.
And so the mechanism poetry is really just
to feed or seed that generator function.
So being literal sometimes is a suboptimal compression
for the thing you're trying to convey.
And it's actually in the process of the user
going through that generation
that they understand what you mean.
Like that's the beautiful part.
It's also like when you look at a beautiful painting,
like it's not the pixels of the painting
that are beautiful, it's the thought process
that occurs when you see that,
the experience of that that actually is a thing that matters.
Yeah, it's resonating with some deep,
thing within you that the artist also experienced
and was able to convey that through the pixels.
And that's actually gonna be relevant for full on telepathy.
It's like if you just read the poetry literally,
that doesn't say much of anything interesting.
It requires a human to interpret it.
So it's the combination of the human mind
and all the experiences the human being has
within the context of the collective intelligence
of the human species that makes that poem make sense.
And they load that in.
And so in that same way, the signal that carries
from human to human meaning may seem trivial,
but may actually carry a lot of power
because of the complexity of the human mind
and the receiving end.
Yeah, that's interesting.
I had poetry still doesn't, who was it?
I think Yoshibaka, first of all,
I said something about all the people
that think we've achieved AGI explain why humans like music.
Oh yeah.
And until the AGI likes music,
you haven't achieved AGI or something like this.
Do you not think that's like some next token,
entropy surprise kind of thing going on there?
I don't know.
I don't know either.
I listen to a lot of classical music
and also read a lot of poetry.
And yeah, I do wonder if like there is some element
of the next token surprise factor going on there.
Yeah, maybe.
Because I mean, like a lot of the tricks
in both poetry and music are like basically
you have some repeated structure and then you do like a twist.
Like it's like, okay, verse or like clause one, two, three
is one thing and then clause four is like,
okay, now we're on to the next theme.
And they kind of play with exactly when the surprise happens
and the expectation of the user.
And that's even true like through history
as musicians evolve music,
they take like some known structure
that people are familiar with
and they just tweak it a little bit.
Like they tweak it and add a surprising element.
This is especially true in like in classical music heritage.
But that's what I'm wondering.
Is it all just entropy?
Like the-
So breaking structure or breaking symmetry
is something that humans seem to like
maybe as simple as that.
Yeah, and I mean, great artists copy
and they also, you know,
knowing which rules to break is the important part.
And fundamentally it must be about the listener
of the piece.
Which rule is the right one to break?
It's about the user or the audience member
perceiving that as interesting.
What do you think is the meaning of human existence?
There's a TV show I really like called The West Wing.
And in The West Wing, there's a character,
he's the president of the United States
who's having a discussion about the Bible
with one of their colleagues.
And the colleague says something about,
you know, the Bible says X, Y, and Z.
And the president says, yeah, but it also says A, B, C.
And the person says, well, do you believe the Bible
to be literally true?
And the president says, yes.
But I also think that neither of us
are smart enough to understand it.
I think to like the analogy here for the meaning of life
is that largely we don't know the right question to ask.
And so I think I'm very aligned with
sort of the hitchhikers guide the galaxy version
of this question, which is basically
if we can ask the right questions,
it's much more likely we find the meaning
of human existence.
And so in the short term as a heuristic
in the sort of search policy space,
we should try to increase the diversity
of people asking such questions
or generally of consciousness
and conscious beings asking such questions.
So again, I think I will take the I don't know card here,
but say I do think there are meaningful things we can do
that improve the likelihood of answering that question.
It's interesting how much value you assign
to the task of asking the right questions.
That's the main thing is not the answers, it's the questions.
This point, by the way, is driven home
in a very painful way when you try to communicate
with someone who cannot speak.
Because a lot of the time, the last thing to go
is they have the ability to somehow wiggle a lip
or move something that allows them to say yes or no.
And in that situation, it's very obvious
that what matters is are you asking them
the right question to be able to say yes or no to?
Wow, that's powerful.
Well, Bliss, thank you for everything you do.
And thank you for being you.
And thank you for talking today.
Thank you.
Thanks for listening to this conversation
with Bliss Chapman.
And now, dear friends, here's Nolan Arba,
the first human being to have a Neuralink device
implanted in his brain.
You had a diving accident in 2016
that left you paralyzed with no feeling
from the shoulders down.
How did that accident change your life?
There's sort of a freak thing that happened.
Imagine you're running into the ocean.
Oh, this is a lake, but you're running into the ocean
and you get to about waist-high
and then you kind of like dive in,
take the rest of the plunge under the wave or something.
That's what I did.
And then I just never came back up.
Not sure what happened.
I did it running into the water with a couple of guys.
And so my idea of what happened is really just that
I took like a stray fist, elbow, knee, foot,
something to the side of my head.
The left side of my head was sore for about a month afterward.
So I must've taken a pretty big knock.
And then they both came up and I didn't.
And so I was facedown in the water for a while.
I was conscious.
And then eventually just realized
I couldn't hold my breath any longer.
And I keep saying, took a big drink.
People, I don't know if they like that I say that.
It seems like I'm making light of it all,
but this is kind of how I am.
And I don't know, like I'm a very
relaxed sort of stress-free person.
I rolled with the punches for a lot of this.
I kind of took it in stride.
It's like, all right, well, what can I do next?
How can I improve my life even a little bit
on a day-to-day basis at first,
just trying to find some way
to heal as much of my body as possible,
to try to get healed, to try to get off a ventilator,
learn as much as I could
so I could somehow survive once I left the hospital.
And then thank God I had my family around me.
If I didn't have my parents, my siblings,
then I would have never made it this far.
They've done so much for me
more than I can ever thank them for, honestly.
And a lot of people don't have that.
A lot of people in my situation,
their families either aren't capable of providing for them
or honestly just don't want to.
And so they get placed somewhere in some sort of home.
So thankfully I had my family.
I have a great group of friends,
a great group of buddies from college
who have all rallied around me
and we're all still incredibly close.
People always say, if you're lucky,
you'll end up with one or two friends from high school
that you keep throughout your life.
I have about 10, 10 or 12 from high school
that have all stuck around
and we still get together, all of us twice a year.
We call it the spring series and the fall series.
This last one we all did, we dressed up like X-Men.
So I did a Professor Xavier and it was freaking awesome.
It was so good.
So yeah, I have such a great support system around me.
And so, being a quadriplegic isn't that bad.
I get waited on all the time.
People bring me food and drinks
and I get to sit around and watch as much TV and movies
and anime as I want.
I get to read as much as I want.
I mean, it's great.
It's beautiful to see that you see the silver lining
in all of this, which is going back.
Do you remember the moment when you first realized
you were paralyzed from the neck down?
Yep, I was face down in the water.
Right when I, whatever, something hit my head,
I tried to get up and I realized I couldn't move
and it just sort of clicked.
I'm like, all right, I'm paralyzed, can't move.
What do I do?
If I can't get up, I can't flip over, can't do anything,
then I'm gonna drown eventually.
And I knew I couldn't hold my breath forever.
So I just held my breath and thought about it
for maybe 10, 15 seconds.
I've heard from other people that like on lookers,
I guess the two girls that pulled me out of the water
were two of my best friends, they were lifeguards.
And one of them said that it looked like my body
was sort of shaking in the water,
like I was trying to flip over and stuff.
But I knew, I knew immediately and I just kind of,
I realized that that's what my situation was
from here on out.
Maybe if I got to the hospital,
they'd be able to do something.
When I was in the hospital, like right before surgery,
I was trying to calm one of my friends down.
I had like brought her with me from college to camp
and she was just bawling over me.
And I was like, hey, it's gonna be fine, like don't worry.
I was cracking some jokes to try to lighten the mood.
The nurse had called my mom and I was like,
don't tell my mom, she's just gonna be stressed out.
Call her after I'm out of surgery
because at least she'll have some answers then.
Like whether I live or not, really.
And I didn't want her to be stressed
through the whole thing, but I knew.
And then when I first woke up after surgery,
I was super drugged up.
They had me on fentanyl like three ways, which was awesome.
I don't recommend it, but I saw some crazy stuff
on that fentanyl and it was still the best I've ever felt
on drugs.
Medication, sorry, on medication.
And I remember the first time I saw my mom in the hospital,
I was just bawling.
I had like ventilator in, like I couldn't talk or anything.
And I just started crying because it was more
like seeing her, not that, I mean, the whole situation
obviously was pretty rough, but it was just like seeing
her face for the first time was pretty hard.
But yeah, I just, I never had like a moment of,
you know, man, I'm paralyzed, this sucks.
I don't want to like be around anymore.
It was always just, I hate that I have to do this,
but like sitting here and wallowing isn't going to help.
So immediate acceptance.
Yeah, yeah.
Has there been low points along the way?
Yeah, yeah, sure.
I mean, there are days when I don't really feel like doing
anything, not so much anymore.
Like not for the last couple of years,
I don't really feel that way.
More so just wanted to try to do anything possible
to make my life better at this point.
But at the beginning, there were some ups and downs.
There were some really hard things to adjust to.
First off, just like the first couple of months,
the amount of pain I was in was really, really hard.
I mean, I remember screaming at the top of my lungs
in the hospital because I thought my legs were on fire.
And obviously I can't feel anything,
but it's all nerve pain.
And so that was a really hard night.
I asked them to give me as much pain meds as possible.
They're like, you've had as much as you can have.
So just kind of deal with it, go to a happy place,
sort of thing.
So that was a pretty low point.
And then every now and again, it's hard,
like realizing things that I wanted to do in my life
that I won't be able to do anymore.
I always wanted to be a husband and father,
and I just don't think that I could do it now
as a quadriplegic.
Maybe it's possible, but I'm not sure I would ever
put someone I love through that,
like having to take care of me and stuff,
not being able to go out and play sports.
I was a huge athlete growing up.
So that was pretty hard.
Just little things too,
when I realized I can't do them anymore.
Like there's something really special
about being able to hold a book and smell a book,
like the feel, the texture, the smell,
like as you turn the pages, like I just love it.
I can't do it anymore.
And it's little things like that.
The two year mark was pretty rough.
Two years is when they say,
you will get back basically as much
as you're ever gonna get back
as far as movement and sensation goes.
And so for the first two years,
that was the only thing on my mind,
was like try as much as I can to move my fingers,
my hands, my feet, everything possible
to try to get sensation and movement back.
And then when the two year mark hit,
so June 30th, 2018,
I was really sad that that's kind of where I was.
And then just randomly here and there,
but I was never like depressed for long periods of time.
Just, it never seemed worthwhile to me.
What gave you strength?
My faith, my faith in God was a big one.
My understanding that it was all for a purpose.
And even if that purpose wasn't anything involving Neuralink,
even if that purpose was,
there's a story in the Bible about Job.
And I think it's a really, really popular story
about how Job has all of these terrible things happen to him
and he praises God throughout the whole situation.
I thought, and I think a lot of people think
for most of their lives that they are Job,
that they're the ones going through something terrible
and they just need to praise God through the whole thing
and everything will work out.
At some point after my accident,
I realized that I might not be Job,
that I might be one of his children
that gets killed or kidnapped or taken from him.
And so it's about terrible things
that happen to those around you who you love.
So maybe, in this case, my mom would be Job
and she has to get through something extraordinarily hard
and I just need to try and make it
as best as possible for her
because she's the one that's really going
through this massive trial.
And that gave me a lot of strength.
And obviously my family, my family and my friends,
they give me all the strength that I need
on a day-to-day basis.
So it makes things a lot easier
when you're having that great support system around me.
From everything I've seen of you online, your streams
and the way you are today, I really admire,
let's say, your unwavering positive outlook on life.
Has that always been this way?
Yeah, yeah, I mean, I've just always
thought I could do anything I ever wanted to do.
There was never anything too big.
Like whatever I set my mind to,
I felt like I could do it.
I didn't want to do a lot.
I wanted to travel around and be sort of like a gypsy
and go work odd jobs.
I had this dream of traveling around Europe
and being like, I don't know, a shepherd in Wales or Ireland
and then going to being a fisherman in Italy,
doing all these things for a year.
It's such cliche things, but I just thought
it would be so much fun to go and travel
and do different things.
And so I've always just seen the best
in people around me too.
And I've always tried to be good to people.
And growing up with my mom too,
she's like the most positive, energetic person in the world.
And we're all just people, people.
Like I just get along great with people.
I really enjoy meeting new people.
And so I just wanted to do everything.
This is just kind of just how I've been.
It's just great to see that cynicism didn't take over
given everything you've been through.
That's, was that like a deliberate choice you made
that you're not going to let this keep you down?
Yeah, a bit.
Also, like I just, it's just kind of how I am.
I just, like I said, I roll with the punches with everything.
I always used to tell people,
like I don't stress about things much.
And whenever I'd see people getting stressed
or just say, you know, like it's not hard.
Just don't stress about it.
And like, that's all you need to do.
And they're like, that's not how that works.
I'm like, it works for me.
I just don't stress and everything will be fine.
Like everything will work out.
Obviously not everything always goes well.
And it's not like it all works out for the best all the time.
But I just don't think stress has had any place in my life
since I was a kid.
What was the experience like of you being selected
to be the first human being to have a Neuralink device
implanted in your brain?
Were you scared?
Excited?
No, no, it was cool.
Like I was, I was never afraid of it.
I had to think through a lot.
Should I, should I do this, like be the first person?
I could wait until number two or three
and get a better version of the Neuralink.
Like the first one might not work.
Maybe it's actually gonna kind of suck.
It's gonna be the worst version ever in a person.
So why would I do the first one?
Like I've already kind of been selected.
I could just tell them, you know, like, okay,
find someone else and then I'll do number two or three.
Like, I'm sure they would let me.
They're looking for a few people anyways.
But ultimately I was like, I don't know.
There's something about being the first one
to do something, it's pretty cool.
I always thought that if I had the chance
that I would like to do something for the first time,
this seemed like a pretty good opportunity.
And I was, I was never scared.
I think my like faith had a huge part in that.
I always felt like God was preparing me for something.
I almost wish it wasn't this
because I had many conversations with God
about not wanting to do any of this as a quadriplegic.
I told him, you know, I'll go out and talk to people.
I'll go out and travel the world
and talk to, you know, stadiums.
Thousands of people give my testimony.
I'll do all of it, but like heal me first.
Don't make me do all of this in a chair.
That sucks.
And I guess he won that argument.
I didn't really have much of a choice.
I always felt like there was something going on.
And to see how,
I guess easily I made it through the interview process
and how quickly everything happened,
how the star sort of aligned with all of this.
It just told me like as the surgery was getting closer,
it just told me that, you know,
it was all meant to happen.
It was all meant to be.
And so I shouldn't be afraid of anything that's to come.
And so I wasn't, I kept telling myself like, you know,
you say that now, but as soon as the surgery comes,
you're probably going to be freaking out.
Like you're about to have brain surgery
and brain surgery is a big deal for a lot of people,
but it's a even bigger deal for me.
Like it's all I have left.
The amount of times I've been like, thank you God
that you didn't take my brain and my personality
and my ability to think, my like love of learning,
like my character, everything, like thank you so much.
Like as long as you left me that,
then I think I can get by.
And I was about to let people go like root around
and they're like, hey, we're going to go
like put some stuff in your brain.
Like hopefully it works out.
And so it was, it was something that gave me pause,
but like I said, how smoothly everything went.
I never expected for a second that anything would go wrong.
Plus the more people I met on the borrows side
and on the knurling side,
they're just the most impressive people in the world.
Like I can't speak enough to how much I trust these people
with my life and how impressed I am with all of them.
And to see the excitement on their faces,
to like walk into a room and roll into a room
and see all of these people looking at me,
like we're just, we're so excited.
Like we've been working so hard on this
and it's finally happening.
It's super infectious
and it just makes me want to do it even more
and to help them achieve their dreams.
Like, I don't know, it's so, it's so rewarding
and I'm so happy for all of them, honestly.
What was the day of surgery like?
What's, when'd you wake up?
What'd you feel?
Yeah. Minute by minute.
Yeah. Were you freaking out?
No, no, I thought I was going to,
but a surgery approach the night before, the morning of,
I was just excited.
I was like, let's make this happen.
I think I said that,
something like that to Elon on the phone.
Beforehand, we were like FaceTiming
and I was like, let's rock and roll.
And he's like, let's do it.
I don't know.
I just, I wasn't scared.
So we woke up,
I think we had to be at the hospital at like 5.30 AM.
I think surgery was at like 7 AM.
So we woke up pretty early.
I'm not sure much of us slept that night.
Got to the hospital at 5.30,
went through like all the pre-op stuff.
Everyone was super nice.
Elon was supposed to be there in the morning,
but something went wrong with his plane.
So we ended up FaceTiming.
That was cool.
Had one of the greatest one-liners of my life
after that phone call.
Hung up with him, there were like 20 people around me.
And I was like, I just hope
he wasn't too starstruck talking to me.
Nice.
Yeah, it was good.
Well done.
Yeah, yeah.
Did you write that ahead of time?
No, it just came to me.
I was like, this is, this seems right.
Went into surgery.
I asked if I could pray right beforehand.
So I like prayed over the room.
I asked God if you'd like be with my mom
in case anything happened to me
and just like calm her nerves out there.
Woke up and played a bit of a prank on my mom.
I don't know if you've heard about it.
Yeah, I've read about it.
Yeah, she was not happy.
Can you take me through the prank?
Yeah, this is something-
You regret doing that now?
Nope, no, not one bit.
It was something I had talked about
ahead of time with my buddy, Bane.
I was like, I would really like to play a prank on my mom.
Very specifically, my mom.
She's very gullible.
I think she had knee surgery once even.
And after she came out of knee surgery,
she was super groggy.
She was like, I can't feel my legs.
And my dad looked at her.
He was like, you don't have any legs.
Like they had to amputate both your legs.
And we just do very mean things to her all the time.
I'm so surprised that she still loves us.
But right after surgery, I was really worried
that I was going to be too groggy, like not all there.
I've had anesthesia once before and it messed me up.
Like I could not function for a while afterwards.
And I said a lot of things that I was like,
I was really worried that I was going to start,
I don't know, dropping some bombs.
And I wouldn't even know, I wouldn't remember.
So I was like, please God, don't let that happen.
And please let me be there enough to do this to my mom.
And so she walked in after surgery.
It was like the first time they had been able to see me
after surgery and she just looked at me.
I said, hi, like, how are you?
How are you doing?
How do you feel?
And I looked at her and this very,
I think the anesthesia helped very like groggy,
sort of confused look on my face.
It's like, who are you?
And she just started looking around the room,
like at the surgeons or the doctors,
like what did you do to my son?
Like you need to fix this right now.
Tears started streaming.
I saw how much she was freaking out.
I was like, I can't let this go on.
And so I was like, mom, I'm fine.
Like, it's all right.
And still, she was not happy about it.
She still says she's gonna get me back someday,
but I mean, I don't know.
I don't know what that's gonna look like.
It's a lifelong battle.
Yeah, but it was good.
In some sense, it was a demonstration that you still got.
That's all I wanted it to be.
That's all I wanted it to be.
And I knew that doing something super mean to her
like that would show her, yeah.
To show that you're still there,
that you love her.
Yeah, exactly, exactly.
It's a dark way to do it, but I love it.
What was the first time you were able to feel
that you can use the Neuralink device
to affect the world around you?
Yeah, the first little taste I got of it
was actually not too long after surgery.
Some of the Neuralink team had brought in
like a little iPad, a little tablet screen,
and they had put up eight different channels
that were recording some of my neuron spikes.
And they put it in front of me,
and they're like, this is like real time your brain firing.
It's like, that's super cool.
My first thought was, I mean, if they're firing now,
let's see if I can affect them in some way.
So I started trying to like wiggle my fingers,
and I just started like scanning through the channels.
And one of the things I was doing
was like moving my index finger up and down.
And I just saw this yellow spike on like top row,
like third box over or something.
I saw this yellow spike every time I did it.
And I was like, oh, that's cool.
And everyone around me was just like, what are you seeing?
I was like, look, look at this one.
Look at like this top row, third box over,
this yellow spike, like that's me right there,
there, there, and everyone was freaking out.
They started like clapping.
I was like, that's super unnecessary.
Like this is what's supposed to happen, right?
So you're imagining yourself moving each individual finger
one at a time, and then seeing like,
that you can notice something.
And then when you did the index finger, you're like, oh.
Yeah, I was wiggling kind of all of my fingers
to see if anything would happen.
There was a lot of other things going on,
but that big yellow spike was the one that stood out to me.
Like, I'm sure that if I would have stared at it
long enough, I could have mapped out
maybe a hundred different things,
but the big yellow spike was the one that I noticed.
Maybe you could speak to what it's like
to sort of wiggle your fingers to like,
to imagine that the mental, the cognitive effort required
to sort of wiggle your index finger, for example.
How easy is that to do?
Pretty easy for me.
It's something that at the very beginning,
after my accident, they told me to try
and move my body as much as possible,
even if, you know, you can't just keep trying,
because that's going to create new like neural pathways
or pathways in my spinal cord
to like reconnect these things,
to hopefully regain some movement someday.
That's fascinating.
Yeah, I know, it's bizarre, but I-
So that's part of the recovery process
is to keep trying to move your body.
Yep.
And that's- Every day as much as you can.
And the nervous system does its thing.
It starts reconnecting.
Yeah, it'll start reconnecting for some people.
Some people, it never works.
Some people, they'll do it.
Like for me, I got some bicep control back,
and that's about it.
I can, if I try enough, I can wiggle some of my fingers,
not like on command.
It's more like if I try to move, say my right pinky,
and I just keep trying to move it
after a few seconds, it'll wiggle.
So I know there's stuff there.
Like I know, and that happens with
a few different of my fingers and stuff.
But yeah, that's what they tell you to do.
One of the people at the time
when I was in the hospital came in and told me
for one guy who had recovered most of his control,
what he thought about every day was actually walking,
like the act of walking, just over and over again.
So I tried that for years.
I tried just imagining walking, which is, it's hard.
It's hard to imagine like all of the steps
that go into, well, taking a step,
like all of the things that have to move,
like all of the activations that have to happen
along your leg in order for one step to occur.
But you're not just imagining, you're like doing it, right?
I'm trying, yeah.
So it's like, it's imagining over again
what I had to do to take a step.
Cause it's not something any of us think about.
We just, you want to walk and you take a step.
You don't think about all of the different things
that are going on in your body.
So I had to recreate that in my head as much as I could.
And then I practice it over and over and over.
So it's not like a third person perspective
as a first person perspective.
You're like, it's not like you're imagining yourself walking.
You're like literally doing this, everything,
all the same stuff as if you're walking.
Which was hard.
It was hard at the beginning.
Like frustrating hard or like actually cognitively hard?
Like which one?
It was both.
There's a scene in one of the Kill Bill movies actually,
oddly enough, where she is like paralyzed.
I don't know from like a drug that was in her system.
And then she like finds some way to get
into the back of a truck or something.
And she stares at her toe and she says, move,
like move your big toe.
And after, you know, a few seconds on screen, she does it.
And she did that with every one of her like body parts
until she can move again.
I did that for years.
Just stared at my body and said, move your index finger.
Move your big toe.
Sometimes vocalizing it like out loud,
but sometimes just thinking it.
I tried every different way to do this,
to try to get some movement back.
And it's hard because it actually is like taxing,
like physically taxing on my body,
which is something I would have never expected
because it's not like I'm moving,
but it feels like there's a buildup of,
I don't know, the only way I can describe it is,
there are like signals that aren't getting through
from my brain down,
because there's that gap in my spinal cord.
So brain down and then from my hand back up to the brain.
And so it feels like those signals get stuck
in whatever body part that I'm trying to move.
And they just build up and build up
and build up until they burst.
And then once they burst,
I get like this really weird sensation
of everything sort of like dissipating back out to level.
And then I do it again.
It's also just like a fatigue thing,
like a muscle fatigue,
but without actually moving your muscles.
It's very, very bizarre.
And then if you try to stare at a body part
or think about a body part and move for two, three, four,
sometimes eight hours,
it's very taxing on your mind.
It takes a lot of focus.
It was a lot easier at the beginning
because I wasn't able to like control a TV in my room
or anything.
I wasn't able to control any of my environment.
So for the first few years,
a lot of what I was doing was staring at walls.
And so obviously I did a lot of thinking
and I tried to move a lot just over and over and over again.
Do you never give up sort of hope there?
No.
Training, heart, ascension.
Yep, and I still do it.
I do it like subconsciously.
And I think that helped a lot
with things with Neuralink, honestly.
It's something that I talked about the other day
at the All Hands that I did at Neuralink's Austin facility.
Welcome to Austin, by the way.
Yeah, hey, thanks, man.
I went to school. Nice hat.
Hey, thanks, thanks, man.
The Gigafactory was super cool.
I went to school at Texas A&M.
So I've been around before.
So you should be saying welcome to me.
Welcome to Texas Lex.
I get you.
But yeah, I was talking about how a lot of what
they've had me do, especially at the beginning,
well, I still do it now, is body mapping.
So like there will be a visualization of a hand
or an arm on the screen and I have to do that motion.
And that's how they sort of train the algorithm
to understand what I'm trying to do.
And so it made things very seamless for me, I think.
That's really, really cool.
So it's amazing to know,
because I've learned a lot about the body mapping procedure.
Like with the interface and everything like that,
it's cool to know that you've been essentially
like training to be like world-class at that task.
Yeah, I don't know if other quadriplegics,
like other paralyzed people give up.
I hope they don't.
I hope they keep trying because I've heard
other paralyzed people say like, don't ever stop.
They tell you two years, but you just never know.
You're the human bodies capable of amazing things.
So I've heard other people say don't give up.
Like I think one girl had spoken to me
through some family members and said that
she had been paralyzed for 18 years.
And she'd been trying to like wiggle her index finger
for all that time.
And she finally got it back like 18 years later.
So like, I know that it's possible
and I'll never give up doing it.
I just, I do it when I'm lying down, like watching TV,
I'll find myself doing it kind of just almost like on its own.
It's just something I've gotten so used to doing
that I don't know, I don't think I'll ever stop.
That's really awesome to hear,
because I think it's one of those things
that can really pay off in the long term.
Because like there is training,
you're not visibly seeing the results of that training
at the moment.
But like there's a like Olympic level nervous system
getting ready for something.
Which honestly was like something that I think
Neuralink gave me that I can't think them enough for.
Like I can't show my appreciation for it enough
was being able to visually see that what I'm doing
is actually having some effect.
It's a huge part of the reason why like I know now
that I'm gonna keep doing it forever.
Because before Neuralink, I was doing it every day
and I was just assuming that things were happening.
Like it's not like I knew,
I wasn't getting back any mobility or sensation or anything.
So I could have been running up against a brick wall
for all I knew.
And with Neuralink, I get to see like all the signals
happening real time.
And I get to see that what I'm doing can actually be mapped.
When we started doing like click calibrations and stuff.
When I go to click my index finger for a left click
that it actually recognizes that.
Like it changed how I think about what's possible
with like retraining my body to move.
And so yeah, I'll never give up now.
And also just the signal that there's still a powerhouse
of a brain there that's like.
Exactly.
And as the technology develops that brain is,
I mean that's the most important thing
about the human body is the brain.
And you can do a lot of the control.
So what did it feel like when you first could wiggle
the index finger and saw the environment respond
like that little thing wherever we're just being way
too dramatic, according to you.
Yeah, it was very cool.
I mean, it was cool, but I keep telling this to people.
It made sense to me.
Like it made sense that, you know,
like there are signals still happening in my brain.
And that as long as you had something near it
that could measure those, that could record those,
then you should be able to like visualize it in some way.
Like see it happen.
And so that was not very surprising to me.
I was just like, oh, cool.
Like we found one, like we found something that works.
It was cool to see that their technology worked
and that everything that they had worked so hard for
was like going to pay off.
But I hadn't like moved a cursor or anything at that point.
I hadn't like interacted with a computer
or anything at that point.
So it just made sense.
It was cool.
Like I didn't really know much about BCI at that point
either.
So I didn't know like what sort of step
this was actually making.
Like I didn't know if this was like a huge deal
or if this was just like, okay, this is, you know,
it's cool that we got this far, but we're actually hoping
for something like much better down the road.
It's like, okay, I just thought that they knew
that it turned on.
So I was like, cool, like this is, this is cool.
Well, did you like read up on the specs
of the hardware you're getting installed?
Like the number of threads?
Yeah, yeah, I knew all of that, but it's all like,
it's all Greek to me.
I was like, okay, threads, 64 threads, 16 electrodes,
1,024 channels, okay, like that, that math checks out.
Sounds right.
When was the first time you were able
to move a mouse cursor?
I know it must've been within the first maybe week,
week or two weeks that I was able to like first move
the cursor and again, like it kind of made sense to me.
Like it didn't seem like that big of a deal.
Like it was like, okay, well, how do I explain this?
When everyone around you starts clapping
for something that you've done, it's easy to say, okay,
like I did something cool.
Like that was impressive in some way.
What exactly that meant, what it was,
hadn't really like set in for me.
So again, I knew that me trying to move a body part
and then that being mapped in some sort
of like machine learning algorithm to be able
to identify like my brain signals and then take that
and give me cursor control.
That all kind of made sense to me.
I don't know like all the ins and outs of it,
but I was like, there are still signals in my brain firing.
They just can't get through because there's like a gap
in my spinal cord.
And so they just, they can't get all the way down
and back up, but they're still there.
So when I moved the cursor for the first time,
I was like, that's cool,
but I expected that that should happen.
Like it made sense to me.
When I moved the cursor for the first time
with just my mind without like physically trying to move.
So I guess I can get into that just a little bit,
like the difference between attempted movement
and imagined movement.
Yeah, that's a fascinating difference
from one to the other.
Yeah, yeah, yeah.
So like attempted movement is me physically trying
to attempt to move, say my hand.
I try to attempt to move my hand to the right,
to the left, forward and back.
And that's all attempted, attempt to like lift my finger
up and down, attempt to kick or something.
I'm physically trying to do all of those things,
even if you can't see it.
Like I'm, this would be like me attempting
to like shrug my shoulders or something.
That's all attempted movement.
That all, that's what I was doing
for the first couple of weeks
when they were going to give me cursor control.
When I was doing body mapping, it was attempt to do this,
attempt to do that.
When Nier was telling me to like imagine doing it,
it like kind of made sense to me,
but it's not something that people practice.
Like if you started school as a child
and they said, okay, write your name with this pencil.
And so you do that like, okay,
now imagine writing your name with that pencil.
Kids would think like, I guess,
like that kind of makes sense and they would do it,
but that's not something we're taught.
It's all like how to do things physically.
We think about like thought experiments and things,
but that's not like a physical action of doing things.
It's more like what you would do in certain situations.
So imagine movement, it never really connected with me.
Like I guess you could maybe describe it
as like a professional athlete,
like swinging a baseball bat or swinging like a golf club,
like imagine what you're supposed to do,
but then you go right to that and physically do it.
Like you, then you get a bat in your hand
and then you do what you've been imagining.
And so I don't have that like connection.
So telling me to imagine something versus attempting it,
there wasn't a lot that I could do there mentally.
I just kind of had to accept what was going on and try.
But the attempted moving thing, it all made sense to me.
Like if I try to move,
then there's a signal being sent in my brain
and as long as they can pick that up,
then they should be able to map it to what I'm trying to do.
And so when I first moved the cursor like that,
it was like, yes, this should happen.
Like I'm not surprised by that.
But can you clarify,
is there supposed to be a difference
between imagined movement and attempted movement?
Yeah, just that in imagined movement,
you're not attempting to move at all.
So it's-
You're like visualizing. Visualizing.
And then theoretically,
is that supposed to be a different part of the brain
that lights up in those two different situations?
Yeah, not necessarily.
I think all these signals can still be represented
in motor cortex,
but the difference I think has to do with the naturalness
of imagining something versus.
Attempting and sort of the fatigue of that over time.
And by the way, on the mic is bliss.
So like, this is just different ways to prompt you
to kind of get to the thing that you're around.
Yeah, yeah.
Attempted movement does sound like the right thing to try.
Yeah, I mean, it makes sense to me.
Because imagine for me, I would start visualizing.
Like in my mind, visualizing.
Attempted, I would actually start trying to like,
yeah, there's a, I mean, I did like,
combat sports my whole life, like wrestling.
When I'm imagining a move, see, I'm like moving my muscle.
Like there's a bit of an activation almost,
versus like visualizing yourself like a picture doing it.
Yeah, it's something that I feel like naturally
anyone would do.
If you try to tell someone to imagine doing something,
they might close their eyes
and then start physically doing it.
But it's just-
Just didn't click.
Yeah, it's hard.
It was very hard at the beginning.
But attempted worked.
Attempted worked, it worked just like it should work like,
work like a charm.
Remember there was like one Tuesday we were messing around
and I think, I forget what swear word you used,
but there was a swear word that came out of your mouth
when you figured out you could just do
the direct cursor control.
Yeah, that's, it blew my mind, like no pun intended.
Blew my mind when I first moved the cursor
just with my thoughts and not attempting to move.
It's something that I found like over the couple of weeks,
like building up to that,
that as I get better cursor controls,
like the model gets better,
then it gets easier for me to like,
like I don't have to attempt as much to move it.
And part of that is something that I'd even talked
with them about when I was watching
the signals of my brain one day.
I was watching when I like attempted to move to the right
and I watched the screen as like I saw the spikes.
So like I was seeing the spike,
the signal was being sent
before I was actually attempting to move.
I imagine just because, you know,
when you go to say move your hand or any body part,
that signal gets sent before you're actually moving,
has to make it all the way down and back up
before you actually do any sort of movement.
So there's a delay there.
And I noticed that there was something going on in my brain
before I was actually attempting to move,
that my brain was like anticipating what I wanted to do.
And that all started sort of, I don't know,
like percolating in my brain.
I get just, it was just sort of there,
like always in the back,
like that's so weird that it could do that.
It kind of makes sense,
but I wonder what that means as far as like
using the Neuralink and, you know,
and then as I was playing around with the attempted movement
and playing around with the cursor,
and I saw that like as the cursor control got better,
that it was anticipating my movements
and what I wanted it to do, like cursor movements,
what I wanted to do a bit better and a bit better.
And then one day I just randomly,
as I was playing web grid,
I like looked at a target
before I had started like attempting to move.
I was just trying to like get over,
like train my eyes to start looking ahead,
like, okay, this is the target I'm on,
but if I look over here to this target,
I know I can like maybe be a bit quicker getting there.
And I looked over and the cursor just shot over.
It was wild.
Like I had to take a step back.
I was like, this should not be happening.
All day I was just smiling.
I was so giddy.
I was like, guys, do you know that this works?
Like I can just think it and it happens,
which like they'd all been saying this entire time,
like I can't believe like you're doing all this
with your mind.
I'm like, yeah, but is it really with my mind?
Like I'm attempting to move and it's just picking that up.
So it doesn't feel like it's with my mind,
but when I moved it for the first time like that,
it was, oh man, it like,
it made me think that this technology,
that what I'm doing is actually way,
way more impressive than I ever thought.
It was way cooler than I ever thought.
And it just opened up a whole new world of possibilities
of like what could possibly happen with this technology
and what I might be able to be capable of with it.
Because to you it felt for the first time
like this was digital telepathy.
Like you're controlling a digital device with your mind.
I mean, that's a real moment of discovery.
That's really cool.
Like you've discovered something.
I've seen like scientists talk about like a big aha moment.
You know, like Nobel prize winning,
they'll have this like, holy crap.
Like, whoa.
That's what it felt like.
Like I didn't feel like,
like I felt like I had discovered something, but for me,
maybe not necessarily for like the world at large
or like this field at large.
It just felt like an aha moment for me.
Like, oh, this works.
Like obviously it works.
And so that's what I do like all the time now.
I kind of intermix the attempted movement
and imagine movement.
I do it all like together
because I've found that there is some interplay with it
that maximizes efficiency with the cursor.
So it's not all like one or the other.
It's not all just, I only use attempted
or I only use like imagine movements.
It's more, I use them in parallel
and I can do one or the other.
I can just completely think about whatever I'm doing,
but I don't know.
I like to play around with it.
I also like to just experiment with these things.
Like every now and again,
I'll get this idea in my head like,
I wonder if this works and I'll just start doing it.
And then afterwards I'll tell them, by the way,
I wasn't doing that like you guys wanted me to.
I thought of something and I wanted to try it.
And so I did, it seems like it works.
So maybe we should like explore that a little bit.
So I think that discovery is not just for you,
at least from my perspective,
that's a discovery for everyone else
who ever uses a new link that this is possible.
Like I don't think there's an obvious thing
that this is even possible.
It's like, I was saying to Bliss earlier,
it's like the four minute mile.
People thought it was impossible
to run a mile in four minutes.
And once the first person did it,
then everyone just started doing it.
So like just to show that it's possible,
that paves the way to like anyone can not do it.
That's the thing that's actually possible.
You don't need to do the attempted movement.
You can just go direct.
That's crazy.
That is crazy.
For people who don't know,
can you explain how the link app works?
You have an amazing stream on the topic.
Your first stream, I think on X describing the app.
Can you just describe how it works?
Yeah, so it's just an app that Neuralink created
to help me interact with the computer.
So on the link app,
there are a few different settings and different modes
and things I can do on it.
So there's like the body mapping,
if we kind of touched on.
There's a calibration.
Calibration is how I actually get cursor control.
So calibrating what's going on in my brain
to translate that into cursor control.
So it will pop out models.
What they use, I think is like time.
So it would be five minutes
and calibration will give me so good of a model.
And then if I'm in it for 10 minutes and 15 minutes,
the models will progressively get better.
And so the longer I'm in it,
generally the better the models will get.
That's right, because you often refer to the models.
The model is the thing that's constructed
once you go through the calibration step.
And then you also talked about sometimes you'll play
like a really difficult game like Snake
just to see how good the model is.
Yeah, so Snake is kind of like my litmus test for models.
If I can control Snake decently well,
then I know I have a pretty good model.
So yeah, the link app has all of those
as web grid in it now.
It's also how I like connect to the computer
just in general.
So they've given me a lot of like voice controls
with it at this point.
So I can say like connect or implant disconnect.
And as long as I have that charger handy,
then I can connect to it.
So the charger is also how I connect to the link app
to connect to the computer.
I have to have the implant charger over my head
when I wanna connect to have it wake up
because the implants in hibernation mode
like always when I'm not using it.
I think there's a setting to like wake it up every so long.
So we could set it to half an hour, five hours or something
if I just want it to wake up periodically.
So yeah, I'll like connect to the link app
and then go through all sorts of things.
Calibration for the day, maybe body mapping.
I have like, I made them give me like a little homework tab
because I am very forgetful and I forget to do things a lot.
So I have like a lot of data collection things
that they want me to do.
Is the body mapping part of the data collection
or is that also part of the collection?
Yeah, it is.
It's something that they want me to do daily
which I've been slacking on
because I've been doing so much media and traveling.
So I've been super famous.
Yeah, I've been a terrible first candidate
for how much I've been slacking on my homework.
But yeah, it's just something that they want me to do every day
to track how well the Neuralink is performing over time
and have something to give.
I imagine to give to the FDA
to create all sorts of fancy charts and stuff
and show like, hey, this is what the Neuralink,
this is how it's performing, you know,
day one versus day 90 versus day 180 and things like that.
What's the calibration step like?
Is it like move left, move right?
It's a bubble game.
So there will be like yellow bubbles
that pop up on the screen.
At first it is open loop.
So open loop, this is something that I still don't fully
understand the open loop and closed loop thing.
Me and Blitz talked for a long time
about the difference between the two
on the technical side.
So it'd be great to hear your side of the story.
Open loop is basically, I have no control over the cursor.
The cursor will be moving on its own across the screen
and I am following by intention the cursor
to different bubbles.
And then the algorithm is training off
of what like the signals it's getting are
as I'm doing this.
There are a couple of different ways that they've done it.
They call it center out target.
So there will be a bubble in the middle
and then eight bubbles around that.
And the cursor will go from the middle to one side.
So say middle to left, back to middle to up to middle,
like up right.
And they'll do that all the way around the circle.
And I will follow that cursor the whole time
and then it will train off of my intentions
what it is expecting my intentions to be
throughout the whole process.
Can you actually speak to when you say follow?
Yes, you don't mean with your eyes,
you mean with your intentions?
Yeah, so generally for calibration,
I'm doing attempted movements
because I think it works better.
I think the better models as I progress through calibration
make it easier to use imagined movements.
Wait, wait, wait.
So calibrated on attempted movement will create a model
that makes it really effective for you
to then use the force.
Yes, I've tried doing calibration with imagined movement
and it just doesn't work as well for some reason.
So that was the center out targets.
There's also one where a random target
will pop up on the screen and it's the same.
I just like move, I follow along
wherever the cursor is to that target
all across the screen.
I've tried those with imagined movement
and for some reason the models just don't,
they don't give as high level as quality
when we get into closed loop.
I haven't played around with it a ton.
So maybe like the different ways
that we're doing calibration now might make it a bit better.
But what I've found is there will be a point in calibration
where I can use imagined movement.
Before that point, it doesn't really work.
So if I do calibration for 45 minutes,
the first 15 minutes, I can't use imagined movement.
It just like doesn't work for some reason.
And after a certain point, I can just sort of feel it.
I can tell it moves different.
That's the best way I can describe it.
It's almost as if it is anticipating
what I am going to do again before I go to do it.
And so using attempted movement for 15 minutes,
at some point I can kinda tell when I move my eyes
to the next target that the cursor is starting to pick up.
It's starting to understand,
it's learning what I'm going to do.
So first of all, it's really cool that,
I mean, you are a true pioneer in all of this.
You're like exploring how to do every aspect
of this most effectively.
And there's just, I imagine so many lessons learned
from this.
So thank you for being a pioneer
in all these kinds of different like super technical ways.
And it's also cool to hear that there's like a different
like feeling to the experience when it's calibrated
in different ways, like just,
because I imagine your brain is doing something different.
And that's why there's a different feeling to it.
And then try and define the words
and the measurements to those feelings
would be also interesting.
But at the end of the day,
you can also measure your actual performance
on whether it's snake or web grid.
You can see like what actually works well.
And you're saying for the open loop calibration,
the attempted movement works best for now.
So the open loop, you don't get the feedback
that's something that you did something.
Is that frustrating?
No, no, it makes sense to me.
Like we've done it with a cursor
and without a cursor in open loop.
So sometimes it's just say for like the center out,
you'll start calibration with a bubble lighting up
and I push towards that bubble.
And then when that bubble,
when it's pushed towards that bubble for say three seconds,
the bubble will pop and then I come back to the middle.
So I'm doing it all just by my intentions.
Like that's what it's learning anyway.
So it makes sense that as long as I follow
what they want me to do,
like follow the yellow brick road,
that it'll all work out.
You're full of great references.
Is the bubble game fun?
Like, yeah.
They always feel so bad making me do calibration.
Like, oh, we're about to do a 40 minute calibration.
I'm like, all right,
would you guys wanna do two of them?
Like I'm always asking to like whatever they need,
I'm more than happy to do.
And it's not bad.
Like I get to lie there or sit in my chair
and like do these things with some great people.
I get to have great conversations.
I can give them feedback.
I can talk about all sorts of things.
I could throw something on on my TV in the background
and kind of like split my attention between them.
Like it's not bad at all.
I don't mind it.
Is there a score that you get?
Like, can you do better on the bubble game?
No, I would love that.
I would love, yeah.
Writing down the suggestions from Nolan.
That's a...
Making more fun, gamified.
Yeah, that's one thing that I really,
really enjoy about Web Grid
is because I'm so competitive.
Like the higher the BPS, the higher the score,
I know the better I'm doing.
And so if I, I think I've asked at one point,
one of the guys,
like if he could give me some sort of numerical feedback
for calibration,
like I would like to know what they're looking at.
Like, oh, you know, it is,
we see like this number while you're doing calibration.
And that means at least on our end
that we think calibration is going well.
And I would love that because I would like to know
if what I'm doing is going well or not.
But then they've also told me like,
not necessarily like one-to-one.
It doesn't actually mean that calibration is going well
in some ways.
So it's not like a hundred percent
and they don't want to like skew what I'm experiencing
or want me to change things based on that.
If that number isn't always accurate
to like how the model will turn out
or how like the end result,
that's at least what I got from it.
One thing I do, I have asked them
and something that I really enjoy striving for
is towards the end of calibration,
there is like a time between targets.
And so I like to keep like at the end
that number as low as possible.
So at the beginning, it can be, you know,
four or five, six seconds between me popping bubbles.
But towards the end, I like to keep it below like 1.5
or if I could get it to like one second between like bubbles
because in my mind that translates really nicely
to something like web grid where I know
if I can hit a target one every second
that I'm doing real, real well.
There you go.
That's the way to get a score on the calibration
is like the speed, how quickly can you get
from bubble to bubble.
So there's the open loop and then it goes to the closed loop.
The closed loop can already start giving you a sense
because you're getting feedback of like how good the model is.
Yeah, so closed loop is when I first get cursor control
and how they've described it to me,
someone who does not understand this stuff.
I am the dumbest person in the room every time.
The humility is that I am closing the loop.
So I am actually now the one that is like finishing
the loop of whatever this loop is.
I don't even know what the loop is.
They've never told me.
They just say there is a loop and at one point it's open
and I can't control and then I get control and it's closed.
So I'm finishing the loop.
So how long the calibration usually takes?
You said like 10, 15 minutes.
Well, yeah, they're trying to get that number down
pretty low.
That's what we've been working on a lot recently
is getting that down as low as possible.
So that way, if this is something that people need to do
on a daily basis or if some people need to do
on a like every other day basis or once a week,
they don't want people to be sitting in calibration
for long periods of time.
I think they wanted to get it down seven minutes or below
at least where we're at right now.
It'd be nice if you never had to do calibration.
So we'll get there at some point, I'm sure,
the more we learn about the brain
and like I think that's the dream.
I think right now for me to get like really,
really good models, I'm in calibration 40 or 45 minutes.
And I don't mind, like I said, they always feel really bad
but if it's going to get me a model
that can like break these records on Webgrid,
I'll stay in it for flipping two hours.
Let's talk business.
So Webgrid, I saw a presentation that where Bliss said,
by March, you selected 89,000 targets in Webgrid.
Can you explain this game?
What is Webgrid and what does it take
to be a world-class performer in Webgrid
as you continue to break world records?
It's like a gold medalist like, wow.
Yeah, I'd like to thank everyone who's helped me get here,
my coaches, my parents, for dropping me to practice
every day at five in the morning, like thank God.
And just overall my dedication to my craft.
The interviews with athletes are always like that exact,
it's like that template.
Yeah, so.
So Webgrid is a grid itself.
It's literally just a grid.
They can make it as big or small as you can make a grid.
A single box on that grid will light up
and you go and click it.
And it is a way for them to benchmark how good a BCI is.
So it's pretty straightforward.
You just click targets.
Only one blue cell appears
and you're supposed to move the mouse to there
and click on it.
So I like playing on bigger grids
because the bigger the grid, the more BPS,
it's bits per second that you get every time you click one.
So I'll say I'll play out like a 35 by 35 grid.
And then one of those little squares,
cell and call it, target, whatever,
will light up and you move the cursor there
and you click it and then you do that forever.
And you've been able to achieve at first eight bits per second
and you've recently broke that.
Yeah, I'm at 8.5 right now.
I would have beaten that literally the day
before I came to Austin,
but I had like a, I don't know,
like a five second lag right at the end.
And I just had to wait until the latency calmed down
and then I kept clicking,
but I was at like 8.01 and then five seconds of lag.
And then the next like three targets I clicked
all stayed at 8.01.
So if I would have been able to click
during that time of lag,
I probably would have hit,
I don't know, I might've hit nine.
So I'm there.
I'm like, I'm really close.
And then this whole Austin trip has really gotten
in the way of my web grid playing ability.
So that's all you're thinking about right now.
Yeah, I know.
I just want, I want to do better.
At nine.
I want to do better.
I want to hit nine.
I think, well, I know nine is very, very achievable.
I'm right there.
I think 10, I could hit maybe in the next month.
Like I could do it probably in the next few weeks
if I really push.
I think you and Ilana are basically the same person.
Cause last time I did a podcast with him,
he came in extremely frustrated that he can't beat
Uber Lilith as a droid.
That was like a year ago, I think.
I forget like solo.
And I could just tell there's some percentage
of his brain the entire time was thinking like,
I wish I was right now attempting.
I think he did it that night.
He did it that night.
He stayed up and did it that night.
Just crazy to me.
I mean, in a fundamental way, it's really inspiring.
And what you're doing is inspiring in that way.
Cause I mean, it's not just about the game.
Everything you're doing there has impact.
By striving to do well on web grid,
you're helping everybody figure out
how to create the system all along.
Like the decoding, the software, the hardware,
the calibration, all of it.
How to make all of that work
so you can do everything else really well.
Yeah, it's just really fun.
Well, that's also, that's part of the thing
is making it fun.
Yeah, it's addicting.
I've joked about like what they actually did
when they went in and put this thing in my brain.
They must've flipped a switch to make me more susceptible
to these kinds of games, to make me addicted
to like web grid or something.
Do you know Bliss' high score?
Yeah, he said like 14 or something.
17.
Oh boy.
17.1 or something.
17.01.
He told me he like does it on the floor with peanut butter
and he like fasts, it's weird.
That sounds like cheating.
Sounds like performance enhancing.
Nolan's like the first time Nolan played this game,
he asked, you know, how could I be at this game?
And I mean, I think you told me right then,
you're gonna try to beat me.
I'm gonna get there someday.
Yeah, I fully believe you.
I think I can.
I'm excited for that.
Yeah, so I've been playing first off with the dwell cursor,
which really hampers my web grid playing ability.
Basically, I have to wait 0.3 seconds for every click.
Oh, so you can't do the clicks.
Yeah, so you have to, so you click by dwelling.
You said 0.3.
0.3 seconds, which sucks.
It really slows down how much I'm able to,
like how high I'm able to get.
I still hit like 50, I think I hit like 50 something trials,
net trials per minute in that,
which was pretty good.
Cause I'm able to like,
there's one of the settings is also like how slow
you need to be moving in order to initiate a click,
to start a click.
So I can tell sort of when I'm on that threshold
to start initiating a click just a bit early.
So I'm not fully stopped over the target when I go to click.
I'm doing it like on my way to the targets a little
to try to time it just right.
Wow, so you're slowing down.
Yeah, just a hair right before the targets.
This is like a lead performance, okay.
But that's still, it sucks that there's a ceiling
of the 0.3.
Well, I can get down to 0.2 and 0.1, 0.1.
Yeah, and I've played with that a little bit too.
I have to adjust a ton of different parameters
in order to play with 0.1.
And I don't have control over all that on my end yet.
It also changes like how the models are trained.
Like if I train a model like in Webgrid,
I like a bootstrap on a model,
which basically is them training models
as I'm playing Webgrid based off of like the Webgrid data.
Then I'm so like, if I play Webgrid for 10 minutes,
they can train off that data specifically
in order to get me a better model.
If I do that with 0.3 versus 0.1,
the models come out different.
The way that they interact, it's just much, much different.
So I have to be really careful.
I found that doing it with 0.3 is actually better
in some ways, unless I can do it with 0.1
and change all of the different parameters,
then that's more ideal
because obviously 0.3 is faster than 0.1.
So I could get there.
I can get there.
Can you click using your brain?
For right now, it's the hover clicking
with the dwell cursor.
We, before all the thread retraction stuff happened,
we were calibrating clicks, left click, right click.
That was my previous ceiling
before I broke the record again with the dwell cursor
was I think on a 35 by 35 grid with left and right click.
And you get more BPS, more bits per second
using multiple clicks, because it's more difficult.
Oh, because what is it?
You're supposed to do either a left click
or a right click.
Yes.
There's a different color.
Yeah, blue targets for left click,
orange targets for right click is what they had done.
So my previous record of 7.5
was with the blue and the orange targets, yeah.
Which I think if I went back to that now,
doing the click calibration,
I would be able to,
and being able to initiate clicks on my own,
I think I would break that 10 ceiling
in a couple of days, max.
Yeah, you would start making Bliss nervous about his 17th.
Why do you think we haven't given him the, exactly.
So what did it feel like with the retractions
that there is some of the threads retracted?
It sucked.
It was really, really hard.
The day they told me was the day of my big Neuralink tour
at their Fremont facility.
And they told me like right before we went over there,
it was really hard to hear.
My initial reaction was, all right, go in, fix it.
Like go and take it out and fix it.
The first surgery was so easy.
Like I went to sleep, a couple hours later I woke up
and here we are.
I didn't feel any pain,
didn't take like any pain pills or anything.
So I just knew that if they wanted to,
they could go in and put in a new one like next day,
if that's what it took.
Because I just wanted, I wanted it to be better
and I wanted not to lose the capability.
I had so much fun playing with it
for a few weeks for a month.
I had like it had opened up so many doors for me
and it opened up so many more possibilities
that I didn't want to lose it after a month.
I thought it would have been a cruel twist of fate
if I had gotten to see the view
from like the top of this mountain
and then have it all come crashing down after a month.
And I knew like say the top of the mountain,
but like I, how I saw it was I was just now starting
to climb the mountain and I was,
like there was so much more than I knew was possible.
And so to have all of that be taken away
was really, really hard.
But then on the drive over to the facility,
I don't know, like five minute drive, whatever it is,
I talked with my parents about it.
I prayed about it.
I was just like, you know,
I'm not gonna let this ruin my day.
I'm not gonna let this ruin this amazing like tour
that they have set up for me.
Like I wanna go show everyone how much I appreciate
all the work they're doing.
I wanna go like meet all of the people
who have made this possible.
And I wanna go have one of the best days of my life.
And I did, and it was amazing.
And it absolutely was one of the best days
I've ever been privileged to experience.
And then for a few days, I was pretty down on the dumps.
But for like the first few days afterwards,
I was just like, I didn't know
if it was ever gonna work again.
And then I just, I made the decision that it,
even if I lost the ability to use the narrow link,
even if I lost, even if I like lost out
on everything to come,
if I could keep giving them data in any way,
then I would do that.
If I needed to just do like some of the data collection
every day or body mapping every day for a year,
then I would do it because I know
that everything I'm doing helps everyone to come after me.
And that's all I wanted.
I guess the whole reason that I did this
was to help people.
And I knew that anything I could do to help,
I would continue to do,
even if I never got to use the cursor again,
then I was just happy to be a part of it.
And everything that I had done was just a perk.
It was something that I got to experience.
And I know how amazing it's gonna be
for everyone to come after me.
So might as well just keep trucking along.
That said, you were able to get to work your way up
to get the performance back.
So this is like going from Rocky one to Rocky two.
So when did you first realize that this is possible
and what gave you sort of the strength,
the motivation, the determination to do it,
to increase back up and be your previous record?
Yeah, it was within a couple of weeks.
Again, this feels like I'm interviewing an athlete.
This is great.
I like the name apparently.
The road back was long and hard,
from many difficulties.
There were dark days.
It was a couple of weeks, I think.
And then there was just a turning point.
I think they had switched how they were measuring
the neuron spikes in my brain like the,
Bliss, help me out.
Yeah, the way in which we were measuring
the behavior of individual neurons.
So we're switching from sort of individual spike detection
to something called spike band power,
which if you watch the previous segments
with either me or DJ, you probably have some confidence.
Yeah, okay.
So when they did that, it was kind of like,
you know, light over the head, like light bulb moment,
like, oh, this works.
And this seems like we can run with this.
And I saw the uptake in performance immediately.
Like I could feel it when they switched over.
I was like, this is better.
Like this is good.
Like everything up till this point for the last few weeks,
last like whatever, three or four weeks,
because it was before they even told me,
like everything before this sucked.
Like let's keep doing what we're doing now.
And at that point, it was not like,
oh, I know I'm still only at like saying web grid terms,
like four or five BPS compared to my 7.5 before,
but I know that if we keep doing this,
then like I can get back there.
And then they gave me the dwell cursor
and the dwell cursor sucked at first.
It's not obviously not what I want,
but it gave me a path forward
to be able to continue using it.
And hopefully to continue to help out.
And so I just ran with it and never looked back.
Like I said, I'm just kind of person.
I roll with the punches anyway, so.
What was the process?
What was the feedback loop on the figuring out
how to do the spike detection in a way
that would actually work well for Nolan?
Yeah, it's a good question.
So maybe just describe first how the actual update worked.
It was basically an update to your implant.
So we just did an over the air software update
to his implant, so we could update your Tesla or your iPhone.
And that firmware change enabled us to record
sort of averages of populations of neurons
nearby individual electrodes.
So we have less resolution about which individual neuron
is doing what, but we have a broader picture
of what's going on nearby an electrode overall.
And that feedback loop, I mean, basically as Nolan described,
it was immediate when we flipped that switch.
I think the first day we did that,
you hit three or four VPS right out of the box.
And that was a light bulb moment for,
okay, this is the right path to go down.
And from there, there's a lot of feedback around
how to make this useful for independent use.
So what we care about ultimately is that you can use it
independently to do whatever you want.
And to get to that point, it required us to re-engineer the UX
as you talked about with the dwell cursor,
to make it something that you can use independently
without us needing to be involved all the time.
And yeah, this is obviously the start of this journey.
So hopefully we get back to the places
where you're doing multiple clicks
and using that to control much more fluidly everything
and much more naturally the applications
that you're trying to interface with.
And most importantly, get that web grid number up.
Yeah, yeah.
So how's the, on the hover click,
do you accidentally click stuff sometimes?
Like what's, how hard is it to avoid accidentally clicking?
I have to continuously keep it moving basically.
So like I said, there's a threshold
where it will initiate a click.
So if I ever drop below that, it'll start
and I have 0.3 seconds to move it
before it clicks anything.
And if I don't want it to ever get there,
I just keep it moving at a certain speed
and like just constantly like doing circles on screen,
moving it back and forth to keep it from clicking stuff.
I actually noticed a couple of weeks back
that I was, when I was not using the implant,
I was just moving my hand back and forth or in circles.
Like I was trying to keep the cursor from clicking
and I was just doing it
like while I was trying to go to sleep.
And I was like, okay, this is a problem.
To avoid the clicking.
I guess, does that create problems?
Like when you're gaming accidentally click a thing, like.
Yeah, yeah, it happens in chess.
I've lost, I've lost a number of games
because I'll accidentally click something.
I think the first time I ever beat you
was because of an accident click.
Yeah, I miss clicking.
It's a nice excuse, right?
Yeah.
Any time you lose, you could just say,
that was accidental.
You said the app improved a lot from version one
when you first started using it.
It was very different.
So can you just talk about the trial and error
that you went through with the team,
like 200 plus pages of notes?
Like what's that process like of going back and forth
and working together to improve the thing?
It's a lot of me just using it like day in and day out
and saying like, hey, can you guys do this for me?
Like give me this.
I want to be able to do that.
I need this.
I think a lot of it just doesn't occur to them maybe
until someone is actually using the app, using the implant.
It's just something that they just never would have thought
of or it's very specific to even like me,
maybe what I want.
It's something I'm a little worried about
with the next people that come is maybe they will want
things much different than how I've set it up
or what the advice I've given the team.
And they're going to look at some of the things
they've added for me.
Like that's a dumb idea.
Like why would he ask for that?
And so I'm really looking forward to get the next people on
because I guarantee that they're going to think of things
that I've never thought of.
They're going to think of improvements.
I'm like, wow, that's a really good idea.
Like I wish I would have thought of that.
And then they're also going to give me some pushback
about like, yeah, what you are asking them to do here.
That's a bad idea.
Let's do it this way.
And I'm more than happy to have that happen.
But it's just a lot of like, you know,
different interactions with different games
or applications, the internet,
just with the computer in general.
There's tons of bugs that end up popping up
left, right, center.
So it's just me trying to use it as much as possible
and showing them what works and what doesn't work
and what I would like to be better.
And then they take that feedback
and they usually create amazing things for me.
They solve these problems in ways I would have never imagined.
They're so good at everything they do.
And so I'm just really thankful that I'm able to give them
some feedback and they can make something of it.
Because a lot of my feedback is like really done.
It's just like, I want this.
Please do something about it.
And we'll come back super well thought out.
And it's way better than anything I could have ever thought
of or implemented myself.
So they're just great.
They're really, really cool.
As the BCI community grows, would you like to hang out
with the other folks with neural links?
Like what relationship, if any,
would you want to have with them?
Because you said like they might have a different set
of like ideas of how to use the thing.
Would you be intimidated by their web grid performance?
No, no, I hope.
Compete.
I hope day one, they like wipe the floor with me.
I hope they beat it and they crush it,
double it if they can.
Just because on one hand,
it's only gonna push me to be better
because I'm super competitive.
I want other people to push me.
I think that is important for anyone trying
to achieve greatness is they need other people around them
who are going to push them to be better.
And I even made a joke about it on X once,
like once the next people get chosen,
like cue buddy cop music.
Like I'm just excited to have other people to do this with
and to like share experiences with.
I'm more than happy to interact with them
as much as they want.
I'm more than happy to give them advice.
I don't know what kind of advice I could give them,
but if they have questions, I'm more than happy.
What advice would you have
for the next participant in clinical trial?
That they should have fun with this
because it is a lot of fun
and that I hope they work really, really hard
because it's not just for us,
it's for everyone that comes after us
and come to me if they need anything
and to go to Nirling if they need anything.
Man, Nirling moves mountains.
Like they do absolutely anything for me that they can.
And it's an amazing support system to have.
It puts my mind at ease for like so many things
that I have had like questions about
or so many things I want to do.
And they're always there.
And that's really, really nice.
And so I would tell them not to be afraid
to go to NurLink with any questions that they have,
any concerns, anything that they're looking to do with this
and any help that NurLink is capable of providing,
I know they will.
And I don't know, I don't know, just work your ass off
because it's really important
that we try to give our all to this.
So have fun and work hard.
Yeah, yeah, there we go.
Maybe that's what I'll just start saying to people.
Have fun, work hard.
Now you're a real pro athlete, just keep it short.
Maybe it's good to talk about what you've been able to do
now that you have a NurLink implant,
like the freedom you gain from this way of interacting
with the outside world.
Like you play video games all night
and you do that by yourself and that's a kind of freedom.
Can you speak to that freedom that you gain?
Yeah, it's what all, I don't know,
people in my position want,
they just want more independence.
The more load that I can take away from people around me,
the better.
If I'm able to interact with the world
without using my family,
without going through any of my friends,
like needing them to help me with things, the better.
If I'm able to sit up on my computer all night
and not need someone to like sit me up,
say like on my iPad, like in a position where I can use it
and then have to have them wait up for me all night
until I'm ready to be done using it.
Like that, it takes a load off of all of us
and it's really like all I can ask for.
It's something that I could never thank NurLink enough for
and I know my family feels the same way.
Just being able to have the freedom to do things on my own
at any hour of the day or night,
it means the world to me and I don't know.
When you're up at 2 a.m. playing Web Grid by yourself,
I just imagine like it's darkness
and then it's just a light glowing
and you're just focused.
What's going through your mind?
Or are you like in a state of flow
where it's like the mind is empty,
like those like Zen masters?
Generally it is me playing music of some sort.
I have a massive playlist
and so I'm just like rocking out to music
and then it's also just like a race against time
because I'm constantly looking at how much battery percentage
I have left on my implant.
I have 30% which equates to X amount of time
which means I have to break this record
in the next hour and a half
or else it's not happening tonight
and so it's a little stressful when that happens.
When it's above 50%, I'm like okay, I got time.
It starts getting down to 30 and then 20.
It's like all right, 10%, a little pop-up's
gonna pop up right here
and it's gonna really screw my web grid flow
and it's gonna tell me that there's a low battery,
low battery pop-up comes up
and I'm like it's really gonna screw me over.
If I'm gonna break this record,
I have to do it in the next 30 seconds
or else that pop-up's gonna get in the way,
cover my web grid.
After that, I go click on it, go back into web grid
and I'm like all right, that means I have 10 minutes left
before this thing's dead.
That's what's going on in my head.
Generally that and whatever song's playing
and I just want to break those records so bad.
It's all I want when I'm playing web grid.
It has become less of like,
this is just a leisurely activity.
I just enjoy doing this because it just feels so nice
and it puts me at ease.
It is no, once I'm in web grid,
you better break this record
or you're gonna waste five hours of your life right now.
And I don't know, it's just fun, it's fun man.
Have you ever tried web grid with like two targets
and three targets?
Can you get higher BPS with that?
Can you do that?
You mean like different color targets or you mean?
Oh, multiple targets, does that change the thing?
Yeah, so BPS is a log of number of targets
times correct minus incorrect divided by time.
And so you can think of like different clicks
as basically double in the number of active targets.
Got it.
So you basically higher BPS the more options
there are, the more difficult to task.
And there's also like Zen mode you've played in before
which is like infinite canvas.
It covers the whole screen with a grid and I don't know.
And so you can go like, that's insane.
He doesn't like it because it didn't show BPS.
So like, you know, oh yeah.
I had them put in a giant BPS in the background.
So now it's like the opposite of Zen mode.
It's like super hard mode, like just metal mode
if it's just like a giant number in the background.
We should be named that metal mode
if it isn't like metal mode.
So you also play Civilization VI.
I love Civ VI, yeah.
You usually go with Korea.
I do.
So the great part about Korea is they focus on like science
tech victories, which was not planned.
Like I've been playing Korea for years
and then all of the Neuralink stuff happened.
So it kind of aligned.
But what I've noticed with tech victories is
if you can just rush tech, rush science,
then you can do anything.
Like at one point in the game,
you will be so far ahead of everyone technologically
that you will have like musket men,
infantry men playing sometimes
and people will still be fighting with like bows and arrows.
And so if you want to win a domination victory,
you just get to a certain point with the science
and then go and wipe out the rest of the world.
Or you can just take science all the way and win that way.
And you're going to be so far ahead of everyone
because you're producing so much science
that it's not even close.
I've accidentally won in different ways
just by focusing on science.
Accidentally won by focusing on science.
Yeah, I was playing only science, obviously,
like just science all the way, just tech.
And I was trying to get like every tech
in the tech tree and stuff.
And then I accidentally won through a diplomatic victory.
And I was so mad.
I was so mad because it just like ends the game one turn.
It was like, oh, you won.
You're so diplomatic.
I'm like, I don't want to do this.
I should have declared war on more people or something.
It was terrible,
but you don't need like giant civilizations with tech,
especially with Korea.
You can keep it pretty small.
So I generally just, you know,
get to a certain military unit
and put them all around my border to keep everyone out.
And then I will just build up.
So very isolationist.
Nice.
Yeah.
Just work on the science of the tech.
That's it.
You're making it sound so fun.
It's so much fun.
And I also saw a civilization seven trailer.
Oh man, I'm so pumped.
And that's probably coming out.
Come on, sim seven, hit me up.
All alpha, beta tests, whatever.
Wait, when is it coming out?
It's in-
Yeah, yeah, next year.
What other stuff would you like to see improved
about the Neuralink app and just the entire experience?
I would like to, like I said,
get back to the like click on demand,
like the regular clicks.
That would be great.
I would like to be able to connect to more devices.
Right now it's just the computer.
I'd like to be able to use it on my phone
or use it on different consoles, different platforms.
I'd like to be able to control as much stuff
as possible, honestly.
Like an Optimus robot would be pretty cool.
That would be sick if I could control an Optimus robot.
The link app itself,
it seems like we are getting pretty dialed in
to what it might look like down the road.
Seems like we've gotten through a lot
of what I want from it, at least.
The only other thing I would say is like more control
over all the parameters that I can tweak
with my like cursor and stuff.
There's a lot of things that go into
how the cursor moves in certain ways.
And I have, I don't know,
like three or four of those parameters.
And they're my gain and friction.
Gain, friction, yeah.
And there's maybe double the amount of those
with just like velocity
and then with the actual dwell cursor.
So I would like all of it.
I want as much control over my environment as possible.
Especially-
So you want like advanced mode.
You know, like in like there's menus,
usually there's basic mode.
And you're like one of those folks,
like the power user advance.
Yeah, that's what I want.
I want as much control over this as possible.
So yeah, that's really all I can ask for.
Just give me everything.
Has speech been useful?
Like just being able to talk also
in addition to everything else?
Yeah, you mean like while I'm using it?
While you're using it, like speech to text?
Oh yeah.
Or do you type or like,
because there's also a keyboard.
Yeah, yeah, so there's a virtual keyboard.
That's another thing I would like to work more on
is finding some way to type or text in a different way.
Right now it is like a dictation basically
and a virtual keyboard that I can use with the cursor.
But we've played around with like finger spelling,
like sign language finger spelling,
and that seems really promising.
So I have this thought in my head
that it's going to be a very similar learning curve
that I had with the cursor
where I went from attempted movement
to imagined movement at one point.
I have a feeling, this is just my intuition,
that at some point I'm going to be doing finger spelling
and I won't need to actually attempt to finger spell anymore,
that I'll just be able to think the letter that I want
and it'll pop up.
That will be epic.
That's challenging, that's hard.
That's a lot of work for you to kind of take that leap,
but that would be awesome.
And then like going from letters to words is another step.
Like you would go from, you know,
right now it's finger spelling
of like just the sign language alphabet,
but if it's able to pick that up,
then it should be able to pick up
like the whole sign language, like language.
And so then if I could do something along those lines
or just the sign language spelled word,
if I can, you know, spell it at a reasonable speed
and it can pick that up,
then I would just be able to think that through
and it would do the same thing.
I don't see why not after what I saw
with the cursor control,
I don't see why it wouldn't work,
but we'd have to play around with it more.
What was the process in terms of like training yourself
to go from attempted movement to imagined movement?
How long does that take?
So like how long would this kind of process take?
Well, it was a couple of weeks
before it just like happened upon me.
But now that I know that that was possible,
I think I can make it happen with other things.
I think it would be much, much simpler.
Would you get an upgraded implant device?
Sure, absolutely, whenever they'll let me.
So you don't have any concerns
for you with the surgery experience,
all of it was like no regrets.
No.
So everything's been good so far.
Yep.
You just keep getting upgrades.
Yeah, I mean, why not?
I've seen how much it's impacted my life already
and I know that everything from here on out
is gonna get better and better.
So I would love to, I would love to get the upgrade.
What future capabilities are you excited about?
Sort of beyond this kind of telepathy.
Is vision interesting?
So for folks who, for example, who are blind,
so you're like enabling people to see or for speech?
Yeah, there's a lot that's very, very cool about this.
I mean, we're talking about the brain.
So there's like, this is just motor cortex stuff.
There's so much more that can be done.
The vision one is fascinating to me.
I think that is going to be very, very cool
to give someone the ability to see
for the first time in their life would just be,
I mean, it might be more amazing
than even helping someone like me.
Like that just sounds incredible.
The speech thing is really interesting,
being able to have some sort of like real time translation
and cut away that language barrier would be really cool.
Any sort of like actual impairments that it could solve
like with speech would be very, very cool.
And then also there are a lot of different disabilities
that all originate in the brain.
And you would be able to,
hopefully be able to solve a lot of those.
I know there's already stuff to help people with seizures
that can be implanted in the brain.
This would do, I imagine the same thing.
And so you could do something like that.
I know that even someone like Joe Rogan has talked about
the possibilities with being able to stimulate the brain
in different ways.
I'm not sure what,
like how ethical a lot of that would be.
That's beyond me, honestly.
But I know that there's a lot that can be done
when we're talking about the brain
and being able to go in and physically make changes
to help people or to improve their lives.
So I'm really looking forward to everything
that comes from this.
And I don't think it's all that far off.
I think a lot of this can be implemented within my lifetime
assuming that I live a long life.
What you were referring to is things like
people suffering from depression
or things of that nature potentially getting help.
Yeah, flip a switch like that, make someone happy.
I know, I think Joe has talked about it more
in terms of like, you want to experience
like what a drug trip feels like.
Like you want to experience what you like to be on.
Yeah, mushrooms or something like that, DMT,
like you can just flip that switch in the brain.
My buddy Bane has talked about being able to
like wipe parts of your memory
and re-experience things that like for the first time,
like your favorite movie or your favorite book,
like just wipe that out real quick
and then re-fall in love with Harry Potter or something.
I told him, I was like, I don't know how I feel
about like people being able to just wipe
parts of your memory.
That seems a little sketchy to me.
He's like, they're already doing it, so.
Sounds legit.
I would love memory replay,
just like actually like high resolution
replayable memories.
I saw an episode of Black Mirror about that one,
so I don't think I want it.
Yeah, so Black Mirror always kind of considers
the worst case, which is important.
I think people don't consider the best case
or the average case enough.
I don't know what it is about us humans.
We want to think about the worst possible thing.
We love drama.
Like how is this new technology going to kill everybody?
We just love that.
Okay, like yes, let's watch.
Hopefully people don't think about that too much with me.
It'll ruin a lot of my plans.
Yeah, I assume you're gonna have to take over the world.
I mean, I love your Twitter.
You tweeted, I'd like to make jokes about hearing voices
in my head since getting the neural link,
but I feel like people would take it the wrong way,
plus the voices in my head told me not to.
Yeah.
Yeah.
Please never stop.
So you were talking about Optimus.
Is that something you would love to be able to do
to control the robotic arm or the entirety of Optimus?
Oh yeah, for sure.
For sure, absolutely.
You think there's something like fundamentally different
by just being able to physically interact with the world?
Yeah, oh, 100%.
I know another thing with like being able to like give
people the ability to like feel sensation and stuff to you
by going in with the brain and having the neural link
maybe do that.
That could be something that could be translated through,
transferred through the Optimus as well.
Like there's all sorts of really cool interplay between that
and then also like you said, just physically interacting.
I mean, 99% of the things that I can't do myself,
obviously need, I need a caretaker for someone
to physically do things for me.
If an Optimus robot could do that,
I could live an incredibly independent life
and not be such a burden on those around me.
And that would, it would change the way people like me live,
at least until whatever this is gets cured.
But being able to interact with the world physically
like that would just be amazing.
And they're not just like for being,
for having it be a caretaker or something,
but something like I talked about,
just being able to read a book.
Imagine an Optimus robot just being able to hold a book open
in front of me, like get that smell again.
I might not be able to feel it at that point.
Or maybe I could again with the sensation and stuff,
but being, there's something different about reading
like a physical book than staring at a screen
or listening to an audio book.
I actually don't like audio books.
I've listened to a ton of them at this point,
but I don't really like them.
I would much rather like read a physical copy.
So one of the things you would love to be able to experience
is opening the book, bringing it up to you
and to feel the touch of the paper.
Yeah. Oh man.
The touch, the smell.
I mean, it's just like just something about the words
on the page and they've replicated that page color
on like the Kindle and stuff.
Yeah. It's just not the same.
Yeah. So just something as simple as that.
So one of the things you miss is touch.
I do. Yeah.
A lot of things that I interact with in the world
like clothes or literally any physical thing
that I interact with in the world,
a lot of times what people around me will do
is they'll just come like rub it on my face.
They'll like lay something on me so I can feel the weight.
They will rub a shirt on me so I can feel fabric.
Like there's something very profound about touch
and it's something that I miss a lot
and something I would love to do again.
We'll see.
What would be the first thing you do
with a hand that can touch?
Your mom will hug after that, right?
Yeah, I know.
It's one thing that I've asked God for basically every day
since my accident was just being able to like one day move
even if it was only like my hand,
so that way like I could squeeze my mom's hand or something
just to like show her that, you know,
like how much I care and how much I love her and everything.
Something along those lines,
being able to just interact with the people around me,
handshake, give someone a hug, I don't know,
anything like that, being able to help me eat,
like I'd probably get really fat,
which would be a terrible, terrible thing.
Also be bliss in chess on a physical chessboard.
Yeah, yeah.
I mean, there are just so many upsides, you know?
And any way to find some way to feel like
I'm bringing bliss down to my level
because he's just such an amazing guy
and everything about him is just so above and beyond
that anything I can do to take him down a notch,
I'm gonna be happy.
Yeah, humble him a bit, he needs it.
Yeah.
Okay, as he's sitting next to me.
Did you ever make sense of why God puts good people
through such hardship?
Oh, man.
I think it's all about
understanding how much we need God
and I don't think that there's any light without the dark.
I think that if all of us were happy all the time,
there would be no reason to turn to God ever.
I feel like there would be no concept of good or bad.
And I think that as much of
like the darkness and the evil that's in the world,
it makes us all appreciate the good
and the things we have so much more.
And I think like when I had my accident,
one of the first things I said to one of my best friends was
and this was within like the first month or two
after my accident, I said,
everything about this accident has just made me understand
and believe that like God is real
and that there really is a God basically
and that like my interactions with him
have all been real and worthwhile.
And he said, if anything, seeing me go through this accident,
he believes that there isn't a God
and it's a very different reaction.
But I believe that it is a way for God to test us,
to build our character,
to send us through trials and tribulations
to make sure that we understand how precious he is
and the things that he's given us
and the time that he's given us.
And then to hopefully grow from all of that
I think that's a huge part of being here
is to not just have an easy life
and do everything that's easy
but to step out of our comfort zones
and really challenge ourselves
because I think that's how we grow.
What gives you hope about this whole thing
we have going on, human civilization?
Man, I think people are my biggest inspiration.
Even just being at Neuralink for a few months,
looking people in the eyes
and hearing their motivations for why they're doing this.
It's so inspiring
and I know that they could be other places
at cushier jobs, working somewhere else,
doing X, Y, or Z, that doesn't really mean that much.
But instead they're here
and they want to better humanity
and they want to better just the people around them,
the people that they've interacted with in their life.
They want to make better lives for their own family members
who might have disabilities
or they look at someone like me and they say,
I can do something about that so I'm going to.
And it's always been what I've connected with most
in the world are people.
I've always been a people person
and I love learning about people
and I love learning how people developed
and where they came from
and to see how much people are willing to do
for someone like me when they don't have to
and they're going out of their way to make my life better.
It gives me a lot of hope for just humanity in general,
how much we care and how much we're capable of
when we all kind of get together
and try to make a difference.
And I know there's a lot of bad out there in the world
but there always has been and there always will be.
And I think that that is, it shows human resiliency
and it shows what we're able to endure
and how much we just want to be there and help each other
and how much satisfaction we get from that
because I think that's one of the reasons
that we're here is just to help each other.
And I don't know, that always gives me hope
is just realizing that there are people out there
who still care and who want to help.
And thank you for being one such human being
and continuing to be a great human being
through everything you've been through
and being an inspiration to many people,
to myself for many reasons,
including your epic, unbelievably great performance
on Web Grid.
I will be training all night tonight
to try to catch up.
You can do it.
And I believe in you that you can, once you come back,
so sorry to interrupt with the Austin trip,
once you come back, eventually beat Bliss.
Yeah, yeah, for sure, absolutely.
I'm rooting for you, the whole world is rooting for you.
Thank you for everything you've done, man.
Thanks, thanks, man.
Thanks for listening to this conversation
with Nolan Arbaugh and before that with Elon Musk,
DJ Saw, Matthew McDougall and Bliss Chapman.
To support this podcast,
please check out our sponsors in the description.
And now let me leave you with some words
from Aldous Huxley in the Doors of Perception.
We live together.
We act on and react to one another,
but always and in all circumstances,
we are by ourselves.
The martyrs go hand in hand into the arena.
They are crucified alone.
Embrace the lovers desperately try to fuse
their insulated ecstasies
into a single self-transcendence in vain.
But it's very nature, every embodied spirit
is doomed to suffer and enjoy its solitude.
Sensations, feelings, insights, fancies,
all these are private and except through symbols
and a secondhand incommunicable.
We can pool information about experiences,
but never the experiences themselves.
From family to nation,
every human group is a society of island universes.
Thank you for listening and hope to see you next time.
Thank you.
