Deep learning works extraordinarily well. And we still largely don't know why.
A new paper from Jamie Simon, Daniel Kunin, and 12 co-authors argues that a scientific theory of deep learning is emerging, and coins a name for the emerging field: learning mechanics.
We sat down with Jamie and Dan on Generally Intelligent to talk about what a physics of deep learning would actually look like, why now, and what's left to figure out.
00:03:05 Learning mechanics as the physics to mechanistic interpretability's biology
00:04:13 Why deep learning needs a theory
00:07:07 Why deep learning is uniquely hard to engineer
00:12:11 How a week in the woods became a paper
00:25:59 The barrier to theory isn't opacity, but complexity
00:36:26 Deep learning's first gas law
00:47:22 Why more particles makes the problem easier
00:56:22 The discretization hypothesis
01:01:50 The strongest signal that a compact theory exists
01:05:07 The Platonic Representation Hypothesis
01:15:41 Why learning mechanics and mech interp need each other
01:25:29 Theory as safety infrastructure
Read the paper
Transcript and links
Learning Mechanics website
Full transcript: https://imbueai.substack.com/p/geoffrey-litt
Generally Intelligent is a podcast by Imbue, a research company building toward a future where AI agents are open and accountable to their users, so people have more power in the digital world.
