I'm not super optimistic about AGI being solved next year. I don't think that's going to happen personally. There has been work on building algebraic reasoning systems and kind of like figuring out more things that feel like proofs. But if you chase that rabbit hole down, the question then becomes, how do you express your intent to the machine? So maybe you want LMS to provide the spec, but you have a different kind of net that actually implements the code.
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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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
