The Problem With Pushing the Envelope of Machine Learning

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A lot of AI research ends up being that it has to go fast enough to get scale. Jeremy actually takes the time where many people don't to really dive deep into why is the beta parameter of the atom optimizer equal to this? Yeah. And as you say, he then brings it and teaches people. His mission is to help lift, you know, his website says, making AI uncool again. Like, like, forget about the hype. It's actually practical and useful. Let's teach people how to do this, right?

Chris Lattner is a legendary software and hardware engineer, leading projects at Apple, Tesla, Google, SiFive, and Modular AI, including the development of Swift, LLVM, Clang, MLIR, CIRCT, TPUs, and Mojo. Please support this podcast by checking out our sponsors:
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Mojo programming language: https://www.modular.com/mojo
Modular AI: https://modular.com/

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OUTLINE:
Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time.
(00:00) – Introduction
(06:38) – Mojo programming language
(16:55) – Code indentation
(25:22) – The power of autotuning
(35:12) – Typed programming languages
(51:56) – Immutability
(1:04:14) – Distributed deployment
(1:38:41) – Mojo vs CPython
(1:54:30) – Guido van Rossum
(2:01:31) – Mojo vs PyTorch vs TensorFlow
(2:04:55) – Swift programming language
(2:10:27) – Julia programming language
(2:15:32) – Switching programming languages
(2:24:58) – Mojo playground
(2:29:48) – Jeremy Howard
(2:40:34) – Function overloading
(2:48:59) – Error vs Exception
(2:56:39) – Mojo roadmap
(3:09:41) – Building a company
(3:21:27) – ChatGPT
(3:27:50) – Danger of AI
(3:31:44) – Future of programming
(3:35:01) – Advice for young people