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The Lost Reading Items of Ilya Sutskever's AI Reading List
11 Nov 2024, Taro Langner I recently shared a summary of a viral AI reading list attributed to Ilya Sutskever, which laid claim to covering ‘90% of what matters’ back in 2020. It boils down the reading items to barely one percent of the original word count to form the TL;DR I would have wished for before reading.
Certain papers about meta-learning and competitive self-play also feature repeatedly in a series of presentations held by Ilya Sutskever around this time and may well have eventually been included in the reading list too. They open with a fundamental motivation of why deep learning works, framing backpropagation with neural networks as a search for small circuits that relate to the Minimum Description Length principle, according to which the shortest program that can explain given data will reach the best generalization possible. Overall, the preserved reading items manage to strike an impressive balance between covering different model classes, applications and theory while also including many famous authors of the field.
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