logo
episode-header-image
Sep 3
1h 12m

Why ML Needs a New Programming Language ...

Jane Street
About this episode

Chris Lattner is the creator of LLVM and led the development of the Swift language at Apple. With Mojo, he’s taking another big swing: How do you make the process of getting the full power out of modern GPUs productive and fun? In this episode, Ron and Chris discuss how to design a language that’s easy to use while still providing the level of control required to write state of the art kernels. A key idea is to ask programmers to fully reckon with the details of the hardware, but making that work manageable and shareable via a form of type-safe metaprogramming. The aim is to support both specialization to the computation in question as well as to the hardware platform. “Somebody has to do this work,” Chris says, “if we ever want to get to an ecosystem where one vendor doesn’t control everything.”

You can find the transcript for this episode on our website.

Some links to topics that came up in the discussion:

Up next
Jul 25
The Thermodynamics of Trading with Daniel Pontecorvo
Daniel Pontecorvo runs the “physical engineering” team at Jane Street. This group blends architecture, mechanical engineering, electrical engineering, and construction management to build functional physical spaces. In this episode, Ron and Dan go deep on the challenge of heat ex ... Show More
58m 46s
May 2025
Building Tools for Traders with Ian Henry
Ian Henry started his career at Warby Parker and Trello, building consumer apps for millions of users. Now he writes high-performance tools for a small set of experts on Jane Street’s options desk. In this episode, Ron and Ian explore what it’s like writing code at a company that ... Show More
1h 19m
Mar 2025
Finding Signal in the Noise with In Young Cho
In Young Cho thought she was going to be a doctor but fell into a trading internship at Jane Street. Now she helps lead the research group’s efforts in machine learning. In this episode, In Young and Ron touch on the porous boundaries between trading, research, and software engin ... Show More
59m 45s
Recommended Episodes
Dec 2024
Adam Brown – How Future Civilizations Could Change The Laws of Physics
Adam Brown is a founder and lead of BlueShift with is cracking maths and reasoning at Google DeepMind and a theoretical physicist at Stanford.We discuss: destroying the light cone with vacuum decay, holographic principle, mining black holes, & what it would take to train LLMs tha ... Show More
2h 43m
May 2020
49: Thinking Machines II (Techniques in Artificial Intelligence)
Machines have been used to simplify labor since time immemorial, and simplify thought in the last few hundred years. We are at a point now where we have the electronic computer to aid us in our endeavor, which allows us to build hypothetical thinking machines by simply writing th ... Show More
57m 55s
Dec 2023
#441: Python = Syntactic Sugar?
See the full show notes for this episode on the website at talkpython.fm/441 
1h 7m
Jan 2025
Erik Bernhardsson on Creating Tools That Make AI Feel Effortless
Today on No Priors, Elad chats with Erik Bernhardsson, founder and CEO of Modal Labs, a platform simplifying ML workflows by providing a serverless infrastructure designed to streamline deployment, scaling, and development for AI engineers. Erik talks about his early work on Spot ... Show More
23m 36s
Feb 2025
Accelerating AI Training and Inference with AWS Trainium2 with Ron Diamant - #720
Today, we're joined by Ron Diamant, chief architect for Trainium at Amazon Web Services, to discuss hardware acceleration for generative AI and the design and role of the recently released Trainium2 chip. We explore the architectural differences between Trainium and GPUs, highlig ... Show More
1h 7m
Jul 2024
Building Real-World LLM Products with Fine-Tuning and More with Hamel Husain - #694
Today, we're joined by Hamel Husain, founder of Parlance Labs, to discuss the ins and outs of building real-world products using large language models (LLMs). We kick things off discussing novel applications of LLMs and how to think about modern AI user experiences. We then dig i ... Show More
1h 20m
Oct 2024
How Meta wants to shape our digital future with open source AI w/ Ragavan Srinivasan
Llama is Meta’s Large Language Model trained on over 15 trillion tokens of publicly available information. It’s available to anyone – from people making custom fan-made entertainment on a smartphone… to, potentially, complex projects that may not have the public’s well-being in m ... Show More
50m 14s