Machine learning is moving things to like data flow graphs and higher level of abstractions. It gives the system a lot more flexibility in terms of how to translate or map it onto the system that you have. And so what you need, the bottomest part of the layer there is a way for all these devices to talk to each other. I think we can help lift the world and make it much simpler and actually get used.
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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EPISODE LINKS:
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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
