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17 件
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1:03EN
MBMatthew Berman· 2026年5月9日Why EVE Online Is an Interesting Environment for AI ResearchMatthew Berman · Forward FutureMatthew Berman explains why the game's persistent economy and player alliances may offer a demanding test of long-term planning.エージェントReinforcement LearningMulti-agent systems+1
スライド 4 枚13:30EN
3Blue1Brown· 2025年8月17日How AlphaGeometry Combines Logical Proof with Creative ConstructionAleph0Aleph0 explains how a symbolic geometry solver gets surprisingly far without AI, and how a language model supplies the auxiliary constructions that take it to Olympiad level.MathematicsReasoning & test-time computeAI research+2
スライド 3 枚10:28EN
MBMatthew Berman· 2025年8月5日OpenAI’s gpt-oss Models: Open Weights, Benchmarks and SafetyMatthew Berman · Forward FutureMatthew Berman reviews the 120b and 20b models, local deployment claims, selected evaluations and the scope of OpenAI’s safety testing.モデルと学習AI research評価(Evals)+1
スライド 4 枚12:14EN
MBMatthew Berman· 2025年7月16日AI News: Windsurf’s Acquisition, Meta Compute Plans, Grok and VoxtralMatthew Berman · Forward FutureA July 2025 roundup examines developer-tool deals, AI infrastructure, model behavior, reward methods and speech recognition.AI researchモデルと学習エージェント+1
スライド 3 枚11:38EN
MBMatthew Berman· 2025年6月12日AI News in June 2025: Magistral, Voice Models and Image GenerationMatthew Berman · Forward FutureMatthew Berman reviews Mistral’s reasoning models, voice and browser updates, reports on Meta and Scale AI, and FLUX.1 Kontext.Reasoning & test-time compute音声 AIGenerative media+2
スライド 3 枚10:05EN
MBMatthew Berman· 2025年1月9日A 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.安全性とガバナンス評価(Evals)AI research
0:35EN
MBMatthew Berman· 2024年11月6日An IBM Visit: Granite Models, InstructLab and Quantum ComputingMatthew Berman · Forward FutureMatthew Berman previews a longer discussion of IBM’s small AI models, model customization and quantum research.モデルと学習AI research
12:42EN
Lex Fridman· 2020年3月28日ポッドキャスト · 2 人The Experience Machine: What Our Refusal to Plug In Says About What We Valueゲスト Nick Bostrom · ホスト Lex FridmanNick Bostrom unpacks Robert Nozick's experience machine, the status quo effect behind our answers, and whether a simulation convincing enough to feel real would have to contain real minds.PhilosophyPhilosophy of MindConsciousness & Intelligence+2
11:11EN
Lex Fridmanポッドキャスト · 2 人Yann LeCun on Benchmarks, Interactive Environments, and Why Human Intelligence Is Not Generalゲスト Yann Lecun · ホスト Lex FridmanIn a 2019 clip, LeCun defends benchmarks as a test of AI claims, explains why interactive environments break the usual train-test protocol, and argues that "general intelligence" is a misnomer.モデルと学習BenchmarksAGI(汎用人工知能)+3
14:41EN
Lex Fridmanポッドキャスト · 2 人Talking for 20 Minutes: How the Alexa Prize Turns Real Customers into a Testbed for Conversational AIゲスト Rohit Prasad · ホスト Lex FridmanRohit Prasad, head scientist of Amazon Alexa, explains the Alexa Prize grand challenge, how it is judged and why true conversation still needs reasoning.プロダクトと UX音声 AIAI research+3
11:41EN
Lex Fridmanポッドキャスト · 2 人Teaching Machines Humility: Stuart Russell on Escaping the King Midas Problemゲスト Stuart Russell · ホスト Lex FridmanIn a short clip from a 2018 conversation, Berkeley's Stuart Russell explains why fixed objectives make superintelligent AI dangerous and why uncertainty about our goals is the fix.セキュリティと安全性安全性とガバナンスExistential Risk+3
10:16EN
Lex Fridmanポッドキャスト · 2 人Reasoning Without Logic: Yann LeCun on Teaching Neural Networks to Thinkゲスト Yann Lecun · ホスト Lex FridmanYann LeCun explains why he believes neural networks can reason, and what memory, recurrence and continuous representations would need to look like.モデルと学習機械学習の基礎Deep Learning+3
13:58EN
Lex FridmanポッドキャストDeep Reinforcement Learning, Explained by the Researcher Behind AlphaGoホスト Lex FridmanDavid Silver of DeepMind walks through what reinforcement learning is, how its building blocks fit together, and why deep neural networks keep surprising even the experts.モデルと学習Reinforcement Learning機械学習の基礎+3
10:34EN
Lex Fridmanポッドキャスト · 2 人Why Deep Learning Needs Rules: Chollet on Interpolation, Abstraction and Hybrid AIゲスト François Chollet · ホスト Lex FridmanFrançois Chollet explains why neural networks only interpolate between points they have seen, and why the best AI systems pair them with symbolic rules.モデルと学習機械学習の基礎Deep Learning+2
10:39EN
Lex FridmanポッドキャストStatistics as Decision-Making: Michael I. Jordan on Bayesians, Frequentists and the Ideas Between Themホスト Lex FridmanBerkeley's Michael I. Jordan explains where statistics came from, why it is really about decisions, and how empirical Bayes and false discovery rate bridge two rival philosophies.モデルと学習機械学習の基礎Decision Making+2
11:26EN
Lex FridmanポッドキャストThe Cookie Your Brain Bakes: Joscha Bach on Happiness, Cells and the Meaning of Godホスト Lex FridmanIn a short clip, Joscha Bach moves from why happiness is not the point to the cell as a cosmic probe and a naturalistic reading of God as civilization.PhilosophyConsciousness & IntelligencePhilosophy of Mind+3
11:23EN
Lex Fridmanポッドキャスト · 2 人Science Is a Self-Improving System That Isn't Exploding: Chollet on the Limits of an Intelligence Explosionゲスト François Chollet · ホスト Lex FridmanFrançois Chollet uses the history of science to argue that recursive self-improvement meets exponential friction and yields linear, not explosive, progress.モデルと学習AGI(汎用人工知能)AI research+3