012 | How Deep Learning Does Magic
Bit of a Tangent
English - August 26, 2019 14:00 - 1 hour - 85.9 MB - ★ - 1 ratingScience Homepage Download Apple Podcasts Google Podcasts Overcast Castro Pocket Casts RSS feed
This is a discussion about why deep neural nets are unreasonably effective. Gianluca and Jared examine the relationships between neural architectures and the laws of physics that govern our Universe—exploring brains, human language, and linear functions. Nothing could have prepared them for the territories this episode expanded to, so strap yourself in!
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Shownotes:
AlphaGo beating Lee Sedol at Go: https://en.wikipedia.org/wiki/AlphaGo_versus_Lee_Sedol
OpenAI Five: https://openai.com/blog/openai-five/
Taylor series/expansions video from 3Blue1Brown: https://www.youtube.com/watch?v=3d6DsjIBzJ4
Physicist Max Tegmark: https://en.wikipedia.org/wiki/Max_Tegmark
Tegmark’s great talk on connections between physics and deep learning (which formed much of the inspiration for this conversation): https://www.youtube.com/watch?v=5MdSE-N0bxs
Universal Approximation Theorem: https://en.wikipedia.org/wiki/Universal_approximation_theorem
A refresher on “Map vs. Territory”: https://fs.blog/2015/11/map-and-territory/
Ada Lovelace (who worked on Babbage’s Analytical Engine): https://en.wikipedia.org/wiki/Ada_Lovelace
Manifolds and their topology: http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/
Binary trees: https://en.wikipedia.org/wiki/Binary_tree
Markov process: http://mathworld.wolfram.com/MarkovProcess.html
OpenAIs GPT-2: https://openai.com/blog/better-language-models/
Play with GPT-2 in your browser here: https://talktotransformer.com/
Lex Fridman’s MIT Artificial Intelligence podcast: https://lexfridman.com/ai/
The Scientific Odyssey podcast: https://thescientificodyssey.libsyn.com/