THE AI READING LIST
The famous list of papers Ilya Sutskever gave John Carmack. He said, “If you really learn all of these, you’ll know 90% of what matters today”.
The audio episodes explain the key insights, giving you a clear overview of every chapter before reading the full PDFs.
Listen to the episodes #
AI-narrated, key-insights chapter by chapter. Free to stream and download.
Also on the list #
Courses, books and code-heavy pieces that don’t fit an audio episode — read them at the source.
- Article
The Annotated Transformer
Sasha Rush et al. (Harvard NLP) · 2018
- Book
Kolmogorov Complexity and Algorithmic Randomness
A. Shen, V. Uspensky, N. Vereshchagin · 2017
- Course
CS231n: Convolutional Neural Networks for Visual Recognition
Andrej Karpathy, Fei-Fei Li et al. (Stanford) · 2016
- Book
Machine Super Intelligence
Shane Legg · 2008
- Paper
A Tutorial Introduction to the Minimum Description Length Principle
Peter Grünwald · 2004
THE STORY #
As the story goes, when legendary game programmer John Carmack (Doom, Quake) decided to move into AI, he asked OpenAI co-founder Ilya Sutskever what he should read. Ilya handed him a list of around thirty papers and said that if Carmack really learned all of them, he’d understand 90% of what matters in modern deep learning.
Carmack has confirmed the exchange in interviews, though he’s said the original list was lost. The version circulated today was reconstructed by the community from his and others’ recollections. It’s a remarkably coherent tour from convolutional and recurrent nets, through attention and Transformers, to scaling laws and the information-theoretic roots of learning.
Reconstructions and background: community reading list, Aman’s AI Journal, Ilya’s List.
Have a paper of your own? #
Upload any PDF or paste a link and ListenDock turns it into a clear audio episode.