Attributed metadata history
Up to 100 retained publisher events per page for 39d8f28bafebd636904a8646510da760e7a8ed0741a52f3978640ca19cd5f849. Withdrawals and unavailable references remain attributed to their publishers.
intelligence_src_33e452677074d4c3bd
observed · 2026-10-13 08:33:03 UTC
To understand the decision-making of AI, developers often look at the text that models produce as they reason through problems. But these “chains of thought” are increasingly hard to read, which is prompting AI developers to consider other approaches. One alternative, known as “mechanistic interpretability,” seeks to explain the inner workings of AI models, rather than just relying on their outward behavior. Leading AI companies have formed mechanistic interpretability teams in recent years, and the field has spawned several startups, the most prominent of which is Goodfire AI, whose chief scientist Tom McGrath joined the latest episode of AI Deep Dive. (The full episode is available here.)
Fingerprint: 7a08b20920f950f48813d3f4de3c9596ee41fc279169fd0e5a84c1bca947f5a1
intelligence_src_33e452677074d4c3bd
observed · 2026-10-12 08:32:02 UTC
available
Fingerprint: 817c8bd41a2e577bace765ae2ce7ccaa3c9c8d6deba0a641a29d3725510ac5c5