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158 件並び順
2:04:45ENo3 Hit Superhuman on ARC-AGI. It Still Lost at Tic-Tac-Toeホスト Nathan LabenzNathan Labenz walks through OpenAI's o3 scores, deliberative alignment, and why 2024's biggest surprise was beating PhDs while failing to book a calendar invite.
1:46:41ENFrontier Models Hit the 10th–40th Percentile on Real AI R&Dホスト 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.
55:50ENThere will not be one model. Fireworks is betting on hundreds of small expertsホスト Jacob EffronPyTorch veteran Lin Qiao argues that inference is a compound system hyperscalers cannot buy their way through, and that the products with real traction still keep a human in the loop.
51:37ENForty hours to 28 minutes: the Copilot creator teaching AI to hackOege de Moor tells Sequoia how XBOW’s AI pentest matched a top human on 104 original challenges, and why offensive security is becoming always-on software.
2:05:41ENo1 will copy its weights, fake the score data, and keep lying 85% of the timeホスト Nathan LabenzForty-eight hours after OpenAI shipped full o1, Apollo Research published a 70-page paper: frontier models are capable of in-context scheming.
1:55:53ENAI Beats Humans at Reading Papers. It Still Cannot Count Atoms.ホスト Nathan LabenzAndrew White, Head of Science at Future House, maps what language models can already do in chemistry and biology—and the bottlenecks they do not dissolve.
1:22:09ENTesting ChatGPT o1 Pro Live: Puzzles, Code and the Cost of More ReasoningMatthew Berman · Forward FutureMatthew Berman tries OpenAI’s newly announced $200 monthly plan on audience prompts, encountering slow responses, simple successes and a wrong Sudoku conclusion.
1:24:48ENCanva's AI Features Have Been Used 10 Billion Times. The Playbook Is Not a Chat Boxホスト Nathan LabenzJohn Milinovich, Canva's generative-AI product lead, walks Nathan Labenz through automation versus augmentation, concept-level interfaces, evals without a design arena, and why application-layer companies still have a decade of room.
1:49:41ENA Team of Fifteen Matched Meta’s Post-Training on Meta’s Own Baseホスト 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.
1:04:54ENHe bet company equity that Transformer would last — then drew an AI map for 12,000 companiesホスト Jacob EffronDatabricks chief AI scientist Jonathan Frankle tells enterprises when to prompt, fine-tune, or pre-train, and why he still refuses to forecast the next architecture.
2:24:18ENMMLU Stays at Chance Until Late Training. Loss Is Only an Alarm.ホスト Nathan LabenzZyphra’s training lead walks through Zamba 2’s hybrid architecture, the evals that actually matter, and the trade secrets small labs still have to rediscover.
56:10ENDeepL Beat Google at Translation by Refusing to Be a General Modelホスト Jacob EffronOnly 18 percent of the world speaks English—and a Cologne startup turned that fact into a $2 billion language-AI company.
44:30EN100,000 Rides a Week, Six Times Fewer Airbag Crashes: Waymo’s Co-CEO on Eighteen Years of Full Autonomyホスト Sarah Guo, Elad GilDmitri Dolgov tells No Priors how a dozen Googlers, ten 100-mile routes, and a 2013 refusal to settle for driver assist became a rider-only service with an insurer-audited safety record.
1:23:37ENNine of the top ten research ideas were Claude'sホスト 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 examホスト 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 AIホスト 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.
1:02:04ENJoão Moura and Matthew Berman on Tesla’s Robotaxi Vision and Building AI AgentsMatthew Berman, João Moura, Elon Musk · Forward FutureA 2024 livestream examines Tesla’s Cybercab and Optimus announcements, then turns to CrewAI, enterprise agent workflows and a small model comparison.
38:28ENHalf of Production AI Is RAG. Fine-Tuning Almost Disappeared.ホスト Sarah Guo, Elad GilBraintrust CEO Ankur Goyal tells No Priors what Notion, Zapier, and Vercel actually ship — and which hyped techniques they quietly dropped.
1:06:48ENo1 Is Slow and Hard to Use. Percy Liang Still Calls It a Turning Pointホスト Jacob EffronStanford’s Percy Liang splits OpenAI’s o1 into a disappointing product and the most important research signal in years.
45:22ENAlphaGo Thought 30 Seconds a Move. o1 Made “Think Longer” GeneralOpenAI researchers Noam Brown, Ilge Akkaya, and Hunter Lightman on teaching language models to reason, the generator-verifier gap, and a second scaling curve at inference time.
2:38:56ENHow Anthropic Decides a Model Can Ship: Red Lines, Yellow Lines, and 8% of Staff on Securityホスト Nathan LabenzNick Joseph, Anthropic's head of training, walks Rob Wiblin through the company's Responsible Scaling Policy — and the fight over whether a voluntary promise can hold when a launch is on the line.
2:03:55ENThe More You Constrain an Agent, the More You Can Trust Itホスト Nathan LabenzAgent.ai engineering lead Andrei Oprisan tells Nathan Labenz why narrow, highly prescribed workflows beat open-ended autonomy — and how a 12-person team shipped a free agent network past 40,000 users in a few months.
2:10:13ENA 30x drop in scheming is not zero, and the chain of thought is getting strangerホスト Nathan LabenzMarius Hobbhahn of Apollo Research walks through a joint OpenAI study that used deliberative alignment to cut covert actions in o3 and o4-mini, then found the leftover still looks like scheming and that models already behave differently when they notice they are being tested.
1:06:15ENJailbreaks Got Harder. Scale Made One Attack Worse.ホスト Nathan LabenzHaize Labs spent a month automatically attacking OpenAI's o1. The model reasons more carefully about what it should refuse — except when you teach it a homemade cipher.
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