Are LLMs a dead end for AGI? Stanford's Edward Y. Chang argues the bottleneck isn't the model, but a missing "coordination layer." The new theory (UCCT) sees reasoning as a phase transition, moving from simple pattern-matching to goal-directed thinking. He built MACI, a
MACHINE LEARNING
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MathWorks Showcases AI Embedded Systems for Smart Vehicles
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Join MathWorks at #CES2026! See how we’re driving smarter, safer, more connected vehicles at Booth 4477: AI for Embedded Model-Based SDV Design Simulink Virtualization Digital Twins w/ Databricks Meet our experts: https://
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Fine-tuning LLMs: The Continued Focus on Reinforcement Learning
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Their hit was fine-tuning LLMs (“RL”), and this is still that. (Even CoT came from Google.)
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Symmetry-Based Learning: ICLR 2014 Invited Talk
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See my ICLR-2014 invited talk on symmetry-based learning:
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Judging YouBuildChallenge hackathon: describe ideas, get apps, $10K+ prizes
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I’m judging the #YouBuildChallenge hackathon (Dec 16–22).
— Robert Scoble (@Scobleizer) 19 décembre 2025
The premise is simple: describe what you want to build, and @YouWareAI turns it into a working app.
$10K+ in prizes.
YouWare is rethinking how software gets created—shifting from writing code to expressing creativity.… https://t.co/O43j3lXImgI’m judging the #YouBuildChallenge hackathon (Dec 16–22). The premise is simple: describe what you want to build, and @YouWareAI turns it into a working app. $10K+ in prizes. YouWare is rethinking how software gets created—shifting from writing code to expressing creativity.
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Poor Generalization in AI: Exploiting Symmetries as Solution
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The #1 problem in AI is poor generalization, and the #1 solution is exploiting symmetries.
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Geoffrey Hinton and Jeff Dean Discuss Old Days in Fireside Chat
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I recently talked about the old days with Jeff Dean. nitter.net/JeffDean/status/200138… Jeff Dean (@JeffDean) It was delightful to do a fireside chat with my longtime colleague @geoffreyhinton a couple of weeks ago, moderated by @JordanJacobs10. The recording of our full discussion is now on Spotify: open.spotify.com/episode/2zM… — https://nitter.net/JeffDean/status/2001389087924887822#m
→ View original post on X — @geoffreyhinton, 2025-12-18 23:07 UTC
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Chain-of-thought monitoring as complementary mechanistic interpretability approach
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We view chain-of-thought monitoring as complementary to mechanistic interpretability, not as a replacement for it. Because we believe that chain-of-thought monitoring is incredibly useful as a window into a model’s brain and could be a loadbearing layer in a scalable control
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RL Reasoning Tradeoff: Monitorability vs Inference Compute
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RL at today’s frontier doesn’t seem to wreck monitorability and can help early reasoning steps. But there’s a tradeoff: smaller models run with higher reasoning effort can be easier to monitor at similar capability — at the cost of extra inference compute (a “monitorability
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GPT-5.2-Codex: Advanced Model for Long-Horizon Agentic Coding
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Just launched GPT-5.2-Codex! The best model for long-horizon agentic coding, including strong performance on refactors and migrations. Codex becoming very magical.