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Articles about In-context learning from podcasts, articles and papers, with links to the original sources.
On Training Data, Box CEO Aaron Levie was asked where enterprise AI memory is heading — retrieval, or models whose weights absorb a company's knowledge. His answer started with a lawyer who can see five matters and whose access changes daily, and ended with a wish for a rubric deciding what gets baked in and what stays a lookup.
Rich Sutton and Khurram Javed want deployed AI to change its underlying weights from individual experience, rather than rely on extra context or shared model updates. Their Oak Lab agenda combines learning rates tailored to each weight with a way to refresh a network’s capacity to learn—supported by earlier experiments, but not yet a demonstrated general-purpose system.
BDH-CQ’s authors report solving 118 of 400 public ARC-AGI-1 tasks at an estimated inference cost of $0.00070 per task, with up to two candidate answers. On Moonshots, Emad Mostaque welcomed architectural experimentation; panelist Alex questioned whether this design offered progress beyond a specialized benchmark.