What the Hugging Face Incident Reveals About AI Alignment - Noam Brown
Full episode: • OpenAI researcher on agent swarms & recurs... Me on twitter: https://x.com/dwarkesh_sp
Research Scientist, OpenAI
Verified interview transcripts, podcast appearances, and direct quotes from Noam Brown, Research Scientist at OpenAI. AI-verified speaker identity with full-text searchable transcripts.
Full episode: • OpenAI researcher on agent swarms & recurs... Me on twitter: https://x.com/dwarkesh_sp
Full episode: • OpenAI researcher on agent swarms & recurs... Me on twitter: https://x.com/dwarkesh_sp
Full episode: • OpenAI researcher on agent swarms & recurs... Me on twitter: https://x.com/dwarkesh_sp
Full episode: • OpenAI researcher on agent swarms & recurs... Me on twitter: https://x.com/dwarkesh_sp
New episode with Noam Brown. We talk about multi-agent, Navier-Stokes, and what the current explosion of maths progress tells us about what happens once you automate AI research. And we also discuss how we will know if the models are actually aligned before we kick off RSI. 𝐄𝐏𝐈𝐒𝐎𝐃𝐄 𝐋𝐈𝐍𝐊𝐒 Transcript: https://www.dwarkesh.com/p/noam-brown Apple Podcasts: https://podcasts.apple.com/us/podcast... Spotify: https://open.spotify.com/episode/3ngD... 𝐒𝐏𝐎𝐍𝐒𝐎𝐑𝐒 Jane Street has been interested in AI for a lot longer than you'd think, and not just for trading. In 2011, a full year before AlexNet and ove…
OpenAI Research Scientist Noam Brown talks with AI Deep Dive host Rocket Drew about AI agents, reinforcement learning and what happens when increasingly capable agents begin reasoning, delegating and coordinating with each other. They dig into the infamous Hugging Face incident, the limits of AI “research taste,” OpenAI’s push toward AI systems that can help improve the next generation of AI, and the growing challenge of monitoring models’ chains of thought as they become more capable.
When a new AI model drops, it’s judged based on a static benchmark grid that doesn’t account for how long the model is allowed to think. How then should we measure a model’s true capability? OpenAI research scientist Noam Brown returns to talk with Sarah Guo about his latest essay on why the AI industry’s traditional benchmark grids are broken, and how large-scale test-time compute is fundamentally changing how models are evaluated. Noam explains how, if properly scaffolded, today’s models can reason for weeks or even months on complex tasks. He also discusses real-world implications of test-t…
Last month, an OpenAI model disproved a long-standing conjecture by Paul Erdős on the planar unit distance problem, producing a construction so intricate that verifying it took a team of external mathematicians a week. In the inaugural episode of Begin Proof, Slater Stich sits down with Noam Brown, who leads work on multi-agent reasoning and test-time compute at OpenAI, and was one of the key people behind o1, OpenAI's first public reasoning model. They talk about what the Erdős result actually means, where the models are superhuman and where they still aren't, and what working mathematicians…
Keynote: Noam Brown, Research Scientist at OpenAI in Conversation with Joe Spisak, Meta A sit down with Noam Brown to chat about the SOTA in reasoning, planning, and inference time scaling and the new methods for how we measure intelligence in this new regime. Hosted by Joe Spisak.
Solving Poker and Diplomacy, Debating RL+Reasoning with Ilya, what's wrong with the System 1/2 analogy, and where Test-Time Compute hits a wall Timestamps 00:00 Intro – Diplomacy, Cicero & World Championship 02:00 Reverse Centaur: How AI Improved Noam’s Human Play 05:00 Turing Test Failures in Chat: Hallucinations & Steerability 07:30 Reasoning Models & Fast vs. Slow Thinking Paradigm 11:00 System 1 vs. System 2 in Visual Tasks (GeoGuessr, Tic-Tac-Toe) 14:00 The Deep Research Existence Proof for Unverifiable Domains 17:30 Harnesses, Tool Use, and Fragility in AI Agents 21:00 The Case…
The evolution of artificial intelligence has seen remarkable milestones, particularly in developing systems capable of advanced reasoning. This panel will explore the journey from AI mastering strategic games to tackling complex, real-world problems. We will delve into the significance of integrating deliberate, System 2 thinking into AI models, enabling them to process information more thoroughly and solve intricate tasks. The discussion will highlight the challenges and breakthroughs in enhancing AI’s reasoning capabilities, the role of reinforcement learning and self-play in training, and…
To get smarter, traditional AI models rely on exponential increases in the scale of data and computing power. Noam Brown, a leading research scientist at OpenAI, presents a potentially transformative shift in this paradigm. He reveals his work on OpenAI's new o1 model, which focuses on slower, more deliberate reasoning — much like how humans think — in order to solve complex problems. (Recorded at TEDAI San Francisco on October 22, 2024) If you love watching TED Talks like this one, become a TED Member to support our mission of spreading ideas: https://ted.com/membership Follow TED! X: /…
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