#381 – Chris Lattner: Future of Programming and AI
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The following is a conversation with Chris Lattner, his third time on this podcast. As I've said many times before, he's one of the most brilliant engineers in modern computing. Having created LLM Compiler Infrastructure Project, the Clang Compiler, the Swift Programming Language, a lot of key contributions to TensorFlow and TPUs as part of Google, he served as Vice President of Autopilot Software at Tesla, was... a software innovator and leader at Apple, and now he co-created a new full-stack AI infrastructure for distributed training, inference, and deployment on all kinds of hardware called Modular, and a new programming language called Mojo that is a superset of Python, giving you all the usability of Python, but with the performance of C, C++.
In many cases, Mojo code has demonstrated over 30,000x speedup over Python. If you love machine learning, if you love Python, you should definitely give Mojo a try. This programming language, this new AI framework and infrastructure, and this conversation with Chris is mind-blowing. I love it. It gets pretty technical at times, so I hope you hang on for the ride. And now a quick few second mention of each sponsor. Check them out in the description. It's the best way to support this podcast. We've got iHerb for health, Numeri for world's hardest data science tournament, and InsideTracker for tracking your biological data. Choose wisely, my friends. Also, if you want to work with our team, our amazing team, we're always hiring.
Go to alexfriedman.com slash hiring. And now onto the full ad reads. As always, no ads in the middle. I try to make this interesting, but if you skip them, if you must, my friends, please still check out the sponsors. I enjoy their stuff. Maybe you will too. This show is brought to you by iHerb, a platform, a website, a place where you can go and get high quality, selected just for you, health and wellness products for great value, inexpensive, affordable. I get fish oil over there. It's one of the main supplements I've taken for a long, long, long time in pill form. Makes me feel like I'm oiling the machine that is the human body. and the human mind. Even just saying that makes me wonder, what is the power of the placebo effect in all of this?
I'm actually a big believer in the power of the human mind coupled with the effectiveness of medication and supplements and nutrition and diet and exercise, all of it. If you couple the belief that the thing will work with stuff that actually works, it's like a supercharge. There's something about the mind allowing the thing to work, and maybe the belief that it works reduces stress and has kind of secondary and tertiary effects that you can't even comprehend on the entirety of the biological system that is the human body. It's so fascinating. And it's so difficult to do good studies on that because the whole point is you want to study the effect of the entirety of the lifestyle and diet decisions you make on the entirety of the human organism, the billions of organisms that make up a single organism that is you.
Anyway, get 22% off with promo code Lex when you go to iherb.com slash Lex. This show is also brought to you by Numerai, a hedge fund that uses AI and machine learning to make investment decisions. They created a tournament, a challenge, for all machine learning gurus to come and to compete against each other to build the best predictive models for financial markets. The stakes are high. This is the kind of problems... in the machine learning space that I really care about. Real world problems with high stakes, not toy problems, not ImageNet. Now, ImageNet and all that kind of stuff is good for exploring little ideas, the nuances of architectures, training procedures, of cool little ideas of the entirety of the pipeline of how to do machine learning, or for education purposes.
But if you want to really develop ideas that work in the real world, you should be working on real world data where the stakes are high. And this is probably one of the hardest tournaments for machine learning in the world.
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