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1:03EN
MBMatthew Berman· 2026년 5월 9일Why 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.에이전트Reinforcement LearningMulti-agent systems+1
슬라이드 4장13:30EN
3Blue1Brown· 2025년 8월 17일How 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
슬라이드 3장10:28EN
MBMatthew Berman· 2025년 8월 5일OpenAI’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.모델과 학습AI research평가(Evals)+1
슬라이드 4장12:14EN
MBMatthew Berman· 2025년 7월 16일AI 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 research모델과 학습에이전트+1
슬라이드 3장11:38EN
MBMatthew Berman· 2025년 6월 12일AI 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 compute음성 AIGenerative media+2
슬라이드 3장10:05EN
MBMatthew Berman· 2025년 1월 9일A 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.안전과 거버넌스평가(Evals)AI research
0:35EN
MBMatthew Berman· 2024년 11월 6일An 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.모델과 학습AI research
12:42EN
Lex Fridman· 2020년 3월 28일팟캐스트 · 2명The Experience Machine: What Our Refusal to Plug In Says About What We Value게스트 Nick Bostrom · 진행자 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 Fridman팟캐스트 · 2명Yann LeCun on Benchmarks, Interactive Environments, and Why Human Intelligence Is Not General게스트 Yann Lecun · 진행자 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.모델과 학습BenchmarksAGI(범용 인공지능)+3
14:41EN
Lex Fridman팟캐스트 · 2명Talking for 20 Minutes: How the Alexa Prize Turns Real Customers into a Testbed for Conversational AI게스트 Rohit Prasad · 진행자 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.제품과 UX음성 AIAI research+3
11:41EN
Lex Fridman팟캐스트 · 2명Teaching Machines Humility: Stuart Russell on Escaping the King Midas Problem게스트 Stuart Russell · 진행자 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.보안과 안전안전과 거버넌스Existential Risk+3
10:16EN
Lex Fridman팟캐스트 · 2명Reasoning Without Logic: Yann LeCun on Teaching Neural Networks to Think게스트 Yann Lecun · 진행자 Lex FridmanYann LeCun explains why he believes neural networks can reason, and what memory, recurrence and continuous representations would need to look like.모델과 학습머신러닝 기초Deep Learning+3
13:58EN
Lex Fridman팟캐스트Deep Reinforcement Learning, Explained by the Researcher Behind AlphaGo진행자 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.모델과 학습Reinforcement Learning머신러닝 기초+3
10:34EN
Lex Fridman팟캐스트 · 2명Why Deep Learning Needs Rules: Chollet on Interpolation, Abstraction and Hybrid AI게스트 François Chollet · 진행자 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.모델과 학습머신러닝 기초Deep Learning+2
10:39EN
Lex Fridman팟캐스트Statistics as Decision-Making: Michael I. Jordan on Bayesians, Frequentists and the Ideas Between Them진행자 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.모델과 학습머신러닝 기초Decision Making+2
11:26EN
Lex Fridman팟캐스트The Cookie Your Brain Bakes: Joscha Bach on Happiness, Cells and the Meaning of God진행자 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 Fridman팟캐스트 · 2명Science Is a Self-Improving System That Isn't Exploding: Chollet on the Limits of an Intelligence Explosion게스트 François Chollet · 진행자 Lex FridmanFrançois Chollet uses the history of science to argue that recursive self-improvement meets exponential friction and yields linear, not explosive, progress.모델과 학습AGI(범용 인공지능)AI research+3