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: @labenz (Nathan) @ZimingLiu11 (Ziming) @cogrev_podcast

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