About this episode
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David Sussillo spent years training neural networks at Google Brain
and now leads research at Meta Reality Labs. His verdict on the
path to artificial consciousness might surprise you.
In this conversation, we discuss why scaling transformers alone
won't produce conscious AI, how recurrent neural networks differ
from the transformer architectures powering today's LLMs, the
FORCE algorithm he developed to train RNNs, what his work on
Meta's EMG wristband reveals about AI and biological signals,
Rich Sutton's Bitter Lesson and whether we're locked into a
GPU-transformer paradigm, and why studying the brain may be
the only way out of AI's current limitations.
David is also the author of Emergence, a braided memoir that
tells the story of his path from foster care to the frontier
of neuroscience and AI research.
David Sussillo is a research scientist at Meta Reality Labs
and an adjunct professor at Stanford University. He completed
his PhD at Columbia University under Larry Abbott and previously
worked at Google Brain.
Key Takeaways:
00:00 Scale alone won't produce consciousness — here's why
00:25 The "bitter lesson": big data crushes every algorithmic invention
01:15 How hallucinations were discovered early at Google
05:00 The lock-in problem: why LLM + GPU may cap AI's ceiling
08:25 What Meta's neural wristband is actually trying to solve
11:20 Rich Sutton's bitter lesson explained
14:15 "More is Different" — why you can't reduce complexity upward
28:30 The book Emergence — two stories braided into one
36:00 Why mentorship matters more than intelligence for at-risk kids
47:00 Stability over intensity: what kids in foster care actually need
➡️ Follow David Sussillo
🌐 Website / Research: Meta Reality Labs
📚 Emergence (memoir): https://www.amazon.com/Emergence-Memoir-Boyhood-Computation-Mysteries/dp/1538768577
🎓 Stanford / Columbia / Carnegie Mellon Alumni
✍️ Email: davidsussillo@gmail.com
🏄♂️ Twitter: https://x.com/SussilloDavid
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