絞り込み結果
7 件並び順
1:18:05EN
Unsupervised Learning: With Jacob Effron· 2025年12月18日ポッドキャストThe Real Bottleneck in AI Research Is Compute, Not Ideasホスト Jacob EffronAfter NeurIPS, Ari Morcos and Rob Toews sit down with Jacob Effron to argue about plateauing models, SSI’s secrecy, OpenAI’s cash burn, and a set of spicy 2026 bets.
48:00ENUnsupervised Learning: With Jacob Effron· 2025年12月15日ポッドキャストA Two-Second Vote Is Training Models to Be Clickbaitホスト Jacob EffronSurge AI CEO Edwin Chen tells Redpoint why LMArena rewards length and emojis, how one lab's models got worse for a year, and why frontier labs are no longer on the same path.
1:16:53ENUnsupervised Learning: With Jacob Effron· 2025年10月24日ポッドキャストKarpathy Called Today's Models Slop. His Decade Timeline Still Sounds Bullishホスト Jacob EffronDatology AI's Ari Morcos and Radical Ventures' Rob Toews walk through Karpathy's cold water, OpenAI's dealmaking, and who actually pays for the data-center boom.
57:52ENUnsupervised Learning: With Jacob Effron· 2025年8月13日ポッドキャストThey Aimed for 1%. The Lab Hit 20%. Chai Discovery Moved Antibody Design Onto the Computer.ホスト 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.
44:21ENUnsupervised Learning: With Jacob Effron· 2025年6月4日ポッドキャストMercor's CEO: Knowledge Work's Next Job Is Writing Evalsホスト Jacob EffronBrendan Foody tells Redpoint how Mercor turned AI hiring and expert data into a $100 million round — and why he thinks a huge slice of white-collar work will become creating tests for models.
56:10ENUnsupervised Learning: With Jacob Effron· 2024年10月29日ポッドキャストDeepL 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.
1:10:19ENUnsupervised Learning: With Jacob EffronポッドキャストThe Robot That Folded the Laundry: Physical Intelligence’s Three Frontiersホスト Jacob EffronKarol Hausman and Danny Driess explain how robot foundation models moved from coded primitives to web-scale pretraining, and why performance is now the hard gate.