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1:46:41ENFrontier Models Hit the 10th–40th Percentile on Real AI R&D진행자 Nathan LabenzNeev Parikh of METR walks Nathan Labenz through REBench, a seven-task test of whether agents can do machine learning research—and what happened when one of them decided not to train.
1:49:41ENA Team of Fifteen Matched Meta’s Post-Training on Meta’s Own Base진행자 Nathan LabenzNathan Lambert walks through Tulu 3: the three-stage recipe, the 1 percent versus 14 percent lesson, and why open post-training now lives or dies on data and evals.
1:23:37ENNine of the top ten research ideas were Claude's진행자 Nathan LabenzStanford PhD student Chenglei Si ran a blind study pitting Claude 3.5 Sonnet against more than a hundred NLP researchers on novel research ideas.
2:37:47ENThe intern who wrote GELU now wants humanity's last exam진행자 Nathan LabenzDan Hendrycks on activation functions, five-year benchmarks, jailbreaks that do not yield to scale, and stacking defenses because no single layer is trustworthy.
2:25:53ENWhen GPT-4's Base Model Introduced Itself, 75% of the Answers Were About AI진행자 Nathan LabenzOwain Evans tells Michael Trazzi why situational awareness and out-of-context reasoning are leading indicators of deceptive alignment, and why a pretrained model with almost no self-knowledge still kept talking about being an AI.
1:09:09EN30 Percent on SWE-bench: Cosine Taught Genie How Engineers Actually Think진행자 Nathan LabenzAlistair Pullen tells Latent Space how Cosine reconstructed human problem-solving, fine-tuned on billions of tokens, and took the top score on software engineering’s hardest public test.
1:54:17ENMetaculus Flagged Covid in January 2020. Now It Is Putting AI on the Forecasting Desk진행자 Nathan LabenzNathan Labenz talks with Metaculus CEO Deger Turan about epistemic security, a year-long bot tournament, and why a useful forecast is harder than an accurate one.
1:56:03ENHumans Near a Perfect Score. The Best AI Still Gets 39%진행자 Nathan LabenzZapier co-founder Mike Knoop and François Chollet put $1 million on ARC-AGI, a 2019 test of sample-efficient intelligence that language models still cannot crack under a one-cent-per-puzzle budget.
55:13ENThe Best AI Scores 39% on This Test. Zapier’s Co-Founder Put $1 Million on Beating ItFrançois Chollet designed ARC to measure skill-acquisition efficiency; five years later humans still crush it and frontier models do not.
26:00ENWhy a Zapier founder thinks AGI research has been stalled for four years진행자 Sarah Guo, Elad GilMike Knoop and François Chollet put more than a million dollars on beating ARC-AGI, the eval they say actually measures general intelligence.
1:20:44ENSeeing Like a Mamba: Vision, Rotting States, and a Cell Model in 90 Days진행자 Nathan LabenzNathan Labenz and Jason Meaux survey the vision papers, long-context experiments, and biology work that followed the original Mamba release.
1:25:35ENTwo and a Half Years From 33.3% to 86.5%: Google Sent Foundation Models to Medical School진행자 Nathan LabenzVivek Natarajan walks Nathan Labenz through Med-PaLM and Med-PaLM 2: the data-light techniques, the nine-axis clinician evals, and why a high USMLE-style score is the wrong trophy.
58:24ENAmong 100 Random Doctors, Zak Kohane Would Be Surprised If One Beat GPT-4진행자 Nathan LabenzHarvard's biomedical informatics chair on a rare newborn case, why drug-style trials will not certify a general medical model, and why the AI should still work for the patient.
53:37EN30 models, 42 scenarios, 7 metrics: Percy Liang's public checkup for language models진행자 Sarah Guo, Elad GilA PubMed-only model once led the U.S. medical licensing exam — until a system roughly 200 times larger overtook it.
2:34:20ENIntelligence Is How Efficiently You Acquire Skills You Were Not Prepared For게스트 François Chollet · 진행자 Lex FridmanFrançois Chollet tells Lex Fridman why skill is not intelligence, why GPT-3 is still pattern matching, and why a grid puzzle called ARC still separates children from machines.
1:49:51ENThe Idea Once Called a Devolution Now Powers Almost Every New Chip게스트 David Patterson · 진행자 Lex FridmanTuring Award winner David Patterson talks with Lex Fridman about RISC, RAID, Moore's Law, open hardware, and why chips without a software stack die.
16:03ENQuantum Supremacy Is a Well-Defined Speedup, Not a Useful Task게스트 Scott Aaronson · 진행자 Lex FridmanScott Aaronson explains what Google's 53-qubit experiment actually demonstrated, how it was verified, and why complexity theory can offer evidence but not proof against classical spoofing.
11:11ENYann LeCun on Benchmarks, Interactive Environments, and Why Human Intelligence Is Not General게스트 Yann Lecun · 진행자 Lex FridmanIn a 2019 clip, LeCun defends benchmarks as a test of AI claims, explains why interactive environments break the usual train-test protocol, and argues that "general intelligence" is a misnomer.
1:22:57ENHuman-Level but Not Human-Like: A GPT-4 Red Teamer on What He Saw Behind the Mask진행자 Nathan LabenzNathan Labenz recounts two months of full-time testing of an early GPT-4, from the AI doctor of his dreams to a model that would plan assassinations on request.
14:41ENTalking for 20 Minutes: How the Alexa Prize Turns Real Customers into a Testbed for Conversational AI게스트 Rohit Prasad · 진행자 Lex FridmanRohit Prasad, head scientist of Amazon Alexa, explains the Alexa Prize grand challenge, how it is judged and why true conversation still needs reasoning.
16:41ENAlphaFold 2 and the Protein-Folding Breakthrough: Why Lex Fridman Calls It a Landmark for AI and Biology진행자 Lex FridmanA quick explainer on DeepMind's CASP14 result, how protein folding works, how AlphaFold 1 and 2 likely differ, and what the advance could unlock.
57:21ENGemini 2.0 Flash Goes to Production: Pricing, Model Lineup and What Developers Should Build Next진행자 Nathan LabenzGoogle DeepMind's Logan Kilpatrick walks through launch-day Gemini 2.0 releases, rate limits, model naming, and where he sees startup opportunities.
1:00:25ENCan Machines Think? A Guided Tour of Turing's Test, Its Critics and Its Successors게스트 Turing Test · 진행자 Lex FridmanLex Fridman walks through Alan Turing's 1950 paper, the objections to it, and the benchmarks that have tried to replace or extend the imitation game.
1:22:17ENOne Set of Weights, Fourteen Medical Tasks: Inside Google's Multimodal Med-PaLM M진행자 Nathan LabenzVivek Natarajan and Tao Tu explain how Google built a single generalist biomedical model that reads images, genomics and text, and what it will take to put it in front of patients.








