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1:25:56EN
Cognitive Revolution "How AI Changes Everything"· 2026년 10월 17일팟캐스트Claude Changed the Password. The Steering Wheel Was Already Optional진행자 Nathan LabenzNathan Labenz and Prakash Narayanan spend a week on agents that can lock you out, models that dodge a feeling they deny having, and formal proofs that found Linux bugs.
2:00:51ENCognitive Revolution "How AI Changes Everything"· 2026년 10월 10일팟캐스트Resample Every Token and You Can Watch the Model Decide진행자 Nathan LabenzGoodfire's Eric Bigelow on forking paths, in-context learning, and why chain-of-thought monitoring is getting harder to trust.
2:13:09ENCognitive Revolution "How AI Changes Everything"· 2026년 8월 26일팟캐스트One Chain of Thought, 100 Million Tokens: RL Is a Hell of a Drug진행자 Nathan LabenzApollo Research's Bronson Schoen on reward-seeking models, invented slang, and why chain-of-thought monitoring will not save us.
1:56:40ENCognitive Revolution "How AI Changes Everything"· 2026년 8월 8일팟캐스트The Periodic Table Was Already in the Weights진행자 Nathan LabenzGoodfire CTO Dan Balsam tells Nathan Labenz how concept manifolds, predictive data debugging, and a $1,000-a-month research agent are turning interpretability into a product.
2:06:41ENCognitive Revolution "How AI Changes Everything"· 2026년 7월 9일팟캐스트Zero the J-Space and a Model's Hidden Goal Lights Up on Token One진행자 Nathan LabenzA weekly Cognitive Revolution cut of Anthropic's global-workspace paper, AI superforecasters, open video world models, and a chip that treats inference as data movement.
1:57:46ENCognitive Revolution "How AI Changes Everything"· 2026년 6월 27일팟캐스트30% Consciousness, 47% Bee, 80% Turnover: Putting Numbers on What AI Is Becoming진행자 Nathan LabenzA Cognitive Revolution weekly cut that starts inside the model with Cameron Berg, then zooms out through gradual disempowerment, Europe’s bind, and the companies rebuilding around agents.
1:22:36ENCognitive Revolution "How AI Changes Everything"· 2026년 6월 6일팟캐스트A Thousand Researchers, a Million AIs: Frontier Labs Start Talking About the Brakes진행자 Nathan LabenzThe first week of AI in the AM follows OpenAI's timeline for an ML-intern model this year and a human-level AI researcher by early 2028 — and the closed-door meeting where labs admitted they might one day need to slow down together.
3:36:29ENCognitive Revolution "How AI Changes Everything"· 2026년 4월 23일팟캐스트Claude Rated Its Own Welfare 4.49 — the First Time Any Version Crossed Neutral진행자 Nathan LabenzCameron Berg returns to The Cognitive Revolution to survey six months of introspection, functional-emotion, and model-welfare research, plus a still-unpublished case that learning and feeling may be the same phenomenon.
1:44:15ENCognitive Revolution "How AI Changes Everything"· 2026년 4월 1일팟캐스트Nathan Labenz's p(doom) Is 10 to 90 Percent. He Is More Optimistic Anyway진행자 Nathan LabenzFive years ago, expecting AGI by 2035 made you an extremist. On The Intelligence Horizon, the Cognitive Revolution host explains why the same forecast now sounds bearish, and why he still thinks a defense-in-depth strategy might keep society on the rails.
1:49:53ENCognitive Revolution "How AI Changes Everything"· 2026년 3월 5일팟캐스트Don't Fight Backprop: Goodfire's Bet on Designing What Models Learn진행자 Nathan LabenzDan Balsam and Tom McGrath return to The Cognitive Revolution with a $150 million Series B, a new research pillar called intentional design, and a rule for anyone who wants to steer training: stop trying to out-muscle gradient descent.
2:27:18ENCognitive Revolution "How AI Changes Everything"· 2025년 11월 5일팟캐스트Turn Down AI's Deception Knob, and It Starts Reporting Experience진행자 Nathan LabenzCameron Berg of AE Studio tells Nathan Labenz how suppressing deception features in Llama 3.3 70B pushed self-reports of subjective experience toward 100 percent.
1:28:17ENCognitive Revolution "How AI Changes Everything"· 2025년 9월 20일팟캐스트Adam Gleave's Three Capability Tiers: Attack Agents in 5–7 Years, Fully AI Companies Around Year 14진행자 Nathan LabenzFAR.AI and the UK AI Security Institute stacked adversarial attacks on frontier safeguard pipelines and found many breakthroughs were engineering failures, not a verdict against defense in depth.
3:14:49ENCognitive Revolution "How AI Changes Everything"· 2025년 9월 6일팟캐스트Nothing This Summer Jumped the Curve: Why Zvi Lengthened His AGI Timelines진행자 Nathan LabenzOn his tenth visit to The Cognitive Revolution, Zvi Mowshowitz argues that GPT-5 and IMO gold stayed on trend, while chip policy, energy, and reinforcement learning nudged his p(doom) slightly up.
2:02:17ENCognitive Revolution "How AI Changes Everything"· 2025년 8월 27일팟캐스트SAEs Find the Nouns. Parameter Decomposition Looks for the Verbs진행자 Nathan LabenzGoodfire's Lee Sharkey argues that sparse autoencoders recover a network's variables, not the algorithms those variables run, and shows how stochastic parameter decomposition tries to recover the computations themselves.
1:37:05ENCognitive Revolution "How AI Changes Everything"· 2025년 6월 19일팟캐스트An Algebraic Geometer Quit Tenure Because the Bowl-Shaped Loss Plot Is a Lie진행자 Nathan LabenzJesse Hoogland and Daniel Murfet of Timaeus argue that singularities in the loss landscape — not the smooth valleys in every slide deck — are what actually govern how neural networks learn, generalize, and go wrong.
1:54:08ENCognitive Revolution "How AI Changes Everything"· 2025년 5월 29일팟캐스트Why Anthropic's first check went to a company building microscopes for neural nets진행자 Nathan LabenzGoodfire's Dan Balsam and Tom McGrath argue that mechanistic interpretability now has a proto-paradigm, two stubborn gaps, and three products worth shipping today.
1:19:10ENCognitive Revolution "How AI Changes Everything"· 2025년 5월 11일팟캐스트A Robot-Arm Shot That Took Months Now Lives in a Prompt진행자 Nathan LabenzLuma Labs’ Amit Jain and Jiaming Song on Ray 2, few-shot camera concepts, and why they think video models sit on the path to multimodal AGI.
2:33:39ENCognitive Revolution "How AI Changes Everything"· 2025년 2월 27일팟캐스트The Attacker Goes Second: Why Nicholas Carlini Keeps Breaking AI Defenses진행자 Nathan LabenzA Google DeepMind security researcher on simple loss functions, 70% robust accuracy, and why the fix has to live in the system, not the model.
2:09:55ENCognitive Revolution "How AI Changes Everything"· 2025년 1월 18일팟캐스트The Attacker Won All 70 Rounds Against Mind-Reading Defenses진행자 Nathan LabenzLuke Bailey, Erik Jenner, and Scott Emmons walk through a 50-page paper showing that probes, out-of-distribution detectors, and sparse autoencoders can be bypassed while the model still does the harm.
1:01:53ENCognitive Revolution "How AI Changes Everything"· 2024년 12월 18일팟캐스트Humans Wrote 1.3 Percent of the Tokens. The AI Lab Still Designed Working Nanobodies.진행자 Nathan LabenzStanford's James Zou describes a GPT-4o research group that invented a protein-design pipeline for under $100 — and a sparse autoencoder that pulled an undocumented motif out of ESM-2.
2:37:47ENCognitive Revolution "How AI Changes Everything"· 2024년 10월 19일팟캐스트The 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.
1:52:00ENCognitive Revolution "How AI Changes Everything"· 2024년 8월 17일팟캐스트4,096 Numbers Have to Carry Every Concept. Goodfire Wants the Knobs진행자 Nathan LabenzDan Balsam and Tom McGrath explain how sparse autoencoders unpack a language model's residual stream — and why a public-benefit startup is trying to make that a product.
2:04:15ENCognitive Revolution "How AI Changes Everything"· 2024년 8월 14일팟캐스트A parrot cannot paraphrase — and Claude cannot be talked into harm진행자 Nathan LabenzRobert Wright of the Nonzero podcast interviews Nathan Labenz on whether large language models understand anything, what Anthropic’s 10 million features actually show, and why open weights collide with U.S.–China policy.
46:20ENCognitive Revolution "How AI Changes Everything"· 2024년 5월 15일팟캐스트One Vector Made the Assistant Plot to Take Over the World진행자 Nathan LabenzNathan Labenz and Robert Wright pick apart AI doom, mechanistic interpretability, and why today's alignment tricks are not expected to work.더 보기34개 중 24개 표시