Introduction

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