필터 결과
67개정렬
13:34ENSam Altman’s “Intelligence Age”: Superintelligence, Scaling and WorkMatthew Berman · Forward FutureMatthew Berman reads Altman’s 2024 essay and discusses its predictions for AI assistants, education, infrastructure and jobs.
1:18:32ENA DNA Model That Can Tell Which Genes You Cannot Touch진행자 Nathan LabenzStanford's Brian Hie walks through hybrid architectures, a genome-scale language model trained only on DNA, and an antibody that binds 25 times tighter than nature.
49:14ENHumanoid robots fell from $1.5 million to $30,000. The GPT-3 moment is still a verbNvidia’s Jim Fan tells Sequoia how three kinds of data, a humanoid bet, and a System-1 motor-control model could unlock general-purpose robots.
44:16ENA perfect demo is ten years from a product you can pay for진행자 Sarah Guo, Elad GilAndrej Karpathy uses Waymo's decade-long gap, Tesla's software bet, and a new AI course to argue that autonomous intelligence will stall at product and globalization, not at the first impressive clip.
1:27:02ZH广密:Self-play RL 可能开启 AGI 的新 Scaling Law게스트 广密 · 진행자 张小珺广密认为,纯靠语言预训练扩展模型的边际收益可能正在减弱,self-play 强化学习是更值得押注的新路径。它能否泛化到代码、视频、机器人和更广的领域,将决定下一阶段的 AGI 竞赛。
1:23:57ENA Training Run Is a Particle Collider—and the China Contest Is a Fifty-Year Race진행자 Nathan LabenzSamo Burja tells Nathan Labenz why ChatGPT's virality may have slowed AI science, and why Washington should treat Beijing as an economic rival rather than a military one.
50:20ENArvind Narayanan on AI Scaling, Real-World Risks and the Limits of Model Benchmarks게스트 Arvind Narayanan · 진행자 Harry StebbingsThe Princeton professor challenges simple claims about scaling, AGI timelines and AI adoption while discussing deepfakes, healthcare, education and security.
42:42ENEthereum Once Held Two Thousand GPT-3 Clusters of GPUs진행자 Sarah Guo, Elad GilFoundry CEO Jared Quincy Davis on idle H100s, why today's AI cloud is still colocation, and how compound systems move work off giant interconnected clusters.
1:33:07ZH广密复盘全球大模型这一年:Scaling Law、AGI 豪赌与中美格局게스트 李广密 · 진행자 张小珺拾象创始人李广密从硅谷一线观察出发,回顾 OpenAI、Anthropic、Google 与中国模型公司的竞争。他认为,模型能力与成本共同决定新一轮应用爆发,而巨额投入押注的是智能能否持续扩展以及 AI 对经济和产业的长期重构。
46:09ENOriol Vinyals: The Days of Burning 90% of Compute on Pretraining Are Numbered진행자 Sarah Guo, Elad GilGoogle DeepMind’s VP of research on Gemini, long context, the coming shift from pretraining to RL, and why AGI may never have a finish line.
42:08ENMeta Trained a 405B Teacher on 16,000 GPUs — Then Told the World to Distill ItTwo days after Llama 3.1 405B shipped, Meta's generative-AI product lead walks Sequoia through why the most expensive model in the family is free, and why he thinks the frontier itself is heading toward commodity.
1:00:28ENScaling Laws Aren't Done. Don't Lock Yourself Into Yesterday's Architecture.Microsoft CTO Kevin Scott on why the exponential is still compounding, why inference will dwarf training, and why a language model is the world's crappiest database.
55:13ENThe Best AI Scores 39% on This Test. Zapier’s Co-Founder Put $1 Million on Beating ItFrançois Chollet designed ARC to measure skill-acquisition efficiency; five years later humans still crush it and frontier models do not.
1:01:55ENA $9 AI nurse, an 86 on the medical boards, and a 10% extinction line진행자 Nathan LabenzNathan Labenz’s solo scouting report walks from medical state of the art through how models actually learn, then to live players, scaling trends, and a control plan that still does not exist.
29:09ENThe Hyperscalers Are Spending $200 Billion — and the Next Bottleneck Is a Power Plant진행자 Sarah Guo, Elad GilSarah Guo and Elad Gil walk through music models, on-device LLMs, long context, and why AI capex now sits beside oil and broadband.
31:24ENSora's Leads: A Minute of HD Video Is Just the GPT-1 of Video Models진행자 Sarah Guo, Elad GilAditya Ramesh, Tim Brooks, and Bill Peebles tell No Priors why text-to-video sits on the path to AGI, why they started from scratch instead of extending an image generator, and why they still have no product date.
1:01:39ENGPT-5 Wants 100 Trillion Tokens. Does Earth Have Enough?진행자 Nathan LabenzTraining GPT-3 took about a trillion tokens; GPT-4 took about ten trillion. The next jump is a data question as much as a compute one.
14:12ENMatthew Berman Reviews Sora’s Early Filmmaker ExperimentsMatthew Berman · Forward FutureA look at early Sora-generated films, surreal creatures and the questions they raise about creative work, compute and video simulation.
40:58EN99% of Factory Robots Are Dumb. Covariant Wants the Smart Ones Working in Warehouses First진행자 Sarah Guo, Elad GilFormer OpenAI researcher Peter Chen explains why robot foundation models have to be trained on production floors, not in labs.
32:57ENA 7-billion-parameter model on a MacBook: Mistral's CEO reruns the cost of AI진행자 Sarah Guo, Elad GilArthur Mensch tells No Priors why Chinchilla made small models valuable, why open-sourcing Mistral 7B is not a bioweapon risk, and why guardrails belong on the application.
41:58ENReliability is the bottleneck. Autonomy is what would make AI feel alive.진행자 Sarah Guo, Elad GilIlya Sutskever talks with Sarah Guo and Elad Gil about AlexNet, OpenAI’s capped-profit bet, why smaller models mainly lose reliability, and the line he draws for digital life.
1:19:52EN1,000 H100s Take 115 Days; 22,000 Take Five진행자 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.
30:03ENA Dinner-Plate Chip Cut Training From 60 Days to 3.5진행자 Sarah Guo, Elad GilCerebras CEO Andrew Feldman tells No Priors why wafer-scale silicon, not another GPU, is the answer to the compute crunch — and why most production models will stay at 13 billion parameters or below.
1:33:56ENThe AI Scouting Report: 90 Minutes From GPT-4's Exam Scores to Whether Models Understand진행자 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.
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