machine-learning

Ilya Sutskever opens this Simons Institute lecture by admitting he almost gave the standard talk and decided against it. Instead he presents a set of results from years earlier at OpenAI, never published, on a question that had bothered him: supervised learning comes with a mathematical guarantee, and unsupervised learning does not. His claim is that it can, if you frame unsupervised learning as compression. This is the “Classic of the Week” pick, and it earns the label for a specific reason....

Anima Anandkumar spent 83 minutes on Latent Space explaining a specific architectural claim: transformers are the wrong tool for physical simulation, and the reason is not compute budget but a mismatch in what the architecture assumes about its inputs. She has the receipts — her group’s weather model, FourCastNet, is deployed at a national weather agency and runs tens of thousands of times faster than the numerical models it replaces....

Weekly Paper Notes — one of the top picks from the 2026-06-13 CS paper digest. Area: Distributed Computing. Authors: Samuel Erickson, Mikael Johansson (KTH) arXiv: 2606.13287 · PDF TL;DR In asynchronous SGD (ASGD), workers compute gradients on possibly stale parameters and push updates without waiting for slow peers. That’s how you keep all the GPUs busy, but it’s also how slow workers (“stragglers”) inject large delays into the update stream, which classical analyses say should slow convergence in proportion to the maximum delay across the workers....