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[George AI Martin: Using Deep Learning to Finish the Game of Thrones Series]-[Ep. 38: Spoiler Alert! AI Predicts Next Chapter in Game of Thrones Saga]

NVIDIA AI Podcast · B2 · 2017-09-14

Technology
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📋 Summary

Bridging Pop Culture and AI: The 'George AI Martin' Project

In a fascinating convergence of modern technology and pop culture, data scientist Zach Tout embarked on a project to utilize deep learning to "finish" the highly anticipated Game of Thrones book series, The Winds of Winter. Frustrated by the delay in George R.R. Martin’s writing, Tout leveraged neural networks to analyze patterns and generate text in the style of the iconic fantasy saga.

The Technical Framework

Tout utilized a recurrent neural network (RNN), specifically a long short-term memory (LSTM) cell, to process the text. Unlike standard machine learning models, LSTMs are designed to remember information over longer sequences, which is crucial for maintaining the tone and character voice within the Song of Fire and Ice books.

To build the model, Tout converted approximately 32,000 unique words from the books into numerical IDs. By feeding these sequences into the network, the model learned to predict the next word in a sentence. Tout initialized the generation process using a "prime word"—typically a character name like "Tyrion"—which served as the anchor for the AI to begin constructing its own narrative output recursively.

Results: Chaos, Creativity, and Absurdity

While the project, dubbed "George AI Martin" by Vice, successfully captured the tone and vocabulary of the series, it struggled with long-term narrative coherence. Tout noted that the AI often produced text that was amusing rather than grammatically perfect or plot-consistent.

Examples of the AI's output included:

  • Surprising Plot Twists: The AI generated bizarre scenarios, such as Sansa Stark being identified as a "Baratheon" and Jon Snow potentially being a "Lannister."
  • Character Anomalies: In one instance, the AI described Jon Snow in a surreal, somewhat perverse manner involving "a fat little blind girl," highlighting the model's struggle to maintain consistent character arcs.
  • Narrative Inconsistency: The model frequently resurrected dead characters or created nonsensical pairings, such as Ned Stark being both "alive and apparently dead," illustrating the system's inability to retain complex, long-term plot points.

The Future of AI in Creative Writing

Despite the current limitations of his model—specifically its inability to track plot holes or maintain a coherent story over a full novel—Tout remains optimistic about the role of AI in creative fields. He suggests that such systems could eventually serve as "co-pilots" for authors, helping them brainstorm plot ideas or check for inconsistencies.

Tout emphasizes that the barrier to entry for deep learning is lower than many imagine. With resources like Udacity and Coursera, and a basic understanding of Python, enthusiasts can replicate similar projects. His journey from an applied math background to a data scientist underscores that persistence and the vast wealth of information available online are the primary requirements for mastering these technologies.

Conclusion

Zach Tout’s experiment is a testament to the intersection of human fandom and machine intelligence. While "George AI Martin" will not replace the actual author, it provides a unique lens through which we can view the current capabilities and future potential of AI in artistic creation. As Tout put it, the goal is to continue refining the model’s grammar and context-awareness, keeping the spirit of the series alive through endless, algorithmically generated chapters.

🎯Key Sentences

1
How did you even get to the point where you were able to do it?
2
And these two things were not coincidental.
3
I've wanted the last book to come out for a while.
4
And did that work pretty well?
5
I was actually pretty surprised at how funny they were.
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📝Key Phrases

1
take it upon yourself to do something
2
get in someone's head
3
come on the air
4
not coincidental
5
put the two together
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📖 Transcript

Welcome to Nvidia's AI podcast. And I think this is the first time we've been able to say this, but there might be spoilers ahead on this segment of the podcast.
Because joining us, we have Zach Tout, who is a data scientist and a developer from Boulder, Colorado.
Zach, welcome. Hi, thanks for having me.
Zach has done something that we've all wanted to have done which is He's working on finishing Game of Thrones, the books, by setting an AI system on it.
Zach, I'm sure you're a fan of the show.
Do you watch it? Do you read it? Do you do all those things?

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