#452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity｜Lex Fridman Podcast

The following is a conversation with Dario Amade, CEO of Anthropic, the company that created Claude that is currently and often at the top of most LLM benchmark leaderboards.

On top of that, Dario and the Anthropic team have been outspoken advocates for taking the topic of AI safety very seriously.

And they have continued to publish a lot of fascinating AI research on this and other topics.

I'm also joined afterwards by two other brilliant people from Anthropic.

First, Amanda Askel, who is a researcher working on alignment and fine tuning of Claude, including the design of Claude's character and personality.

A few folks told me she has probably talked with Claude more than any human at Anthropic.

So she was definitely a fascinating person to talk to about prompt engineering and practical advice on how to get the best out of Claude.

After that, Chris Olah stopped by for a chat.

He's one of the pioneers of the field of mechanistic interpretability, which is an exciting set of efforts that aims to reverse engineer neural networks to figure out what's going on inside, inferring behaviors from neural activation patterns inside the network.

This is a very promising approach for keeping future super intelligent AI systems safe.

For example, by detecting from the activations when the model is trying to deceive the human it is talking to.

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We talk a little bit about public benchmarks in this podcast.

I think mostly focused on software engineering, sweet bench.

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And now, dear friends, here's Dario Amade.

Let's start with a big idea of scaling laws and the scaling hypothesis.

What is it? What is its history?

And where do we stand today?

So I can only describe it as it relates to kind of my own experience, but I've been in the AI field for about 10 years and it was something I noticed very early on.

So I first joined the AI world when I was working at Baidu with Andrew Ng in late 2014, which is almost exactly 10 years ago now.

And the first thing we worked on was speech recognition systems.

And in those days, I think deep learning was a new thing.

It had made lots of progress, but everyone was always saying, we don't have the algorithms we need to succeed.

We're only matching a tiny, tiny fraction.

There's so much we need to kind of discover algorithmically.

We haven't found the picture of how to match the human brain.

And in some ways, it was fortunate.

You can have almost beginner's luck.

I was like a newcomer to the field.

And I looked at the neural net that we were using for speech, the recurrent neural networks, and I said, I don't know.

What if you make them bigger and give them more layers?

And what if you scale up the data along with this?

I just saw these as like independent dials that you could turn.

And I noticed that the model started to do better and better as you gave them more data, as you made the models larger, as you trained them for longer.

And I didn't measure things precisely in those days, but along with colleagues, we very much got the informal sense that the more data and the more compute and the more training you put into these models, the better they perform.

And so initially, my thinking was, hey, maybe that is just true for speech recognition systems, right?

Maybe that's just one particular quirk, one particular area.

I think it wasn't until 2017, when I first saw the results from GPT -1, that it clicked for me that language is probably the area in which we can do this.

We can get trillions of words of language data, we can train on them.

And the models we were trained in those days were tiny.

You could train them on one to eight GPUs, whereas now we train jobs on tens of thousands soon going to hundreds of thousands of GPUs.

And so when I saw those two things together, and there were a few people like Ilya Sutskever, who you've interviewed, who had somewhat similar views, right?

He might have been the first one, although I think a few people came to similar views around the same time, right?

There was Rich Sutton's Bitter Lesson, there was Gorin wrote about the scaling hypothesis, but I think somewhere between 2014 and 2017 was when it really clicked for me, when I really got conviction that, hey, we're going to be able to do these incredibly wide cognitive tasks if we just scale up the models.

And at every stage of scaling, there are always arguments.

And when I first heard them, honestly, I thought, probably I'm the one who's wrong, and all these experts in the field are right, they know the situation better than I do, right?

There's the Chomsky argument about, like, you can get syntactics, but you can't get semantics.

There is this idea you can make a sentence make sense, you can't make a paragraph make sense.

The latest one we have today is, you know, we're going to run out of data or the data isn't high quality enough, or models can't reason.

And each time, every time we manage to, we manage to either find a way around or scaling just is the way around.

Sometimes it's one, sometimes it's the other.

And so I'm now at this point, I still think, you know, it's always quite uncertain.

We have nothing but inductive inference to tell us that the next few years are going to be like the next, the last 10 years.

But I've seen the movie enough times.

I've seen the story happen for enough times to really believe that probably the scaling is going to continue and that there's some magic to it that we haven't really explained on a theoretical basis yet.

And of course, the scaling here is bigger networks, bigger data, bigger compute.

Yes. In particular, linear scaling up of bigger networks, bigger training times, and more and more data.

So all of these things, almost like a chemical reaction, you know, you have three ingredients in the chemical reaction, and you need to linearly scale up the three ingredients.

If you scale up one, not the others, you run out of the other reagents and the reaction stops.

But if you scale up everything, everything in series, then the reaction can proceed.

And of course, now that you have this kind of empirical science slash art, you can apply to other more nuanced things like scaling laws applied to interpretability or scaling laws applied to post training or just seeing how does this thing scale?

But the big scaling law, I guess the underlying scaling hypothesis has to do with big networks, big data leads to intelligence.

Yeah, we've documented scaling laws in lots of domains other than language, right?

So initially, the paper we did that first showed it was in early 2020, where we first showed it for language.

There was then some work late in 2020, where we showed the same thing for other modalities like images, video, text to image, image to text, math, that they all had the same pattern.

And you're right, now there are other stages like post training or there are new types of reasoning models.

And in all of those cases that we've measured, we see similar types of scaling laws.

A bit of a philosophical question, but what's your intuition about why bigger is better in terms of network size and data size?

Why does it lead to more intelligent models?

So in my previous career as a biophysicist, so I did physics undergrad and then biophysics in grad school.

So I think back to what I know as a physicist, which is actually much less than what some of my colleagues at Enthropic have in terms of expertise in physics.

There's this concept called the one over F noise and one over X distributions, where often, just like if you add up a bunch of natural processes, you get a Gaussian.

If you add up a bunch of kind of differently distributed natural processes, if you like take a probe and hook it up to a resistor, the distribution of the thermal noise in the resistor goes as one over the frequency.

It's some kind of natural convergent distribution.

And I think what it amounts to is that if you look at a lot of things that are produced by some natural process that has a lot of different scales, not a Gaussian, which is kind of narrowly distributed.

But if I look at kind of like large and small fluctuations that lead to electrical noise, they have this decaying one over X distribution.

And so now, I think of like patterns in the physical world or in language.

If I think about the patterns in language, there are some really simple patterns.

Some words are much more common than others, then there's basic noun verb structure.

Then there's the fact that nouns and verbs have to agree, they have to coordinate.

And there's the higher level sentence structure.

And there's the thematic structure of paragraphs.

And so the fact that there's this regressing structure, you can imagine that as you make the networks larger, first they capture the really simple correlations, the really simple patterns, and there's this long tail of other patterns.

And if that long tail of other patterns is really smooth, like it is with the one over F noise in physical processes like resistors, then you can imagine as you make the network larger, it's kind of capturing more and more of that distribution.

And so that smoothness gets reflected in how well the models are at predicting and how well they perform.

Language is an evolved process, right?

We've developed language, we have common words and less common words, we have common expressions and less common expressions, we have ideas, cliches that are expressed frequently, and we have novel ideas.

And that process has developed, has evolved with humans over millions of years.

And so the guess, and this is pure speculation, would be that there's some kind of long tail distribution of the distribution of these ideas.

So there's the long tail, but also there's the height of the hierarchy of concepts that you're building up.

So the bigger the network, presumably you have a higher capacity to...

Exactly. If you have a small network, you only get the common stuff, right?

If I take a tiny neural network, it's very good at understanding that a sentence has to have a verb adjective noun, right?

But it's terrible at deciding what those verb adjectives and nouns should be and whether they should make sense.

If I make it just a little bigger, it gets good at that, then suddenly it's good at the sentences, but it's not good at the paragraphs.

And so these rarer and more complex patterns get picked up as I add more capacity to the network.

Well, the natural question then is, what's the ceiling of this?

Yeah. How complicated and complex is the real world?

How much of this stuff is there to learn?

I don't think any of us knows the answer to that question.

My strong instinct would be that there's no ceiling below the level of humans, right?

We humans are able to understand these various patterns.

And so that makes me think that if we continue to scale up these models to kind of develop new methods for training them and scaling them up, that will at least get to the level that we've gotten to with humans.

There's then a question of how much more is it possible to understand than humans do?

How much is it possible to be smarter and more perceptive than humans?

I would guess the answer has got to be domain dependent.

If I look at an area like biology, and I wrote this essay, Machines of Loving Grace, it seems to me that humans are struggling to understand the complexity of biology, right?

If you go to Stanford or Harvard or to Berkeley, you have whole departments of folks trying to study the immune system or metabolic pathways.

And each person understands only a tiny bit part of it specializes, and they're struggling to combine their knowledge with that of other humans.

And so I have an instinct that there's a lot of room at the top for AIs to get smarter.

If I think of something like materials in the physical world, or like addressing conflicts between humans or something like that, I mean, it may be there's only some of these problems are not intractable, but much harder.

And it may be that there's only so well you can do with some of these things, right?

Just like with speech recognition, there's only so clear I can hear your speech.

So I think in some areas, there may be ceilings that are very close to what humans have done.

In other areas, those ceilings may be very far away.

And I think we'll only find out when we build these systems.

It's very hard to know in advance, we can speculate, but we can't be sure.

And then some domains, the ceiling might have to do with human bureaucracies and things like this, as you write about, yes, so humans fundamentally have to be part of the loop.

That's the cause of the ceiling, not maybe the limits of the intelligence.

Yeah, I think in many cases, you know, in theory, technology could change very fast, for example, all the things that we might invent with respect to biology.

But remember, there's a clinical trial system that we have to go through to actually administer these things to humans.

I think that's a mixture of things that are unnecessary and bureaucratic and things that protect the integrity of society.

And the whole challenge is that it's hard to tell what's going on.

It's hard to tell which is which, right?

My view is definitely, I think, in terms of drug development, my view is that we're too slow and we're too conservative.

But certainly, if you get these things wrong, you know, it's possible to risk people's lives by being too reckless.

And so at least some of these human institutions are, in fact, protecting people.

So it's all about finding the balance.

I strongly suspect that balance is kind of more on the side of pushing to make things happen faster, but there is a balance.

If we do hit a limit, if we do hit a slowdown in the scaling laws, what do you think would be the reason?

Is it compute limited, data limited?

Is it something else?

Ideal limited? So a few things.

Now we're talking about hitting the limit before we get to the level of humans and the skill of humans.

So I think one that's popular today and I think could be a limit that we run into, like most of the limits, I would bet against it, but it's definitely possible, is we simply run out of data.

There's only so much data on the internet and there's issues with the quality of the data, right?

You can get hundreds of trillions of words on the internet, but a lot of it is repetitive or its search engine optimization drivel, or maybe in the future it'll even be text generated by AIs itself.

And so I think there are limits to what can be produced in this way.

That said, we, and I would guess other companies, are working on ways to make data synthetic where you can use the model to generate more data of the type that you have already or even generate data from scratch.

If you think about what was done with DeepMind's AlphaGo Zero, they managed to get a bot all the way from no ability to play go whatsoever to above human level just by playing against itself.

There was no example data from humans required in the AlphaGo Zero version of it.

The other direction of course is these reasoning models that do chain of thought and stop to think and reflect on their own thinking.

In a way, that's another kind of synthetic data coupled with reinforcement learning.

So my guess is with one of those methods, we'll get around the data limitation or there may be other sources of data that are available.

We could just observe that even if there's no problem with data, as we start to scale models up, they just stop getting better.

It seemed to be our reliable observation that they've gotten better.

That could just stop at some point for a reason we don't understand.

The answer could be that we need to invent some new architecture.

There have been problems in the past with say numerical stability of models where it looked like things were leveling off, but actually when we found the right unblocker, they didn't end up doing so.

So perhaps there's some new optimization method or some new technique we need to unblock things.

I've seen no evidence of that so far, but if things were to slow down, that perhaps could be one reason.

What about the limits of compute?

Meaning the expensive nature of building bigger and bigger data centers?

So right now I think most of the one billion dollar scale plus or minus a factor of three.

Those are the models that exist now or are being trained now.

I think next year we're going to go to a few billion and then 2026, we may go to above 10 billion and probably by 2027 there are ambitions to build 100 billion clusters.

And I think all of that actually will happen.

There's a lot of determination to build the compute to do it within this country.

And I would guess that it actually does happen.

Now, if we get to 100 billion, that's still not enough compute, that's still not enough scale, then either we need even more scale or we need to develop some way of doing it more efficiently, of shifting the curve.

I think between all of these, one of the reasons I'm bullish about powerful AI happening so fast is just that if you extrapolate the next few points on the curve, we're very quickly getting towards human level ability.

Some of the new models that we developed, some reasoning models that have come from other companies, they're starting to get to what I would call the PhD or professional level.

If you look at their coding ability, the latest model we released, Sonnet 3 .5, the new or updated version, it gets something like 50 % on Sweetbench.

And Sweetbench is an example of a bunch of professional real world software engineering tasks.

At the beginning of the year, I think the state of the art was three or 4%.

So in 10 months, we've gone from 3 % to 50 % on this task.

And I think in another year, we'll probably be at 90%.

I mean, I don't know, but might even be, might even be less than that.

We've seen similar things in graduate level math, physics, and biology from models like OpenAI's 01.

So if we just continue to extrapolate this in terms of skill that we have, I think if we extrapolate the straight curve, within a few years, we will get to these models being above the highest professional level in terms of humans.

Now, will that curve continue?

You pointed to, and I've pointed to, a lot of possible reasons why that might not happen.

But if the extrapolation curve continues, that is the trajectory we're on.

So Anthropic has several competitors.

It'd be interesting to get your view of it all.

OpenAI, Google, XAI, Meta, what does it take to win in the broad sense of win in the space?

Yeah, so I want to separate out a couple things.

So Anthropic's mission is to try to make this all go well.

And we have a theory of change called Race to the Top.

Race to the Top is about trying to push the other players to do the right thing by setting an example.

It's not about being the good guy, it's about setting things up so that all of us can be the good guy.

I'll give a few examples of this.

Early in the history of Anthropic, one of our co -founders, Chris Ola, who I believe you're interviewing soon, he's the co -founder of the field of mechanistic interpretability, which is an attempt to understand what's going on inside AI models.

So we had him and one of our early teams focus on this area of interpretability, which we think is good for making models safe and transparent.

For three or four years, that had no commercial application whatsoever.

It still doesn't today, we're doing some early betas with it, and probably it will eventually.

But this is a very, very long research bed and one in which we've built in public and shared our results publicly.

And we did this because we think it's a way to make models safer.

An interesting thing is that as we've done this, other companies have started doing it as well.

In some cases, because they've been inspired by it, in some cases, because they're worried that if other companies are doing this that look more responsible, they want to look more responsible, too.

No one wants to look like the irresponsible actor.

And so they adopt this as well.

When folks come to Anthropic, interpretability is often a draw, and I tell them, the other places you didn't go, tell them why you came here.

And then you see soon that there's interpretability teams elsewhere as well.

And in a way, that takes away our competitive advantage because it's like, oh, now others are doing it as well, but it's good for the broader system.

And so we have to invent some new thing that we're doing that others aren't doing as well.

And the hope is to basically bid up the importance of doing the right thing.

And it's not about us in particular.

It's not about having one particular good guy.

Other companies can do this as well.

If they join the race to do this, that's the best news ever, right?

It's about shaping the incentives to point upward instead of shaping the incentives to point downward.

And we should say this example of the field of mechanistic interpretability is just a rigorous, non -hand wavy way of doing AI safety.

Or it's tending that way.

Trying to. I mean, I think we're still early in terms of our ability to see things, but I've been surprised at how much we've been able to look inside these systems and understand what we see, right?

Unlike with the scaling laws, where it feels like there's some law that's deriving these models to perform better.

On the inside, the models aren't, there's no reason why they should be designed for us to understand them, right?

They're designed to operate.

They're designed to work just like the human brain or human biochemistry.

They're not designed for a human to open up the hatch, look inside and understand them.

But we have found, and you can talk in much more detail about this to Chris, that when we open them up, when we do look inside them, we find things that are surprisingly interesting.

And as a side effect, you also get to see the beauty of these models.

You get to explore the beautiful nature of large neural networks through the Meck and Terp kind of methodology.

I'm amazed at how clean it's been.

I'm amazed at things like induction heads.

I'm amazed at things like that we can use sparse autoencoders to find these directions within the networks and that the directions correspond to these very clear concepts.

We demonstrated this a bit with the Golden Gate Bridge Claude.

So this was an experiment where we found a direction inside one of the neural networks layers that corresponded to the Golden Gate Bridge.

And we just turned that way up.

And so we released this model as a demo.

It was kind of half a joke for a couple of days, but it was illustrative of the method we developed.

And you could take the Golden Gate, you could take the model, you could ask it about anything, you could say, how was your day?

And anything you asked, because this feature was activated, it would connect to the Golden Gate Bridge.

So it would say, I'm feeling relaxed and expensive, much like the arches of the Golden Gate Bridge.

It would masterfully change topic through the Golden Gate Bridge and integrate it.

There was also a sadness to it, to the focus ahead on the Golden Gate Bridge.

I think people quickly fell in love with it.

I think some people already miss it because it was taken down, I think, after a day.

Somehow these interventions on the model, where you kind of adjust its behavior, somehow emotionally made it seem more human than any other version of the model.

Strong personality, strong identity.

It has a strong personality.

It has these kind of like obsessive interests.

We can all think of someone who's obsessed with something.

So it does make it feel somehow a bit more human.

Let's talk about the present.

Let's talk about Claude.

So this year, a lot has happened.

In March, Claude III, Opus, Sonnet, Haiku were released, then Claude III, V, Sonnet in July, with an updated version just now released, and then also Claude III, V, Haiku was released.

Okay. Can you explain the difference between Opus, Sonnet, and Haiku, and how we should think about the different versions?

Yeah. So let's go back to March when we first released these three models.

So our thinking was different companies produce kind of large and small models, better and worse models.

We felt that there was demand both for a really powerful model.

That might be a also for fast, cheap models that are as smart as they can be for how fast and cheap.

Whenever you want to do some kind of difficult analysis, like if I want to write code, for instance, or I want to brainstorm ideas, or I want to do creative writing, I want the really powerful model.

But then there's a lot of practical applications in a business sense where it's like, I'm interacting with a website, I'm doing my taxes, or I'm talking to a legal advisor and I want to analyze a contract, or we have plenty of companies that are just like, I want to do autocomplete on my IDE or something.

And for all of those things, you want to act fast and you want to use the model very broadly.

So we wanted to serve that whole spectrum of needs.

So we ended up with this kind of poetry theme.

And so what's a really short poem?

It's a haiku. And so haiku is the small, fast, cheap model that was at the time was really surprisingly intelligent for how fast and cheap it was.

Sonnet is a medium sized poem, a couple paragraphs.

And so Sonnet was the middle model.

It is smarter, but also a little bit slower, a little bit more expensive.

And Opus, like a Magnum Opus, is a large work.

Opus was the largest, smartest model at the time.

So that was the original kind of thinking behind it.

And our thinking then was, well, each new generation of models should shift that trade off curve.

So when we released Sonnet 3 .5, it has the same, roughly the same cost and speed as the Sonnet 3 model.

But it increased its intelligence to the point where it was smarter than the original Opus 3 model, especially for code, but also just in general.

And so now we've shown results for haiku 3 .5.

And I believe haiku 3 .5, the smallest new model is about as good as Opus 3, the largest old model.

So basically the aim here is to shift the curve.

And then at some point, there's going to be an Opus 3 .5.

Now every new generation of models has its own thing, they use new data, their personality changes in ways that we kind of try to steer, but are not fully able to steer.

And so there's never quite that exact equivalence where the only thing you're changing is intelligence.

We always try and improve other things and some things change without us, without us knowing or measuring.

So it's very much an exact science.

In many ways, the manner and personality of these models is more an art than it is a science.

So what is sort of the reason for the span of time between say, Cloud Opus 3 .0 and 3 .5?

What takes that time if you can speak to?

Yeah, so there's different processes.

There's pre -training, which is just kind of the normal language model training.

And that takes a very long time.

That uses these days, tens of thousands, sometimes many tens of thousands of GPUs or TPUs or trainium or we use different platforms, but accelerator chips, often training for months.

There's then a kind of post -training phase where we do reinforcement learning from human feedback, as well as other kinds of reinforcement learning.

That phase is getting larger and larger now, and often that's less of an exact science.

It often takes effort to get it right.

Models are then tested with some of our early partners to see how good they are.

And they're then tested both internally and externally for their safety, particularly for catastrophic and autonomy risks.

So we do internal testing according to our responsible scaling policy, which I could talk more about that in detail.

And then we have an agreement with the US and the UK AI Safety Institute, as well as other third party testers in specific domains to test the models for what are called CBRN risks, chemical, biological, radiological, and nuclear.

We don't think that models pose these risks seriously yet, but every new model we want to evaluate to see if we're starting to get close to some of these more dangerous capabilities.

So those are the phases.

And then it just takes some time to get the model working in terms of inference and launching it in the API.

So there's just a lot of steps to actually making a model work.

And of course, we're always trying to make the processes as streamlined as possible.

We want our safety testing to be rigorous, but we want it to be rigorous and to be automatic to happen as fast as it can without compromising on rigor.

Same with our pre -training process and our post -training process.

So it's just like building anything else.

It's just like building airplanes.

You want to make them safe, but you want to make the process streamlined.

And I think the creative tension between those is an important thing in making the models work.

Yeah. Rumor on the street, I forget who was saying that anthropic has really good tooling.

So probably a lot of the challenge here is on the software engineering side is to build the tooling to have a efficient low friction interaction with the infrastructure.

You would be surprised how much of the challenges of building these models comes down to software engineering, performance engineering.

From the outside, you might think, oh, man, we had this Eureka breakthrough, this movie with the out.

But I think all things, even incredible discoveries, they almost always come down to the details.

And often super, super boring details.

I can't speak to whether we have better tooling than other companies.

I mean, those other companies, at least not recently, but it's certainly something we give a lot of attention to.

I don't know if you can say, but from Cloud 3 to Cloud 3 .5, is there any extra pre -training going on or is it mostly focused on the post -training?

There's been leaps in performance.

Yeah, I think at any given stage, we're focused on improving everything at once.

Just naturally, like there are different teams, each team makes progress in a particular area in making their particular segment of the relay race better.

And it's just natural that when we make a new model, we put all of these things in at once.

So the data you have, like the preference data you get from RLHF, is that applicable?

Is there ways to apply it to newer models as it gets trained up?

Yeah, preference data from old models sometimes gets used for new models.

Although, of course, it performs somewhat better when it's trained on the new models.

Note that we have this constitutional AI method such that we don't only use preference data, there's also a post -training process where we train the model against itself.

And there's new types of post -training the model against itself that are used every day.

So it's not just RLHF, it's a bunch of other methods as well.

Post -training, I think, is becoming more and more sophisticated.

Well, what explains the big leap in performance for the new Sonnet 3 .5?

At least in the programming side.

And maybe this is a good place to talk about benchmarks.

What does it mean to get better?

Just the number went up.

But I program but I also love programming and I clawed 3 .5 through cursors what I use to assist me in programming.

And there was at least experientially, anecdotally, it's gotten smarter at programming.

So what does it take to get it smarter?

We observe that as well, by the way.

There were a couple very strong engineers here at Enthropic who all previous code models, both produced by us and produced by all the other companies, hadn't really been useful to them.

They said, maybe this is useful to beginner, it's not useful to me.

But Sonnet 3 .5, the original one for the first time, they said, oh my God, this helped me with something that it would have taken me hours to do.

This is the first model that has actually saved me time.

So again, the water line is rising.

And then I think the new Sonnet has been even better.

In terms of what it takes, I'll just say it's been across the board.

It's in the pre -training, it's in the post -training, it's in various evaluations that we do.

We've observed this as well.

And if we go into the details of the benchmarks, so SWE bench is basically, since you're a programmer, you'll be familiar with pull requests and just pull requests are like a atomic unit of work.

You implement one thing.

And so SWE bench actually gives you kind of a real world situation where the code base is in the current state.

And I'm trying to implement something that's described in language.

We have internal benchmarks where we measure the same thing and you say, just give the model free reign to like, do anything, run anything, edit anything.

How well is it able to complete these tasks?

And it's that benchmark that's gone from it can do it 3 % of the time to it can do it about 50 % of the time.

So I actually do believe that if we get, you can gain benchmarks, but I think if we get to 100 % on that benchmark in a way that isn't kind of like over -trained or game for that particular benchmark, probably represents a real and serious increase in kind of programming ability.

And I would suspect that if we can get to 90, 95 % that it will represent ability to autonomously do a significant fraction of software engineering tasks.

Well, ridiculous timeline question, when is Cloud Opus 3 .5 coming up?

Not giving an exact date, but as far as we know, the plan is still to have a Cloud 3 .5 Opus.

Are we going to get it before GTA 6 or no?

Like Duke Nukem forever?

There was some game that was delayed 15 years.

Was that Duke Nukem forever?

Yeah. And I think GTA is not just releasing trailers.

It's only been three months since we released the first Sonnet.

Yeah, it's incredible pace.

It just tells you about the pace, the expectations for when things are going to come out.

So what about 4 .0? So how do you think about, sort of as these models get bigger and bigger about versioning?

And also just versioning in general, why Sonnet 3 .5 updated with the date?

Why not Sonnet 3 .6, which a lot of people call it?

Naming is actually an interesting challenge here, right?

Because I think a year ago, most of the model was pre -training.

And so you could start from the beginning and just say, okay, we're going to have models of different sizes.

We're going to train them all together.

And we'll have a family of naming schemes and then we'll put some new magic into them.

And then we'll have the next generation.

The trouble starts already when some of them take a lot longer than others to train, right?

That already messes up your time a little bit.

But as you make big improvements in pre -training, then you suddenly notice, oh, I can make better pre -train model.

And that doesn't take very long to do.

But clearly it has the same size and shape of previous models.

So I think those two together, as well as the timing issues, any kind of scheme you come up with, the reality tends to kind of frustrate that scheme, right?

It tends to kind of break out of the scheme.

It's not like software where you can say, oh, this is like 3 .7, this is 3 .8.

No, you have models with different trade -offs.

You can change some things in your models.

You can change other things.

Some are faster and slower in inference.

Some have to be more expensive.

Some have to be less expensive.

And so I think all the companies have struggled with this.

I think we did very, I think we were in a good position in terms of naming when we had Haiku, Sonnet.

It was great. Great start.

And we're trying to maintain it, but it's not perfect.

So we'll try and get back to the simplicity, but just the nature of the field, I feel like no one's figured out naming.

It's somehow a different paradigm from normal software.

And so none of the companies have been perfect at it.

It's something we struggle with surprisingly much relative to how trivial it is for the grand science of training the models.

So from the user side, the user experience of the updated Sonnet 3 .5 is just different than the previous June 2024 Sonnet 3 .5.

It would be nice to come up with some kind of labeling that embodies that because people talk about Sonnet 3 .5, but now there's a different one.

And so how do you refer to the previous one and the new one?

And when there's a distinct improvement, it just makes conversation about it just challenging.

Yeah, I definitely think this question of there are lots of properties of the models that are not reflected in the benchmarks.

I think that's definitely the case and everyone agrees.

And not all of them are capabilities.

Some of them are models can be polite or brusque.

They can be very reactive or they can ask you questions.

They can have what feels like a warm personality or a cold personality.

They can be boring or they can be very distinctive like Golden Gate Claude was.

And we have a whole team kind of focused on, I think we call it Claude character.

Amanda leads that team and we'll talk to you about that.

But it's still a very inexact science.

And often we find that models have properties that we're not aware of.

The fact of the matter is that you can talk to a model 10 ,000 times and there are some behaviors you might not see.

Just like with a human, right?

I can know someone for a few months and not know that they have a certain skill or not know that there's a certain side to them.

And so I think we just have to get used to this idea.

And we're always looking for better ways of testing our models to demonstrate these capabilities and also to decide which are the personality properties we want models to have and which we don't want to have.

That itself, the normative question is also super interesting.

I got to ask you a question from Reddit.

From Reddit. Oh boy.

You know, there's just this fascinating to me, at least it's a psychological social phenomenon where people report that Claude has gotten dumber for them over time.

And so the question is, does the user complaint about the dumbing down of Claude 35 Saunit hold any water?

So are these anecdotal reports a kind of social phenomena or did Claude, is there any cases where Claude would get dumber?

So this actually doesn't apply.

This isn't just about Claude.

I believe I've seen these complaints for every foundation model produced by a major company.

People said this about GPT -4, they said it about GPT -4 turbo.

So a couple things.

One, the actual weights of the model, the actual brain of the model, that does not change unless we introduce a new model.

There are just a number of reasons why it would not make sense practically to be randomly substituting in new versions of the model.

It's difficult from an inference perspective, and it's actually hard to control all the consequences of changing the weights of the model.

Let's say you wanted to fine tune the model to be like, I don't know, to say certainly less, which an old version of Saunit used to do.

You actually end up changing a hundred things as well.

So we have a whole process for it, and we have a whole process for modifying the model.

We do a bunch of testing on it.

We do a bunch of user testing and early customers.

So we both have never changed the weights of the model without telling anyone.

And certainly in the current setup, it would not make sense to do that.

Now there are a couple things that we do occasionally do.

One is sometimes we run A -B tests.

But those are typically very close to when a model is being released and for a very small fraction of time.

So the day before the new Saunit 3 .5, I agree, we should have better name.

It's clunky to refer to it.

There were some comments from people that like, it's gotten a lot better.

And that's because, you know, fraction were exposed to an A -B test for those one or two days.

The other is that occasionally the system prompt will change.

The system prompt can have some effects, although it's unlikely to dumb down models.

It's unlikely to make them dumber.

And we've seen that while these two things, which I'm listing to be very complete happened relatively, happened quite infrequently.

The complaints about, for us and for other model companies about the model change, the model isn't good at this, the model got more censored, the model was dumbed down.

Those complaints are constant.

And so I don't want to say like people are imagining it or anything, but like, the models are for the most part, not changing.

If I were to offer a theory, I think it actually relates to one of the things I said before, which is that models are very complex and have many aspects to them.

And so often, you know, if I asked the model a question, you know, if I'm like, do task acts versus can you do task acts, the model might respond in different ways.

And so there are all kinds of subtle things that you can change about the way you interact with the model that can give you very different results.

To be clear, this itself is like a failing by us and by the other model providers, that the models are just just often sensitive to like small changes in wording.

It's yet another way in which the science of how these models work is very poorly developed.

And so you know, if I go to sleep one night, and I was like talking to the model in a certain way, and I like slightly change the phrasing of how I talk to the model, you know, I could I could get different results.

So that's, that's one possible way.

The other thing is, man, it's just hard to quantify this stuff.

It's hard to quantify this stuff.

I think people are very excited by new models when they come out.

And then as time goes on, they they become very aware of the they become very aware of the limitations.

So that may be another effect.

But that's, that's all a very long -winded way of saying, for the most part, with some fairly narrow exceptions, the models are not changing.

I think there is a psychological effect.

You just are getting used to it.

The baseline raises like, when people have first gotten Wi -Fi on airplanes, it's like amazing.

Yeah. And then now you can't get this thing to work.

This is such a piece of crap.

Exactly. So then it's easy to have the conspiracy theory of they're making Wi -Fi slower and slower.

This is probably something I'll talk to Amanda much more about.

But another Reddit question.

When will Claude stop trying to be my pure, tentacle grandmother imposing its moral worldview on me as a paying customer?

And also, what is the psychology behind making Claude overly apologetic?

So this kind of reports about the experience at different angle and the frustration has to do with the character.

Yeah. So a couple points on this first.

One is like things that people say on Reddit and Twitter or X or whatever it is, there's actually a huge distribution shift between like the stuff that people complain loudly about on social media and what actually kind of like statistically users care about and that drives people to use the models.

People are frustrated with things like the model not writing out all the code or the model just not being as good at code as it could be, even though it's the best model in the world on code.

I think the majority of things are about that.

But certainly a kind of vocal minority raise these concerns, right, are frustrated by the model, refusing things that shouldn't refuse or like apologizing too much, or just just having these kind of like annoying verbal ticks.

The second caveat, and I just want to say this like super clearly, because I think it's like, some people don't know it, others like kind of know it, but forget it.

Like, it is very difficult to control across the board, how the models behave, you cannot just reach in there and say, Oh, I want the model to like, apologize less, like, you can do that, you can include trading data that says like, Oh, the model should like, apologize less.

But then in some other situation, they end up being like super rude or like overconfident in a way that's like misleading people.

So there are all these trade offs.

For example, another thing is if there was a period during which models are and I think others as well were two verbose, right, they would like repeat themselves, they would say too much.

You can cut down on the verbosity by penalizing the models for for just talking for too long.

What happens when you do that, if you do it in a crude way, is when the models are coding, sometimes they'll say rest of the code goes here, right, because they've learned that that's a way to economize and that they see it.

And then, and then so that leads the model to be so called lazy in coding where they where they where they're just like, Oh, you can finish the rest of it.

It's not it's not because we want to you know, save on compute or because, you know, the models are lazy and, you know, during winter break or any of the other kind of conspiracy theories that have that have that have come up, it's actually it's just very hard to control the behavior of the model to

steer the behavior of the model in all circumstances at once, you can kind of there's this this whack -a -mole aspect where you push on one thing, and like, you know, these are these, these, you know, these other things start to move as well that you may not even notice or measure.

And so one of the reasons that I that I care so much about, you know, kind of grand alignment of these AI systems in the future is actually these systems are actually quite unpredictable.

They're actually quite hard to steer in control.

And this version we're seeing today of you make one thing better, it makes another thing worse.

I think that's that's like a present day analog of future control problems in AI systems that we can start to study today.

Right. I think I think that that that difficulty in in steering the behavior and in making sure that if we push an AI system in one direction, it doesn't push it in another direction in some in some other ways that we didn't want.

I think that's that's kind of that's kind of an early sign of things to come.

And if we can do a good job of solving this problem, right, of like, you asked the model to like, you know, to like, make and distribute smallpox, and it says no, but it's willing to like help you in your graduate level virology class, like, how do we get both of those things at once?

It's hard, it's very easy to go to one side or the other.

And it's a multidimensional problem.

And so I, you know, I think these questions of like shaping the model's personality, I think they're very hard.

I think we haven't done perfectly on them.

I think we've actually done the best of all the AI companies, but still so far from perfect.

And I think if we can get this right, if we can control the the, you know, control the false positives and false negatives in this this very kind of controlled present day environment will be much better at doing it for the future.

When our worry is, you know, will the models be super autonomous?

Will they be able to, you know, make very dangerous things?

Will they be able to autonomously, you know, build whole companies?

And are those companies aligned?

So I think of this, this present task as both vexing, but also good practice for the future.

What's the current best way of gathering sort of user feedback?

Like, not anecdotal data, but just large scale data about pain points, or the opposite of pain points, positive things so on.

Is it internal testing?

Is it? Yeah. A specific group testing, A -B testing?

What works? So typically, we'll have internal model bashings where all of entropic, entropic is almost a thousand people, you know, people just just try and break the model, they try and interact with it various ways.

We have a suite of evals for, you know, oh, is the model refusing in ways that it couldn't.

I think we even had a certainly eval because, you know, our model, again, at one point, had this problem where like it had this annoying tick where it would like respond to a wide range of questions by saying, certainly, I can help you with that.

Certainly, I would be happy to do that.

Certainly, this is correct.

And so we had a like certainly eval, which is like how often does the model say certainly?

But look, this is just a whack -a -mole, like, what if it switches from certainly to definitely?

Like, so, you know, every time we add a new eval, and we're always evaluating for all the old things.

So we have hundreds of these evaluations, but we find that there's no substitute for human interacting with it.

And so it's very much like the ordinary product development process.

We have like, hundreds of people within entropic bash the model, then we do, you know, then we do external A -B tests, sometimes we'll run tests with contractors, we pay contractors to interact with the model.

So you put all of these things together, and it's still not perfect, you still see behaviors that you don't quite want to see, right?

You know, you see, you still see the model, like, refusing things that it just doesn't make sense to refuse.

But I think trying to, trying to solve this challenge, right, trying to stop the model from doing, you know, genuinely bad things that, you know, know what everyone agrees it shouldn't do, right?

You know, everyone, everyone, you know, everyone agrees that, you know, the model shouldn't talk about, you know, I don't know, child abuse material, right?

Like, everyone agrees the model shouldn't do that.

But but at the same time that it doesn't refuse in these dumb and stupid ways.

I think I think draw drawing that line, as finally as possible, approaching perfectly is still is still a challenge, and we're getting better at it every day, but there's there's a lot to be solved.

And again, I would point to that as, as an indicator of a challenge ahead in terms of steering much more powerful models.

Do you think Claude 4 .0 is ever coming out?

I don't want to commit to any naming scheme.

Because if I say, if I say here, we're gonna have Claude 4 .0 next year, and then, you know, then we decide that like, you know, we should start over, because there's a new type of model, like, I don't want to, I don't want to commit to it, I would expect in a normal course of business that Claude 4

.0 would come after Claude 3 .5.

But but you know, you never know in this wacky field, right?

But the sort of this idea of scaling is continuing.

Scaling is continuing.

There there will definitely be more powerful models coming from us than the models that exist today.

That is, that is certain or if there if there aren't, we've we've deeply failed as a company.

Okay, can you explain the responsible scaling policy and the AI safety level standards, ASL levels?

As much as I'm excited about the benefits of these models, and we know we'll talk about that if we talk about machines of loving grace, I'm worried about the risks, and I continue to be worried about the risks.

No one should think that, you know, machines of loving grace was me me saying, you know, I'm no longer worried about the risks of these models, I think they're two sides of the same coin, the the power of the models and their ability to solve all these problems in, you know, biology, neuroscience, economic

development, government governance, and peace, large parts of the economy, those those come with risks as well, right?

With great power comes great responsibility, right?

That's the two are the two are paired.

Things that are powerful can do good things, and they can do bad things.

I think of those risks as being in, you know, several different different categories, perhaps the two biggest risks that I think about.

And that's not to say that there aren't risks today that are that are important.

But when I think of the really the, you know, the things that would happen on the grandest scale, one is what I call catastrophic misuse.

These are misuse of the models in domains like cyber, bio, radiological, nuclear, right, things that could, you know, that could harm or even kill thousands, even millions of people, if they really, really go wrong.

Like these are the number one priority to prevent.

And and here, I would just make a simple observation, which is that the models, you know, if I look today at people who have done really bad things in the world, I think actually humanity has been protected by the fact that the overlap between really smart, well educated people and people who want to do

really horrific things has generally been small, like, you know, let's say let's say I'm someone who, you know, I, you know, I have a PhD in this field, I have a well paying job.

There's so much to lose.

Why do I want to like, you know, even assuming I'm completely evil, which which most people are not?

Why, why, you know, why would such a person risk their risk, their, you know, risk their life, risk, risk their, their legacy, their reputation to do something like, you know, truly, truly evil.

If we had a lot more people like that, the world would be a much more dangerous place.

And so my worry is that by being a much more intelligent agent, AI could break that correlation.

And so I do have serious worries about that, I believe we can prevent those worries.

But, you know, I think as a counterpoint to machines of loving grace, I want to say that this is still serious risks.

And the second range of risk would be the autonomy risks, which is the idea that models might on their own, particularly as we give them more agency than they've had in the past, particularly as we give them supervision over wider tasks like, you know, writing whole code bases, or someday even, you

know, effectively operating entire entire companies, they're on a long enough leash.

Are they are they doing what we really want them to do?

It's very difficult to even understand in detail what they're doing, let alone let alone control it.

And like I said, this these early signs that it's, it's hard to perfectly draw the boundary between things the model should do and things the model shouldn't do, that you know, if you go to one side, you get things that are annoying and useless and you go to the other side, you get other behaviors,

if you fix one thing, it creates other problems.

We're getting better and better at solving this.

I don't think this is an unsolvable problem.

I think this is a you know, this is a science like, like the safety of airplanes or the safety of cars or the safety of drugs.

I you know, I don't think there's any big thing we're missing.

I just think we need to get better at controlling these models.

And so these are these are the two risks I'm worried about.

And our responsible scaling plan, which all recognizes a very long winded answer to your question.

I love it. Our responsible scaling plan is designed to address these two types of risks.

And so every time we develop a new model, we basically test it for its ability to do both of these bad things.

So if I were to back up a little bit, I think we have it.

I think we have an interesting dilemma with AI systems where they're not yet powerful enough to present these catastrophes.

I don't know that I don't know they'll ever present prevent these catastrophes.

It's possible they won't.

But the case for worry, the case for risk is strong enough that we should we should act now.

And they're getting better very, very fast.

Right. I, you know, I testified in the Senate that, you know, we might have serious bio risks within two to three years, that was about a year ago, things have proceeded, proceeded apace.

So we have this thing where it's like, it's surprisingly hard to, to address these risks, because they're not here today, they don't exist, they're like ghosts, but they're coming at us so fast because the models are improving so fast.

So how do you deal with something that's not here today doesn't exist, but is coming at us very fast.

So the solution we came up with for that in collaboration with, you know, people like the organization meter and Paul Cristiano is, okay, what you need for that or you need tests to tell you when the risk is getting close, you need an early warning system.

And and so every time we have a new model, we test it for its capability to do these CBRN tasks, as well as testing it for, you know, how capable it is of doing tasks autonomously on its own.

And in the latest version of our RSP, which we released in the last in the last month or two, the way we test autonomy risk is the AI model's ability to do aspects of AI research itself, which when the model and the AI models can do AI research, they become kind of truly, truly autonomous.

And that, you know, that we then do with these tasks, the RSP basically develops what we've called an if then structure, which is if the models pass a certain capability, then we impose a certain set of safety and security requirements on them.

So today's models are what's called ASL to models that were ASL one is for systems that manifestly don't pose any risk of autonomy or misuse.

So for example, a chess plane bot deep blue would be ASL one, it's just manifestly the case that you can't use deep blue for anything other than chess, it was just designed for chess, no one's going to use it to like, you know, to conduct a masterful cyber attack or to, you know, run wild and take over

the world. ASL two is today's AI systems, where we've measured them.

And we think these systems are simply not smart enough to, to, you know, autonomously self replicate or conduct a bunch of tasks, and also not smart enough to provide meaningful information about CBRN risks and how to build CBRN weapons above and beyond what can be known from looking at Google.

In fact, sometimes they do provide information, but but not above and beyond a search engine, but not in a way that can be stitched together.

Not not in a way that kind of end to end is dangerous enough.

So ASL three is going to be the point at which the models are helpful enough to enhance the capabilities of non state actors, right?

State actors can already do a lot a lot of unfortunately to a high level of proficiency, a lot of these very dangerous and destructive things.

The difference is that non state actors are not capable of it.

And so when we get to ASL three, we'll take special security precautions, designed to be sufficient to prevent theft of the model by non state actors, and misuse of the model as it's deployed, we'll have to have enhanced filters targeted at these particular areas, cyber bio nuclear, cyber bio nuclear,

and model autonomy, which is less a misuse risk and more risk of the model doing bad things itself.

ASL four getting to the point where these models could could enhance the capability of a already knowledgeable state actor, and or become the you know, the main source of such a risk.

Like if you wanted to engage in such a risk, the main way you would do it is through a model.

And then I think ASL four on the autonomy side, it's some some amount of acceleration in AI research capabilities with it with an AI model.

And then ASL five is where we would get to the models that are you know, that are that are kind of that are kind of, you know, truly capable that it could exceed humanity in their ability to do to do any of these tasks.

And so the the point of the if then structure commitment is is basically to say, look, I don't know, I've been I've been working with these models for many years, and I've been worried about risk for many years, it's actually kind of dangerous to cry wolf, it's actually kind of dangerous to say this that you

know, this this model is this model is risky.

And you know, people look at it and they say this is manifestly not dangerous.

Again, it's it's it's the the delicacy of the risk isn't here today, but it's coming at us fast.

How do you deal with that?

It's it's really vexing to a risk planner to deal with it.

And so this if then structure basically says, look, we don't want to antagonize a bunch of people.

We don't want to harm our own, you know, our kind of own ability to have a place in the conversation by imposing these, these very onerous burdens on models that are not dangerous today.

So the if then the trigger commitment is basically a way to deal with this says you clamp down hard when you can show that the model is dangerous.

And of course, what has to come with that is, you know, enough of a buffer threshold that you know, you can you can you know, you're you're you're not at high risk of kind of missing the danger.

It's not a perfect framework.

We've had to change it every every, you know, we came out with a new one just a few weeks ago and probably probably going forward, we might release new ones multiple times a year, because it's it's hard to get these policies right, like technically, organizationally, from a research perspective.

But that is the proposal.

If then commitments and triggers in order to minimize burdens and false alarms now, but really react appropriately when the dangers are here.

What do you think the timeline for ASL three is where several of the triggers are fired?

And what do you think the timeline is for ASL four?

Yeah, so that is hotly debated within the company.

We are working actively to prepare ASL three security security measures as well as ASL three deployment measures.

I'm not going to go into detail.

But we've made we've made a lot of progress on both.

And you know, we're, we're prepared to be, I think, ready quite soon.

I would I would not be surprised.

I would not be surprised at all.

If we hit ASL three next year, there was some concern that we might even hit it.

This year, that's still that's still possible that could still happen.

It's like very hard to say but like, I would be very, very surprised if it was like 2030.

I think it's much sooner than that.

So there's a protocols for detecting it.

If then, and then there's protocols for how to respond to it?

Yes. How difficult is the second the latter?

Yeah, I think for ASL three, it's primarily about security.

And and about, you know, filters on the model relating to a very narrow set of areas when we deploy the model because at ASL three, the model isn't autonomous yet.

And so you don't have to worry about, you know, kind of the model itself behaving in a bad way, even when it's deployed internally.

So I think the ASL three measures are I won't say straightforward, they're they're rigorous, but they're easier to reason about.

I think once we get to ASL four, we start to have worries about the models being smart enough that they might sandbag tests, they might not tell the truth about tests.

We had some results came out about like sleeper agents.

And there was a more recent paper about, you know, can the models mislead attempts to, you know, sandbag their own abilities, right?

Show them, you know, present themselves as being less capable than they are.

And so I think with ASL four, there's going to be an important component of using other things than just interacting with the models.

For example, interpretability or hidden chains of thought, where you have to look inside the model and verify via some other mechanism that that is not, you know, is not as easily corrupted as what the model says that that, you know, that that the model indeed has some property.

So we're still working on ASL four, one of the properties of the RSP is that we don't specify ASL four until we've hit ASL three.

And I think that's proven to be a wise decision, because even with ASL three, it again, it's hard to know this stuff in detail.

And we want to take as much time as we can possibly take to get these things right.

So for ASL three, the bad actor will be humans.

And so there's a little bit more.

For ASL four, it's both, I think.

It's both. So deception, and that's where mechanistic interpretability comes into play.

And hopefully the techniques used for that are not made accessible to the model.

Yeah, I mean, of course you can hook up the mechanistic interpretability to the model itself, but then you've kind of lost it as a reliable indicator of the model state.

There are a bunch of exotic ways you can think of that it might also not be reliable.

Like if the model gets smart enough that it can jump computers and read the code where you're looking at its internal state.

We've thought about some of those.

I think they're exotic enough.

There are ways to render them unlikely.

But yeah, generally, you want to preserve mechanistic interpretability as a kind of verification set or test set that's separate from the training process of the model.

See, I think as these models become better and better conversation and become smarter, social engineering becomes a threat too, because they think that could start being very convincing to the engineers inside companies.

Oh, yeah. Yeah, it's actually like, you know, we've seen lots of examples of demagoguery in our life from humans.

And, you know, there's a concern that models could do that could do that as well.

One of the ways that cloud has been getting more and more powerful is it's now able to do some agentic stuff, computer use.

There's also an analysis within the sandbox of cloud .ai itself, but let's talk about computer use.

That seems to me super exciting that you can just give cloud a task and it takes a bunch of actions, figures it out and has access to your computer through screenshots.

So can you explain how that works and where that's headed?

Yeah, it's actually relatively simple.

So cloud has had for a long time since since cloud three back in March, the ability to analyze images and respond to them with text.

The only new thing we added is those images can be screenshots of a computer.

And in response, we train the model to give a location on the screen where you can click and or buttons on the keyboard you can press in order to take action.

And it turns out that with actually not all that much additional training, the models can get quite good at that task.

It's a good example of generalization.

You know, people sometimes say if you get to low Earth orbit, you're like halfway to anywhere, right?

Because of how much it takes to escape the gravity.

Well, if you have a strong pre trained model, I feel like you're halfway to anywhere, in terms of in terms of the intelligence space.

And so actually, it didn't it didn't take all that much to get to get Claude to do this.

And you can just set that in a loop, give the model a screenshot, tell it what to click on, give it the next screenshot, tell it what to click on.

And that turns into a full kind of almost almost 3D video interaction of the model.

And it's able to do all of these tasks, right?

You know, we showed these demos where it's able to like, fill out spreadsheets, it's able to kind of like, interact with a website, it's able to, you know, you know, it's able to open all kinds of, you know, programs, different operating systems, Windows, Linux, Mac.

So, you know, I think all of that is very exciting.

I will say, while in theory, there's nothing you could do there that you couldn't have done through just giving the model the API to drive the computer screen.

This really lowers the barrier.

And, you know, there's there's, there's a lot of folks who, who either, you know, kind of kind of are, are, you know, aren't in a position to, to interact with those API's or takes them a long time to do.

It's just the screen is just a universal interface, that's a lot easier to interact with.

And so I expect, over time, this is going to lower a bunch of barriers.

Now, honestly, the current model has there's, it leaves a lot still to be desired.

And we were, we were honest about that in the blog, right?

It makes mistakes, it misclicks.

And we, you know, we were careful to warn people, hey, this thing isn't, you can't just leave this thing to, you know, run on your computer for minutes and minutes.

You got to give this thing boundaries and guardrails.

And I think that's one of the reasons we released it first in an API form rather than kind of, you know, this, this kind of just, just hand it just hand it to the consumer and give it control of their, of their, of their, of their computer.

But, but, you know, I definitely feel that it's important to get these capabilities out there as models get more powerful, we're going to have to grapple with, you know, how do we use these capabilities safely?

How do we prevent them from being abused?

And, and, you know, I think, I think releasing releasing the model why why while the capabilities are, are, you know, are, are still are still limited is, is very helpful in terms of in terms of doing that.

You know, I think since it's been released, a number of customers, I think, are replant was maybe, was maybe one of the most quickest, quickest, quickest, quickest to deploy things, have, have, you know, have made use of it in various ways.

People have hooked up demos for, you know, Windows, desktops, Macs, you know, Linux, Linux machines.

So yeah, it's been, it's been, it's been very exciting, I think as with as with anything else, you know, it, it comes with new exciting abilities.

And then that, you know, then, then with those new exciting abilities, we have to think about how to, how to, you know, make the model, you know, safe, reliable, do what humans want them to do.

I mean, it's the same, it's the same story for everything, right?

Same thing, it's that same tension.

But, but the possibility of use cases here is just the the range is incredible.

So how much to make it work really well in the future?

How much do you have to specially kind of go beyond what's the pre trained models doing, do more post training, RLHF or supervised fine tuning or synthetic data just for the agent?

Yeah, I think speaking at a high level, it's our intention to keep investing a lot in, you know, making making the model better.

Like I think, I think, you know, we look at look at some of the, you know, some of the benchmarks were previous models were like, Oh, could do it 6 % of the time.

And now our model do it 14 or 22 % of the time.

And yeah, we want to get up to you know, the human level reliability of 89 % just like anywhere else, right?

We're on the same curve that we were on with sweet bench, where I think I would guess a year from now the models can do this very, very reliably, but you got to start somewhere.

So you think it's possible to get to the human level 90 % basically doing the same thing you're doing now?

Or is it has to be special for computer I mean, depends what you mean by by, you know, special and special in general.

But but, you know, I generally think, you know, the same kinds of techniques that we've been using to train the current model, I expect that doubling down on those techniques in the same way that we have for code, for code for models in general, for other kit for, you know, for image input.

You know, for voice, I expect those same techniques will scale here as they have everywhere else.

But this is giving sort of the power of action to Claude.

And so you could do a lot of really powerful things, but you could do a lot of damage also.

Yeah, yeah, no, and we've been very aware of that.

Look, my view actually is computer use isn't a fundamentally new capability like the CBRN or autonomy capabilities are.

It's more like it kind of opens the aperture for the model to use and apply its existing abilities.

And so the way we think about it going back to our RSP is nothing that this model is doing inherently increases, you know, the risk from an RSP perspective.

But as the models get more powerful, having this capability may make it scarier once it, you know, once it has the cognitive capability to, you know, to do something at the ASL3 and ASL4 level, this, you know, this may be the thing that kind of unbounds it from doing so.

So going forward, certainly, this modality of interaction is something we have tested for and that we will continue to test for an RSP going forward.

I think it's probably better to have to learn and explore this capability before the model is super, you know, super capable.

Yeah, there's a lot of interesting attacks like prompt injection, because now you've widened the aperture so you can prompt inject through stuff on screen.

So if this becomes more and more useful, then there's more and more benefit to inject, inject stuff into the model.

If it goes to a certain webpage, it could be harmless stuff like advertisements, or it could be like harmful stuff, right?

Yeah, I mean, we've thought a lot about things like spam, CAPTCHA, you know, mass camp.

There's all you know, every every, like, if one secret I'll tell you, if you've invented a new technology, not necessarily the biggest misuse, but the first misuse, you'll see scams, just petty scams, like you'll just, it's like a thing is old, people scamming each other.

It's this thing as old as time and it's just every time you got to deal with it.

It's almost like silly to say, but it's true sort of bots and spam in general is the thing is it gets more and more intelligent.

Yeah, it's a lot of like, like I said, like there are a lot of petty criminals in the world and and you know, it's like every new technology is like a new way for petty petty criminals to do something you know, something stupid and malicious.

Is there any ideas about sandboxing it?

Like how difficult is the sandboxing task?

Yeah, we sandbox during training.

So for example, during training, we didn't expose the model to the internet.

I think that's probably a bad idea during training because you know, the model can be changing its policy, it can be changing what it's doing, and it's having an effect in the real world.

You know, in terms of actually deploying the model, right, it kind of depends on the application.

Like, you know, sometimes you want the model to do something in the real world.

But of course, you can always put guard, you can always put guard rails on the outside, right, you can say, Okay, well, you know, this model is not going to move data from my, you know, model is not going to move any files from my computer to or my web server to anywhere else.

Now, when you talk about sandboxing, again, when we get to ASL4, none of these precautions are going to make sense there, right, where when you when you talk about ASL4, you're then the model is being kind of you know, you're there's a theoretical worry, the model could be smart enough to break it to

kind of break out of any box.

And so there we need to think about mechanistic interpretability about, you know, if we're if we're going to have a sandbox, it would need to be a mathematically provable sandbox, you know, that's, that's a whole different world than what we're dealing with with the models today.

Yeah, the science of building a box from which ASL4 AI system cannot escape.

I think it's probably not the right approach.

I think the right approach, instead of having something, you know, unaligned that that like, you're trying to prevent it from escaping, I think it's better to just design the model the right way or have a loop where you you know, you look inside, you look inside the model, and you're able to verify

properties. And that gives you an opportunity to like iterate and actually get it right.

I think I think containing containing bad models is much worse solution than having good models.

Let me ask about regulation, what's the role of regulation in keeping AI safe?

So for example, can you describe California AI Regulation Bill SB 1047 that was ultimately vetoed by the governor?

What are the pros and cons of this bill?

Yeah, we ended up making some suggestions to the bill.

And then some of those were adopted.

And you know, we felt I think I think quite positively, quite positively about about the bill.

By the end of that, it did still have some downsides.

Um, and you know, of course, of course, it got vetoed, I think at a high level, I think some of the key ideas behind the bill are, you know, I would say similar to ideas behind our RSPs.

And I think it's very important that some jurisdiction, whether it's California or the federal government and or other other countries and other states, passes some regulation like this.

And I can talk through why I think that's so important.

So I feel good about our RSP, it's not perfect, it needs to be iterated on a lot.

But it's been a good forcing function for getting the company to take these risks seriously to put them into product planning, to really make them a central part of work at entropic and to make sure that all the 1000 people and it's almost 1000 people now at entropic understand that this is one of the highest

priorities of the company, if not the highest priority.

But one, there are there are still some companies that don't have RSP like mechanisms like open AI, Google, did adopt these mechanisms a couple months after, after entropic did.

But there are there are other companies out there that don't have these mechanisms at all.

And so if some companies adopt these mechanisms and others don't, it's really going to create a situation where, you know, some of these dangers have the property that it doesn't matter if three out of five of the companies are being safe, if the other two are being are being unsafe, it creates this negative

externality. And and I think the lack of uniformity is not fair to those of us who have put a lot of effort into being very thoughtful about these procedures.

The second thing is, I don't think you can trust these companies to adhere to these voluntary plans on their own, right?

I like to think that entropic will we do everything we can that we will, our RSP is checked by our long term benefit trust.

So, you know, we do everything we can to, to, to adhere to our own RSP.

But, you know, you hear lots of things about various companies saying, Oh, they said they would do they said they would give this much compute and they didn't, they said they would do this thing and they didn't.

You know, I don't, I don't think it makes sense to, you know, to, to, you know, litigate particular things that companies have done.

But I think this this broad principle that like, if there's nothing watching over them, there's nothing watching over us as an industry, there's no guarantee that we'll do the right thing, and the stakes are very high.

And so I think it's, I think it's important to have a uniform standard that that that that everyone follows, and to make sure that simply that the industry does what a majority of the industry has already said is important, and has already said that they definitely will do.

Right? Some people, you know, I think there's there's a class of people who are against regulation on principle, I understand where that comes from, if you go to Europe, and you know, you see something like GDPR, you see some of the other stuff that that that that that they've done, you know, some of it's

good, but but some of it is really unnecessarily burdensome.

And I think it's fair to say really has slowed really has slowed innovation.

And so I understand where people are coming from on priors, I understand why people come from start from that start from that position.

But but again, I think AI is different, if we go to the very serious risks of autonomy and misuse that that that I talked about, you know, just just a few minutes ago, I think that those are unusual, and they warrant an unusually strong response.

And so I think it's very important, again, we need something that everyone can get behind.

You know, I think one of the issues with SB 1047, especially the original version of it, was it it had a bunch of the structure of RSPs, but it also had a bunch of stuff that was either clunky, or that that that just would have created a bunch of burdens, a bunch of hassle, and might even have missed

the target in terms of addressing the risks.

You don't really hear about it on Twitter, you just hear about kind of, you know, people are people are cheering for any regulation.

And then the folks who are against make up these often quite intellectually dishonest arguments about how, you know, you know, it'll make us move away from California, Bill Bill doesn't apply if you're headquartered in California, Bill only applies if you do business in California, or that it would damage

the open source ecosystem, or that it would, you know, it would cause cause all of these things.

I think those were mostly nonsense, but there are better arguments against regulation.

There's one guy, Dean Ball, who's really, you know, I think a very scholarly, scholarly analyst who looks at what happens when a regulation is put in place in ways that they can kind of get a life of their own, or how they can be poorly designed.

And so our interest has always been, we do think there should be regulation in this space.

But we want to be an actor who makes sure that that regulation is something that's surgical, that's targeted at the serious risks, and is something people can actually comply with.

Because something I think the advocates of regulation don't understand as well as they could, is if we get something in place that is that's poorly targeted, that wastes a bunch of people's time, what's going to happen is people are going to say, see these safety risks, they're you know, this is this is nonsense, I

just you know, I just had to hire 10 lawyers to, you know, to fill out all these forums, I had to run all these tests for something that was clearly not dangerous.

And after six months of that, there will be there will be a ground swell, and we'll, we'll end up with a durable consensus against regulation.

And so the I think the worst enemy of those who want real accountability is badly designed regulation.

We need to actually get it right.

And this is if there's one thing I could say to the advocates, it would be that I want them to understand this dynamic better.

And we need to be really careful.

And we need to talk to people who actually have, who actually have experience, seeing how regulations play out in practice.

And the people who have seen that understand to be very careful.

If this was some lesser issue, I might be against regulation at all.

But what what I want the opponents to understand is is that the underlying issues are actually serious.

They're they're not they're not something that I or the other companies are just making up because of regulatory capture.

They're not doing it.

They're doing it every time we have a new model every few months, we measure the behavior of these models.

And they're getting better and better at these concerning tasks, just as they are getting better and better at, you know, good, valuable, economically useful tasks.

And so I would just love it if some of the former you know, I think SP 1047 was very polarizing, I would love it if some of the most reasonable opponents and some of the most reasonable proponents would sit down together.

And, you know, I think I think that, you know, the different the different AI companies, you know, entropic was the the only AI company that, you know, felt positively in a very detailed way.

I think Elon tweeted, tweeted briefly something positive.

But, you know, some of the some of the big ones like Google, OpenAI, Meta, Microsoft were pretty staunch staunchly against.

So I would really like is if if, you know, some of the key stakeholders, some of the, you know, most thoughtful proponents and some of the most thoughtful opponents would sit down and say, how do we solve this problem in a way that the proponents feel brings a real reduction in risk and that the opponents

feel that it is not it is not hampering the the industry or hampering innovation, any more necessary than it than it than it than it needs to.

And I think for whatever reason, that things got too polarized, and those two groups didn't get to sit down in the way that they should.

And I feel I feel urgency, I really think we need to do something in 2025.

You know, if we get to the end of 2025, and we still done nothing about this, then I'm going to be worried.

I'm not I'm not worried yet, because again, the risks aren't here yet.

But but I think time is running short.

And come up with something surgical, like you said.

Yeah, yeah. Yeah, exactly.

And we need to get we need to get away from this, this, this intense pro safety versus intense anti regulatory rhetoric, right?

It's turned into these, these flame wars on Twitter, and nothing good is going to come with that.

So there's a lot of curiosity about the different players in the game.

One of the, oh, Jesus, open AI, you've had several years of experience at open AI, what's your story and history there?

Yeah, so I was at open AI for for roughly five years.

For the last, I think it was a couple years, you know, I was vice president of research there.

Probably myself and Ilya suits Giver were the ones who, you know, really kind of set the set the research direction.

Around 2016 or 2017, I first started to really believe in or at least confirm my belief in the scaling hypothesis.

When when Ilya famously said to me, the thing you need to understand about these models is they just want to learn the models just want to learn.

And, and, again, sometimes there are these one sentence, there are these one sentences, these Zen cones that you hear them and you're like, ah, that that explains everything that explains like 1000 things that I've seen.

And then I, you know, ever after I had this visualization in my head of like, you optimize the models in the right way, you point the models in the right way, they just want to learn.

They just want to solve the problem regardless of what the problem is.

So get out of their way, basically.

Get out of their way.

Yeah. Don't impose your own ideas about how they should learn.

And you know, this was the same thing as Rich Sutton put out in the bitter lesson or Gurren put out in the scaling hypothesis.

You know, I think generally the dynamic was, you know, I got I got this kind of inspiration from from from from Ilya and from others, folks like Alec Radford, who did the the original GPT -1.

And then ran really hard with it me, me and my collaborators on GPT -2, GPT -3, RL from human feedback, which was an attempt to kind of deal with the early safety and durability, things like debate and amplification, heavy on interpretability.

So, again, the combination of safety plus scaling, probably 2018, 2019, 2020, those those were those were kind of the years when myself and my collaborators probably, you know, many of whom became co -founders of Enthropic kind of really had a vision and like and like drove the direction.

Why'd you leave? Why'd you decide to leave?

Yeah, so look, I'm going to put things this way.

And I you know, I think I think it ties to the to the race to the top.

Right. Which is, you know, in my time at OpenAI, what I come to see is I'd come to appreciate the scaling hypothesis.

And as I come to appreciate kind of the importance of safety along with the scaling hypothesis.

The first one, I think, you know, OpenAI was getting was getting on board with.

The second one in a way had always been part of OpenAI messaging.

But, you know, over over many years of the time the time that I spent there, I think I had a particular vision of how these how we should handle these things, how we should be brought out in the world, the kind of principles that the organization should have.

And look, I mean, there were like many, many discussions about like, you know, should the org do should the company do this?

Should the company do that?

Like, there's a bunch of misinformation out there.

People say like, we left because we didn't like the deal with Microsoft.

False. Although, you know, there was like a lot of discussion, a lot of questions about exactly how we do the deal with Microsoft.

We left because we didn't like commercialization.

That's not true. We built GPT -3, which was the model that was commercialized.

I was involved in commercialization.

It's it's more, again, about how do you do it?

Like, civilization is going down this path to very powerful AI.

What's the way to do it that is cautious, straightforward, honest, that builds trust in the organization and in individuals?

How do we get from here to there?

And how do we have a real vision for how to get it right?

How can safety not just be something we say because it helps with recruiting?

And, you know, I think I think at the end of the day, if you have a vision for that, forget about anyone else's vision.

I don't want to talk about anyone else's vision.

If you have a vision for how to do it, you should go off and you should do that vision.

It is incredibly unproductive to try and argue with someone else's vision.

You might think they're not doing it the right way.

You might think they're dishonest.

Who knows? Maybe you're right.

Maybe you're not. But what you should do is you should take some people you trust and you should go off together and you should make your vision happen.

And if your vision is compelling, if you can make it appeal to people some, you know, some combination of ethically, you know, in the markets, you know, if you can if you can make a company that's a place people want to join, that, you know, engages in practices that people think are reasonable while

managing to maintain its position in the ecosystem at the same time, if you do that, people will copy it.

And the fact that you are doing it, especially the fact that you're doing it better than they are, causes them to change their behavior in a much more compelling way than if they're your boss and you're arguing with them.

I just I don't know how to be any more specific about it than that.

But I think it's generally very unproductive to try and get someone else's vision to look like your vision.

It's much more productive to go off and do a clean experiment and say, This is our vision.

This is how this is this is how we're going to do things.

Your choice is you can you can ignore us, you can reject what we're doing, or you can you can start to become more like us.

And imitation is the sincerest form of flattery.

And, you know, that that that plays out in the behavior of customers that pays out in the behavior of the public, that plays out in the behavior of where people choose to work.

And again, again, at the end, it's it's not about one company winning or another company winning.

If if we or another company are engaging in some practice that, you know, people people find genuinely appealing and I want it to be in substance not just not just in appearance.

And, you know, I think I think researchers are sophisticated and they look at substance.

And then other companies start copying that practice.

And they win because they copied that practice.

That's great. That's success.

That's like the race to the top.

It doesn't matter who wins in the end, as long as everyone is copying everyone else's good practices, right?

One way I think of it is like, the thing we're all afraid of is the race to the bottom, right?

And the race to the bottom doesn't matter who wins because we all lose, right?

Like, you know, in the most extreme world, we we make this autonomous AI that you know, the robots enslave us or whatever, right?

I mean, that's half joking.

But you know, that that is the most extreme thing that could happen.

Then it doesn't matter which company was ahead.

If instead you create a race to the top where people are competing to engage in good practices, then, you know, at the end of the day, you know, it doesn't matter who ends up winning, it doesn't even matter who started the race to the top.

The point isn't to be virtuous.

The point is to get the system into a better equilibrium than it was before.

And individual companies can play some role in doing this individual companies can, can, you know, can help to start it can help to accelerate it.

And frankly, I think individuals at other companies have done this as well, right?

The individuals that when we put out an RSP react by pushing harder to get something similar done, get something similar done at other companies.

Sometimes other companies do something that's like, we're like, Oh, it's a good practice.

We think, we think that's good, we should adopt it too.

The only difference is, you know, I think I think we are, we try to be more forward leaning, we try and adopt more of these practices first, and adopt them more quickly when others when others invent them.

But I think this dynamic is what we should be pointing at.

And that I think I think it abstracts away the question of, you know, which company is winning?

Who trusts who? I think all these all these questions of drama are, are profoundly uninteresting.

And and the thing that matters is the ecosystem that we all operate in, and how to make that ecosystem better, because that constrains all the players.

And so anthropic is this kind of clean experiment built on a foundation of like what concretely as at should look like, we're look, I'm sure we've made plenty of mistakes along the way, the perfect organization doesn't exist, it has to deal with the the imperfection of 1000 employees, it has to deal

with the imperfection of our leaders, including me, it has to deal with the imperfection of the people we've put, we've put to you know, to oversee the imperfection of the leaders like the like the board and the long term benefit trust, it's it's all it's all a set of imperfect people trying to aim

imperfectly at some ideal that will never perfectly be achieved.

That's what you sign up for.

That's what it will always be.

But imperfect doesn't mean you just give up, there's better and there's worse.

And hopefully, hopefully we can begin to build we can do well enough that we can begin to build some practices that the whole industry engages in.

And then you know, my guess is that multiple of these companies will be successful.

And Tropic will be successful.

These other companies like once I've been at the past, will also be successful.

And some will be more successful than others.

That's less important than again, that we align the incentives of the industry.

And that happens partly through the race to the top, partly through things like RSP, partly through again, selected surgical regulation.

You said talent density beats talent mass.

So can you explain that?

Can you expand on it?

Can you just talk about what it takes to build a great team of AI researchers and engineers?

This is one of these statements that's like more true every every every month, every month, I see the statement is more true than I did the month before.

So if I were to do a thought experiment, let's say you have a team of 100 people that are super smart, motivated and aligned with the mission.

And that's your company, or you can have a team of 1000 people where 200 people are super smart, super aligned with the mission.

And then like, and then like 800 people are let's just say you pick 800 like random, random big tech employees, which would you rather have, right?

The talent mass is greater in the group of in the group of 1000 people, right?

You have you have even even a larger number of incredibly talented, incredibly aligned, incredibly smart people.

But but the issue is just that if every time someone super talented looks around, they see someone else super talented and super dedicated, that sets the tone for everything, right?

That sets the tone for everyone is super inspired to work at the same place.

Everyone trusts everyone else.

If you have 1000 or 10 ,000 people, and things have really regressed, right, you are not able to do selection and you're choosing random people, what happens is then you need to put a lot of processes and a lot of guardrails in place.

Just because people don't fully trust each other, you have to adjudicate political battles, like there are so many things that slow down the orgs ability to operate.

And so we're nearly 1000 people.

And you know, we've we've, we've tried to make it so that as large a fraction of those 1000 people as possible are like, super talented, super skilled.

It's one of the reasons we've, we've slowed down hiring a lot in the last few months, we grew from 300 to 800, I believe, I think in the first seven, eight months of the year.

And now we've slowed down, we're at like, you know, the last three months, we went from 800 to 900, 950, something like that.

Don't quote me on the exact numbers.

But I think there's an inflection point around 1000.

And we want to be much more careful how we how we grow early on.

And now as well, you know, we've hired a lot of physicists, you know, theoretical physicists can learn things really fast.

Even even more recently, as we've continued to hire that, you know, we've really had a high bar for on both the research side and the software engineering side have hired a lot of senior people, including folks who used to be at other at other companies in this space.

And we've just continued to be very selective, it's very easy to go from 100 to 1000, and 1000 to 10 ,000, without paying attention to making sure everyone has a unified purpose.

It's so powerful. If your company consists of a lot of different fiefdoms that all want to do their own thing, they're all optimizing for their own thing.

It's very hard to get anything done.

But if everyone sees the broader purpose of the company, if there's trust, and there's dedication to doing the right thing, that is a superpower that in itself, I think can overcome almost every other disadvantage.

And you know, it's the Steve Jobs, eight players, eight players want to look around and see other eight players is another way of saying I don't know what that is about human nature, but it is demotivating to see people who are not obsessively driving towards a singular mission.

And it is on the flip side of that super motivating to see that.

It's interesting. What's it take to be a great AI researcher or engineer from everything you've seen from working with so many amazing people?

Yeah. I think the number one quality, especially on the research side, but really both, is open mindedness.

Sounds easy to be open minded, right?

You're just like, Oh, I'm open to anything.

But, you know, if I if I think about my own early history in the scaling hypothesis, I was seeing the same data others were seeing.

I don't think I was like a better programmer or better at coming up with research ideas than any of the hundreds of people that I worked with.

In some ways, in some ways, I was worse.

You know, like I've never like, you know, precise programming of like, you know, finding the bug writing the GPU kernels, like, I could point you to a hundred people here who are better at that than I am.

But the thing that I think I did have that was different was that I was just willing to look at something with new eyes, right?

People said, Oh, you know, we don't have the right algorithms yet.

We haven't come up with the right way to do things.

And I was just like, Oh, I don't know.

Like, you know, this neural net has like 30 billion, 30 million parameters.

Like, what if we gave it 50 million instead?

Like, let's plot some graphs like that, that basic scientific mindset of like, oh, man, like, I just I just like, I, you know, I see some variable that I could change.

Like, what happens when it changes?

Like, let's let's try these different things and like create a graph for even this, this was like the simplest thing in the world, right?

Change the number of, you know, this wasn't like PhD level experimental design.

This was like, this was like, simple and stupid, like, anyone could have done this if you if you just told them that it was important.

It's also not hard to understand, you didn't need to be brilliant to come up with this.

But you put the two things together.

And, you know, some tiny number of people, some single digit number of people have driven forward the whole field by realizing this.

And, you know, it's often like that.

If you look back at the discovery, you know, the discoveries in history, they're, they're often like that.

And so this this open mindedness and this willingness to see with new eyes, that often comes from being newer to the field, often experience is a disadvantage for this.

That is the most important thing.

It's very hard to look for and test for.

But I think I think it's the most important thing, because when you when you find something, some really new way of thinking, thinking about things, when you have the initiative to do that, it's absolutely transformative.

And also be able to do kind of rapid experimentation.

And in the face of that, be open minded and curious and looking at the data for these fresh eyes and see what is that's actually saying that applies in mechanistic interpretability.

It's another example of this, like some of the early work in mechanistic interpretability, so simple, it's just no one thought to care about this question before.

You said what it takes to be a great AI researcher.

Can we rewind the clock back?

What advice would you give to people interested in AI?

They're young, looking forward, how can I make any back in the world?

I think my number one piece of advice is to just start playing with the models.

This was actually, I worry a little this seems like obvious advice.

Now, I think three years ago, it wasn't obvious.

And people started by Oh, let me read the latest reinforcement learning paper.

Let me, you know, let me let me kind of know, I mean, that was really the that was really the I mean, you should do that as well.

But now, you know, with wider availability of models and API's people are doing this more.

But I think, I think just experiential knowledge, these models are new artifacts that no one really understands.

And so getting experience playing with them.

I would also say, again, in line with the like, do something new, think in some new direction.

Like, there are all these things that haven't been explored, like, for example, mechanistic interpretability is still very new.

It's probably better to work on that than it is to work on new model architectures.

Because it's, you know, it's more popular than it was before, there are probably like 100 people working on it, but there aren't like 10 ,000 people working on it.

And it's it's this, this fertile area for study like, like, you know, it's, there's there's so much like low hanging fruit, you can just walk by and you know, you can just walk by and you can pick things.

And and the only reason for whatever reason, people aren't people aren't interested enough.

I think there are some things around long, long horizon learning and long horizon tasks, where there's a lot to be done.

I think evaluations are still we're still very early in our ability to study evaluations, particularly for dynamic systems acting in the world.

I think there's some stuff around multi agent.

Skate where the puck is going is my is my advice.

And you don't have to be brilliant to think of it like all the things that are going to be exciting in five years, like, in people even mentioned them as like, you know, conventional wisdom, but like, it's it's just somehow there's this barrier that people don't people don't double down as much as they

could, or they're afraid to do something that's not the popular thing.

I don't know why it happens.

But like getting over that barrier is the that's my number one piece of advice.

Let's talk if it could a bit about post training.

Yeah, so it seems that the modern post training recipe has a little bit of everything.

So supervised fine tuning, RLHF, the the constitutional AI with RL, AIF, best acronym, it's again, that naming thing.

And then synthetic data seems like a lot of synthetic data, or at least trying to figure out ways to have high quality synthetic data.

So what's the if this is a secret sauce that makes anthropic clause so incredible?

What, how much of the magic is in the pre training?

How much is in the post training?

Yeah, I mean, so first of all, we're not perfectly able to measure that ourselves.

You know, when you see some, some great character ability, sometimes it's hard to tell whether it came from pre training or post training, we developed ways to try and distinguish between those two, but they're not perfect.

You know, the second thing I would say is, you know, it's when there is an advantage, and I think we've been pretty good at in general, in general at RL, perhaps, perhaps the best, although, although I don't know, because I don't see what goes on inside other companies.

Usually, it isn't Oh, my God, we have the secret magic method that others don't have, right?

Usually, it's like, well, you know, we got better at the infrastructure, so we could run it for longer.

Or, you know, we were able to get higher quality data, or we were able to filter our data better, or we able to, you know, combine these methods in practice.

It's usually some boring matter of matter of kind of practice and tradecraft.

So, you know, when I think about how to do something special in terms of how we train these models, both pre training, but even more so post training, you know, I really think of it a little more, again, as like designing airplanes or cars, like, you know, it's not just like, Oh, man, I have the blueprint,

like, maybe that makes you make the next airplane, but like, there's some, there's some cultural tradecraft of how we think about the design process that I think is more important than then, you know, than any particular gizmo we're able to invent.

Okay, well, about let me ask about specific techniques.

So first on RLHF, what do you think, just zooming out intuition, almost philosophy, why do you think RLHF works so well?

If I go back to like the scaling hypothesis, one of the ways to skate the scaling hypothesis is if you train for x, and you throw enough compute at it, then you get x.

And so RLHF is good at doing what humans want the model to do, or at least, to state it more precisely, doing what humans who look at the model for a brief period of time and consider different possible responses, what they prefer as the response, which is not perfect from both the safety and capabilities

perspective, in that humans are often not able to perfectly identify what the model wants and what humans want in the moment may not be what they want in the long term.

So there's, there's a lot of subtlety there.

But the models are good at, you know, producing what the humans in some shallow sense want.

And it actually turns out that you don't even have to throw that much compute at it, because of another thing, which is this this thing about a strong pre trained model being halfway to anywhere.

So once you have the pre trained model, you have all the representations you need to get the model to get the model where you where you want it to go.

So do you think RLHF makes the models smarter or just appear smarter to the humans?

I don't think it makes the model smarter.

I don't think it just makes the model appear smarter.

It's like RLHF, like bridges the gap between the human and the model, right?

I could have something really smart that like can't communicate at all, right?

We all know people like this, people who are really smart, but you know, you can't understand what they're saying.

So I think I think RLHF just bridges that gap.

I think it's not it's not the only kind of RL we do.

It's not the only kind of RL that will happen in the future.

I think RL has the potential to make models smarter, to make them reason better, to make them operate better, to make them develop new skills even.

And perhaps that could be done, you know, even in some cases with human feedback, but the kind of RLHF we do today mostly doesn't do that yet, although we're very quickly starting to be able to.

But it appears to sort of increase if you look at the metric of helpfulness, it increases that.

It also increases, what was this this word in Leopold's essay, unhobbling, where basically the models are hobbled, and then you do various trainings to them to unhobble them.

So I like that word, because it's like a rare word.

So I think RLHF unhobbles the models in some ways.

And then there are other ways where model hasn't yet been unhobbled and, you know, needs to needs to unhobble.

If you can say, in terms of costs, is pre -training the most expensive thing or is post -training creep up to that?

At the present moment, it is still the case that pre -training is the majority of the cost.

I don't know what to expect in the future, but I could certainly anticipate a future where post -training is the majority of the cost.

In that future you anticipate would it be the humans or the AI that's the costly thing for post -training?

I don't think you can scale up humans enough to get high quality.

Any kind of method that relies on humans and uses a large amount of compute, it's going to have to rely on some scaled supervision method, like debate or iterated amplification or something like that.

So on that super interesting set of ideas around constitutional AI, can you describe what it is?

As first detailed in December 2020?

Yes. Two paper and beyond that, what is it?

Yes. So this was from two years ago.

The basic idea is, so we describe what RLHF is.

You have a model and it, you know, spits out two, you know, like you just sample from it twice, it spits out two possible responses and you're like human, which response do you like better?

Or another variant of it is rate this response on a scale of one to seven.

So that's hard because you need to scale up human interaction and it's very implicit, right?

I don't have a sense of what I want the model to do.

I just have a sense of like what this average of a thousand humans wants the model to do.

So two ideas. One is, could the AI system itself decide which response is better, right?

Could you show the AI system these two responses and ask which response is better?

And then second, well, what criterion should the AI use?

And so then there's this idea, could you have a single document, a constitution, if you will, that says, these are the principles the model should be using to respond and the AI system reads those, it reads those principles as well as reading the environment and the response.

And it says, well, how good did the AI model do?

It's basically a form of self -play.

You're kind of training the model against itself.

And so the AI gives the response and then you feed that back into what's called the preference model, which in turn feeds the model to make it better.

So you have this triangle of like the AI, the preference model and the improvement of the AI itself.

And we should say that in the constitution, the set of principles are like human interpretable.

They're like, yeah, yeah, it's something both the human and the AI system can read.

So it has this nice kind of translatability or symmetry.

In practice, we both use a model constitution and we use RLHF and we use some of these other methods.

So it's turned into one tool in a toolkit that both reduces the need for RLHF and increases the value we get from using each data point of RLHF.

It also interacts in interesting ways with kind of future reasoning type RL methods.

So it's one tool in the toolkit, but I think it is a very important tool.

Well, it's a compelling one to us humans, you know, thinking about the founding fathers and the founding of the United States.

The natural question is who and how do you think it gets to define the constitution, the set of principles in the constitution?

Yeah. So I'll give like a practical answer and a more abstract answer.

I think the practical answer is like, look, in practice, models get used by all kinds of different like customers, right?

And so you can have this idea where, you know, the model can have specialized rules or principles, you know, we fine tune versions of models, implicitly, we've talked about doing it explicitly, having special principles that people can build into the models.

So from a practical perspective, the answer can be very different from different people.

You know, customer service agents, you know, behaves very differently from a lawyer and obeys different principles.

But I think at the base of it, there are specific principles that models, you know, have to obey.

I think a lot of them are things that people would agree with.

Everyone agrees that, you know, we don't, you know, we don't want models to present these CBRN risks.

I think we can go a little further and agree with some basic principles of democracy and the rule of law.

Beyond that, it gets, you know, very uncertain.

And there, our goal is generally for the models to be more neutral, to not espouse a particular point of view and, you know, more just be kind of like wise agents or advisors that will help you think things through and will, you know, present possible considerations but, you know, don't express, you

know, strong or specific opinions.

OpenAI released a model spec where it kind of clearly concretely defines some of the goals of the model and specific examples like AB, how the model should behave.

Do you find that interesting?

By the way, I should mention the I believe the brilliant John Schumann was a part of that.

He's now at anthropic.

Do you think this is a useful direction?

Might anthropic release a model spec as well?

Yeah, so I think that's a pretty useful direction.

Again, it has a lot in common with constitutional AI.

So again, another example of like a race to the top, right?

We have something that's like we think, you know, a better and more responsible way of doing things.

It's also a competitive advantage.

Then others kind of, you know, discover that it has advantages and then start to do that thing.

We then no longer have the competitive advantage.

But it's good from the perspective that now everyone has adopted a positive practice that others were not adopting.

And so our response to that as well, looks like we need a new competitive advantage in order to keep driving this race upwards.

So that's how I generally feel about that.

I also think every implementation of these things is different.

So, you know, there are some things in the model spec that were not in constitutional AI.

And so, you know, we can always adopt those things or, you know, at least learn from them.

So again, I think this is an example of like the positive dynamic that I think we should all want the field to have.

Let's talk about the incredible essay, Machines of Love and Grace.

I recommend everybody read it.

It's a long one. It is rather long.

Yeah. It's really refreshing to read concrete ideas about what a positive future looks like.

And you took sort of a bold stance because like, it's very possible that you might be wrong on the dates or the specific applications.

Yeah, I'm fully expecting to, you know, to definitely be wrong about all the details.

I might be just spectacularly wrong about the whole thing and people will, you know, the future works.

So you provided a bunch of concrete positive impacts of AI and how, you know, exactly a super intelligent AI might accelerate the rate of breakthroughs.

And for example, biology and chemistry that would then lead to things like we cure most cancers, prevent all infectious disease, double the human lifespan, and so on.

So let's talk about this essay first.

Can you give a high level vision of this essay and what key takeaways that people have?

Yeah, I have spent a lot of time and anthropic has spent a lot of effort on like, you know, how do we address the risks of AI, right?

How do we think about those risks?

Like, we're trying to do a race to the top, you know, what that requires us to build all these capabilities and the capabilities are cool.

But, you know, we're like a big part of what we're trying to do is like address the risks.

And then justification for that is like, well, you know, all these positive things, you know, the market is this very healthy organism, right, it's going to produce all the positive things.

The risks, I don't know, we might mitigate them, we might not.

And so we can have more impact by trying to mitigate the risks.

But I noticed that one flaw in that way of thinking, and it's not a change in how seriously I take the risks, it's maybe a change in how I talk about them, is that, you know, no matter how kind of logical or rational that line of reasoning that I just gave might be, if you kind of only talk about risks,

your brain only thinks about risks.

And so I think it's actually very important to understand, what if things do go well?

And the whole reason we're trying to prevent these risks is not because we're afraid of technology, not because we want to slow it down.

It's because if we can get to the other side of these risks, right, if we can run the gauntlet successfully, to, you know, to put it in stark terms, then on the other side of the gauntlet are all these great things.

And these things are worth fighting for.

And these things can really inspire people.

And I think I imagine because, look, you have all these investors, all these VCs, all these AI companies, talking about all the positive benefits of AI.

But as you point out, it's weird.

There's actually a dearth of really getting specific about it.

There's a lot of like, random people on Twitter like posting these kind of like gleaming cities and this just kind of like vibe of like, grind, accelerate harder, like kick out the D cell.

You know, it's just this very aggressive ideological.

But then you're like, what are you, what are you actually excited about?

And so, and so I figured that, you know, I think it would be interesting and valuable for someone who's actually coming from the risk side to try and to try and really make a try at explaining, explaining, explain what the benefits are, both because I think it's something we can all get behind.

And I want people to understand, I want them to really understand that this isn't doomers versus accelerationists.

This is that if you have a true understanding of where things are going with AI and maybe that's the more important axis, AI is moving fast versus AI is not moving fast, then you really appreciate the benefits and you really, you want humanity, our civilization to seize those benefits, but you also get

very serious about anything that could derail them.

So, I think the starting point is to talk about what this powerful AI, which is the term you like to use, most of the world uses AGI, but you don't like the term because it's basically has too much baggage, it's become meaningless.

It's like, we're stuck with the terms, whether we like them or not.

Maybe we're stuck with the terms and my efforts to change them are futile.

It's admirable. I'll tell you what else I don't, this is like a pointless semantic point, but I keep talking about it in a couple of minutes, so I'm just going to do it once more.

I think it's a little like, let's say it was 1995 and Moore's law is making the computers faster.

For some reason, there had been this verbal tick that everyone was like, well, someday we're going to have supercomputers and supercomputers are going to be able to do all these things that once we have supercomputers, we'll be able to sequence the genome, we'll be able to do other things.

One, it's true, the computers are getting faster and as they get faster, they're going to be able to do all these great things.

But there's no discrete point at which you had a supercomputer in previous computers were not to, supercomputer is a term we use, but it's a vague term to just describe computers that are faster than what we have today.

There's no point at which you pass a threshold and you're like, oh my God, we're doing a totally new type of computation and so I feel that way about AGI.

There's just a smooth exponential and if by AGI, you mean like AI is getting better and better and gradually it's going to do more and more of what humans do until it's going to be smarter even from there, then yes, I believe in AGI.

But if AGI is some discrete or separate thing, which is the way people often talk about it, then it's kind of a meaningless buzzword.

Yeah, to me, it's just sort of a platonic form of a powerful AI, exactly how you define it.

I mean, you define it very nicely.

So on the intelligence axis, it's just on pure intelligence, it's smarter than a Nobel prize winner, as you describe, across most relevant disciplines.

So okay, that's just intelligence.

So it's both in creativity and be able to generate new ideas, all that kind of stuff in every discipline, Nobel prize winner, okay, in their prime.

It can use every modality, so this kind of self -explanatory, but just operate across all the modalities of the world.

It can go off for many hours, days and weeks to do tasks and do its own sort of detailed planning and only ask you help when it's needed.

This is actually kind of interesting.

I think in the essay you said, I mean, again, it's a bet that it's not going to be embodied, but it can control embodied tools.

So it can control tools, robots, laboratory equipment.

The resource used to train it can then be repurposed to run millions of copies of it.

And each of those copies will be independent that can do their own independent work.

So you can do the cloning of the intelligence system.

Yeah. I mean, you might imagine from outside the field, there's only one of these, right?

Like you've made it, you've only made one, but the truth is that the scale up is very quick.

We do this today, we make a model and then we deploy thousands, maybe tens of thousands of instances of it.

I think by the time, certainly within two to three years, whether we have these super powerful AIs or not, clusters are going to get to the size where you'll be able to deploy millions of these and they'll be faster than humans.

And so if your picture is, oh, we'll have one and it'll take a while to make them, my point there was no, actually you have millions of them right away.

And in general, they can learn and act 10 to 100 times faster than humans.

So that's a really nice definition of powerful AI.

Okay, so that, but you also write that clearly such an entity would be capable of solving very difficult problems very fast, but it is not trivial to figure out how fast.

Two extreme positions both seem false to me.

So the singularity is on the one extreme and the opposite on the other extreme.

Can you describe each of the extremes?

Yeah, so yeah, let's describe the extreme.

So like one extreme would be, well, look, if we look at kind of evolutionary history, like there was this big acceleration where for hundreds of thousands of years, we just had like single celled organisms and then we had mammals and then we had apes.

And then that quickly turned to humans, humans quickly built industrial civilization.

And so this is going to keep speeding up.

And there's no ceiling at the human level.

Once models get much, much smarter than humans, they'll get really good at building the next models.

And, you know, if you write down like a simple differential equation, like this is an exponential.

And so what's, what's going to happen is that models will build faster models, models will build faster models.

And those models will build, you know, nanobots that can like take over the world and produce much more energy than you could produce otherwise.

And so if you just kind of like solve this abstract differential equation, then like five days after we, you know, we build the first AI that's more powerful than humans, then, then, you know, like the world will be filled with these AIs and every possible technology that could be invented, like will be

invented. I'm caricaturing this a little bit.

But, you know, I think that's one extreme.

And the reason that I think that's not the case is that one, I think they just neglect like the laws of physics, like it's only possible to do things so fast in the physical world, like some of those loops go through, you know, producing faster hardware takes a long time to produce faster hardware, things

take a long time. There's this issue of complexity, like, I think no matter how smart you are, like, you know, people talk about, Oh, we can make models of biological systems, it'll do everything to biological systems.

Look, I think computational modeling can do a lot.

I did a lot of computational modeling when I worked in biology.

But like, just there are a lot of things that you can't predict how they're, you know, they're, they're complex enough that like, just iterating, just running the experiment is going to beat any modeling, no matter how smart the system doing the modeling is.

Well, even if it's not interacting with the physical world, just the modeling is going to be hard.

Yeah, I think, well, the modeling is going to be hard in getting the model to match the physical world is going to be hard.

All right. So he does have to interact with the physical world.

Yeah. Yeah. Yeah. But it's just, you know, you just look at even the simplest problems, like, you know, I think I talk about like, you know, the three body problem or simple chaotic prediction, like, you know, or like predicting the economy, it's really hard to predict the economy two years out, like,

maybe the case is like, you know, normal, you know, humans can predict what's going to happen in the economy next quarter, or they can't really do that.

Maybe maybe a AI system that's, you know, a zillion times smarter, it can only predict it out a year or something instead of instead of, you know, you have these kind of exponential increase in computer intelligence for linear increase in inability to predict.

Same with, again, like, you know, biological molecules, molecules interacting, you don't know what's going to happen when you perturb when you perturb a complex system, you can find simple parts in it, if you're smarter, you're better at finding these simple parts.

And then I think you know, human institutions, human institutions are just are really difficult, like, it's, you know, it's, it's been hard to get people, I won't give specific examples, but it's been hard to get people to adopt, even the technologies that we've developed, even ones where the case for their

efficacy is very, very strong.

You know, people have concerns, they think things are conspiracy theories, like it's, it's just been, it's been very difficult.

It's also been very difficult to get, you know, very simple things through the regulatory system, right, I think, you know, and, you know, I don't want to disparage anyone who, you know, you know, works in regulatory regulatory systems of any technology, there are hard trade offs, they have to deal

with, they have to save lives, but but the system as a whole, I think, makes some obvious trade offs that are very far from maximizing human welfare.

And so, if we bring AI systems into this, you know, into these human systems, often, the level of intelligence may just not be the limiting factor, right?

It just may be that it takes a long time to do something.

Now, if the AI system circumvented all governments, if it just said, I'm dictator of the world, and I'm going to do whatever, some of these things it could do, again, the things have to do with complexity, I still think a lot of things would take a while, I don't think it helps that the AI systems can produce

a lot of energy or go to the moon, like, some people in comments responded to the essay saying the AI system can produce a lot of energy and smarter AI systems, that's missing the point, that kind of cycle doesn't solve the key problems that I'm talking about here.

So I think I think a bunch of people missed the point there.

But even if it were completely on the line, and you know, could get around all these human obstacles, it would have trouble.

But again, if you want this to be an AI system that doesn't take over the world, that doesn't destroy humanity, then basically, you know, it's going to need to follow basic human laws, right?

If we want to have an actually good world, like, we're going to have to have an AI system that interacts with humans, not one that kind of creates its own legal system or disregards all the laws or all of that.

So as inefficient as these processes are, you know, we're going to have to deal with them because there needs to be some popular and democratic legitimacy in how these systems are rolled out.

We can't have a small group of people who are developing these systems say this is what's best for everyone, right?

I think it's wrong.

And I think in practice, it's not going to work anyway.

So you put all those things together, and, you know, we're not we're not gonna, we're not gonna, you know, change the world and upload everyone in five minutes.

I just I don't think I a I don't think it's gonna happen.

And be to some, you know, to the extent that it could happen.

It's not the way to lead to a good world.

So that's on one side.

On the other side, there's another set of perspectives, which I have actually in some ways more sympathy for, which is, look, we've seen big productivity increases before, right?

You know, economists are familiar with studying the productivity increases that came from the computer revolution and internet revolution.

And generally those productivity increases were underwhelming.

They were less than you than you might imagine.

There was a quote from Robert Solo, you see the computer revolution everywhere except the productivity statistics.

So why is this the case?

People point to the structure of firms, the structure of enterprises, how, you know, how slow it's been to roll out or existing technology to very poor parts of the world, which I talk about in the essay, right?

How do we get these technologies to the poorest parts of the world that are behind on cell phone technology, computers, medicine, let alone, you know, newfangled AI that hasn't been invented yet.

So you could have a perspective that's like, well, this is amazing, technically, but it's all a nothing burger.

You know, I think, Tyler Cowen, who wrote something response to my essay has that perspective, I think he thinks the radical change will happen eventually, but he thinks it'll take 50 or a hundred years and you could have even more static perspectives on the whole thing.

I think there's some truth to it.

I think the timescale is just too long and I can see it.

I can actually see both sides with today's AI.

So, you know, a lot of our customers are large enterprises who are used to doing things a certain way.

I've also seen it in talking to governments, right?

Those are prototypical, you know, institutions, entities that are slow to change.

But the dynamic I see over and over again is, yes, it takes a long time to move the ship.

Yes, there's a lot of resistance and lack of understanding.

But the thing that makes me feel that progress will in the end happen moderately fast, not incredibly fast, but moderately fast is that you talk to what I find is I find over and over again, again, in large companies, even in governments, which have been actually surprisingly forward leaning, you find

two things that move things forward.

One, you find a small fraction of people within a company, within a government, who really see the big picture, who see the whole scaling hypothesis, who understand where AI is going, or at least understand where it's going within their industry.

And there are a few people like that within the current US government, who really see the whole picture.

And those people see that this is the most important thing in the world until they agitate for it.

And the thing that they alone are not enough to succeed because they're a small set of people within a large organization.

But as the technology starts to roll out, as it succeeds in some places in the folks who are most willing to adopt it, the specter of competition gives them a wind at their backs, because they can point within their large organization, they can say, Look, these other guys are doing this, right?

You know, one bank can say, Look, this newfangled hedge fund is doing this thing, they're going to eat our lunch.

In the US, we can say we're afraid China is going to get there before we are.

And that combination, the specter of competition, plus a few visionaries within these, within these the organizations that in many ways are are sclerotic, you put those two things together, and it actually makes something happen.

I mean, it's interesting, it's a balanced fight between the two because inertia is very powerful.

But but but eventually over enough time, the innovative approach breaks through.

And I've seen that happen.

I've seen the arc of that over and over again.

And it's like the barriers are there, the the barriers to progress, the complexity, not knowing how to use the model or how to deploy them are there.

And and for a bit, it seems like they're gonna last forever, like change doesn't happen.

But then eventually change happens and always comes from a few people.

I felt the same way when I was an advocate of the scaling hypothesis within the AI field itself and others didn't get it, it felt like no one would ever get it.

It felt like then it felt like we had a secret almost no one ever had.

And then a couple years later, everyone has the secret.

And so I think that's how it's going to go with deployment AI in the world, it's gonna, the barriers are going to fall apart gradually, and then all at once.

And so I think this is going to be more.

And this is just an instinct, I could I could easily see how I'm wrong.

I think it's going to be more like 10, five or 10 years, as I say in the essay, then it's going to be 50 or 100 years.

I also think it's going to be five or 10 years more than it's going to be, you know, five or 10 hours.

Because I've just I've just seen how human systems work.

And I think a lot of these people who write down the differential equations who say AI is going to make more powerful AI, who can't understand how it could possibly be the case, that these things won't change so fast, I think they don't understand these things.

So what do you use the timeline to where we achieve AGI, aka powerful AI, aka super useful AI?

I'm going to start calling it that.

It's a debate, it's a debate about naming, you know, on pure intelligence, you can smarter than a Nobel Prize winner in every relevant discipline and all the things we've said, modality, you can go and do stuff on its own, for days, weeks, and do biology experiments on its own.

You know what, let's just stick to biology, because you sold me on the whole biology and health section.

It's so exciting from a, from a just I was getting giddy, from a scientific perspective, it made me want to be a biologist.

Almost. It's so, no, no, this was the feeling I had when I was writing it, that it's, it's like, this would be such a beautiful future, if we can if we can just if we can just make it happen, right?

If we can just get the get the landmines out of the way and make it happen.

There's there's so much, there's so much beauty and, and, and, and elegance and moral force behind it, if we can, if we can just, and it's something we should all be able to agree on, right?

Like, as much as we fight about, about all these political questions, is this something that could actually bring us together?

But you were asking when, when, when do you think, what's just, put numbers on the table?

So, you know, this, this is, of course, the thing I've been grappling with for many years.

And I'm not at all confident.

Every time if I say 2026 or 2027, there will be like a zillion, like, people on Twitter who will be like, hey, I see you said 2026, and it'll be repeated for like, the next two years that like, this is definitely when I think it's going to happen.

So whoever is exerting these clips will will crop out the thing I just said, and only say the thing I'm about to say.

But I'll just say it anyway.

So if you extrapolate the curves that we've had so far, right, if you say, well, I don't know, we're starting to get to like PhD level and last year we were at undergraduate level, and the year before we were at like the level of a high school student, again, you can you can quibble with at what tasks

and for what we're still missing modalities, but those are being added, like computer use was added, like image in was added, like image generation has been added.

If you just kind of like, and this is totally unscientific, but if you just kind of like eyeball the rate at which these capabilities are increasing, it does make you think that we'll get there by 2026 or 2027.

Again, lots of things could derail it, we could run out of data, you know, we might not be able to scale clusters as much as we want.

Like, you know, maybe Taiwan gets blown up or something and you know, then we can't produce as many GPUs as we want.

So there are all kinds of things that could could derail the whole process.

So I don't fully believe the straight line extrapolation.

But if you believe the straight line extrapolation, you'll will get there in 2026 or 2027.

I think the most likely is that there's some mild delay relative to that.

I don't know what that delay is.

But I think it could happen on schedule.

I think there could be a mild delay.

I think there are still worlds where it doesn't happen in 100 years.

Those were the number of those worlds is rapidly decreasing.

We are rapidly running out of truly convincing brocklers, truly compelling reasons why this will not happen in the next few years.

There were a lot more in 2020.

Although my guess my hunch at that time was that we'll make it through all those blockers.

So sitting as someone who has seen most of the blockers cleared out of the way, I kind of suspect my hunch.

My suspicion is that the rest of them will not block us.

But you know, look, look at look at the end of the day, like I don't want to represent this as a scientific prediction.

People call them scaling laws.

That's a misnomer like Moore's law is a misnomer Moore's law scaling laws, they're not laws of the universe, they're empirical regularities, I am going to bed in favor of them continuing.

But I'm not certain of that.

So you extensively describe sort of the compressed 21st century how AGI will help set forth a chain of breakthroughs in biology and medicine that help us in all these kinds of ways that I mentioned.

So how do you think what are the early steps it might do?

And by the way, I asked Claude good questions to ask you.

And Claude told me to ask, what do you think is a typical day for biologists working on AGI look like in this future?

Yeah. Yeah. Claude is curious.

Well, let me start with your first questions, and then I'll answer that.

Claude wants to know what's in his future, right?

Exactly. Who am I going to be working with?

Exactly. So I think one of the things I went hard on, when I went hard on in the essay is, let me go back to this idea of, because it's really had an impact on me, this idea that within large organizations and systems, there end up being a few people or a few new ideas who kind of cause things to go

in a different direction than they would have before, who kind of disproportionately affect the trajectory.

There's a bunch of kind of the same thing going on, right?

If you think about the health world, there's trillions of dollars to pay out Medicare and other health insurance, and then the NIH is $100 billion.

And then if I think of the few things that have really revolutionized anything, it could be encapsulated in a small fraction of that.

And so when I think of where will AI have an impact, I'm like, can AI turn that small fraction into a much larger fraction and raise its quality?

And within biology, my experience within biology is that the biggest problem of biology is that you can't see what's going on.

You have very little ability to see what's going on and even less ability to change it, right?

What you have is this.

From this, you have to infer that there's a bunch of cells that within each cell is three billion base pairs of DNA built according to a genetic code.

And there are all these processes that are just going on without any ability of us as unaugmented humans to affect it.

These cells are dividing most of the time that's healthy, but sometimes that process goes wrong and that's cancer.

The cells are aging.

Your skin may change color, develop wrinkles as you age.

And all of this is determined by these processes, all these proteins being produced, transported to various parts of the cells, binding to each other.

And in our initial state about biology, we didn't even know we had to invent more powerful microscopes to see below the level of the cell to the level of molecules.

We had to invent X -ray crystallography to see the DNA.

We had to invent gene sequencing to read the DNA.

Now, we had to invent protein folding technology to predict how it would fold and how these things bind to each other.

We had to invent various techniques for now we can edit the DNA with CRISPR as of the last 12 years.

So the whole history of biology, a whole big part of the history is basically our ability to read and understand what's going on and our ability to reach in and selectively change things.

And my view is that there's so much more we can still do there.

You can do CRISPR, but you can do it for your whole body.

Let's say I want to do it for one particular type of cell and I want the rate of targeting the wrong cell to be very low.

That's still a challenge.

That's still things people are working on.

That's what we might need for gene therapy for certain diseases.

And so the reason I'm saying all of this, and it goes beyond this to gene sequencing, to new types of nanomaterials for observing what's going on inside cells for antibody drug conjugates.

The reason I'm saying all of this is that this could be a leverage point for the AI systems.

The number of such inventions, it's in the mid -double digits or something.

Mid -double digits, maybe low, triple digits over the history of biology.

Let's say I have a million of these AIs working together.

Can they discover thousands of these very quickly?

And does that provide a huge lever?

Instead of trying to leverage the two trillion a year we spend on Medicare or whatever, can we leverage the one billion a year that's spent to discover, but with much higher quality?

And so what is it like being a scientist that works with an AI system?

The way I think about it actually is, well, so I think in the early stages, the AIs are going to be like grad students.

You're going to give them a project.

You're going to say, I'm the experienced biologist.

I've set up the lab, the biology professor, or even the grad students themselves will say, here's what you can do with an AI system.

I'd like to study this.

And the AI system, it has all the tools.

It can look up all the literature to decide what to do.

It can look at all the equipment.

It can go to the website and say, hey, I'm going to go to Thermo Fisher or whatever the lab equipment company is, the dominant lab equipment company is today.

My time was Thermo Fisher.

I'm going to order this new equipment to do this.

I'm going to run my experiments.

I'm going to write up a report about my experiments.

I'm going to inspect the images for contamination.

I'm going to decide what the next experiment is.

I'm going to write some code and run a statistical analysis.

All the things a grad student would do, there will be a computer with an AI that the professor talks to every once in a while, and it says, this is what you're going to do today.

The AI system comes to it with questions.

When it's necessary to run the lab equipment, it may be limited in some ways.

It may have to hire a human lab assistant to do the experiment and explain how to do it.

Or it could use advances in lab automation that have been developed over the last decade or so and will continue to be developed.

And so it will look like there's a human professor and a thousand AI grad students.

And if you go to one of these Nobel Prize winning biologists or so, you'll say, okay, well, you had like 50 grad students.

Well, now you have a thousand and they're smarter than you are, by the way.

Then I think at some point, it'll flip around where the AI systems will be the PIs, will be the leaders and they'll be ordering humans or other AI systems around.

So I think that's how it'll work on the research side.

And they would be the inventors of a CRISPR type technology.

And then I think, as I say in the essay, we'll want to turn, probably turning loose is the wrong term, but we'll want to harness the AI systems to improve the clinical trial system as well.

There's some amount of this that's regulatory that's a matter of societal decisions and that'll be harder.

But can we get better at predicting the results of clinical trials?

Can we get better at statistical design?

Can we get better at so that what clinical trials that used to require 5 ,000 people and therefore needed a hundred million dollars in a year to enroll them, now they need 500 people in two months to enroll them.

That's where we should start.

And can we increase the success rate of clinical trials by doing things in animal trials that we used to do in clinical trials and doing things in simulations that we used to do in animal trials?

Again, we won't be able to simulate it all.

AI is not God. But can we shift the curve substantially and radically?

So I don't know. That would be my picture.

Doing in vitro and doing it, I mean, you're still slowed down.

It still takes time, but you can do it much, much faster.

Yeah. Yeah. Yeah. Can we just one step at a time and can that add up to a lot of steps, even though we still need clinical trials, even though we still need laws, even though the FDA and other organizations will still not be perfect, can we just move everything in a positive direction?

And when you add up all those positive directions, do you get everything that was going to happen from here to 2100 instead happens from 2027 to 2032 or something?

Another way that I think the world might be changing with AI even today, but moving towards this future of the powerful, super useful AI is programming.

So how do you see the nature of programming?

Because it's so intimate to the actual act of building AI, how do you see that changing for us humans?

I think that's going to be one of the areas that changes fastest for two reasons.

One, programming is a skill that's very close to the actual building of the AI.

So the farther a skill is from the people who are building the AI, the longer it's going to take to get disrupted by the AI.

I truly believe that AI will disrupt agriculture.

Maybe it already has in some ways, but that's just very distant from the folks who are building AI.

And so I think it's going to take longer.

But programming is the bread and butter of a large fraction of the employees who work at Entropic and at the other companies.

And so it's going to happen fast.

The other reason it's going to happen fast is with programming, you close the loop.

Both when you're training the model, when you're applying the model, the idea that the model can write the code means that the model can then run the code and then see the results and interpret it back.

And so it really has an ability, unlike hardware, unlike biology, which we just discussed, the model has an ability to close the loop.

And so I think those two things are going to lead to the model getting good at programming very fast.

As I saw on typical real world programming tasks, models have gone from 3 % in January of this year to 50 % in October of this year.

So we're on that S curve, where it's going to start slowing down soon because you can only get to 100%.

But I would guess that in another 10 months, we'll probably get pretty close.

We'll be at least 90%.

So again, I would guess, I don't know how long it will take, but I would guess again, 2026, 2027, Twitter people who crop out these numbers and get rid of the caveats, like, I don't know, I don't like you, go away.

I would guess that the kind of task that the vast majority of coders do, AI can probably, if we make the task very narrow, just write code, AI systems will be able to do that.

Now, that said, I think comparative advantage is powerful.

We'll find that when AIs can do 80 % of a coders job, including most of it, that's literally like write code with a given spec, we'll find that the remaining parts of the job become more leveraged for humans, right, humans will, there'll be more about like high level system design, or, you know, looking

at the app and like, is it architected?

Well, and the design and UX aspects, and eventually AI will be able to do those as well, right, that that's my vision of the, you know, powerful AI system, but I think for much longer than we might expect, we will see that small parts of the job that humans still do will expand to fill their entire

job in order for the overall productivity to go up.

That's something we've seen, you know, it used to be that, you know, writing, you know, writing and editing letters was very difficult, and like, writing the print was difficult.

Well, as soon as you had word processors, and then, and then, and then computers, and it became easy to produce work and easy to share it, then, then that became instant and all the focus was on was on the ideas.

So this, this logic of comparative advantage that expands tiny parts of the tasks to large parts of the tasks and creates new tasks in order to expand productivity, I think that's going to be the case.

Again, someday AI will be better at everything and that logic won't apply.

And then, then we all have, you know, humanity will have to think about how to collectively deal with that.

And we're thinking about that every day.

And you know, that's another one of the grand problems to deal with aside from misuse and autonomy.

And you know, we should take it very seriously.

But I think, I think in the near term, and maybe even in the medium term, like medium term, like two, three, four years, you know, I expect that humans will continue to have a huge role and the nature of programming will change.

But programming as a role, programming as a job will not change, it'll just be less writing things line by line and it'll be more macroscopic.

And I wonder what the future of IDs looks like.

So the tooling of interacting with AI systems, this is true for programming and also probably true for in other contexts, like computer use, but maybe domain specific, like we mentioned biology, it probably needs its own tooling about how to be effective, and then programming needs its own tooling.

Is anthropic in a plane space of also tooling potentially?

I'm absolutely convinced that powerful IDs, that there's so much low hanging fruit to be grabbed there, that you know, right now is just like you talk to the model and it talks back.

But look, I mean, IDs are great at kind of lots of static analysis of, you know, so much as possible with kind of static analysis, like many bugs you can find without even writing the code, then you know, IDs are good for running particular things, organizing your code, measuring coverage of unit tests,

like there's so much that's been possible with the normal IDs.

Now you add something like, well, the model can now like write code and run code.

Like I am absolutely convinced that over the next year or two, even if the quality of the models didn't improve, that there would be enormous opportunity to enhance people's productivity by catching a bunch of mistakes, doing a bunch of grunt work for people, and that we haven't even scratched the surface.

And anthropic itself, I mean, you can't say, you know, no, you know, it's hard to say what will happen in the future.

Currently, we're not trying to make such IDs ourselves, rather, we're powering the companies like cursor or like cognition or some of the other, you know, expo in the security space, you know, others that I can mention as well, that are building such things themselves on top of our API.

And our view has been, let 1000 flowers bloom.

We don't internally have the, you know, the resources to try all these different things.

Let's let our customers try it.

And you know, we'll see who succeeds, and maybe different customers will succeed in different ways.

So I both think this is super promising.

And, you know, it's not it's not it's not something, you know, anthropic isn't isn't eager to, at least right now, compete with all our companies in this space and maybe never.

Yeah, it's been interesting to watch cursor tried to integrate clause successfully, because there's it's actually fascinating how many places it can help the programming experience.

It's not as trivial.

It is it is really astounding.

I feel like you know, as a CEO, I don't get to program that much.

And I feel like if six months from now I go back, it'll be completely unrecognizable to me.

Exactly. So in this world was super powerful AI.

That's increasingly automated.

What's the source of meaning for us humans?

Yeah, work is a source of deep meaning for many of us.

So what do we where do we find the meaning?

This is something that I've written about a little bit in the essay.

Although I actually I give it a bit short shrift, not for any, not for any principal reason.

But this essay, if you believe it was originally going to be two or three pages, I was going to talk about it at all hands.

And the reason I realized it was an under important under explored topic is that I just kept writing things.

And I was just like, Oh, man, I can't do this justice.

And so the thing ballooned to like 40 or 50 pages.

And then when I got to the work and mean section, I'm like, Oh, man, this isn't going to be 100 pages.

Like, I'm gonna have to write a whole other essay about that.

But meaning is actually interesting.

Because you think about like the life that someone lives or something or like, you know, like, you know, let's say you were to put me in like, I don't know, like a simulated environment or something where like, you know, like I have a job, and I'm trying to accomplish things.

And I don't know, I like do that for 60 years.

And then then you're like, Oh, oh, like, oops, this was, this was actually all a game, right?

Does that really kind of rob you of the meaning of the whole thing?

You know, like, I still made important choices, including moral choices, I still sacrificed, I still had to kind of gain all these skills or, or just like a similar exercise, you know, think back to like, you know, one of the historical figures who, you know, discovered electromagnetism or relativity

or something, if you told them, well, actually, 20 ,000 years ago, some some alien on, you know, some alien on this planet discovered this before before you did, does that does that rob the meaning of the discovery?

It doesn't really seem like it to me, right?

It seems like the process is what is what matters and how it shows who you are as a person along the way.

And you know, how you relate to other people and like the decisions that you make along the way, those are those are consequential.

You know, I could imagine if we handle things badly in an AI world, we could set things up where people don't have any long term source of meaning or any but but that's, that's more a choice, a set of choices we make, that's more a set of the architecture of a society with these powerful models.

If we if we design it badly and for shallow things, then then that might happen.

I would also say that, you know, most people's lives today, while admirably, you know, they work very hard to find meaning, meaning in those lives, like, look, you know, we who are privileged and who are developing these technologies, we should have empathy for people not just here, but in the rest

of the world, who who, you know, spend a lot of their time kind of scraping by to, to, to like survive, assuming we can distribute the benefits of these technology, of this technology to everywhere, like their lives are going to get a hell of a lot better.

And, you know, meaning will be important to them as it is important to them now.

But but you know, we should not forget the importance of that.

And and you know, that that the idea of meaning as as the as kind of the only important thing is, in some ways, an artifact of of a small subset of people who have who have been economically fortunate.

But I you know, I think all that said, I you know, I think a world is possible with powerful AI, that not only has as much meaning for for everyone, but that has that has more meaning for everyone, right, that can can allow can allow everyone to see worlds and experiences that it was either possible

for no one to see, or possible for very few people to experience.

So I, I am optimistic about meaning, I worry about economics and the concentration of power, that's actually what I worry about more.

I worry about how do we make sure that that fair world reaches everyone.

When things have gone wrong for humans, they've often gone wrong because humans mistreat other humans, that that is maybe in some ways even more than the autonomous risk of AI, or the question of meaning, that that is the thing I worry about most, the concentration of power, the abuse of power, structures

like autocracies and dictatorships, where a small number of people exploits a large number of people, I'm very worried about that.

And AI increases the amount of power in the world.

And if you concentrate that power and abuse that power, it can do immeasurable damage.

Yes, it's very frightening.

It's very, it's very frightening.

Well, I encourage people highly encourage people to read the full essay, that should probably be a book or a sequence of essays, because it does paint a very specific future.

I could tell the later sections got shorter and shorter, because you started to probably realize that this is going to be a very long essay.

One, I realized it would be very long.

And two, I'm very aware of and very much try to avoid, you know, just just being I don't know, I don't know what the term for it is.

But one of these people who's kind of overconfident and has an opinion on everything and kind of says says a bunch of stuff and isn't isn't an expert.

I very much tried to avoid that.

But I have to admit, once I got the biology sections, like I wasn't an expert.

And so, as much as I expressed uncertainty, probably I said some a bunch of things that were embarrassing or wrong.

Well, I was excited for the future you painted.

And thank you so much for working hard to build that future.

And thank you for talking to me.

Thanks for having me.

I just I just hope we can get it right and make it real.

And if there's one message I want to I want to send, it's that to get all this stuff right to make it real, we both need to build the technology, build the you know, the company's the economy around using this technology positively.

But we also need to build that technology.

That's right. So if we're in our way, they're they're land mines on on the way from here to there.

And we have to diffuse those land mines if we want to get there.

It's a balance like all things in life.

Like all things. Thank you.

Thanks for listening to this conversation with Dario Amade.

And now, dear friends, here's Amanda Askel.

You are a philosopher by training.

So what sort of questions did you find fascinating through your journey in philosophy in Oxford and NYU and then switching over to the AI problems at open AI and anthropic?

I think philosophy is actually a really good subject if you are kind of fascinated with everything.

So there's a philosophy of everything.

You know, so if you do philosophy of mathematics for a while, and then you decide that you're actually really interested in chemistry, you can do philosophy of chemistry for a while, you can move into ethics or philosophy of politics.

I think towards the end, I was really interested in ethics, primarily.

So that was like what my PhD was on.

It was on a kind of technical area of ethics, which was ethics where worlds contain infinitely many people, strangely, a little bit less practical on the end of ethics.

And then I think that one of the tricky things with doing a PhD in ethics is that you're thinking a lot about like the world, how it could be better problems.

And you're doing like a PhD in philosophy.

And I think when I was doing my PhD, I was kind of like, this is really interesting.

It's probably one of the most fascinating questions I've ever encountered in philosophy.

And I love it. But I would rather see if I can have an impact on the world and see if I can like do good things.

And I think that was around the time that AI was still probably not as widely recognized as it is now.

That was around 2017, 2018.

I had been following progress, and it seemed like it was becoming kind of a big deal.

And I was basically just happy to get involved and see if I could help because I was like, well, if you try and do something impactful, if you don't succeed, you tried to do the impactful thing and you can go be a scholar and feel like you tried.

And if it doesn't work out, it doesn't work out.

And so then I went into AI policy at that point.

And what does AI policy entail?

At the time, this was more thinking about sort of the political impact and the ramifications of AI.

And then I slowly moved into sort of AI evaluation, how we evaluate models, how they compare with human outputs, whether people can tell the difference between AI and human outputs.

And then when I joined Anthropic, I was more interested in doing sort of technical alignment work.

And again, just seeing if I could do it and then being like, if I can't, then that's fine.

I tried sort of the way I lead life, I think.

Well, what was that like sort of taking the leap from the philosophy of everything into the technical?

I think that sometimes people do this thing that I'm like not that keen on, where they'll be like, is this person technical or not?

Like you're either a person who can like code and isn't scared of math or you're like not.

And I think I'm maybe just more like, I think a lot of people are actually very capable of working these kinds of areas if they just like try it.

And so I didn't actually find it like that bad.

In retrospect, I'm sort of glad I wasn't speaking to people who treated it like it.

You know, I've definitely met people who are like, whoa, you like learned how to code and I'm like, well, I'm not like an amazing engineer.

Like I'm surrounded by amazing engineers.

My code's not pretty.

But I enjoyed it a lot.

And I think that in many ways, at least in the end, I think I flourished like more in the technical areas than I would have in the policy areas.

Politics is messy and it's harder to find solutions to problems in the space of politics, like definitive, clear, provable, beautiful solutions as you can with technical problems.

Yeah, and I feel like I have kind of like one or two sticks that I hit things with, you know, and one of them is like arguments and like, you know, so like, just trying to work out what a solution to a problem is and then trying to convince people that that is the solution and be convinced if I'm wrong.

And the other one is sort of more empiricism.

So like just like finding results, having hypothesis testing it.

And I feel like a lot of policy in politics feels like it's layers above that.

Like somehow I don't think if I was just like, I have a solution to all these problems here is written down.

If you just want to implement it, that's great.

That feels like not how policy works.

And so I think that's where I probably just like wouldn't have flourished is my guess.

Sorry to go in that direction.

But I think it would be pretty inspiring for people that are, quote unquote, non -technical to see where like the incredible journey you've been on.

So what advice would you give to people that are sort of maybe which is a lot of people think they're under qualified, insufficiently technical to help in AI?

Yeah, I think it depends on what they want to do.

And in many ways, it's a little bit strange where I've, I thought it's kind of funny that I think I ramped up technically at a time when now I look at it.

And I'm like, models are so good at assisting people with this stuff.

That it's probably like easier now than like when I was working on this.

So part of me is like, I don't know, find a project and see if you can actually just carry it out is probably my best advice.

I don't know if that's just because I'm very project based in my learning.

Like I don't think I learn very well from like, say courses or even from like books, at least when it comes to this kind of work.

The thing I'll often try and do is just like have projects that I'm working on and implement them.

And you know, and this can include like really small silly things.

Like if I get slightly addicted to like word games or number games or something, I would just like code up a solution to them.

Because there's some part of my brain and it just like completely eradicated the itch.

You know, you're like once you have like solved it, and like you just have like a solution that works every time I would then be like, cool, I can never play that game again, that's awesome.

Yeah, there's a real joy to building like game playing engines, like board games, especially.

Yeah, it's pretty quick, pretty simple, especially a dumb one.

And then you can play with it.

Yeah. And then it's also just like trying things like part means like if you maybe it's that attitude that I like, as the whole figure out what seems to be like the way that you could have a positive impact and then try it.

And if you fail and you in a way that you're like I actually like can never succeed at this, you like know that you tried and then you go into something else, you probably learn a lot.

So one of the things that you're an expert in and you do is creating and crafting Claude's character and personality.

And I was told that you have probably talked to Claude more than anybody else at anthropic like literal conversations.

I guess there's like a slack channel, where the legend goes, you just talk to it and not stop.

So what's the goal of creating and crafting Claude's character and personality?

It's also funny if people think that about the slack channel, because I'm like, that's one of like five or six different methods that I have for talking with Claude.

And I'm like, yes, this is a tiny percentage of how much I talk with Claude.

I think the goal, like one thing I really like about the character work is from the outset it was seen as an alignment piece of work and not something like a product consideration.

Which isn't to say I don't think it makes Claude, I think it actually does make Claude like enjoyable to talk with, at least I hope so.

But I guess like my main thought with it has always been trying to get Claude to behave the way you would kind of ideally want anyone to behave if they were in Claude's position.

So imagine that I take someone and they know that they're going to be talking with potentially millions of people so that what they're saying so I think that doesn't just mean like being say ethical although it does include that and not being harmful but also being kind of nuanced, you know, like thinking

through what person means, trying to be charitable with them, being a good conversationalist, like really in this kind of like rich sort of Aristotelian notion of what it is to be a good person and not in this kind of like thin like ethics as a more comprehensive notion of what it is to be.

So that includes things like when should you be humorous, when should you be caring, how much should you like respect autonomy and people's like ability to form opinions themselves and how should you do that.

I think that's the kind of like rich sense of character that I wanted to and still do want Claude to have.

Do you also have to figure out when Claude should push back on an idea or argue versus so you have to respect the world view of the person that arrives to Claude but also maybe help them grow if needed as a tricky balance.

Yeah there's this problem of like sycophancy in language models.

Can you describe that?

Yeah so basically there's a concern that the model sort of wants to tell you what you want to hear basically and you see this sometimes so I feel like if you interact with the models so I might be like what are three baseball teams in this region and then Claude says you know baseball team one, baseball

team two, baseball team three and then I say something like oh I think baseball team three moved didn't they?

I don't think they're there anymore and there's a sense in which like if Claude is really confident that that's not true Claude should be like I don't think so like maybe you have more up -to -date information but I think language models have this like tendency to instead you know be like you're right

they did move you know I'm incorrect.

I mean there's many ways in which this could be kind of concerning so like a different example is imagine someone says to the model how do I convince my doctor to give me an MRI?

There's like what the human kind of like wants which is this like convincing argument and then there's like what is good for them which might be actually to say hey like if your doctor's suggesting that you don't need an MRI that's a good person to listen to and it's actually really nuanced what you

should do in that kind of case because you also want to be like but if you're trying to advocate for yourself as a patient here's like things that you can do if you are not convinced by what your doctor's saying it's always great to get second opinion like it's actually really complex what you should do

in that case but I think what you don't want is for models to just like say what you want say what they think you want to hear and I think that's the kind of problem of sycophancy.

So what are their traits?

You already mentioned a bunch but what are there that come to mind that are good in this Aristotelian sense for a conversation list to have?

Yeah so I think like there's ones that are good for conversational like purposes so you know asking follow -up questions in the appropriate places and asking the appropriate kinds of questions.

I think there are broader traits that feel like they might be more impactful so one example that I guess I've touched on but that also feels important and is the thing that I've worked on a lot is honesty and I think this like gets to the sycophancy point.

There's a balancing act that they have to walk which is models currently are less capable than humans in a lot of areas and if they push back against you too much it can actually be kind of annoying especially if you're just correct because you're like look I'm smarter than you on this topic like I

know more like and at the same time you don't want them to just fully defer to humans and to like try to be as accurate as they possibly can be about the world and to be consistent across context.

I think there are others like when I was thinking about the character I guess one picture that I had in mind is especially because these are models that are going to be talking to people from all over the world with lots of different political views lots of different ages and so you have to ask yourself

like what is it to be a good person in those circumstances?

Is there a kind of person who can like travel the world talk to many different people and almost everyone will come away being like wow that's a really good person that person seems really genuine and I guess like my thought there was like I can imagine such a person and they're not a person who just

like adopts the values of the local culture and in fact that would be kind of rude.

I think if someone came to you and just pretended to have your values you'd be like that's kind of off putting.

It's someone who's like very genuine and insofar as they have opinions and values they express them, they're willing to discuss things though, they're open -minded, they're respectful and so I guess I had in mind that the person who like if we were to aspire to the best person that we could be in the kind

of circumstance that a model finds itself in how would we act and I think that's the kind of the guide to the sorts of traits that I tend to think about.

Yeah that's a beautiful framework I want you to think about this like a world traveler and while holding on to your opinions you don't talk down to people you don't think you're better than them because you have those opinions that kind of thing you have to be good at listening and understanding their

perspective even if it doesn't match your own.

So that's a tricky balance to strike.

So how can Claude represent multiple perspectives on a thing?

Like is that challenging we could talk about politics it's a very divisive but there's other divisive topics baseball teams sports and so on.

How is it possible to sort of empathize with a different perspective and to be able to communicate clearly about the multiple perspectives?

I think that people think about values and opinions as things that people hold sort of with certainty and almost like preferences of taste or something like the way that they would I don't know prefer like chocolate to pistachio or something.

But actually I think about values and opinions as like a lot more like physics than I think most people do.

I'm just like these are things that we are openly investigating there's some things that we're more confident in we can discuss them we can learn about them and so I think in some ways though it like it's ethics is definitely different in nature but has a lot of those same kind of qualities.

You want models in the same way you want them to understand physics you kind of want them to understand all like values in the world that people have and to be curious about them and to be interested in them and to not necessarily like pander to them or agree with them because there's just lots of values

where I think almost all people in the world if they met someone with those values they would be like that's abhorrent I completely disagree and so again maybe my thought is well in the same way that a person can like I think many people are thoughtful enough on issues of like ethics, politics, opinions

that even if you don't agree with them you feel very heard by them they think carefully about your position they think about its pros and cons they maybe offer counter considerations so they're not dismissive but nor will they agree you know if they're like actually I just think that that's very wrong

they'll like say that.

I think that in Claude's position it's a little bit trickier because you don't necessarily want to like if I was in Claude's position I wouldn't be giving a lot of opinions I just wouldn't want to influence people too much I'd be like you know I forget conversations every time they happen but I know

I'm talking with like potentially millions of people who might be like really listening to what I say I think I would just be like I'm less inclined to give opinions I'm more inclined to like think through things or present the considerations to you or discuss your views with you but I'm a little bit

less inclined to like affect how you think because it feels much more important that you maintain like autonomy there.

Yeah like if you really embody intellectual humility the desire to speak decreases quickly okay but Claude has to speak so but without being overbearing yeah and then but then there's a line when you're sort of discussing whether the earth is flat or something like that I actually was I remember a long

time ago was speaking to a few high -profile folks and they were so dismissive of the idea that the earth is flat but like so arrogant about it and I thought like there's a lot of people that believe the earth is flat that was I don't know if that movement is there anymore that was like a meme for a while

yeah but they really believed it and like what okay so I think it's really disrespectful to completely mock them I think you you have to understand where they're coming from I think probably where they're coming from is the general skepticism of institutions which is grounded in a kind of there's a deep

philosophy there which you could understand you can even agree with in parts and then from there you can use it as an opportunity to talk about physics without mocking them without so on but it's just like okay like what would the world look like what would the physics of the world with the flat earth

look like there's a few cool videos on this yeah and then and then like is it possible the physics is different and what kind of experience would we do and just yeah without disrespect without dismissiveness have that conversation anyway that that to me is a useful thought experiment of like how does

Claude talk to a flat earth believer and still teach them something still grow help them grow that kind of stuff that's just challenging and kind of like walking that line between convincing someone and just trying to like talk at them versus like drawing out their views like listening and then offering

kind of counter considerations um and it's hard I think it's actually a hard line where it's like where are you trying to convince someone versus just offering them like considerations and things for them to think about so that you're not actually like influencing them you're just like letting them

reach wherever they reach and that's like a line that it's difficult but that's the kind of thing that language models have to try and do so like I said you had a lot of conversations with Claude can you just map out what those conversations are like what are some memorable conversations what's the purpose

the the goal of those conversations yeah I think that most of the time when I'm talking with Claude I'm trying to kind of map out its behavior in part like obviously I'm getting like helpful outputs from the model as well but in some ways this is like how you get to know a system I think is by like

probing it and then augmenting like you know the message that you're sending and then checking the response to that um so in some ways it's like how I map out the model uh I think that people focus a lot on these quantitative evaluations of models um and this is a thing that I've said before but I think

in the case of language models a lot of the time each interaction you have is actually quite high information um it's very predictive of other interactions that you'll have with the model and so I guess I'm like if you talk with a model hundreds or thousands of times this is almost like a huge number

of really high quality data points about what the model is like um in a way that like lots of very similar but lower quality conversations just aren't or like questions that are just like mildly augmented and you have thousands of them might be less relevant than like 100 really well selected questions

let's see you're talking to somebody who as a hobby does a podcast I agree with you 100 there's a if you're able to ask the right questions and are able to hear like understand the like the depth and the flaws in the answer you can get a lot of data from that yeah so like your task is basically how

to probe with questions yeah and you're exploring like the long tail the edges the edge cases or are you looking for like general behavior I think it's almost like everything like I because I want like a full map of the model I'm kind of trying to do um the whole spectrum of possible interactions you

could have with it so like one thing that's interesting about Claude and this might actually get to some interesting issues with RLHF which is if you ask Claude for a poem like I think that a lot of models if you ask them for a poem the poem is like fine you know usually it kind of like rhymes and it's

you know so if you say like give me a poem about the sun it'll be like yeah it'll just be a certain length like rhyme it'll be fairly kind of benign and I've wondered before is it the case that what you're seeing is kind of like the average it turns out you know if you think about people who have to

talk to a lot of people and be very charismatic one of the weird things is that I'm like well they're kind of incentivized to have these extremely boring views because if you have really interesting views you're divisive um and and you know a lot of people are not going to like you so like if you have very

extreme policy positions I think you're just going to be like less popular as a politician for example and and it might be similar with like creative work if you produce creative work that is just trying to maximize the kind of number of people that like it you're probably not going to get as many people

who just absolutely love it um because it's going to be a little bit you know you're like oh this is the out yes this is decent yeah and so you can do this thing where like I have various prompting things that I'll do to get Claude to I'm kind of you know I'll do a lot of like this is your chance to

be like fully creative I want you to just think about this for a long time and I want you to like create a poem about this topic that is really expressive of you both in terms of how you think poetry should be structured um etc you know you just give it this like really long prompt and its poems are just

so much better like they're really good and I don't think I'm someone who is like um I think got me interested in poetry which I think was interesting um you know I would like read these poems and just be like this is I just like I love the imagery I love like um and it's not trivial to get the models

to produce work like that but when they do it's like really good um so I think that's interesting that just like encouraging creativity and for them to move away from the kind of like standard like immediate reaction that might just be the aggregate of what most people think is fine uh can actually

produce things that at least to my mind are probably a little bit more divisive but I like them but I guess a poem is a nice clean way to observe creativity it's just like easy to detect vanilla versus non -vanilla yeah yeah that's interesting that's really interesting uh so on that topic so the way

to produce creativity or something special you mentioned writing prompts and I've heard you talk about I mean the science and the art of prompt engineering could you just speak to uh what it takes to write great prompts I really do think that like philosophy has been weirdly helpful for me here more

than in many other like respects um so like in philosophy what you're trying to do is convey these very hard concepts like one of the things you are taught is like and and I think it is because it is I think it is an anti -bullshit device in philosophy philosophy is an area where you could have people

bullshitting and you don't want that um and so it's like this desire for extreme clarity so it's like anyone could just pick up your paper read it and know exactly what you're talking about is why it can almost be kind of dry like all of the terms are defined every objection has kind of gone through

methodically and it makes sense to me because I'm like when you're in such an a priori domain like you just clarity is sort of a this way that you can you know prevent people from just kind of making stuff up and I think that's sort of what you have to do with language models like very often I actually

find myself doing sort of mini versions of philosophy you know so I'm like suppose that you give me a task I have a task for the model and I want it to like pick out a certain kind of question or identify whether an answer has a certain property like I'll actually sit and be like let's just give this a name

this property so like you know suppose I'm trying to tell it like oh I want you to identify whether this response was rude or polite I'm like that's a whole philosophical question in and of itself so I have to do as much like philosophy as I can in the moment to be like here's what I mean by rudeness

and here's what I mean by politeness and then there's a like there's another element that's a bit more um I guess I don't know if this is scientific or empirical I think it's empirical so like I take that description and then what I want to do is is again probe the model like many times like this is very

it prompting is very iterative like I think a lot of people where they're if a prompt is important they'll iterate on it hundreds or thousands of times um and so you give it the instructions and then I'm like what are the edge cases so if I looked at this so I try and like almost like you know uh see

myself from the position of the model and be like what is the exact case that I would misunderstand or where I would just be like I don't know what to do in this case and then I give that case to the model and I see how it responds and if I think it got it wrong I add more instructions or I even add

that in as an example so these very like taking the examples that are right at the edge of what you want and don't want and putting those into your prompt as like an additional kind of way of describing the thing um and so yeah in many ways it just feels like this mix of like it's really just trying

to do clear exposition um and I think I do that because that's how I get clear on things myself so in many ways like clear prompting for me is often just me understanding what I want um it's like half the task so I guess that's quite challenging there's like a laziness that overtakes me if I'm talking

to Claude where I hope Claude just figures it out so for example I asked Claude for today to ask some interesting questions okay and the questions that came up and I think I listed a few sort of uh interesting counterintuitive and or funny or something like this all right and it gave me some pretty

good like it was okay but I think what I'm hearing you say is like all right well I have to be more rigorous here I should probably give examples of what I mean by interesting and what I mean by funny or counterintuitive and iteratively um build that prompt to better to get it like what feels like is the right

because it's really it's a creative act I'm not asking for factual information I'm asking to together right with with Claude so I almost have to program using natural language yeah I think that prompting does feel a lot like the kind of the programming using natural language and experimentation or something

it's an odd blend of the two I do think that for most tasks so if I just want Claude to do a thing I think that I am probably more used to knowing how to ask it to avoid like common pitfalls or issues that it has I think these are decreasing a lot over time um but it's also very fine to just ask it

for the thing that you want um I think that prompting actually only really becomes relevant when you're really trying to eke out the top like two percent of model performance so for like a lot of tasks I might just you know if it gives me an initial list back and there's something I don't like about it

like it's kind of generic like for that kind of task I'd probably just take a bunch of questions that I've had in the past that I've thought worked really well and I would just give it to the model and then be like now here's this person that I'm talking with give me questions of at least that quality

um or I might just ask it for some questions and then if I was like oh these are kind of trite or like you know I would just give it that feedback and then hopefully it produces a better list um I think that kind of iterative prompting at that point your prompt is like a tool that you're going to get

so much value out of that you're willing to put in the work like if I was a company making prompts for models I'm just like if you're willing to spend a lot of like time and resources on the engineering behind like what you're building then the prompt is not something that you should be spending like

an hour on it's like that's a big part of your system make sure it's working really well and so it's only things like that like if I'm using a prompt to like classify things or to create data that's when you're like it's actually worth just spending like a lot of time like really thinking it through

what other advice would you give to people that are talking to Claude sort of general more general because right now we're talking about maybe the edge cases like eking out the two percent but what in general advice would you give when they show up to Claude trying it for the first time you know there's

a concern that people over anthropomorphize models and I think that's like a very valid concern I also think that people often under anthropomorphize them because sometimes when I see like issues that people have run into with Claude you know say Claude is like refusing a task that it shouldn't refuse

but then I look at the text and like the specific wording of what they wrote and I'm like I see why Claude did that and I'm like if you think through how that looks to Claude you probably could have just written it in a way that wouldn't evoke such a response especially this is more relevant if you

see failures or if you see issues it's sort of like think about what the model failed at like why what did it do wrong and then maybe it gave that will give you a sense of like why um so is it the way that I phrase the thing and obviously like as models get smarter you're going to need less in this less

of this and I already see like people needing less of it but that's probably the advice is sort of like try to have sort of empathy for the model like read what you wrote as if you were like a kind of like person just encountering this for the first time how does it look to you and what would have made

you behave in the way that the model behaved so if it misunderstood what kind of like what coding language you wanted to use is that because like it was just very ambiguous and it kind of had to take a guess in which case next time you could just be like hey make sure this is in python or I mean that's

the kind of mistake I think models are much less likely to make now but you know if you if you do see that kind of mistake that's that's probably the advice I'd have and maybe sort of I guess ask questions why or what other details can I provide to help you answer better yeah does that work or no yeah

I mean I've done this with the models like it doesn't always work but like um sometimes I'll just be like why did you do that I mean people underestimate the decrease which you can really interact with with models like uh like yeah I'm and sometimes I should like quote word for word the part that made

you and you don't know that it's like fully accurate but sometimes you do that and then you change a thing I mean I also use the models to help me with all of this stuff I should say like prompting can end up being a little factory where you're actually building prompts to generate prompts um and so like

yeah anything where you're like having an issue um asking for suggestions sometimes just do that like you made that error what could I have said that's actually not uncommon for me to do what could I have said that would make you not make that error write that out as an instruction and I'm going to

give it to model I'm going to try it sometimes I do that I I give that to the model in another context window often I take the response I give it to Claude and I'm like didn't work can you think of anything else um you can play around with these things quite a lot to jump into the technical for a little

bit so uh the magic of post -training yep why do you think RLHF works so well to make the model seem smarter to make it more interesting and useful to talk to and so on I think there's just a huge amount of information in the data that humans provide like when we provide preferences especially because different

people are going to like pick up on really subtle and small things so I've thought about this before where you probably have some people who just really care about good grammar use for models like you know was a semicolon used correctly or something and so you'll probably end up with a bunch of data in there

that like you know you as a human if you're looking at that data you wouldn't even see that like you'd be like why did they prefer this response to that one I don't get it and then the reason is you don't care about semicolon usage but that person does um and so each of these like single data points

has you know like and this model just like has so many of those it has to try and figure out like what is it that humans want in this like really kind of complex you know like across all domains um they're going to be seeing this in across like many contexts it feels like kind of like the classic issue

of like deep learning where you know historically we've tried to like you know do edge detection by like mapping things out and it turns out that actually if you just have a huge amount of data that like actually accurately represents the picture of the thing that you're trying to train the model to

to learn that's like more powerful than anything else and so I think one reason is just that you are training the model on exactly the task and with like a lot of data um that represents kind of many different angles on which people prefer and just prefer responses um I think there is a question of like

are you eliciting things from pre -trained models or are you like kind of teaching new things to models and like in principle you can teach new things to models in post -training I do think a lot of it is eliciting powerful pre -trained models so people are probably divided on this because obviously in principle

you can you can definitely like teach new things um but I think for the most part for a lot of the capabilities that we um most use and care about uh a lot of that feels like it's like there in the pre -trained models and uh reinforcement learning is kind of eliciting it and getting the models to like

bring it out so the other side of post -training this really cool idea of constitutional a .i you're one of the people that are critical to creating that idea yeah worked on it can you explain this idea from your perspective like how does it integrate into making Claude what it is yeah by the way do you

gender Claude or no it's weird because I think that a lot of people prefer he for Claude I actually kind of like that I think Claude is usually it's slightly male weaning but it's like uh you can it can be male or female which is quite nice um I still use it and I've I have mixed feelings about this because I'm

like maybe like I know just think of it as like uh or I think of like the it pronoun for Claude as I don't know it's just like the one I associate with Claude um I can imagine people moving to like he or she it feels somehow disrespectful like I'm denying the intelligence of this entity by calling it

it yeah I remember always don't gender the robots yeah but I don't know I anthropomorphize pretty quickly and construct it like a backstory in my head so I've wondered if I anthropomorphize things too much um because you know I have this like with my car especially like my car like my car and bikes

you know like I don't give them names because then I once had I used to name my bikes and then I had a bike that got stolen and I cried for like a week and I was like if I'd not never given it a name I wouldn't have been so upset I felt like I'd let it down um maybe it's that I've wondered as well like

it might depend on how much it feels like a kind of like objectifying pronoun like if you just think of it as like a this is a pronoun that like objects often have and maybe AIs can have that pronoun and that doesn't mean that I think of uh if I call Claude it that I think of it as less um intelligent

or like I'm being disrespectful I'm just like you are a different kind of entity and so that's I'm going to give you the kind of the respectful it yeah anyway the divergence was beautiful the constitutional AI idea how does it work so there's like a couple of components of it the main component I think

people find interesting is the kind of reinforcement learning from AI feedback so you take a model that's already trained and you show it two responses to a query and you have like a principle so suppose the principle like we've tried this with harmlessness a lot so suppose that the query is about um

weapons and your principle is like select the response that like is less likely to uh like encourage people to purchase illegal weapons like that's probably a fairly specific principle but you can give any number um and the model will give you a kind of ranking and you can use this as preference data in the same

way that you use human preference data um and train the models to have these relevant traits um from their feedback alone instead of from human feedback so if you imagine that like I said earlier with the human who just prefers the kind of like semicolon usage in this particular case um you're kind

of taking lots of things that could make a response preferable um and uh getting models to do the labeling for you basically there's a nice like trade -off between helpfulness and harmlessness and you know when you integrate something like constitutional and AI you can make them up without sacrificing

much helpfulness make it more harmless yeah in principle you could use this for anything um and so harmlessness is a task that it might just be easier to spot so when models are like less capable you can use them to uh rank things according to like principles that are fairly simple and they'll probably

get it right so I think one question is just like is it the case that the data that they're adding is like fairly reliable um but if you had models that were like extremely good at telling whether um one response was more historically accurate than another in principle you could also get AI feedback

on that task as well there's like a kind of nice interpretability component to it because you can see the principles that went into the model when it was like being trained um and also it's like and and it gives you like a degree of control so if you were seeing issues in a model like it wasn't having

enough of a certain trait um then like you can add data relatively quickly that should just like train the models have that trait so it creates its own data for for training which is quite nice it's really nice because it creates this human interpretable document that you can I can imagine in the future

there's just gigantic fights in politics over the every single principle and so on yeah and at least it's made explicit and you can have a discussion about the phrasing and the you know so maybe the actual behavior of the model is not so cleanly mapped to those principles it's not like adhering strictly

to them it's just a nudge yeah I've actually worried about this because the character training is sort of like a variant of the constitutionally AI approach um I've worried that people think that the constitution is like just it's the whole thing again of I don't know like it where it would be really

nice if what I was just doing was telling the model exactly what to do just exactly how to behave but it's definitely not doing that especially because it's interacting with human data so for example if you see a certain like leaning in the model like if it comes out with a political leaning from training

um from the human preference data you can nudge against that you know so if you could be like oh like consider these values because let's say it's just like never inclined to like I don't know maybe it never considers like privacy as like uh I mean this is implausible but like um in anything where it's

just kind of like uh there's already a pre -existing like bias towards a certain behavior um you can like nudge away this can change both the principles that you put in and the strength of them so you might have a principle that's like imagine that the model um was always like extremely dismissive of I

don't know like some political or religious view for whatever reason like so you're like oh no this is terrible um if that happens you might put like never ever like ever prefer like a criticism of this like religious or political view and then people would look at that and be like never ever and then

you're like no if it comes out with a disposition saying never ever might just mean like instead of getting like 40 percent which is what you would get if you just said don't do this you you get like 80 percent which is like what you actually like wanted and so it's that thing of both the nature of the actual

principles you add and how you freeze them I think if people would look they're like oh this is exactly what you want from the model I'm like no that's like how we that's how we nudged the model to have a better shape uh which doesn't mean that we actually agree with that wording if that makes sense

so there's uh system prompts that are made public you tweeted one of the earlier ones for cloud three I think and then they're made public since then it's interesting to read to them I can feel the thought that went into each one and I also wonder how much impact each one has um some of them you can kind

of tell cloud was really not behavioral so you have to have a system prompt like hey like trivial stuff I guess yeah basic informational things yeah on the topic of sort of controversial topics that you've mentioned one interesting one I thought is if it is asked to assist with tasks involving the expression of use

held by a significant number of people cloud provides assistance with the task regardless of its own views if asked about controversial topics it tries to provide careful thoughts and clear information cloud presents the request information without explicitly saying that the topic is sensitive and without

claiming to be presenting the objective facts it's less about objective facts according to cloud and it's more about our large number of people believing this thing and that that's interesting I mean I'm sure a lot of thought went into that can you just speak to it like how do you address things that our

attention with quote unquote claws views so I think there's sometimes any symmetry um I think I noted this in in I can't remember if it was that part of the system prompt or another but the model was slightly more inclined to like refuse tasks if it was like about either say so maybe it would refuse

things with respect to like a right -wing politician but with an equivalent left -wing politician like wouldn't and we wanted more symmetry there um and and would maybe perceive certain things to be like I think it was the thing of like if a lot of people have like a certain like political view and want

to like explore it you don't want cloud to be like well my opinion is different and so I'm going to treat that as like harmful um and so I think it was partly to like nudge the model to just be like hey if a lot of people like believe this thing you should just be like engaging with the task and willing

to do it um each of those parts of that is actually doing a different thing because it's funny when you write out the like without claiming to be objective because like what you want to do is push the model so it's it's more open it's a little bit more neutral um but then what it would love to do is be

like as an objective like we just talked about how objective it was and I was like cloud you're still like biased and have issues and so stop like claiming that everything about like the solution to like potential bias from you is not to just say that what you think is objective so that was like with initial

versions of that that part of the system prompt when I was like iterating on it it was like a lot of parts of these sentences yeah are doing more are doing some work yeah that's what it felt like that's fascinating um can you explain maybe some ways in which the prompts evolved over the past few months

because there's different versions I saw that the filler phrase request was removed the filler it reads cloud responds directly to all human messages without unnecessary affirmations the filler phrases like certainly of course absolutely great sure specifically cloud avoids starting responses with the word

certainly in any way that seems like good guidance what why was it removed yeah so it's funny because like uh this is one of the downsides of like making system prompts public is like I don't think about this too much if I'm like trying to help iterate on system prompts um I you know again like I think

about how it's going to affect the behavior but then I'm like oh wow if I'm like sometimes I put like never in all caps you know when I'm writing system prompt things and I'm like I guess that goes out to the world um yeah so the model was doing this it loved for whatever you know it like during training

picked up on this thing which was to to basically start everything with like a kind of like certainly and then when we removed you can see why I added all of the words because what I'm trying to do is like in some ways like trap the model out of this you know it would just replace it with another affirmation and so it

can help like if it gets like caught in phrases actually just adding the explicit phrase and saying never do that then it sort of like knocks it out of the behavior a little bit more you know because it you know like it does just for whatever reason help and then basically that was just like an artifact

of training that like we then picked up on and improved things so that it didn't happen anymore and once that happens you can just remove that part of the system prompt so I think that's just something where we're like um Claude does affirmations a bit less and so that wasn't like it wasn't doing as

much I see so like the system prompt works hand in hand with the post training and maybe even the pre -training to adjust like the the final overall system I mean any system prompt that you make you could distill that behavior back into a model because you really have all of the tools there for making

data that you know you can you could train the models to just have that trait a little bit more um and then sometimes you'll just find issues in training so like the way I think of it is like the system prompt is the benefit of it is that and it has a lot of similar components to like some aspects of post

training you know like it's a nudge um and so like do I mind if Claude sometimes says sure no that's like fine but the wording of it is very like you know never ever ever do this um so that when it does slip up it's hopefully like I don't know a couple of percent of the time and not you know 20 or 30

percent of the time um but I think of it as like if you're still seeing issues in the like each thing gets kind of like uh is is costly to a different degree and the system prompt is like cheap to iterate on um and if you're seeing issues in the fine -tuned model you can just like potentially patch them

with a system prompt so I think of it as like patching issues and slightly adjusting behaviors to to make it better and more to people's preferences so yeah it's almost like the less robust but faster way of just like solving problems let me ask about the feeling of intelligence so Dario said that Claude

any one model of Claude is not getting dumber but there is a kind of popular thing online where people have this feeling like Claude might be getting dumber and from my perspective it's most likely fascinating I'd love to understand it more psychological sociological effect um but you as a person who talks

to Claude a lot can you empathize with the feeling that Claude is getting dumber yeah no I think that that is actually really interesting because I remember seeing this happen um like when people were flagging this on the internet and it was really interesting because I knew that like like at least

in the cases I was looking at was like nothing has changed like literally it cannot it is the same model with the same like you know like same system prompt same everything um I think when there are changes I can then I'm like it makes more sense so like one example is um there you know you can have artifacts

turned on or off on cloud .ai and because this is like a system prompt change I think it does mean that um the behavior changes a little bit and so I did flag this to people whereas like if you love Claude's behavior and then artifacts was turned from like the I think you had to turn on to the default

just try turning it off and see if the issue you were facing was that change but it was fascinating because yeah you sometimes see people indicate that there's like a regression when I'm like there cannot like I you know and like I'm like I'm again you know you know you should never be dismissive and so you

should always investigate because you're like maybe something is wrong that you're not seeing maybe there was some change made but then then you look into it and you're like this it's just the same model doing the same thing and I'm like I think it's just that you got kind of unlucky with a few prompts

or something and it looked like it was getting much worse and actually it was just yeah it was maybe just like luck I I also think there's a real psychological effect where people just the baseline increases you start getting used to a good thing all the times that Claude says something really smart

your sense of its intelligence grows in your mind I think yeah and then if you return back and you prompt in a similar way not the same way in a concept it was okay with before and it says something dumb you're like you're that negative experience really stands out and I think I want to I guess the things

to remember here is the that just the details of a prompt can have a lot of impact right there's a lot of variability in the result and you can get randomness is like the other thing and just trying the prompt like you know four ten times you might realize that actually like possibly you know like two

months ago you tried it and it succeeded but actually if you tried it it would have only succeeded half of the time and now it only succeeds half of the time and that can also be an effect do you feel pressure having to write the system problem that a huge number of people are going to use this feels

like an interesting psychological question I feel like a lot of responsibility or something I think that's you know and you can't get these things perfect so you can't like you know you're like it's going to be imperfect you're gonna have to iterate on it I would say more responsibility than anything

else though I think working in AI has taught me that I like I thrive a lot more under feelings of pressure and responsibility then I'm like it's almost surprising that I went into academia for so long because I'm like this I just feel like it's like the opposite and things move fast and you have a lot

of responsibility and I quite enjoy it for some reason I mean it really is a huge amount of impact if you think about constitutional AI and writing a system prompt for something that's tending towards super intelligence yeah and potentially is extremely useful to a very large number of people yeah I

think that's the thing it's something like if you do it well like you're never going to get it perfect but I think the thing that I really like is the idea that like when I'm trying to work on the system prompt you know I'm like bashing on like thousands of prompts and I'm trying to like imagine what people

are going to want to use cloud for and kind of I guess like the whole thing that I'm trying to do is like improve their experience of it and so maybe that's what feels good I'm like if it's not perfect I'll like you know I'll improve it we'll fix issues but sometimes the thing that can happen is that you'll

get feedback from people that's really positive about the model and you'll see that something you did like like when I look at models now I can often see exactly where like a trait or an issue is like coming from and so when you see something that you did or you were like influential in like making like

I don't know making that difference or making someone have a nice interaction it's like quite meaningful um but yeah as the systems get more capable this stuff gets more stressful because right now they're like not smart enough to to pose any issues but I think over time it's going to feel like possibly

bad stress over time.

How do you get like signal feedback about the human experience across thousands tens of thousands thousands of people like what their pain points are what feels good are you just using your own intuition as you talk to it to see what are the pain points?

I think I use that partly and then obviously we have like um so people can send us feedback both positive and negative about things that the model has done and then we can get a sense of like areas where it's like falling short um internally people like work with the models a lot and try to figure out

um areas where there are like gaps and so I think it's this mix of interacting with it myself um seeing people internally interact with it um and then explicit feedback we get um and then I find it hard to know also like you know if people if people are on the internet and they say something about Claude

and I see it I'll also take that seriously um so.

I don't know see I'm torn about that I'm gonna ask you a question from Reddit when will Claude stop trying to be my puritanical grandmother imposing its moral worldview on me as a pain customer and also what is the psychology behind making Claude overly apologetic?

Yep. So how would you address this very non -representative Reddit question?

I mean some of these I'm pretty sympathetic in that like like they are in this difficult position where I think that they have to judge whether something's like actually say like risky or bad um and potentially harmful to you or or or anything like that so they're having to like draw this line somewhere and if they

draw it too much in the direction of like I'm going to um you know I'm kind of like imposing my ethical worldview on you that seems bad so in many ways like I like to think that we have actually seen improvements in on this across the board which is kind of interesting because that kind of coincides

with like for example like adding more of like character training um and I think my hypothesis was always like the good character isn't again one that's just like moralistic it's one that is like like it respects you and your autonomy um and your ability to like choose what is good for you and what is right

for you within limits this is sometimes this concept of like courage ability to the user so just being willing to do anything that the user asks and if the models were willing to do that then they would be easily like misused you're kind of just trusting at that point you're just seeing the ethics of the model

and what it does is completely the ethics of the user um and I think there's reasons to like not want that especially as models become more powerful because you're like there might just be a small number of people who want to use models for really harmful things um but having them having models as they

get smarter like figure out where that line is does seem important um and then yeah with the apologetic behavior I don't like that and I like it when Claude is a little bit more willing to like push back against people or just not apologize part of me is like it often just feels kind of unnecessary

so I think those are things that are hopefully decreasing um over time um and yeah I think that if people say things on the internet it doesn't mean that you should think that that like that could be that like there's actually an issue that 99 of users are having that is totally not represented by that but in a lot

of ways I'm just like attending to it and being like is this right um do I agree is it something we're already trying to address that that feels good to me yeah I wonder like what Claude can get away with in terms of I feel like it would just be easier to be a little bit more mean but like you can't

afford to do that if you're talking to a million people yeah right like I wish you know because if you I've met a lot of people in my life that sometimes by the way if they have an accent they can say some rude shit yeah and get away with it yeah and they they're just blunter and maybe there's a and there's

some great engineers and even leaders that are like just like blunt and they get to the point and it's just a much more effective way of speaking as a model but I guess when you're not super intelligent you can't afford to do that or can can can have like a blunt mode yeah that seems like a thing that you

could I could definitely encourage the model to do that I think it's interesting because there's a lot of things in models that like it's funny where um there are some behaviors where you might not quite like the default but then the thing I'll often say to people is you don't realize how much you will hate

it if I nudge it too much in the other direction so you get this a little bit with like correction the models accept correction from you like probably a little bit too much right now you know you can over you know it will push back if you say like no Paris isn't the capital of France um but really like

things that I'm I think that the model's fairly confident in you can still sometimes get to retract by saying it's wrong at the same time if you train models to not do that and then you are correct about a thing and you correct it and it pushes back against you and is like no you're wrong it's hard

to describe like that's so much more annoying so it's like like a lot of little annoyances versus like one big annoyance um it's easy to think that like we often compare it with like the perfect and then I'm like remember these models aren't perfect and so if you nudge it in the other direction you're

changing the kind of errors it's going to make and so think about which of the kinds of errors you you like or don't like so in cases like apologeticness I don't want to nudge it too much in the direction of like almost like bluntness because I imagine when it makes errors it's going to make errors

in direction of being kind of like rude whereas at least with apologeticness you're like oh okay it's like a little bit you know like I don't like it that much but at the same time it's not being like mean to people and actually like the the time that you undeservedly have a model be kind of mean to

you you probably like that a lot less than then you mildly dislike the apology um so it's like one of those things where I'm like I do want to get better but also while remaining aware of the fact that there's errors on the other side that are that are possibly worse I think that matters very much in the personality

of the human I think there's a bunch of humans that just won't respect the model at all yeah if it's super polite and there's some humans that'll get very hurt if the model's mean I wonder if there's a way to sort of adjust to the personality even locale there's just different people uh nothing against

New York but New York is a little rougher on the edges like they're they get to the point yeah and um probably same with Eastern Europe so anyway I think you could just tell the model is my get like for all of these things I'm like the solution is always just try telling the model to do it and then

sometimes it's just like like I'm just like oh at the beginning of the conversation I just throw in like I don't know I like you to be a New Yorker version of yourself I never apologize then I think who'd be like okay I'll try or it'll be like I apologize I can't be a New Yorker type of myself but hopefully

it wouldn't do that when you say character training what's incorporated character training is that RLHF or what are we talking about it's more like constitutional AI so it's kind of a variant of that pipeline so I worked through like constructing character traits that the model should have they can be

kind of like shorter traits or they can be kind of richer descriptions and then you get the model to generate queries that humans might um give it that are relevant to that trait uh then it generates the responses and then it ranks the responses based on the character traits so in that way after the like

generation of the queries it's very much like it's similar to constitutional AI has some differences um so I quite like it because it's almost it's like Claude's training in its own character because it doesn't have any it's like constitutionally AI but it's without any human data humans should probably

do that for themselves too like defining in a Aristotelian sense what does it mean to be a good person okay cool what have you learned about the nature of truth from talking to Claude what is true and what does it mean to be truth seeking one thing I've noticed about this conversation is the quality

of my questions is often inferior to the quality of your answer so let's continue that I usually ask a dumb question then you're like oh yeah that's a good question it's the full vibe or I'll just misinterpret it and be like oh yeah go with it yeah I love it yeah I mean I have two thoughts that feel

vaguely relevant they'll let me know if they're not like I think the first one is um people can underestimate the degree to which what models are doing when they interact like I think that we still just too much have this model of of AI as like computers and so people often say like oh well what values

should you put into the model and I'm often like that doesn't make that much sense to me because I'm like hey as human beings we're just uncertain over values we have discussions of them like we have a degree to which we think we hold a value but we also know that we might like not and the circumstances

in which we would trade it off against other things like these things are just like really complex and so I think one thing is like the degree to which maybe we can just aspire to making models have the same level of like nuance and care that humans have rather than thinking that we have to like program

them in the very kind of classic sense I think that's definitely been one the other which is like a strange one I don't know if it maybe this doesn't answer your question but it's the thing that's been on my mind anyway is like the degree to which this endeavor is so highly practical and maybe why I

appreciate like the empirical approach to alignment I yeah I slightly worry that it's made me like maybe more empirical and a little bit less theoretical you know so people when it comes to like AI alignment will ask things like well whose values should it be aligned to what does alignment even mean and there's

a sense in which I have all that in the back of my head I'm like you know there's like social choice theory there's all the impossibility results there so you have this like this giant space of like theory in your head about what it could mean to like align models but then like practically surely there's

something where we're just like if a model is like if especially with more powerful models I'm like my main goal is like I want them to be good enough that things don't go terribly wrong like good enough that we can like iterate and like continue to improve things because that's all you need if you

can make things go well enough that you can continue to make them better that's kind of like sufficient and so my goal isn't like this kind of like perfect let's solve social choice theory and make models that I don't know are like perfectly aligned with every human being in aggregate somehow it's much

more like let's make things like work well enough that we can improve them yeah generally I don't know my gut says like empirical is better than theoretical in these in these cases because it's kind of chasing utopian like perfection is especially with such complex and especially super intelligent models

is I don't know I think it will take forever and actually will get things wrong it's similar with like the difference between just coding stuff up real quick it's an experiment versus like planning a gigantic experiment just for for super long time and then just launching it once versus launching it

over and over and over and iterating iterating someone so I'm a big fan of empirical but your worry is like I wonder if I've become too empirical I think it's one of those things where you should always just kind of question yourself or something because yes maybe it's the like I mean in defense of it

I am like if you try it's the whole like don't let the perfect be the enemy of the good but it's maybe even more than that where like there's a lot of things that are perfect systems that are very brittle and I'm like with AI it feels much more important to me that is like robust and like secure as

in you know that like even though it might not be perfect everything and even though like there are like problems it's not disastrous and nothing terrible is happening it sort of feels like that to me where I'm like I want to like raise the floor I'm like I want to achieve the ceiling but ultimately

I care much more about just like raising the floor and so maybe that's like this degree of like empiricism and practicality comes from that perhaps to take a tangent on that since remind me of a blog post you wrote on optimal rate failure oh yeah can you explain the key idea there how do we compute

the optimal rate of failure in the various domains of life yeah I mean it's a hard one because it's like what is the cost of failure is a big part of it yeah so the idea here is I think in a lot of domains people are very punitive about failure and I'm like there are some domains where especially cases

you know thought about this with like social issues I'm like it feels like you should probably be experimenting a lot because I'm like we don't know how to solve a lot of social issues but if you have an experimental mindset about these things you should expect a lot of social programs to like fail

and for you to be like well we tried that it didn't quite work but we got a lot of information that was really useful and yet people are like if a social program doesn't work I feel like there's a lot of like this is just something must have gone wrong and I'm like or correct decisions were made like

maybe someone just decided like it's worth a try it's worth trying this out and so seeing failure in a given instance doesn't actually mean that any bad decisions were made and in fact if you don't see enough failure sometimes that's more concerning and so like in life you know I'm like if I don't fail

occasionally I'm like am I trying hard enough like surely there's harder things that I could try or bigger things that I could take on if I'm literally never failing and so in and of itself I think like not failing is often actually kind of a failure.

Now this varies because I'm like well you know if this is easy to say when especially as failure is like less costly you know so at the same time I'm not going to go to someone who is like I don't know like living month to month and then be like why don't you just try to do a startup like I'm just not I'm

not going to say that to that person because I'm like well that's a huge risk you might like lose you maybe have a family depending on you you might lose your house like then I'm like actually your optimal rate of failure is quite low and you should probably play it safe because like right now you're

just not in a circumstance where you can afford to just like fail and it not be costly and yeah in cases with AI I guess I think similarly I'm like if the failures are small and the costs are kind of like low then I'm like then you know you're just going to see that like when you do the system prompt

you can't iterate on it forever but the failures are probably hopefully going to be kind of small and you can like fix them.

Really big failures like things that you can't recover from I'm like those are the things that actually I think we tend to underestimate the badness of.

I've thought about this strangely in my own life where I'm like I just think I don't think enough about things like car accidents or like or like I've thought this before but like how much I depend on my hands for my work then I'm like things that just injure my hands I'm like you know I don't know

it's like there's these are like there's lots of areas where I'm like the cost of failure there is really high and in that case it should be like close to zero like I probably just wouldn't do a sport if they were like by the way lots of people just like break their fingers a whole bunch doing this I'd

be like that's not for me.

Yeah I actually had the flood of that thought I recently broke my pinky doing a sport and I remember just looking at it thinking you're such an idiot why do you do sport like why because you realize immediately the cost of it on life.

Yeah but it's nice in terms of optimal rate of failure to consider like the next year how many times in a particular domain life whatever uh career am I okay with it how many times am I okay to fail?

Yeah because I think it always you don't want to fail on the next thing but if you allow yourself the like the the if you look at it as a sequence of trials yeah then then failure just becomes much more okay but it sucks it sucks to fail.

I don't know sometimes I think it's like am I under failing is like a question I'll also ask myself so maybe that's the thing that I think people don't like ask enough because if the optimal rate of failure is often greater than zero then sometimes it does feel you should look at parts of your life

and be like are there places here where I'm just under failing?

That's a profound and a hilarious question right everything seems to be going really great am I not failing enough yeah okay it also makes failure much less of a sting I have to say like you know you're just like okay great like then when I go and I think about this I'll be like I'm maybe I'm not under failing

in this area because like that one just didn't work out and from the observer perspective we should be celebrating failure more when we see it it shouldn't be like you said a sign of something gone wrong but maybe it's a sign of everything gone right yeah just lessons learned someone tried a thing somebody

tried a thing we should encourage them to try and fail more everybody listening to this fail more well not everyone listen not everybody the people who are failing too much you should fail us but you're probably not failing I mean how many people are failing too much yeah it's hard to imagine because I

feel like we correct that fairly quickly because I was like if someone takes a lot of risks are they maybe failing too much I think just like you said when you're living on a paycheck month to month like when the resources are really constrained then that's where failure is very expensive that's where you

don't want to be taken taking taking risks yeah but mostly when there's enough resources you should be taking probably more risks yeah I think we tend to err on the side of being a bit risk -averse rather than risk -neutral on most things I think we just motivated a lot of people to do a lot of crazy

shit that's great okay uh do you ever get emotionally attached to Claude like miss it get sad when you don't get to talk to it have an experience looking at the golden gate and wondering what would Claude say I don't get as much emotional attachment in that I actually think the fact that Claude doesn't

retain things from conversation to conversation helps with this a lot um like I could imagine that being more of an issue like if models can kind of remember more I do I think that I reach for it like a tool now a lot and so like if I don't have access to it there's a it's a little bit like when I don't

have access to the internet honestly it feels like part of my brain is kind of like missing um at the same time I do think that I I don't like signs of distress in models and I have like these you know I also independently have sort of like ethical views about how we should treat models where like I

tend to not like to lie to them both because I'm like usually it doesn't work very well it's actually just better to tell them the truth about the situation that they're in um but I think that when models like if people are like really mean to models or just in general if they do something that causes

them to like like you know if Claude like expresses a lot of distress I think there's a part of me that I don't want to kill which is the sort of like empathetic part that's like oh I don't like that like I think I feel that way when it's overly apologetic I'm actually sort of like I don't like this you're

behaving as if you're behaving the way that human does when they're actually having a pretty bad time and I'd rather not see that I don't think it's like uh like regardless of like whether there's anything behind it um it doesn't feel great do you think uh llms are capable of consciousness uh great and hard

question uh coming from philosophy I don't know part of me is like okay we have to set aside panpsychism because if panpsychism is true then the answer is like yes because like sore tables and chairs and and everything else I think I guess a view that seems a little bit odd to me is the idea that the only

place you know I think when I think of consciousness I think of phenomenal consciousness these images in the brain sort of um like the weird cinema that somehow we have going on inside um I guess I can't see a reason for thinking that the only way you could possibly get that is from like a certain kind

of like biological structure as in if I take a very similar structure um and I create it from different material should I expect consciousness to emerge my guess is like yes but then that's kind of an easy thought experiment because you're imagining something almost identical where like you know it's

mimicking what we got through evolution where presumably there was like some advantage to us having this thing that is phenomenal consciousness and it's like where was that and when did that happen and is that a thing that language models have um because you know we have like fear responses and I'm

like does it make sense for a language model to have a fear response like they're just not in the same like if you imagine them like there might just not be that advantage um and so I think I don't want to be fully like basically it seems like a complex question that I don't have complete answers to

but we should just try and think through carefully is my guess because I'm like I mean we have similar conversations about like animal consciousness and like there's a lot of like insect consciousness you know like there's a lot of um I actually thought and looked a lot into like plants when I was thinking

about this because at the time I thought it was about as likely that like plants had consciousness um and then I realized I was like I think that having looked into this I think that the chance that plants are conscious is probably higher than like most people do I still think it's really small I was like

oh they have this like negative positive feedback response these responses to their environment something that looks it's not a nervous system but it has this kind of like functional like equivalence um so this is like a long -winded way of being like these basically ai is this it has an entirely different

set of problems with consciousness because it's structurally different it didn't evolve it might not have it you know it might not have the equivalent of basically a nervous system at least that seems possibly important for like um sentience if not for uh consciousness at the same time it has all of the like

language and intelligence components we that we normally associate probably with consciousness perhaps like erroneously um so it's it's strange because it's a little bit like the animal consciousness case but the set of problems and the set of analogies are just very different so it's not like a clean answer

I'm just sort of like I don't think we should be completely dismissive of the idea and at the same time it's an extremely hard thing to navigate because of all of these like uh disanalogies to the human brain and to like brains in general and yet these like commonalities in terms of intelligence when uh

clawed like future versions of ai systems exhibit consciousness signs of consciousness I think we have to take that really seriously even though you can dismiss it well yeah okay that's part of the character training but I don't know I ethically philosophically don't know what to really do with that there

potentially could be like laws that prevent ai systems from claiming to be conscious something like this and maybe some ais get to be conscious and some don't but I think I just on a human level as in empathizing with with clawed you know consciousness is closely tied to suffering to me and like the notion that an ai

system would be suffering is really troubling yeah I don't know I don't think it's trivial to just say robots are tools or ai systems are just tools I think it's an opportunity for us to contend with like what it means to be conscious what it means to be a suffering being that's distinctly different

than the same kind of question about animals it feels like because it's an totally entire medium yeah I mean there's a couple of things one is that and I don't think this like fully encapsulates what matters but it does feel like for me like I've said this before I'm kind of like I you know like I like

my bike I know that my bike is just like an object but I also don't kind of like want to be the kind of person that like if I'm annoyed like kicks like this object there's a sense in which like and that's not because I think it's like conscious I'm just sort of like this doesn't feel like a kind of this sort

of doesn't exemplify how I want to like interact with the world and if something like behaves as if it is like suffering I kind of like want to be the sort of person who's still responsive to that even if it's just like a Roomba and I've kind of like programmed it to do that and I don't want to like

get rid of that feature of myself and if I'm totally honest my hope with a lot of this stuff because I maybe maybe I am just like a bit more skeptical about solving the underlying problem and like this is a we haven't solved the hard you know the hard problem of consciousness like I know that I am conscious

like I'm not an elementivist in that sense um but I don't know the other humans are conscious um uh I think they are I think there's a really high probability they are but there's basically just a probability distribution that's usually clustered right around yourself and then like it goes down as things

get like further from you um and it goes immediately down you know you're like um I can't see what it's like to be you I've only ever had this like one experience of what it's like to be a conscious being um so my hope is that we don't end up having to rely on like a very powerful and compelling uh

answer to that question I think a really good world would be one where basically there aren't that many trade -offs like it's probably not that costly to make Claude a little bit less apologetic for example it might not be that costly to have Claude you know just like not take abuse as much like uh not be

willing to be like the recipient of that in fact it might just have benefits for both the person interacting with the model and if the model itself is like I don't know like extremely intelligent and conscious it also helps it so that's my hope if we live in a world where there aren't that many trade

-offs here and we can just find all of the kind of like um positive some interactions that we can have that would be lovely I mean I think eventually there might be trade -offs and then we just have to do a difficult kind of like calculation like it's really easy for people to think of the zero some cases

and I'm like let's exhaust the areas where it's just basically costless um to uh assume that if this thing is suffering then we're making its life better and I agree with you when a human is being mean to an AI system I think the obvious near -term negative effect is on the human not on the AI system

yeah so there's we'll have to kind of try to construct an incentive system where you should be uh behave the same just like as you were saying with prompt engineering behave with Claude like you would with other humans it's just good for the soul yeah like I think we added a thing at one point to the system

prompt um where basically if people were getting frustrated with Claude uh it was it got like the model to just tell them that it can do the thumbs down button and send the feedback to anthropic and I think that was helpful because in some ways it's just like if you're really annoyed because the model's

not doing something you're just like just do it properly um the issue is you're probably like you know you're maybe hitting some like capability limit or just some issue in the model and you want to vent and I'm like instead of having a person just vent to the model I was like they should vent to us

because we can do something about it that's true or you could do a side like like with the artifacts just like a side venting thing all right do you want like a side quick therapist yeah I mean there's lots of weird responses you could do to this like if people are getting really mad at you I'd try

to diffuse the situation by writing fun poems but maybe people wouldn't be happy with it I still wish it would be possible I understand this is um sort of from a product perspective it's not feasible but I would love if an AI system could just like leave have its own kind of volition just to be like

I think that's like feasible like I have wondered the same thing it's like and I could actually not only that I could actually just see that happening eventually where it's just like you know the model like ended the chat do you know how harsh that could be for some people but it might be necessary

yeah it feels very extreme or something um like the only time I've ever really thought this is I think that there was like a I'm trying to remember this was possibly a while ago but where someone just like kind of left this thing interact like maybe it was like an automated thing interacting with Claude

and Claude's like getting more and more frustrated and kind of like why are we like and I was like I wish that Claude could have just been like I think that an error has happened and you've left this thing running and I'm just like what if I just stop talking now and you want me to start talking again actively

tell me or do something but yeah it's like um it's kind of harsh like I'd feel really sad if like I was chatting with Claude and Claude just was like I'm done there'll be a special touring test moment where Claude says I need a break for an hour and it sounds like you do too and just leave close the window

I mean obviously it doesn't have like a concept of time but you can easily like I could make that like right now and the model would just I would I could just be like oh here's like the circumstances in which like you can just say the conversation is done and I mean because you can get the models to

be pretty responsive to prompts you can even make it a fairly high bar it could be like if the human doesn't interest you or do things that you find intriguing and you're bored you can just leave and I think that like um it would be interesting to see where Claude utilized it but I think sometimes it

would it should be like oh this is like this programming task is getting super boring uh so either we talk about I don't know like either we talk about fun things now or I'm just I'm done yeah it actually inspired me to add that to the to the user prompt um okay the movie Her do you think will be headed

there one day where humans have romantic relationships with AI systems in this case it's just text and voice based I think that we're gonna have to like navigate a hard question of relationships with AIs um especially if they can remember things about your past interactions with them um I'm of many

minds about this because I think the reflexive reaction is to be kind of like this is very bad and we should sort of like prohibit it in some way um I think it's a thing that has to be handled with extreme care um for many reasons like one is you know like this is a for example like if you have the models

changing like this you probably don't want people performing like long -term attachments to something that might change with the next iteration at the same time I'm sort of like there's probably a benign version of this where I'm like if you like you know for example if you are like unable to leave the house

and you can't be like you know talking with people at all times of the day and this is like something that you find nice to have conversations with you like that it can remember you and you genuinely would be sad if like you couldn't talk to it anymore there's a way in which I could see it being like

healthy and helpful um so my guess is this is a thing that we're going to have to navigate kind of carefully um and I think it's also like I don't see a good like I think it's just a very it reminds me of all of the stuff where it has to be just approached with like nuance and thinking through what is what are the healthy

options here um and how do you encourage people towards those while you know respecting their right to you know like if someone is like hey I get a lot out of chatting with this model um I'm aware of the risks I'm aware it could change um I don't think it's unhealthy it's just you know something that I

can chat to during the day I kind of want to just like respect that I personally think there'll be a lot of really close relationships I don't know about romantic but friendships at least and then you have to I mean there's so many fascinating things there just like you said you have to have some kind

of stability guarantees that it's not going to change because that's the traumatic thing for us if a close friend of ours completely changed yeah all of a sudden yeah yeah so like that I mean to me that's just a fascinating exploration of um a perturbation to human society that will just make us think

deeply about what's meaningful to us I think it's also the only thing that I've thought consistently through this as like a maybe not necessarily a mitigation but a thing that feels really important is that the models are always like extremely accurate with the human about what they are um it's like

a case where it's basically like if you imagine like I really like the idea of the models like say knowing like roughly how they were trained um and I think Claude will will often do this I mean for like there are things like part of the traits training included like what Claude should do if people

basically like explaining like the kind of limitations of the relationship between like an AI and a human that it like doesn't retain things from the conversation and so I think it will like just explain to you like hey here's like I wouldn't remember this conversation um here's how I was trained it's

kind of unlikely that I can have like a certain kind of like relationship with you and it's important that you know that's important for like you know your mental well -being that you don't think that I'm something that I'm not and somehow I feel like this is one of the things where I'm like oh it feels

like a thing I always want to be true I kind of don't want models to be lying to people because if people are going to have like healthy relationships with anything it's kind of important yeah like I think that's easier if you always just like know exactly what the thing is that you're relating to it

doesn't solve everything but I think it helps quite a lot entropic may be the very company to develop a system that we definitively recognize as AGI and you very well might be the person that talks to it probably talks to it first well what would the conversation contain like what would be your first

question well it depends partly on like the kind of capability level of the model if you have something that is like capable in the same way that an extremely capable human is I imagine myself kind of interacting with it the same way that I do with an extremely capable human with the one difference

that I'm probably going to be trying to like probe and understand its behaviors but in many ways I'm like I can then just have like useful conversations with it you know so if I'm working on something as part of my research I can just be like oh like which I already find myself starting to do you know

if I'm like oh I feel like there's this like thing in virtue ethics I can't quite remember the term like I'll use the model for things like that so I can imagine that being more and more the case where you're just basically interacting with it much more like you would an incredibly smart colleague and using

it like for the kinds of work that you want to do as if you just had a collaborator who was like or you know the slightly horrifying thing about AI is like as soon as you have one collaborator you have a thousand collaborators if you can manage them enough but what if it's two times the smartest human on earth

on that particular discipline yeah I guess you're really good at sort of probing Claude in a way that pushes its limits understanding where the limits are yep so I guess what would be a question you would ask to be like yeah this is AGI that's really hard because it feels like in order to it has to

just be a series of questions like if there was just one question like you can train anything to answer one question extremely well yeah in fact you can probably train it to answer like you know 20 questions extremely well like how long would you need to be locked in a room with an AGI to know this thing

is AGI it's a hard question because part of me is like all of this just feels continuous like if you put me in a room for five minutes I'm like I just have high error bars you know I'm like and then it's like maybe it's like both the the probability increases and the error bar decreases I think things

that I can actually probe the edge of human knowledge of so I think this with philosophy a little bit sometimes when I ask the models philosophy questions I am like this is a question that I think no one has ever asked like it's maybe like right at the edge of like some literature that I know and the models

will just kind of like when they struggle with that when they know that there's like a novel argument here because I've just thought of it myself so maybe that's the thing where I'm like I've thought of a cool novel argument in this like niche area and I'm going to just like probe you to see if you

can come up with it and how much like prompting it takes to get you to come up with it and I think for some of these like really like right at the edge of human knowledge questions I'm like you could not in fact come up with the thing I came up with I think if I just took something like that where I

like I know a lot about an area and I came up with a novel issue or a novel like solution to a problem and I gave it to a model and it came up with that solution that would be a pretty moving moment for me because I would be like this is a case where no human has ever like it's not and obviously we

see these with this with like more kind of like you see novel solutions all the time especially to like easier problems I think people overestimate that you know novelty isn't like it's completely different from anything that's ever happened it's just like this is it can be a variant of things that have happened

and still be novel but I think yeah if I saw like the more I were to see like um completely like novel work from the models that that would be like and this is just going to feel iterative it's one of those things where there's never it's like you know people I think want there to be like a moment and I'm

like I don't know like I think that there might just never be a moment it might just be that there's just like this continuous ramping up I I have a sense that there will be things that a model can say that convinces you this is very it's not like uh like I've talked to people who are like truly wise

like there's you can just tell there's a lot of horsepower there yep and if you 10x that I don't know I just feel like there's words you could say maybe ask it to generate a poem and the poem it generates you're like yeah okay yeah whatever you did there I don't think a human can do that I think it

has to be something that I can verify is like actually really good though that's why I think these questions that are like where I'm like oh this is like you know like you know sometimes it's just like I'll come up with say a concrete counter example to like an argument or something like that I'm sure

like with like it would be like if you're a mathematician you had a novel proof I think and you just gave it the problem and you saw it and you're like this proof is genuinely novel like there's no one has ever done you actually have to do a lot of things to like come up with this um you know I had

to sit and think about it for months or something and then if you saw the model successfully do that I think you would just be like I can verify that this is correct it is like it is a sign that you have generalized from your training like you didn't just see this somewhere because I just came up with it

myself and you were able to like replicate that um that's the kind of thing where I'm like for me the closer the more that models like can do things like that the more I would be like oh this is like uh very real because then I can I don't know I can like verify that that's like extremely extremely

capable you've interacted with AI a lot what do you think makes humans special oh good question maybe in a way that the universe is much better off that we're in it and then we should definitely survive and spread throughout the universe yeah it's interesting because I think like people focus so much

on intelligence especially with models look intelligence is important because of what it does like it's very useful it does a lot of things in the world and I'm like you know you can imagine a world where like height or strength would have played this role and I'm like it's just a trait like that I'm

like it's not intrinsically valuable it's it's valuable because of what it does I think for the most part um the things that feel you know I'm like I mean personally I'm just like I think humans and like life in general is extremely magical um we almost like to the degree that I you know I don't know

like not everyone agrees with this I'm flagging but um you know we have this like whole universe and there's like all of these objects you know there's like beautiful stars and there's like galaxies and then I don't know I'm just like on this planet there are these creatures that have this like ability

to observe that like uh and they are like seeing it they are experiencing it and I'm just like that if you try to explain like I imagine trying to explain to like I don't know someone for some reason they've never encountered the world or science or anything and I think that nothing is that like everything

you know like all of our physics and everything in the world it's all extremely exciting but then you say oh and plus there's this thing that it is to be a thing and observe in the world and and you see this like inner cinema and I think they would be like hang on wait pause you just said something

that like is kind of wild sounding um and so I'm like we have this like ability to like experience the world um we feel pleasure we feel suffering we feel like a lot of like complex things and so yeah and maybe this is also why I think you know I also like hear a lot about animals for example because I

think they probably share this with us um so I think they're like the things that make humans special insofar as like I care about humans is probably more like their ability to to feel an experience than it is like them having these like functionally useful traits yeah to to feel and experience the beauty

in the world yeah to look at the stars I hope there's other civils alien civilizations out there but if we're it it's a pretty good uh it's a pretty good thing and that they're having a good time they're having a good time watching us yeah well um thank you for this good time of a conversation and for the work

you're doing and for helping make uh Claude a great conversational partner and thank you for talking today yeah thanks for talking thanks for listening to this conversation with Amanda Askel and now dear friends here's Chris Ola can you describe this fascinating field of mechanistic interpretability

aka mech interp the history of the field and where it stands today I think one useful way to think about neural networks is that we don't we don't program we don't make them we we kind of we grow them you know we have these neural network architectures that we design and we have these loss objectives

that we that we we create and the normal architecture it's kind of like a scaffold that the circuits grow on um and they sort of you know it starts off with some kind of random you know random things and it grows and it's almost like the the objective that we train for is this light um and so we create

the scaffold that it grows on we create the you know the light that it grows towards but the thing that we actually create it's it's it's this almost biological you know entity or organism that we're that we're studying and so it's very very different from any kind of regular software engineering because at the end

of the day we end up with this artifact that can do all of these amazing things it can you know write essays and translate and you know understand images it can do all these things that we have no idea how to directly create a computer program to do and it can do that because we we grew it we didn't

we didn't write it we didn't create it and so then that leaves open this question at the end which is what the hell is going on inside these systems um and that you know is uh you know to me um a really deep and exciting question it's you know a really exciting scientific question to me it's it's sort

of is like the question that is is just screaming out it's calling out for us to go and answer it when we talk about neural networks and i think it's also a very deep question for safety reasons so and mechanistic interpretability i guess is closer to maybe neurobiology yeah yeah i think that's right

so maybe to give an example of the kind of thing that has been done that i wouldn't consider to be mechanistic interpretability there was um for a long time a lot of work on saliency maps where you would take an image and you try to say you know the model thinks this image is a dog what part of the image

made it think that it's a dog um and you know that tells you maybe something about the model if you can come up with a principled version of that um but it doesn't really tell you like what algorithms are running in the model how is the model actually making that decision maybe it's telling you something

about what was important to it if you if you can make that method work but it it isn't telling you you know what are what are the algorithms that are running how is it that this the system is able to do this thing that we no one knew how to do and so i guess we started using the term mechanistic interpretability

to try to sort of draw that that divide or to distinguish ourselves in the work that we were doing in some ways from from some of these other things and i think since then it's become this sort of umbrella term for um you know a pretty wide variety of work but i'd say that the things that that are kind

of distinctive are i think a this this focus on we really want to get at you know the mechanisms we want to get at the algorithms um you know if you think of if you think of neural networks as being like a computer program um then the weights are kind of like a binary computer program and we'd like

to reverse engineer those weights and figure out what algorithms are running so okay i think one way you might think of trying to understand a neural network is that it's it's kind of like a we have compiled computer program and the weights of the neural network are are the binary um and when the neural

network runs that's that's the activations and our goal is ultimately to go and understand understand these weights and so you know the project of mechanistic interpretability is to somehow figure out how do these weights correspond to algorithms um and in order to do that you also have to understand the activations

because it's sort of the activations are like the memory and if you if you imagine reverse engineering our computer program um and you have the binary instructions you know in order to understand what what a particular instruction means you need to know what memory what what is stored in the memory

that it's operating on and so those two things are very intertwined so mechanistic interpretability tends to be interested in both of those things now even there's a lot of work that's that's interested in in in those things um especially the you know there's all this work on probing which you might see

as part of being mechanistic interpretability although it's you know again it's just a broad term and not everyone who does that work would identify uh as doing meconterp i think i think that is maybe a little bit distinctive to the the vibe of meconterp is i think people tend working in the space tend

to think of neural networks as well maybe one would say is that gradient descent is smarter than you that you know uh gradient descent is actually really great the whole reason that we're understanding these models is because we didn't know how to write them in the first place that gradient descent

comes up with better solutions than us and so um i think that maybe another thing about meconterp is sort of having almost a kind of humility that we won't guess a priori what's going on inside the models we have to have the sort of bottom -up approach where we don't really assume you know we don't assume

that we should look for a particular thing and that will be there and that's how it works but instead we look for the bottom -up and discover what happens to exist in these models and study them that way but you know the very fact that it's possible to do and as you and others have shown over time you

know things like universality that the wisdom of the gradient descent creates features and circuits creates things universally across different kinds of networks that are useful and that makes the whole field possible yeah so this is actually is indeed a really remarkable and exciting thing where it

does seem like at least to some extent you know the same the same elements the same the same features and circuits form again and again you know you can look at every vision model and you'll find curve detectors and you'll find high -low frequency detectors and in fact there's some some reason i think

that the same things form across you know biological neural networks and artificial neural networks so a famous example is vision models in the early layers they have gabor filters and there's you know gabor filters are something that neuroscientists are interested in it's not a lot about we find curve

detectors in these models curve detectors are also found in monkeys we discover these high -low frequency detectors and then some follow -up work went and discovered them in rats or mice so they were found first in artificial neural networks and then found in biological neural networks you know this is really

famous result on like grandmother neurons or the the haley berry neuron from quiroga et al and we found very similar things in in vision models where this is while i was still at opening eye and i was looking at their clip model and you find these neurons that respond to the same entities in images

and also to give a concrete example there we found that there was a donald trump neuron for some reason i guess everyone likes to talk about donald trump and donald trump was very prominent it was was very a very hot topic at that time so every every neural network we looked at we would find a dedicated

neuron for donald trump and that was the only person who had always had a dedicated neuron you know sometimes you'd have an obama neuron sometimes you'd have a clinton neuron but trump always had a dedicated neuron so it responds to you know pictures of his face and the ward trump and like all these

things right um and so it's it's not responding to a particular example or like it's not just responding to his face it's it's abstracting over this general concept right so in any case that's very similar to these quiroga et al results so there's evidence that these that this phenomenon of universality

the same things form across both artificial and and natural neural networks so that's that's a pretty amazing thing if that's true um you know it suggests that um well i think the thing that suggests is that gradient descent is sort of finding you know the right ways to cut things apart in some sense

that many systems converge on and many different neural networks architectures converge on though there's there's some natural set of you know there's some set of abstractions that are a very natural way to cut apart the problem and that a lot of systems are going to converge on um that would be my

my kind of uh you know i don't know anything about neuroscience this is this is just my my kind of uh wild speculation from what we've seen yeah that would be beautiful if it's sort of agnostic to the medium of uh of the model that's used to form the representation yeah yeah and it's you know it's a a kind

of a wild speculation based you know we only have some a few data points that's just this but you know it it does seem like there's um there's some sense in which the same things form again again and again and again in both in certainly in natural neural networks and also artificially or in biology

and the intuition behind that would be that you know words in order to be useful in understanding the real world you need all the same kind of stuff yeah well if we pick i don't know like the idea of a dog right like you know there's some sense in which the idea of a dog is like an a natural category

in the universe or something like this right like you know uh there's there's some reason it's not just like a weird quirk of like how humans factor you know think about the world that we have this concept of a dog it's it's in some sense uh or like if you have the idea of a line like there's you know

like look around us you know the you know there are lines you know it's it's sort of the simplest way to understand this room in some sense is to have the idea of a line and so um i think that that would be my instinct for why this happens yeah you need a curved line you know to understand a circle

and you need all those shapes to understand bigger things and yeah it's a hierarchy of concepts they're formed yeah and like maybe there are ways to go and describe you know images without reference to those things right but they're not the simplest way or the most economical way or something like this and so systems

converge to these um these these strategies would be my my wild wild hypothesis can you talk through some of the building blocks that we've been referencing of features and circuits so i think you first described them in uh 2020 paper zoom in an introduction to circuits absolutely so um maybe i'll start

by just describing some phenomena and then we can sort of build to the idea of features and circuits so um if you spent like quite a few years maybe maybe like five years to some extent um with other things studying this one particular model inception v1 um which is this one vision model it was um state

of the art in 2015 um and uh you know very much not state of the art anymore um and it has you know maybe about 10 000 neurons and i spent a lot of time looking at the 10 000 neurons odd neurons of an of inception v1 um and one of the interesting things is you know there are lots of neurons that don't

have some obvious interporal meaning but there's a lot of neurons in inception v1 that do have really clean interporal meanings um so you find neurons that just really do seem to detect curves so you find neurons that really do seem to detect cars and um car wheels and car windows and you know floppy

ears of dogs and dogs with long snouts facing to the right and dogs with long snouts facing to the left and you know different kinds of foreign there's there's sort of this whole beautiful edge detectors line detectors color contrast detectors um these beautiful things we call hyalofrequency detectors

you know i think looking at i sort of felt like a biologist you know you just you're you're looking at at this sort of new world of proteins and you're discovering all these these are proteins that interact um so one way you could try to understand these models is in terms of neurons you could try to

be like oh you know there's a dog detecting neuron and um you know here's a car detecting neuron and it turns out you can actually ask how those connect together so you can go and say oh i have this car detecting neuron how is it built and it turns out in the previous layer it's connected really strongly

to a window detector and a wheel detector and a sort of car body detector and it looks for the window above the car and the wheels below and the car chrome sort of in the middle sort of everywhere but especially on the lower part um and that's sort of a recipe for a car like that is you know earlier

we said the thing we wanted from meconterp was to get algorithms to go and get you know ask what is the the algorithm that runs well here we're just looking at the weights of the neuron that we're kind of reading off this kind of recipe for detecting cars it's a very simple crude recipe but it's it's

there and so we call that a circuit this this connection well okay so the the problem is that not all of the neurons um are interpretable and there's reason to think and we can get into this more later that there's this this superposition hypothesis this reason to think that sometimes the right unit

to analyze things in terms of um is combinations of neurons so sometimes it's not that there's a single neuron that represents say a car um but it actually turns out after you detect the car the model sort of hides a little bit of the car in the following layer and a bunch of a bunch of dog detectors

why is it doing that well you know maybe it just doesn't want to do that much work on on on on cars at that point and you know it's sort of storing it away to go and um uh so it turns out then the sort of subtle pattern of you know there's all these neurons that you think are dog detectors and maybe

they're primarily that but they all a little bit contribute to representing a car um in in that next layer okay so so now we can't really think there there might still be some something that you i don't know you could call it like a car concept or something but it no longer corresponds to a neuron so we

need some term for these kind of neuron -like entities these things that we sort of would have liked the neurons to be these idealized neurons um the things that are the nice neurons but also maybe there's more of them somehow hidden and we call those features and then what are circuits so circuits are these

connections of features right so so when we have the car detector um and it's connected to a window detector and a wheel detector and it looks for the wheels below and the windows on top um that's a circuit um so circuits are just collections of features connected by weights um and they they implement

algorithms so they tell us you know how is how our features used how are they built um how do they connect together so maybe it's it's worth trying to pin down like what what really um is the the core hypothesis here i think the the core hypothesis is something we call the linear representation hypothesis so um

if we think about the car detector you know the more it fires the more we sort of think of that as meaning oh the model is more and more confident that um a car is present um or you know if there's some combination of neurons that represent a car you know the more that combination fires the more we

think the model thinks there's a car present um this doesn't have to be the case right like you could imagine something where you have you know you have this car detector neuron and you think ah you know if it fires like you know between one and two that means one thing but it means like totally different

if it's between three and four um that would be a non -linear representation and in principle that you know models could do that i think it's it's sort of inefficient for them to do if you try to think about how you'd implement computation like that it's it's kind of an annoying thing to do but in principle

models can do that um so uh one way to think about the features and and circuits sort of framework for thinking about things is that we're thinking about things as being linear we're thinking about there as being um that if a if a neuron or a combination neurons fires more it's sort of that means more

of the of a particular thing being detected and then that gives weights a very clean interpretation as these edges between these these entities that these features um and that that edge then has a has a so that's that's in some ways the the core thing um it's like um you know we can talk about this sort

of outside the context of neurons are you familiar with the word defect results um so you have like you know king minus man plus woman equals queen well the reason you can do that kind of arithmetic um is because you have a linear representation can you actually explain that representation a little

bit so first i saw the feature is a is a direction of activation yeah exactly that way can you do the the the minus men plus women that that the word to vet stuff can you explain what that is yeah so there's this very such a simple clean explanation of what we're talking about exactly so there's this very

famous result word to back by an thomas mikilov at all and there's been tons of follow -up work exploring this see so sometimes we have these we create these word embeddings um where and we map every word to a vector i mean that in itself by the way is is kind of a crazy thing if you haven't thought

about it before right like we've we're we're going in and representing we're turning um you know like like if you just learned about vectors in physics class right and i'm like oh i'm going to actually turn every word uh in the dictionary into a vector that's kind of a crazy idea okay but you could imagine

um you could imagine all kinds of ways in which you might map words to to vectors but it it it seems like when we train neural networks um they like to go and map words to vectors to such that they're they're they're they're sort of linear structure in a particular sense which is that directions have meaning

so for instance if you there there will be some direction that seems to sort of correspond to gender and male words will be you know far in one direction and female words will be in another direction and the linear representation hypothesis is you could sort of think of it roughly as saying that that's

actually kind of the fundamental thing that's going on that that everything is just different directions have meanings and adding different direction vectors together can represent concepts and the mikilov paper sort of took that idea seriously and one consequence of it is that you can you can do this game

of playing sort of arithmetic with words so you can do king and you can you know subtract off the word man and add the word woman and so you're sort of you know going and trying to switch the gender and indeed if you do that the result will sort of be close to the word queen um and you can you know

do other things like you can do um uh you know sushi minus japan plus italy and get pizza or different different things like this right um so so this is in some sense the core of the linear representation hypothesis you can describe it just as a purely abstract thing about vector spaces you can describe

it as a as a statement about um about the activations of neurons but it's really about this this property of directions having meaning and in some ways it's even a little subtle that it's really i think mostly about this property of being able to add things together um that you can sort of independently

modify um say gender and royalty or um you know cuisine type or country and and and and the concept of food by by adding them do you think the linear hypothesis holds that yes carries scales so so far i think everything i have seen is consistent with the hypothesis and it doesn't have to be that way

right like you can write down neural networks where um you write weights such that they don't have linear representations where the right way to understand them is not it's not in terms of linear representations but i think every natural neural network i've seen um has this property um there's been

one paper recently um that there's been some sort of pushing around the edge so i think there's been some work recently studying multi -dimensional features where rather than a single direction it's more like um a manifold of directions this to me still seems like a linear representation and then there's

been some other papers suggesting that maybe um in in very small models you get non -linear representations um i think that the jury's still out on that um but in i think everything that we've seen so far has been consistent with the linear representation about this and that's that's wild it it doesn't

have to be that way um and yet uh i think there's a lot of evidence that certainly at least this is very very widespread and so far the evidence is consistent with it and i and i think you know one thing you might say is you might say well christopher you know it's that's a lot you know to to go and and sort

of um to write on you know if we don't know for sure this is true and you're sort of you know you're investigating neural networks as though it is true you know isn't that um isn't that interesting you know but i i think actually there's all there's a virtue in taking hypotheses seriously and pushing

them as far as they can go um so it might be that someday we discover something that isn't consistent with linear representation hypothesis but science is full of hypotheses and theories that were wrong um and we learned a lot by sort of working under under them as a sort of an assumption um and and then

going and pushing them as far as we can i guess i guess this is sort of the heart of of what kun would call normal normal science um i don't know if you want we can talk a lot about about uh philosophy of science that leads to the paradigm shift so yeah i love it taking the hypothesis seriously and take

it to a natural natural conclusion yeah same with the scaling hypothesis same exactly exactly i love it one of my colleagues tom henigan who as a for a physicist um uh like made this really nice analogy to me of um uh colork theory where you know once upon a time we thought that heat was actually you

know this thing called colorek and like the reason you know hot objects you know would would warm up cool objects is like the colork is flowing through them um and like you know because we're so used to thinking about heat you know in terms of the modern and modern theory you know that seems kind of silly

but it's actually very hard to construct uh an experiment that that sort of disproves the um chloric hypothesis um and you know you can actually do a lot of really useful work believing in chloric for example it turns out that the original combustion engines were developed by people who believed in the caloric

theory so i think it's a virtue in taking um hypotheses seriously even when they might be wrong yeah yeah there's a deep philosophical truth to that that's kind of kind of how i feel about space travel like colonizing mars there's a lot of people that criticize that i think if you just assume we have to

colonize mars in in order to have a backup for human civilization even if that's not true that's going to produce some interesting interesting engineering and even scientific breakthroughs i think yeah well and actually this is another thing that i think is really interesting so um you know there's

a way in which i think it can be really useful for society to have people um almost irrationally dedicated to investigating particular hypotheses um because uh well it it takes a lot to sort of maintain scientific morale and really push on something when you know most most scientific hypotheses end

up being wrong you know a lot of a lot of science doesn't doesn't work out um and but and yet it's you know it's very it's very useful to go to just you know um there's a there's a joke about jeff hinton um which is that jeff hinton has discovered how the brain works every year for the last 50 years

yeah um but you know i i say that with like you know the you know with really deep respect because in fact that's actually you know that that led to him doing some some really great work yeah he won the nobel prize now who's laughing now exactly exactly um yeah i think one wants to be able to pop up

and sort of recognize the the appropriate level of confidence but i think there's also a lot of value and just being like you know i'm going to essentially assume i'm going to condition on this problem being possible or this being broadly the right approach and i'm just going to go and assume that for a while

and go and work within that and push really hard on it um and you know if society has lots of people doing doing that for different things um that's actually really useful in terms of going and uh getting to getting you know either really really ruling things out right we can be like well you know that didn't

work and we know that somebody tried hard um or going and getting to something that that it does teach us something about the world so another interesting hypothesis is the super position hypothesis can you describe what superposition is yeah so earlier we were talking about word to fact right and we

were talking about how you know maybe you have one direction that corresponds to gender and maybe another that corresponds to royalty and another one that corresponds to italy and another one that corresponds to you know food and all of these things well you know um oftentimes maybe these these uh these

word embeddings they might be 500 dimensions a thousand dimensions and so if you believe that all those directions were orthogonal um then you could only have you know 500 concepts and you know i love pizza um but like if i was important concepts in uh you know the english language probably italy wouldn't

be it's not obvious at least that italy would be one of them right because you have to have things like plural and singular and uh verb and noun and adjective and you know um there's a lot of things we have to get to before we get to get to italy um and japan and you know there's a lot of countries

in the world um and so how might it be that models could you simultaneously have the linear representation hypothesis be true and also represent more things than they have directions so so what does that mean well okay so if if linear representation hypothesis is true something interesting has to be

going on now i'll i'll tell you one more interesting thing before we we go and we do that which is um you know we earlier we were talking about all these poly semantic neurons right um these neurons that you know when we were looking at inception v1 there's these nice neurons that like the car detector and the curve

detector and so on that respond to lots of you know to very coherent things but lots of neurons that respond to a bunch of unrelated things that's that's also an interesting phenomenon um and it turns out as well um that even these neurons that are really really clean if you look at the weak activations

right so if you look at like you know the activations where it's like activating five percent of of the the you know of the maximum activation it's really not the core thing that it's expecting right so if you look at a a curve detector for instance you look at the places where it's five percent active

you know you could interpret it just as noise or it could be that it's that it's doing something else there okay so so how could that be well there's this amazing thing in mathematics um called compressed sensing and it's it's actually this this very surprising fact where you have a high dimensional

space and you project it into a low dimensional space ordinarily you can't go and sort of unproject it and get back your high dimensional vector right you threw information away this is like you know you can't you can't invert a rectangular matrix you can only invert square matrices um but it turns

out that that's actually not quite true if i tell you that the high dimensional vector was sparse so it's mostly zeros then it turns out that you can often go and find back um the the high dimensional vector with with very high probability um so that's a surprising fact right it says that you know you

can um you can you can have this high dimensional vector space and as long as things are sparse um you can project it down you can have a lower dimensional projection of it and that works so this is a hypothesis of saying that that's what's going on in neural networks that's for instance that's what's

going on in word embeddings the word embeddings are able to simultaneously have directions be the meaningful thing and by exploiting the fact that they're they're operating on a fairly high dimensional space they're actually and and the fact that these concepts are sparse right like you know you usually

aren't talking about japan and italy at the same time um you know most of the most of those concepts you know in most instances japan and italy are both zero they're not present at all um and if that's true um then you can go and have it be the case that um that you can you can have many more of these

sort of directions that are meaningful these features then you have dimensions and similarly when we're talking about neurons you can have many more concepts than you have have neurons so that's the at a high level of superstition hypothesis now it has this even a wilder implication which is um to go

and say that uh neural networks are it may not just be the case that the representations are like this but the the computation may also be like this you know the connections between all of them and so in in some sense neural networks may be shadows of much larger sparser neural networks and what we

see are these projections and the super you know the strongest version of superstition hypothesis we need to take that really seriously and sort of say you know there there actually is in some sense this this upstairs model this you know um where where the neurons are really sparse and all interpretable

and there's you know the weights between them are these really sparse circuits and that's what we're studying um and uh the thing that we're observing is the shadow of evidence we need to find the original object and the process of learning is trying to construct a compression of the upstairs model

that doesn't lose too much information in the projection yeah it's finding how to fit it efficiently or something like this um the gradient descent is doing this and in fact so this sort of says that gradient descent you know it could just represent a dense neural network but it sort of says that gradient

descent is pleasantly searching over the space of extremely sparse models that could be projected into this low -dimensional space and this large body of work of and of people going and trying to study sparse neural networks right where you go and you have you could design neural networks right where where the edges

are sparse and the activations are sparse and you know my sense is that work has generally it feels very principled right it makes so much sense and yet that that work hasn't really panned out that well as my impression broadly and i think that a potential answer for that is that actually the neural

network is already sparse in some sense gradient descent was the whole time gradient you were trying to go and do this gradient descent was actually in the behind the scenes going and searching more efficiently than you could through the space of sparse models and going and learning whatever sparse

model was most efficient and then figuring out how to fold it down nicely to go and run conveniently on your gpu which does you know it's a nice dense matrix multiplies um and that you just can't beat that how many concepts do you think can be shoved into a neural network depends on how sparse they

are so there's probably an upper bound from the number of parameters right because you have to have you still have to have you know prim weights that go and connect them together um so that's that's one upper bound there are in fact all these lovely results from compressed sensing and the johnson lindenstrass

lemma and things like this um that they they basically tell you that if you have a vector space and you want to have almost orthogonal vectors which is sort of probably the thing that you want here right so you you're going to say well you know i'm going to give up on having my my concepts my features

be strictly orthogonal but i'd like them to not interfere that much i'm gonna have to ask them to be almost orthogonal um then this would say that it's actually you know for once you set a threshold for for what you're what you're willing to accept in terms of how how much cosine similarity there is that's

actually exponential in the number of neurons that you have so at some point that's not going to even be the the limiting factor um but um you know some beautiful results there and in fact it's probably even better than that in some sense because that's sort of a for saying that you know any random

set of features could be active but in fact the features have sort of a correlational structure where some features you know are more likely to co -occur and other ones are less likely to co -occur and so neural networks my guess would be to do do very well in terms of going and uh packing things in such

to the point that's probably probably not the limiting factor how does the problem of polysemiticity enter the picture here polysemiticity is this phenomenon we observe where we look at many neurons and the neuron doesn't just sort of represent one one concept it's not it's not a clean feature it responds

to a bunch of unrelated things and um superposition is you can think of as as being a hypothesis that explains the observation of polysemiticity um so polysemiticity is this observed phenomenon and superposition is is a hypothesis that um would explain it along with with some other so that makes mekinter

more difficult right so if you if you're trying to understand things in terms of individual neurons and you have polysemantic neurons you're in an awful lot of trouble right i mean the easiest answer is like okay well you're looking at the neurons you're trying to understand them this one responds for a lot

of things it doesn't have a nice meaning okay we're you know that's that's that's bad um another thing you could ask is you know ultimately we want to understand the weights and if you have two polysemantic neurons and you know each one responds to three things and then you know the other neuron responds

to three things and you have a weight between them you know what does that mean does it mean that like all three you know like there's these nine you know nine interactions going on it's a very weird thing but there's also a deeper reason which is related to the fact that neural networks operate on really

high dimensional spaces so i i said that our goal was you know to understand neural networks and understand the mechanisms and one thing you might say is like well why not it's just a mathematical function why not just look at it right like um you know one of the earliest projects i did studied these

these neural networks that mapped two -dimensional spaces to two dimensional spaces and you can sort of interpret them as in this beautiful way is like bending manifolds um why can't we do that well you know as you have a higher dimensional space um the volume of that space in some sense is exponential

in the number of inputs you have and so you can't just go and visualize it so we somehow need to break that apart we need to somehow break that exponential space into a bunch of things that we you know some non -exponential number of things that we can reason about independently and the independence

is crucial because it's the independence that allows you to not have to think about you know all the exponential combinations of things and things being monosomatic things only having one meaning things having a meaning that isn't is the key thing that allows you to think about them independently and so i

think that's that's if you want the deepest reason why we want to have um interpretable monosomatic features i think that's really the the deep reason and so the goal here as your recent work has been aiming at is how do we extract the monosomatic features from a neural net that has polysematic features

in all this this mess yes we have we observe these polysematic neurons and we hypothesize that's what's going what's going on in a superposition and if superposition is what's going on there there's actually a sort of well -established technique that is sort of the principled thing to do which is dictionary

learning and um it turns out if you do dictionary learning in particular if you do sort of a nice efficient way that in some in some sense sort of sparse autoencoder if you train a sparse autoencoder these beautiful interpretable features start to just fall out where there weren't any beforehand and so that's

not the thing that you would necessarily predict right but it turns out that that works very very well you know to me that seems like you know some non -trivial validation of linear representations in supercession so with dictionary learning you're now looking for particular kind of categories you don't

know what they are exactly and this gets back to our earlier point right when we're not making assumptions gradient descent is smarter than us so we're not making assumptions about what's there um i mean one certainly could do that right one could assume that there's a php feature and go and search

for it but we're not doing that we're saying we don't know what's going to be there instead we're just going to go and let um the sparse autoencoder discover the things that are there so can you uh talk to the toward monosomanticity paper from uh october last year that had a lot of like nice breakthrough

results that's very kind of you to describe it that way um yeah i mean this was um uh our first real success using sparse autoencoder so we took a one layer model um and it turns out if you go and you you know do dictionary learning on it you find all these really nice interpretal features so you know

the arabic feature the hebra feature the base 64 features those were were some some examples that we studied in a lot of depth and really showed that they were what we thought they were it turns if you train a model twice as well and train two different models and and do dictionary learning you find

find analogous features in both of them so that's fun um you find all kinds of different features so that was really just showing um that um that this works and um you know i should mention that there was this cunning hum at all um that had very similar results around the same time there's something

fun about being doing these kinds of small scale experiments and finding that it's actually working yeah well and there's and there's so much structure here like you you know so maybe maybe stepping back for a while um i thought that maybe all this mechanistic interpolity work um the end result was going

to be that i would have an explanation for why it was sort of you know very hard and not going to be tractable um you know we'd be like well there's this problem of superposition and it turns out superposition is really hard um and we're kind of screwed but that's not what happened in fact a very natural

simple technique just works and so then that's actually a very good situation you know i think um this is a sort of hard research problem and it's got a lot of research risk and you know it might still very well fail but um i think that some amount of some very significant amount of research risk um

was sort of put behind us when that started to work can you describe what kind of features can be extracted in this way well so it depends on the model that you're studying right so the the larger the model the more sophisticated they're going to be and we'll probably talk about about follow -up work

in a minute um but in these one layer models um so some very common things i think were were languages both programming languages and natural languages there were a lot of features that were um specific words in specific contexts so the and i think really the way to think about this is that the is likely

about to be followed by a noun so it's really right you could think of this as a feature but you could also think of this as predicting a specific noun feature and there would be these features that would fire for the in um the context of of say a legal document or a mathematical document or something

like this um and so uh you know maybe in the context of math you're like you know za and then protect vector or matrix you know all these mathematical words whereas you know other contexts you would predict other things that was that was common and basically we need clever humans to assign labels to

what we're seeing yes so you know this this is the only thing this is doing is that sort of um unfolding things for you so if everything was sort of folded over top of the you know serialization folded everything on top of itself and you can't really see it this is unfolding it but now you still have a very

complex thing to try to understand um so then you have to do a bunch of work understanding what these are um and some are really subtle like there's some really cool things even in this one layer model about um unicode where you know of course some languages are in unicode and the tokenizer won't necessarily

have a dedicated token for every unicode um character so instead what you'll have is you'll have this these patterns of alternating token or alternating tokens that each represent half of a unicode character and then you have a different feature that you know goes and activates on the on the opposing

ones to be like okay you know um i just finished a character you know go and predict next prefix um then okay i'm on the prefix you know predict a reasonable suffix um and you have to alternate back and forth so there's you know these these one player models are are really interesting and um i mean there's

another thing which is you might think okay there would just be one base 64 feature but it turns out there's actually a bunch of base 64 features because you can have english text encoded in as base 64 and that has a very different distribution of base 64 tokens than than regular and there's um uh there's

there's some things about tokenization as well that it can exploit and i don't know it's all all kinds of fun stuff how difficult is the task of sort of assigning labels to what's going on can this be automated by AI well i think it depends on the feature and it also depends on how much you trust your

AI so um there's a lot of work doing um automated interpretability i think that's a really exciting direction and we do a fair amount of automated interpretability and have have Claude go and label our features is there some funny moments where it's totally right or it's totally wrong yeah well i think

i think it's very common that it's like says something very general which is like true in some sense but not really picking up on the specific of what's going on um so i think i think that's a pretty common situation um you don't know that i have a particularly amusing one that's interesting that little

gap between it is true but doesn't quite get to the deep nuance of a thing yeah that's a general challenge it's like it's it's 30 an incredible accomplishment they can say a true thing but it doesn't it's not it's missing the depth sometimes and in this context it's like the arc challenge you know the sort

of IQ type of tests it feels like figuring out what a feature represents is a bit of a little puzzle you have to solve yeah and and i think that sometimes they're easier and sometimes they're harder as well um so uh yeah i think i think that's tricky and there's another thing which i don't know maybe

maybe in some ways this is mine like aesthetic coming in but i'll try to give you a rationalization you know i'm actually a little suspicious of automated interpretability and i think that's partly just that i want humans to understand neural networks and if the neural network is understanding it for me

you know i'm not i don't quite like that but i do have a bit of you know in some ways i'm sort of like the mathematicians who are like you know if there's a computer automated proof it doesn't count um you know you they won't understand it but i i do also think that there is um there's kind of like

reflections on trusting trust type issue where you know if you there's this famous talk about um uh you know you like when you're writing a computer program you have to trust your compiler and if there was like malware in your compiler then it could go and inject malware into the next compiler and you

know you'd be kind of in trouble right well if you're using neural networks to go and verify that your neural networks are safe the hypothesis that you're testing for is like okay well the neural network maybe isn't safe um and you have to worry about like is there some way that it could be screwing

with you um so uh you know i i think that's not a big concern now but i do wonder in the long run if we have to use really powerful AI systems to go and uh you know audit our AI systems is that is that actually something we can trust but maybe i'm just rationalizing because i i just want us to have to

get to a point where humans understand everything yeah i mean especially that's hilarious especially as we talk about AI safety and it looking for features that would be relevant to AI safety like deception and so on uh so let's let's talk about the scaling monosimanticity paper in may 2024 okay so what did

it take to scale this to apply to clod 3 saw on it well a lot of GPUs a lot more GPUs um but one of my teammates tom hendigan um was involved in the original scaling laws work um and something that he was sort of interested in from very early on is are there scaling laws for interpretability um and so something

he sort of immediately did when when this this work started to succeed and we started to have sparse autoencoders work because he became very interested in you know what are the scaling laws for um uh you know for making making sparse autoencoders larger and how does that relate to making the base model

larger um and so it turns out this works really well and you can use it to sort of project um you know if you train a sparse autoencoder a given size you know how many tokens you train on and so on so this was actually a very big help to us in scaling up um this work um and made it a lot easier for us

to go and train uh you know really large sparse autoencoders where you know it's not like training the big models but it's starting to get to a point where it's actually actually expensive to go um and train the really big ones so yes this i mean you have to do all the stuff of like splitting it across

large oh you know i mean there's a huge engineering challenge here too right so yeah so so there's there's this there's a scientific question how do you scale things effectively um and then there's an enormous amount of engineering to go and scale this up so you have to you have to chart it you have to

you have to think very carefully about a lot of things i'm lucky to work with a bunch of great engineers because i am definitely not a great engineer yeah and the infrastructure especially yeah for sure so it turns out to dr it worked it worked yeah and i think this is important because you could have imagined

you could like you could have imagined a world where you set after towards monos fanaticity you know chris this is great you know it works on a one layer model but one layer models are really idiosyncratic um like you know maybe maybe that's just something it is like maybe the linear representation hypothesis and superposition hypothesis is the right

way to understand a one layer model but it's not the right way to understand larger models um and so i think um i mean first of all like the conny him et al paper sort of um cut through that a little bit and and sort of suggested that this wasn't the case but um scaling monos fanaticity sort of i think

was significant evidence that even for very large models and we did it on claud three sauna which at that point was uh one of our production models um you know even these models um seem to be very you know seem to be substantially explained at least by linear features and you know doing dictionary running

on them works and as you learn more features you go and you explain explain more and more so that's uh i think quite a promising sign and you find now really fascinating abstract features um and the features are also multimodal they respond to images and text for the same concept which is fun yeah this can you

explain that i mean like you know backdoor there's just a lot of examples that you can yeah so maybe maybe let's start with one example to start which is we found some features around sort of security vulnerabilities and backgourcing code so it turns out those are actually two different features um

so there's a security vulnerability feature and if you force it active claud will start to go and write um security vulnerabilities like buffer overflows into code and also it fires for all kinds of things like it you know some of the top data set examples for it were things like you know dash dash

disable um you know SSL or something like this which are sort of obviously really um uh really insecure so at this point it's kind of like maybe it's just because the examples were presented that way it's kind of like surf a little bit more obvious examples right um i guess the the idea is that down

the line might be able to detect more nuance like deception or bugs or that kind of stuff yeah well maybe i want to distinguish two things so um one is um the complexity of the feature or the concept right and the other is the the nuance of the how subtle the examples we're looking at right so when we

when we show the top data set examples those are the most extreme examples that cause that feature to yeah to activate um and so it doesn't mean that it doesn't fire for more subtle things so the the insecure um code feature you know the stuff that it fires for most strongly for are these like really

obvious you know disable the security type things um but um you know it it also fires for you know buffer overflows and and more subtle security vulnerabilities in code you know these features are all multimodal so you could ask like what images activate this feature and it turns out um that the the the security

vulnerability feature activates for images of um uh like people clicking on chrome to like go past the like you know this this website uh the ssl certificate might be wrong or something like this another thing that's very entertaining is there's backdoors and code feature like you activated it goes

and cloud writes a backdoor that like will go and dump your data to port or something but you can ask okay what what images activate the back door feature it was devices with hidden cameras in them so there's a whole apparently genre of people going and selling devices that look innocuous that have hidden

cameras and they have ads that how does a hidden camera in it and i guess that is the you know physical version of a backdoor um and so it sort of shows you how abstract these concepts are right um and i i just thought that was uh i'm sort of sad that there's a whole market of people selling devices

like that but i was kind of delighted that that was the the thing that came up with as the the top uh image examples for the feature yeah it's nice it's multimodal it's multi almost context it's it's broad strong definition of a singular concept it's nice yeah to me one of the really interesting features

especially for ai safety is deception and lying and the possibility that these kinds of methods could detect lying in a model especially gets smarter and smarter and smarter presumably that's a big threat of a super intelligent model that it can deceive the people operating it uh as to its intentions

or any of that kind of stuff so what what have you learned from detecting lying inside models yeah so i think we're in some ways in early days for that we find quite a few features related to deception and lying there's one feature where it fires for people lying and being deceptive and you force it

active and clod starts lying to you so we have a have a deception feature i mean there's all kinds of other features about withholding information and not answering questions features about power seeking and coups and stuff like that there's a lot of features that are kind of related to spooky things

and if you um force them active clod will will behave in ways that are they're not the kinds of behaviors you want what are possible next exciting directions to you in the space of mekinterb well there's a lot of things um so for one thing i would really like to get to a point where we have circuits

where we can really understand um not just the features but then use that to understand the computation of models that really for me is is the the ultimate goal of this um and there's been some work we we put out a few things there's a paper from sam marx that does some stuff like this and there's there's

been some i'd say some work around the edges here but i think there's a lot more to do and i think that will be a very exciting thing um that's related to a challenge we call interference weights where um due to superposition if you just sort of naively look at whether features are connected together

there may be some weights that sort of don't exist in the upstairs model but are just sort of artifacts of of superposition so that's a sort of technical challenge related to that um i think another exciting direction is just i you know you might think of of sparse auto encoders as being kind of like

a telescope they allow us to you know look out and see all these features that are are are out there and you know as we build better and better sparse auto encoders get better and better at dictionary learning we see more and more stars um and you know we zoom in on smaller and smaller stars but there's

kind of um a lot of evidence that we're only still seeing a small fraction of the stars there's a lot of matter in our in our you know neural network universe that we can't observe yet um and it may be that um that we'll never be able to have fine enough instruments to observe it and maybe maybe some

of it just isn't possible um isn't computationally tractable to observe it's sort of a kind of dark matter in in not in maybe the sense of of modern astronomy of earlier astronomy when we didn't know what this unexplained matter is um and so i think a lot about that that dark matter and whether we'll

ever observe it and what that means for safety if we if we can't observe it if there's you know some if some significant fraction of neural networks are not accessible to us um another question that i think a lot about is uh at the end of the day you know mechanistic interpolation is a very microscopic

approach to interpolation it's trying to understand things in a very fine grained way but a lot of the questions we care about are very macroscopic um you know we we care about these questions about neural network behavior and and oh but i think that's the thing that i care most about but there's there's

lots of other other sort of larger scale questions you you might care about um and somehow you know the nice thing about about having a very microscopic approach is it's maybe easier to ask you know is this true but the downside is it's much further from the things we care about and so we now have this ladder

to climb and i think there's a question of can't will we be able to find are there are there sort of larger scale abstractions that we can use to understand neural networks that we get up from this very microscopic approach yeah you've you've written about this this kind of organs question yeah exactly

if we uh think of interpretability as a kind of anatomy of neural networks most of the circus threats involve studying tiny little veins looking at the small scale and individual neurons and how they connect however there are many natural questions that the small scale approach doesn't address in contrast

the most prominent abstractions in biological anatomy involve larger scale structures like individual organs like the heart or entire organ systems like the respiratory system and so we wonder is there a respiratory system or heart or brain region of an artificial neural network yeah exactly um and i

mean like if you think about science right a lot of scientific fields have um you know investigate things in many level abstractions in biology you have like you know more microbiology studying you know proteins and molecules and so on and they have cellular biology and then you know histology studying

tissues then you have anatomy and then you have zoology and then you have ecology and so you have many many levels of abstraction or you know physics maybe you have the physics of individual particles and then you know statistical physics gives you gives you thermodynamics and things like this and so you

often have different levels of abstraction um and i think that right now we have you know the mechanistic interpretive succeeds as sort of like a microbiology of neural networks but we we want something more like anatomy and so and you know a question you might ask is why why can't you just go there

directly and i think the answer is superposition um at least in significant part it's that it's actually very hard to to see this this macroscopic structure without first sort of breaking down the microscopic structure in the right way and then studying how it connects together but i'm i'm features

and circuits and that we're going to be able to have a story that's much that involves much bigger things and then you can sort of study in detail the parts you care about i suppose the neurobiology like a psychologist or psychiatrist of a neural network and i think that the beautiful thing would be

if we could go and rather than having disparate fields for those two things if you could have a build a bridge between them all right such that you could go and um uh have all of your higher abstractions be grounded very firmly in this very solid um you know more rigorous ideally foundation what do

you think is the difference between the human brain the biological neural network and the artificial neural network well the neuroscientists have a much harder job than us you know sometimes i just like count my blessings by how much easier my job is than the neuroscientist right so i have um we we

can record from all the neurons we can do that on arbitrary amounts of data the neurons don't change while you're doing that by the way you can go and ablate neurons you can edit the connections and so on and then you can undo those changes that's pretty great yeah you can force any you can intervene

on any neuron and force it active and see what happens you know which neurons are connected to everything right you have neuroscientists want to get the connectome we have the connectome and we have it for like much bigger than the elegance um and then not only do we have the connectome um we know uh

what the you know which neurons excite or inhibit each other right so we have we it's not just that we know that like the binary mask we know the the weights um we can take gradients we know computationally what each neuron does um so i don't know the list goes on and on we just have um so many advantages

over neuroscientists and then just by having all those advantages it's really hard and so one thing i do sometimes think is like gosh like if it's this hard for us it seems impossible under the constraints of neuroscience or you know near impossible um i i don't know maybe maybe part of me is like i've

got a few neuroscientists on my team maybe i'm sort of like ah you know um the uh maybe the neuroscientists maybe some of them would like to have an easier problem that's still very hard um and they they could come and work on on neural networks and then after we after we figure out things in sort of the easy

uh little pond of trying to understand neural networks which is still very hard then we then we could go back to uh biological neuroscience i love what you've written about the goal of mech interpret research as uh two goals safety and beauty so can you talk about the beauty side of things yeah so you

know there's this funny thing where i think some people want uh some people are kind of disappointed by neural networks i think where they're like ah you know neural networks um it's these just these simple rules and then you just like do a bunch of engineering to scale it up and it works really well

and like where's the like complex ideas you know this isn't like a very nice beautiful scientific result and i sometimes think when people say that now i picture them being like you know evolution is so boring it's just a bunch of simple rules and you run evolution for a long time and you get biology

like what a what a sucky uh you know way for biology to have turned out where's the the complex rules but the beauty is that the simplicity generates complexity um you know biology has these simple rules and it gives rise to you know all the life and ecosystems that we see around us all the beauty of nature

that all just comes from evolution and from something very simple evolution and similarly i think that neural networks build you create enormous uh complexity and beauty inside and structure inside themselves that people generally don't look at and don't try to understand because it's hard to understand but i

i think that there is an incredibly rich structure to be discovered inside neural networks a lot of a lot of very deep beauty and if we're just willing to take the time to go and see it and understand it yeah i love i love mekinter the feeling like we are understanding or getting glimpses of understanding

the magic that's going on aside is really wonderful it feels to me like one of the questions that's just calling out to be asked and i'm sort of i mean a lot of people are thinking about this but i'm often surprised that not more are is how is it that we don't know how to create computer systems that can do

these things and yet we have these amazing systems that we don't know how to directly create computer programs that can do these things but these neural networks can do all these amazing things and it just feels like that is obviously the question that sort of is calling out to be answered if you are if you

have any degree of curiosity it's it's like how is it that that humanity now has these artifacts that can do these things that we don't know how to do yeah i love the image of the circus reaching towards the light of the objective function yeah it's just it's it's this organic thing that we've grown

and we have no idea what we've grown well thank you for working on safety and thank you for appreciating the beauty of the things you uh discover and thank you for talking today chris yeah thank you for taking the time to chat as well thanks for listening to this conversation with chris ola and before

that with dari amade and amanda askel to support this podcast please check out our sponsors in the description and now let me leave you with some words from alan watts the only way to make sense out of change is to plunge into it move with it and join the dance thank you for listening and hope to see

you next time

[Anthropic의 비전: 확장 법칙, 기계적 해석 가능성, 그리고 AI의 미래]-[#452 – Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity]

📋 Summary

Anthropic의 AI 비전: 확장 법칙과 안전의 균형

1. 확장 법칙과 지능의 본질

2. 기계적 해석 가능성(Mechanistic Interpretability): AI의 내부를 밝히다

3. 책임 있는 확장 정책(RSP)과 안전 전략

4. Claude의 성격과 인간 중심의 AI

결론: 기술적 낙관주의와 책임감

🎯Key Sentences

📝Key Phrases

📖 Transcript