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Shownotes
Yann LeCun – Why LLMs Will Never Get Us to AGI
"The path to superintelligence - just train up the LLMs, train on more synthetic data, hire thousands of people to school your system in post-training, invent new tweaks on RL-I think is complete bullshit. It's just never going to work."
After 12 years at Meta, Turing Award winner Yann LeCun is betting his legacy on a radically different vision of AI. In this conversation, he explains why Silicon Valley's obsession with scaling language models is a dead end, why the hardest problem in AI is reaching dog-level intelligence (not human-level), and why his new company AMI is building world models that predict in abstract representation space rather than generating pixels.
Timestamps
() – Intro and welcome
() – AMI: Why start a company now?
() – Will AMI do research in the open?
() – World models vs LLMs
() – History of self-supervised learning
() – Siamese networks and contrastive learning
() – JEPA and learning in representation space
() – Abstraction hierarchies in physics and AI
() – World models as abstract simulators
() – Object permanence and learning basic physics
() – Game AI: Why NetHack is still impossible
() – Moravec's Paradox and chess
() – AI safety by construction, not fine-tuning
() – Constrained generation techniques
() – Meta's reorganization and FAIR's future
() – SSI, Physical Intelligence, and Wayve
() – Silicon Valley's "LLM-pilled" monoculture
() – China vs US: The open source paradox
() – Why start a company at 65?
() – The AGI hype cycle has happened 6 times before
() – Family and personal background
() – Career advice: Learn things with a long shelf life
() – Neuroscience and machine learning connections
() – Continual learning: Is catastrophic forgetting solved?
Music:
"Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.
"Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.
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About
The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.
