In episode 66 of The Gradient Podcast, Daniel Bashir speaks to Soumith Chintala.
Soumith is a Research Engineer at Meta AI Research in NYC. He is the co-creator and lead of Pytorch, and maintains a number of other open-source ML projects including Torch-7 and EBLearn. Soumith has previously worked on robotics, object and human detection, generative modeling, AI for video games, and ML systems research.
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Outline:
* (00:00) Intro
* (01:30) Soumith’s intro to AI journey to Pytorch
* (05:00) State of computer vision early in Soumith’s career
* (09:15) Institutional inertia and sunk costs in academia, identifying fads
* (12:45) How Soumith started working on GANs, frustrations
* (17:45) State of ML frameworks early in the deep learning era, differentiators
* (23:50) Frameworks and leveling the playing field, exceptions
* (25:00) Contributing to Torch and evolution into Pytorch
* (29:15) Soumith’s product vision for ML frameworks
* (32:30) From product vision to concrete features in Pytorch
* (39:15) Progressive disclosure of complexity (Chollet) in Pytorch
* (41:35) Building an open source community
* (43:25) The different players in today’s ML framework ecosystem
* (49:35) ML frameworks pioneered by Yann LeCun and Léon Bottou, their influences on Pytorch
* (54:37) Pytorch 2.0 and looking to the future
* (58:00) Soumith’s adventures in household robotics
* (1:03:25) Advice for aspiring ML practitioners
* (1:07:10) Be cool like Soumith and subscribe :)
* (1:07:33) Outro
Links:
* Soumith’s Twitter and homepage
* Papers
* Convolutional Neural Networks Applied to House Numbers Digit Classification
* GANs: LAPGAN, DCGAN, Wasserstein GAN
* Automatic differentiation in PyTorch
* PyTorch: An Imperative Style, High-Performance Deep Learning Library