Show Notes
Racing is a sport of tiny margins and mountains of data. OpenAI researcher Joyce Ruffell and RaceTek Systems co-founder Chase Holden are each using AI to help teams make better use of information from the track, the garage, and the people behind the wheel. They discuss OpenAI’s research collaboration with Chip Ganassi Racing and how Chase used ChatGPT and Codex to go from hosting a NASCAR podcast to building a racing intelligence company. They also examine how AI could give smaller teams an edge without replacing the human expertise at the heart of racing, and why car culture runs so deep at OpenAI.
Chapters
00:43 Intro to OpenAI researcher Joyce Ruffell and Chip Ganassi Racing 02:43 Intro to Chase Holden and RaceTek Systems 07:45 Joyce’s path into racing 10:00 Helping racing teams work with AI 15:31 Combining human feedback with racing data 18:26 How AI helps smaller teams compete 21:43 Spreadsheets and the “data wars” in racing 27:03 Getting started with AI and Codex 30:08 How RaceTek won its first customer 33:01 Humans, robot racing, and the future of competition 39:26 Using AI beyond the racetrack
Hosted on Acast. See acast.com/privacy for more information.
More from OpenAI Podcast
The old tests are getting too easy. Tejal Patwardhan leads OpenAI’s frontier evals team, which is finding new ways to measure and forecast progress as models become more capable. She and host Andrew Mayne discuss why evals matter for research, how benchmarks can break or get gamed, and what models need to be judged on next.
Last month AI found something mathematicians had missed for decades. Reasoning researchers Alexander Wei, Hongxun Wu, and Lijie Chen join the podcast to discuss how a general-purpose model helped disprove an 80-year-old conjecture from famed mathematician Paul Erdős. They walk through the moment the result started looking real, what it took to verify the proof, and what’s happened since sharing the discovery with the world. They also explore what this means for the future of math and for researchers learning to work with AI.
People are generating over 1.5 billion images a week in ChatGPT. In this episode, Product lead Adele Li and researcher Kenji Hata share some of the new use cases and trends since the launch of Images 2.0. Together with host Andrew Mayne, they trace the progress from the early DALL-E days and dive into the latest capabilities, including better text rendering, photorealism, multilingual support, world knowledge, aspect ratios, and character consistency. They also explore what comes next as image generation models evolve into more capable creative assistants.
Training frontier models isn’t as simple as adding more GPUs—one small problem and the whole coordinated dance falls apart. OpenAI’s Mark Handley and Greg Steinbrecher discuss how a new supercomputer network design, used to train some of the company’s latest models, keeps the whole system moving in lockstep, even with record numbers of GPUs. They break down Multipath Reliable Connection, a new protocol OpenAI developed with AMD, Broadcom, Intel, Microsoft, and Nvidia, and why they’re making it available for the whole industry to use.
Math is one of the clearest ways to see how far AI has come in a short span. OpenAI researchers Sébastien Bubeck and Ernest Ryu join host Andrew Mayne to explain what changed and what it could mean for the future of research. They reflect on how Ernest used ChatGPT to help solve a 42-year-old open problem, the difference between deep literature search and original mathematical discovery, and what changes when AI can work over longer timelines.