Researchers Name Foundational Papers in ML Thread

Rohan Anil, formerly of Google DeepMind, started a public discussion asking technologists to identify the single most influential paper shaping their education in hardware, systems, ML optimization, or LLMs, referencing lists such as those from Ilya Sutskever. Replies included recommendations for Neural Turing Machines, the information bottleneck method, Alex Graves PhD thesis, Gwern scaling hypothesis work, SHA-RNN, Inception v3, and Galactica. The exchange highlights how practitioners revisit specific seminal works for core conceptual grounding amid rapid AI progress.