最近更新されたチャンネル

選ぶとそのチャンネルだけを表示すべてのチャンネル →

絞り込み結果

248 件
並び順
2:13:07EN
Cognitive Revolution "How AI Changes Everything"· 2023年7月27日ポッドキャストHalf an Hour to a Circuit in GPT-2 Small — and a Quadratic Wall Before GPT-3ホスト 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.モデルと学習Model InterpretabilityAI research+3
スライド 3 枚12:20EN
MBMatthew Berman· 2023年7月10日Testing UltraLM 13B: A Small Model’s Strengths and Reasoning ErrorsMatthew Berman · Forward FutureA 2023 local test finds useful writing and simple coding alongside failures in games, arithmetic and logic.Open-weight models評価(Evals)モデルと学習+1
1:33:56EN
Cognitive Revolution "How AI Changes Everything"· 2023年7月6日ポッドキャストThe AI Scouting Report: 90 Minutes From GPT-4's Exam Scores to Whether Models Understandホスト 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.モデルと学習機械学習の基礎Scaling Laws+3
1:32:22EN
Cognitive Revolution "How AI Changes Everything"· 2023年6月29日ポッドキャストA Million Bytes and 256 Symbols: Meta's MEGABYTE Wants to Throw the Tokenizer Awayホスト Nathan LabenzLili Yu of Meta AI explains a multiscale transformer that predicts million-byte sequences, skips tokenization, and aims to model any file as raw bytes.モデルと学習Multimodal AITransformers & attention+3
1:52:46EN
Cognitive Revolution "How AI Changes Everything"· 2023年6月27日ポッドキャストCharge Neural Nets by Connection Length, and the Black Box Splits Into Modules You Can Seeホスト 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.モデルと学習Model Interpretability機械学習の基礎+3
59:28EN
Cognitive Revolution "How AI Changes Everything"· 2023年6月16日ポッドキャストFrom $500,000 to $50,000: MosaicML Made Training Your Own Model a Priced Productホスト Nathan LabenzJonathan Frankle and Abhi Venigalla explain who is training custom LLMs, why extraction beats chat, and how ALiBi stretches a 65k context window.モデルと学習ファインチューニングPretraining & midtraining+3
2:00:19EN
Cognitive Revolution "How AI Changes Everything"· 2023年6月6日ポッドキャスト1.5 Million GPT-4 Fairy Tales Let a 33-Million-Parameter Model Out-Reason GPT-2ホスト 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…モデルと学習Synthetic dataData quality & curation+3
1:25:35EN
Cognitive Revolution "How AI Changes Everything"· 2023年5月21日ポッドキャストTwo and a Half Years From 33.3% to 86.5%: Google Sent Foundation Models to Medical Schoolホスト Nathan LabenzVivek Natarajan walks Nathan Labenz through Med-PaLM and Med-PaLM 2: the data-light techniques, the nine-axis clinician evals, and why a high USMLE-style score is the wrong trophy.モデルと学習AI in healthcareファインチューニング+3
42:18EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023年5月18日ポッドキャスト · 2 人Ten Percent of Search Is Health — and the Doctor Is Googling Tooホスト Sarah Guo, Elad GilGoogle researcher Karan Singhal on Med-PaLM 2, the real bar for medical language models, and why medicine is a harder alignment lab than chatbots.モデルと学習AI in healthcare安全性とガバナンス+3
スライド5:24EN
MBMatthew Berman· 2023年5月12日Google I/O 2023: Bard, PaLM 2 and AI Across Google's ProductsMatthew Berman · Forward FutureMatthew Berman reviews Google's 2023 AI announcements and weighs its focus on improving established products.AI Strategyプロダクトと UXモデルと学習+1
4:14:33EN
Lex Fridman· 2023年5月9日ポッドキャスト · 2 人The Next Token Is Not a Proof: Stephen Wolfram on ChatGPT and Computational Truthゲスト Stephen Wolfram · ホスト Lex FridmanIn his fourth conversation with Lex Fridman, Stephen Wolfram contrasts large language models with Wolfram|Alpha, then follows computational irreducibility from ChatGPT through observers, thermodynamics, and the three great theories of twentieth-century physics.モデルと学習PhilosophyAI research+3
40:25EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023年4月6日ポッドキャスト · 2 人A 6-Billion-Parameter Clone of ChatGPT’s Core Trickホスト Sarah Guo, Elad GilDatabricks CTO Matei Zaharia tells Sarah Guo and Elad Gil how Dolly learned instruction-following on fifty times less data than GPT-3 — and why he would rather look facts up than bake them into a trillion-parameter network.モデルと学習Open-weight modelsファインチューニング+3
3:28:47EN
Lex Fridman· 2022年10月29日ポッドキャスト · 2 人Karpathy Calls Trained Neural Nets Complicated Alien Artifactsゲスト Andrej Karpathy · ホスト Lex FridmanOn Lex Fridman's podcast, Andrej Karpathy refuses the brain analogy, walks the Fermi paradox through Nick Lane's chemistry, and treats the Transformer as a general-purpose differentiable computer.モデルと学習AGI(汎用人工知能)Autonomous Driving+3
2:10:08EN
Lex Fridman· 2022年7月26日ポッドキャスト · 2 人Gato Is Only the Beginningゲスト Oriol Vinyals · ホスト Lex FridmanDeepMind's Oriol Vinyals talks with Lex Fridman about generalist agents, thrown-away weights, and whether human-level AGI arrives in his lifetime.モデルと学習AGI(汎用人工知能)AI research+3
2:10:39EN
Lex Fridman· 2022年7月1日ポッドキャスト · 2 人Demis Hassabis: Solve Intelligence, Then Solve Everything Elseゲスト Demis Hassabis · ホスト Lex FridmanThe DeepMind CEO tells Lex Fridman why the Turing Test is the wrong exam, how AlphaFold rewrote biology, and why the quiet radio sky suggests we are probably alone.モデルと学習AI researchAGI(汎用人工知能)+3
2:45:10EN
Lex Fridman· 2022年1月22日ポッドキャスト · 2 人A Teenager Learns to Drive in Twenty Hours. A Car Still Cannot.ゲスト Yann Lecun · ホスト Lex FridmanYann LeCun tells Lex Fridman why self-supervised world models are the missing dark matter of intelligence, and why machines are not even at the level of a house cat.モデルと学習World modelsComputer Vision+3
2:31:57EN
Lex Fridman· 2021年9月20日ポッドキャスト · 2 人Jay McClelland on How Thought Emerges From Neural Networksゲスト Jay McClelland · ホスト Lex FridmanThe Stanford cognitive scientist talks with Lex Fridman about connectionism, backpropagation, David Rumelhart, and why intuition discovers what logic later proves.モデルと学習機械学習の基礎Deep Learning+3
2:51:33EN
Lex Fridman· 2021年8月29日ポッドキャスト · 2 人The Self Is a GPT Prompt: Wojciech Zaremba on Consciousness, Codex, and Fermi's Silenceゲスト Wojciech Zaremba · ホスト Lex FridmanOpenAI co-founder Wojciech Zaremba tells Lex Fridman he thinks we might be alone in the universe — and that GPT-3, Codex, and a one-handed robot already hint at how intelligence is built.モデルと学習コーディングエージェント機械学習の基礎+3
2:18:29EN
Lex Fridman· 2021年8月8日ポッドキャスト · 2 人Your Cortex Is 150,000 Brains Voting on a Coffee Cupゲスト Jeff Hawkins · ホスト Lex FridmanJeff Hawkins tells Lex Fridman that intelligence is learning a model of the world, the neocortex is tens of thousands of independent maps that vote, and the fastest path to machine intelligence is reverse-engineering that sheet.モデルと学習Neuroscience & AI機械学習の基礎+3
2:30:30EN
Lex Fridman· 2021年7月31日ポッドキャスト · 2 人The Fallacy of the Successful First Stepゲスト Ishan Misra · ホスト Lex FridmanBerkeley's Jitendra Malik explains why computer vision still looks easy, why fully autonomous driving is not near, and what children know that today's networks do not.モデルと学習Computer VisionMultimodal AI+3
1:56:17EN
Lex Fridman· 2021年4月19日ポッドキャスト · 2 人If You Reran Earth a Million Times, Would Anything Like Us Appear?ゲスト Risto Miikkulainen · ホスト Lex FridmanRisto Miikkulainen on neuroevolution, hyena raids, basil that never sleeps, and why variation-plus-selection still finds solutions backpropagation cannot see.モデルと学習機械学習の基礎AI research+3
2:01:07EN
Lex Fridman· 2020年12月12日ポッドキャスト · 2 人Michael Littman isn’t waiting for a runaway god. He’s watching us.ゲスト Michael Littman · ホスト Lex FridmanThe Brown reinforcement-learning researcher tells Lex Fridman why accidental superintelligence feels like a 747 assembling itself — and why social media, rote language, and theory of mind worry him more.モデルと学習Reinforcement LearningAGI(汎用人工知能)+3
2:10:06EN
Lex Fridman· 2020年8月14日ポッドキャスト · 2 人You Might Have to Understand the World to Understand the Letter Aゲスト Dileep George · ホスト Lex FridmanDileep George on recursive cortical networks, why a five-year-old can read any new CAPTCHA, and what the Wright brothers actually learned from birds.モデルと学習Neuroscience & AIWorld models+3
1:41:37EN
Lex Fridman· 2020年7月21日ポッドキャスト · 2 人The Fallacy of the Successful First Step: Jitendra Malik on Why Seeing Still Isn't Solvedゲスト Jitendra Malik · ホスト Lex FridmanBerkeley's Jitendra Malik tells Lex Fridman why computer vision keeps underestimating itself, why full self-driving still needs cognition, and why vision is the parent of language.モデルと学習画像と動画Autonomous Driving+3
もっと見る248 件中 24 件を表示