Ziming Liu is a Physics PhD student at MIT and IAIFI, advised by Prof. Max Tegmark. Ziming’s research is at the intersection of AI and physics. Today’s discussion goes in-depth on Liu’s paper “Seeing is Believing” where he presents Brain-Inspired Modular Training (BIMT), a method for making neural networks more modular and interpretable. The ability to see modules visually can complement current mechanistic interpretability strategies.
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Watch this episode on YouTube to see research figures and graphics: https://www.youtube.com/watch?v=VKxwlxb2XWw
Links:
Seeing is Believing, authors: Ziming Liu, Eric Gan, and Max Tegmark https://arxiv.org/pdf/2305.08746.pdf
Ziming Liu: https://kindxiaoming.github.io/
TIMESTAMPS:
(00:00) Episode preview
(01:16) Nathan introduces Ziming Liu and his research
(04:50) What motivated Ziming's research into neural networks and modularity
(08:06) Key Visuals of "Seeing is Believing"
(08:44) Biology analogy & paper overview
(15:07) Sponsor: Omneky
(26:08) Research precedents
(30:00) Locality vs modularity
(37:40) Shoutout Tiny Stories : Episode 33
(39:00) Quantization Model of Neuro Scaling.
(1:00:00) How can this work impact mechanistic interpretability strategies
(1:27:00) Training
(1:46:30) Downscaling large-scale models
TWITTER:
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@ZimingLiu11 (Ziming)
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