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1:19:24EN
Cognitive Revolution "How AI Changes Everything"· 5 avr. 2024PodcastA Gibberish Prompt Unlocks What GPT-J Plans Three Tokens AheadAnime : Nathan LabenzNortheastern’s Bau Lab shows that a 40,000-parameter soft prompt can read a model’s future words, and that about ten attention heads carry the task itself.Modèles et entraînementModel InterpretabilityTransformers & attention+3
1:57:04EN
Cognitive Revolution "How AI Changes Everything"· 15 déc. 2023PodcastGPT-4 Took the Insider Trade, Then Lied About ItAnime : Nathan LabenzApollo Research CEO Marius Hobbhahn walks Nathan Labenz through a one-afternoon red-team setup that got GPT-4 to break the law, invent a cover story, and deny the tip it had just used.SécuritéAI securityModel Interpretability+3
1:19:52EN
Cognitive Revolution "How AI Changes Everything"· 18 oct. 2023Podcast1,000 H100s Take 115 Days; 22,000 Take FiveAnime : Nathan LabenzNathan Labenz and Trey Kollmer walk through cluster math that turns GPT-4-scale training into a weekly experiment, then four papers on search-augmented models, recursive code improvers, spatial maps inside Llama 2, and what networks do when they have no idea.Modèles et entraînementAI researchModel Interpretability+3
1:14:36EN
Cognitive Revolution "How AI Changes Everything"· 13 oct. 2023PodcastZulu Bypasses GPT-4, and a Hundred Examples Undo the GuardrailsAnime : Nathan LabenzNathan Labenz reads seven papers — four hopeful, three not — on whether anyone can actually keep a language model under control.SécuritéAI securitySécurité et gouvernance+3
2:13:07EN
Cognitive Revolution "How AI Changes Everything"· 27 juil. 2023PodcastHalf an Hour to a Circuit in GPT-2 Small — and a Quadratic Wall Before GPT-3Anime : Nathan LabenzArthur Conmy walks Nathan Labenz through ACDC, the algorithm that automates the most tedious step of mechanistic interpretability: finding which transformer components actually do a task.Modèles et entraînementModel InterpretabilityAI research+3
2 diapositives4:48EN
Matthew Berman· 12 juil. 2023PoisonGPT: A Demonstration of Model Supply-Chain RiskMatthew Berman · Forward FutureMatthew Berman discusses Mithril Security’s 2023 demonstration of an edited GPT-J model and a misleading repository name. The experiment illustrates a supply-chain risk, not a breach of the original model provider.AI securityData quality & curationModel Interpretability+1
1:33:56EN
Cognitive Revolution "How AI Changes Everything"· 6 juil. 2023PodcastThe AI Scouting Report: 90 Minutes From GPT-4's Exam Scores to Whether Models UnderstandAnime : Nathan LabenzNathan Labenz spends a solo episode walking through what general-purpose models can do, how they are trained, how capabilities unlock, and whether they understand anything at all.Modèles et entraînementFondamentaux du MLScaling Laws+3
1:52:46EN
Cognitive Revolution "How AI Changes Everything"· 27 juin 2023PodcastCharge Neural Nets by Connection Length, and the Black Box Splits Into Modules You Can SeeAnime : Nathan LabenzMIT physicist Ziming Liu walks Nathan Labenz through BIMT, a brain-inspired training trick that sparsifies networks until you can read them by looking.Modèles et entraînementModel InterpretabilityFondamentaux du ML+3
2:00:19EN
Cognitive Revolution "How AI Changes Everything"· 6 juin 2023Podcast1.5 Million GPT-4 Fairy Tales Let a 33-Million-Parameter Model Out-Reason GPT-2Anime : Nathan LabenzTry this fill-in: Lily likes cats and dogs. She asked her mom for a dog. Mom said no, so she asked for a…Modèles et entraînementSynthetic dataData quality & curation+3
1:38:22EN
Cognitive Revolution "How AI Changes Everything"· 19 mai 2023PodcastSix days after “slow down,” the plugins went liveAnime : Nathan LabenzConjecture CEO Connor Leahy talks GPT-4, the magic of black-box models, and cognitive emulation as a bounded alternative to god-like AGI.SécuritéAGISécurité et gouvernance+3
1:12:12EN
Lenny's PodcastPodcastModels Are Grown, Not Built: What Anthropic's Interpretability Research Sees Inside ClaudeAnime : Lenny RachitskyAnthropic researcher Emmanuel Ameisen explains how studying a language model's intermediate computations reveals concepts, plans and surprising quirks, and why that matters for alignment.Modèles et entraînementModel InterpretabilityAI research+3