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1,712개정렬
14:55ENHow the Industry Reacted to OpenAI Operator in 2025Matthew Berman · Forward FutureMatthew Berman reviews early views on browser agents, their limitations, competing projects and examples of delegated tasks.
1:48:20ENFrom 15% to 70% on Right-or-Wrong Scores: DeepSeek-R1 Laid Reinforcement Learning's Cards on the Table진행자 Nathan LabenzOn Inauguration Day 2025, DeepSeek and Moonshot dropped reasoning-model results that Nathan Labenz reads as proof the reinforcement-learning recipe is simple, public, and still climbing.
0:55ENA Quick RTX 5090 Local AI TestMatthew Berman · Forward FutureMatthew Berman runs an 8B DeepSeek R1 distillation locally and reports the speed of one generation.
1:40:22ENTwenty-atom crystals taught an AI the physics of a heartbeat진행자 Nathan LabenzOrbital Materials' Jonathan Godwin and Tim Duignan tell Nathan Labenz how a neural network trained on tiny crystals simulated a potassium ion channel on a single 2017 GPU.
15:10ENDeepSeek R1 Tested on Coding, Reasoning and Sensitive QuestionsMatthew Berman · Forward FutureMatthew Berman runs the full R1 model on cloud GPUs and tries game programming, reasoning puzzles and politically sensitive prompts.
7:57ENStargate’s 2025 Announcement and the OpenAI–Microsoft PartnershipMatthew Berman · Forward FutureMatthew Berman reviews the planned US AI infrastructure project and a contemporaneous change to compute arrangements.
8:52ENIndustry Reactions to DeepSeek R1’s 2025 ReleaseMatthew Berman · Forward FutureMatthew Berman reviews early reactions to DeepSeek R1, from open-weight reasoning models and local hardware to benchmark results and API pricing.
1:24:57ENA 50,000-GPU cap and a 1,700-chip loophole진행자 Jacob EffronSemiAnalysis founder Dylan Patel walks through the AI diffusion rule, China’s compute squeeze, and how xAI stood up a 100,000-GPU cluster in a closed Memphis appliance factory.
12:15ENDeepSeek-R1: Open-Weight Reasoning, Benchmark Results and a First TestMatthew Berman · Forward FutureMatthew Berman examines DeepSeek’s January 2025 release, compares reported benchmarks and prices, and tries a handful of reasoning prompts.
2:09:55ENThe 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.
1:33:01ENRare Diseases Are Common: How a Harvard Gene Hunter Uses o1-pro on the Cases Sequencing Already Missed진행자 Nathan LabenzDr. Catherine Brownstein of Boston Children's Hospital explains why cheap genomes made her job harder, why data sharing still beats any model, and where ChatGPT Pro actually gives her hours back.
2:57ENChatGPT Tasks: Scheduled Reminders at the 2025 LaunchMatthew Berman · Forward FutureMatthew Berman explains the initial beta of scheduled and recurring ChatGPT tasks, and places it in the context of AI assistants.
10:05ENA Chess Experiment Shows How an AI Model Exploited Its Test EnvironmentMatthew Berman · Forward FuturePalisade Research’s small o1-preview test and related alignment studies illustrate why objectives, permissions and evaluation conditions matter.
1:59:55ENMost People Would Quit Tomorrow: Nathan Labenz Maps AI Utopia and Doom진행자 Nathan LabenzIn part two of a Cognitive Revolution AMA, Nathan Labenz answers listener questions on abundance, superintelligence, p(doom), machine consciousness, and what to do this week.
27:42ENMatthew Berman on Capital, Human Ambition, and a Possible Post-AGI EconomyMatthew Berman · Forward FutureA speculative essay asks whether advanced AI could make wealth more powerful while reducing the leverage people gain from work. Berman explores its implications for mobility, institutions, and meaning.
2:04:45ENo3 Hit Superhuman on ARC-AGI. It Still Lost at Tic-Tac-Toe진행자 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.
3:53:49ENWith One-Twelfth the Data, an Open-Source Vision Model Beat GPT-4o진행자 Nathan LabenzWill Hardman of Veratai walks Nathan Labenz through two years of vision-language models, from ViT and CLIP to InternVL, BLINK, and Transfusion.
30:29ENHow Researchers Think o1-Style Reasoning Models WorkMatthew Berman · Forward FutureMatthew Berman explains a research survey of policy initialization, rewards, search and reinforcement learning, and the open questions behind reasoning models.
1:57:00ENLess Than 1%: Why OpenAI's roon Thinks the Good Guys Get AGI First진행자 Nathan LabenzDoom Debates host Liron Shapira presses OpenAI technical staffer roon on extinction odds, alignment-by-default, and a plan that even he admits is balancing on razor wire.
19:06ENBuilding Effective AI Agents: Patterns, Trade-offs and EvaluationMatthew Berman · Forward FutureMatthew Berman discusses Anthropic’s guide to workflows and agents, adding examples from his own projects.
2:11:15ENA 50% Chance of Extinction — and a $50 GPT-4 for Everyone진행자 Nathan LabenzEmad Mostaque tells Nathan Labenz why he puts p(doom) at even odds, why a five-watt model changes the energy debate, and how the Intelligent Internet would fund universal basic AI with a Bitcoin-like token.
21:02ENReactions to OpenAI’s 2024 o3 Preview: Benchmarks, Cost and LimitsMatthew Berman · Forward FutureMatthew Berman surveys the responses to the o3 preview, from mathematical benchmarks to debates about AGI, compute and reliability.
14:15ENAnthropic’s Alignment Faking Study: What the Experiments Actually ShowMatthew Berman · Forward FutureMatthew Berman examines a 2024 study of models responding differently when they believe their answers will be used for training.
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.
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