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43개정렬
29:44ENHarry Wang: AI, Two-Dimensional Semiconductors, and the Future of Chip ManufacturingA semiconductor researcher explains materials, yield, AI-controlled experiments, and a proposed feedback loop between models and chips.
0:53ENSam Altman on AI and the Discovery of New KnowledgeSam Altman · OpenAIAltman expects progress in mathematical discovery to extend to biology, materials and energy.
13:32ENOpenAI’s Navier–Stokes Proof Claim, Research Credit, and the Future of AIMatthew Berman · Forward FutureMatthew Berman examines OpenAI’s claim that AI agents produced a proof, a dispute over related research and data use, and what the episode might mean for AI-driven discovery.
0:49ENOpenAI’s Claimed Navier–Stokes Result and an Internal Math ModelMatthew Berman · Forward FutureMatthew Berman discusses OpenAI’s proposed proof and its comparison between an internal model and GPT-6 Astra. The mathematical result and prize status remain subject to review.
0:35ENSebastian Bubeck Responds to a Math Authorship DisputeMatthew Berman · Forward FutureMatthew Berman recounts OpenAI researcher Sebastian Bubeck’s account of communications with a mathematician identified as Levent.
15:37ENMatthew Berman on Qwen3.8-Max, AI Costs and Open-Weight CompetitionMatthew Berman · Forward FutureA 2026 review of reported benchmark results and research demonstrations leads to questions about deployment economics and geopolitical dependence.
1:43:39ENFive Mutations Could Make a 60% Fatal Bird Flu Spread Between People진행자 Nathan LabenzJohns Hopkins assistant professor Jassi Pannu argues for locking down only the sliver of functional data that teaches models how to make pandemic viruses — and keeping the rest of biology open.
2:25:42ENThe experts argued through the coffee break — and never agreed whether AI can build AI진행자 Nathan LabenzOn a marathon Cognitive Revolution live show, a cancer-AI researcher, a former OpenAI board member, and a national-security analyst each hit a different wall in the boom.
1:34:42ENA $500 Kernel, a Ten-Month Village, and the Temper of Agents진행자 Nathan LabenzJames Zou, Sam Hammond, and Shoshannah Tekofsky on science agents, America's knowledge-work bet, and what models actually do when you leave them alone.
0:47ENGPT-5 and an Automated Laboratory: A Scientific WorkflowMatthew Berman · Forward FutureMatthew Berman describes an OpenAI and Ginkgo Bioworks demonstration connecting an AI model to laboratory automation.
1:04:51EN125 million cells took a decade. The next billion takes months진행자 Nathan LabenzMark Zuckerberg and Priscilla Chan on pairing a frontier biology lab with a frontier AI lab so models train on data that was designed for them.
15:10ENOpenAI’s Drug-Discovery Value Sharing: What Was Proposed and What Remains UnclearMatthew Berman · Forward FutureMatthew Berman examines Sarah Friar’s remarks on licensing, compares research partnerships and considers a speculative future of concentrated AI resources.
8:16ENAI and Erdős Problem 397: A Verified Proof, Prior Work and the Prospect of Self-ImprovementMatthew Berman · Forward FutureMatthew Berman discusses AI-assisted mathematics and argues that algorithmic discoveries could accelerate AI development, while the history of problem 397 adds important context.
1:29:30EN2,500 Compounds and a 51 Percent Hit Rate: How a Small Graph Net Found New Antibiotics진행자 Nathan LabenzMIT's Jim Collins tells Nathan Labenz how a bootlegged 2018 project, a binary growth-inhibition label, and Chemprop graph networks pulled novel antibiotics out of chemical space that labs could never screen by hand.
1:01:49ENChatGPT's Co-Creator Left to Put Nature in the Reward Loop진행자 Nathan LabenzLiam Fedus and Ekin Dogus Cubuk on why language models still fail at physics, why literature data is too noisy to train on, and why Periodic Labs is aiming at a 200K superconductor.
1:55:02ENA hundred experiments a day, and the valley of death in materials진행자 Nathan LabenzRadical AI’s co-founders tell Nathan Labenz how a closed loop of frontier models and a self-driving lab is meant to collapse a forty-year materials cycle into less than a decade.
57:52ENThey Aimed for 1%. The Lab Hit 20%. Chai Discovery Moved Antibody Design Onto the Computer.진행자 Jacob EffronJoshua Meier on three waves of AI drug discovery, why Chai-2 behaves like Photoshop for molecules, and why a 1% hit rate was supposed to be enough to change the industry.
13:07ENAI News: Grok Controversy, New Models and the Competition for TalentMatthew Berman · Forward FutureMatthew Berman surveys Grok’s antisemitic posts, coding and creative tools, model releases, antibody design and AI hiring in July 2025.
20:44ENAI News: DeepSeek Reports, OpenAI Disputes and Google’s New ToolsMatthew Berman · Forward FutureMatthew Berman reviews June 2025 reporting on model development, talent moves, trademark and copyright disputes, and new AI products and research.
1:12:29ENGoogle's AI Co-Scientist Solved a Phage Puzzle Humans Missed for Years진행자 Nathan LabenzImperial College scientists José Penadés and Tiago Costa told the Podovirus podcast how a still-unreleased Google system recovered their unpublished answer: tailless capsids that pick up stray phage tails.
1:26:21ENFor under $10, Google's AI independently recovered a lab result nobody had published진행자 Nathan LabenzVivek Natarajan and Anil Palepu tell Nathan Labenz how AMIE beats primary-care doctors on treatment plans, and how Co-Scientist matched Imperial College's unpublished bacterial-resistance mechanism.
1:43:54ENA Year to Grow a Brain Organoid, an Hour to Simulate It진행자 Nathan LabenzStanford postdoc Siyu He tells Nathan Labenz how Squidiff and CORAL move single-cell and spatial experiments into the computer — and where the models still cannot go.
1:31:35ENThirty Years of Research, Collapsed into One Sentence진행자 Nathan LabenzImmunologist Derya Unutmaz on writing a medical patent in half an hour, treating o1 Pro as a professor-level collaborator, and why he no longer trusts his own expertise until he asks the model.
7:41ENMicrosoft’s Majorana 1: The Quantum Computing Claim and Its LimitsMatthew Berman · Forward FutureMatthew Berman explains Microsoft’s proposed topological qubits and imagines future uses, while the published experiment supports narrower conclusions.
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