4 free resources on this topic. Everything here is free and hand-picked. You can also search within this topic.
Jürgen Schmidhuber's page tracing the idea of recursive self-improvement — systems that rewrite their own learning algorithms — back to his 1987 diploma thesis and later work.
Kevin Roose's newsletter post that publishes Dario Amodei's 2017 internal OpenAI memo, "The Big Blob of Compute Hypothesis," in full for the first time, with background on how it argued for making models bigger rather than more specialized and how it shaped OpenAI's early direction. Free to read on Substack.
Jürgen Schmidhuber's technical report (IDSIA-22-22, arXiv:2212.11279) tracing the history of machine learning and deep neural networks, with his annotations on who contributed what.
Google Scholar profile listing Geoffrey Hinton's papers in citation order — backpropagation, dropout, AlexNet, t-SNE and the rest of the deep learning canon.
From the site: Emeritus Prof. Computer Science, University of Toronto - Cited by 1.089.325 - machine learning - psychology - artificial intelligence - cognitive science - computer science
Why I recommend it: The single best index of the papers that made current AI work. Sort by year to see the ideas arrive. Scholar blocks automated visits, so the picture may be a screenshot.
