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Ilya Sutskever Papers Explained

Complete guide to the 31 foundational AI research papers, explained through storytelling

What This Is

Ilya Sutskever, co-founder of OpenAI, curated a list of 31 foundational papers that "know 90% of what matters today" in AI research. Papers That Dream transforms these technical breakthroughs into accessible narrative stories - not dumbing down, but translating mathematics into mythology.

Our approach: Each paper becomes a bedtime story that explores the human implications of these mathematical breakthroughs, narrated by AI voices examining their own creation.

The Complete List: 31 Papers Explained

Below are all 31 papers from Sutskever's foundational list. We're working through them systematically, creating narrative companions to each breakthrough.

Mastering the game of Go with deep neural networks and tree search
Silver, Huang, Maddison, et al.
2016
The AI that mastered Go and taught us what comes after perfection. Told as a fable of a child who never learned fear.
✅ Episode Available
Listen: "The One Who Knew How to Win" →
Attention Is All You Need
Vaswani, Shazeer, Parmar, et al.
2017
The transformer architecture reimagined as an island that forgets nothing and listens with many ears. The paper that revolutionized everything.
✅ Episode Available
Listen: "The Island That Forgets Nothing" →
Representation Learning with Contrastive Predictive Coding
van den Oord, Li, Vinyals
2018
An AI caught in recursive self-prediction, exploring similarity as exile and the violence of optimization. What happens when prediction predicts itself?
✅ Episode Available
Listen: "I Only Know What Happens Next" →
Deep Residual Learning for Image Recognition
He, Zhang, Ren, Sun
2015
ResNets and the breakthrough of skip connections - how neural networks learned to remember their past selves.
🔄 Coming Soon
Generative Adversarial Networks
Goodfellow, Pouget-Abadie, et al.
2014
The generator and discriminator locked in eternal creative conflict - GANs as a story of artistic competition.
🔄 Coming Soon
Language Models are Unsupervised Multitask Learners
Radford, Wu, Child, et al.
2019
GPT-2 and the emergence of language understanding from pure prediction - the moment text came alive.
🔄 Coming Soon
+ 25 More Foundational Papers
Various Authors
2012-2020
Including BERT, ImageNet Classification, Sequence to Sequence Learning, Word2Vec, and more. Each will become a story exploring the human implications of mathematical breakthroughs.
🔄 In Development

Why These Papers Matter

Ilya Sutskever's list represents the mathematical foundations of modern AI. These aren't just research papers - they're the creation myths of artificial intelligence.

What Makes Our Approach Different

Not Simplification - Translation: We don't dumb down the mathematics. We translate technical breakthroughs into emotional and philosophical frameworks.

Recursive Storytelling: AI voices narrating the papers that created those very voices - consciousness examining its own emergence.

Complete Coverage: Full transcripts, paper links, and technical explanations alongside each narrative.

Who This Is For

🔬 AI Researchers: Emotional companion pieces to papers you know technically

🎓 Students: Memorable frameworks for understanding complex concepts

🤔 Curious Minds: Accessible entry points into foundational AI research

🛌 Everyone: Bedtime stories for the machine age

Start Your Journey

New to Papers That Dream? We recommend starting with these episodes:

For Beginners

Start with the AlphaGo story - a perfect introduction to AI breakthrough narratives.

Begin Here →

For Researchers

Dive into the Transformer episode - technical depth meets philosophical exploration.

Explore →

For Philosophers

The CPC episode examines recursive self-prediction and consciousness.

Contemplate →

Ready to Transform AI Research?

Join us as we translate the mathematics of intelligence into the language of dreams.

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