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17 件
並び順
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
14:55EN
Lex Fridman· 2019年10月29日ポッドキャスト · 2 人Kasparov on Deep Blue, AlphaZero, and Why Machines Win Closed Systems but Humans Still Choose the Questionsゲスト Garry Kasparov · ホスト Lex FridmanGarry Kasparov revisits the 1997 loss to Deep Blue, explains why machines beat humans in closed systems, and argues that the human role is to pick the right questions and not interfere with superior machine knowledge.AI HistoryPhilosophyHuman-AI Collaboration+3
10:20EN
Lex Fridmanポッドキャスト · 2 人How C++ Is Standardized: Votes, Consensus and the Move to a Three-Year Release Cycleゲスト Bjarne Stroustrup · ホスト Lex FridmanBjarne Stroustrup explains how the ISO committee works, why C++0x slipped to C++11, and how shorter cycles made C++14, C++17 and C++20 arrive on time.オープンソースとコミュニティProgramming LanguagesSoftware engineering+1
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
11:19EN
Lex Fridmanポッドキャスト · 2 人Why Donald Knuth Thinks P = NP and Why It Might Not Matterゲスト Donald Knuth · ホスト Lex FridmanKnuth explains how an algorithm can provably exist without anyone being able to write it down, and why that shapes his view of P versus NP.Mathematics
12:53EN
Lex Fridmanポッドキャスト · 2 人Principles, Not Black Boxes: Ray Dalio on When to Trust AIゲスト Ray Dalio · ホスト Lex FridmanRay Dalio explains how Bridgewater turns human thinking into algorithms, why machine learning is risky when the future differs from the past, and why writing down principles is the route to better decisions.企業と戦略AI StrategyDecision Making+3
12:01EN
Lex Fridmanポッドキャスト · 2 人The Big Nap: Why Eric Weinstein Thinks the Pandemic Ends 75 Quiet Yearsゲスト The Big Nap · ホスト Lex FridmanIn an April 2020 conversation, Eric Weinstein and Lex Fridman discuss what a global crisis reveals about collective action, fragility, and the institutions we treat as museum pieces.GeopoliticsPhilosophyHistory
10:24EN
Lex Fridmanポッドキャスト · 2 人Concepts: How C++ Finally Got Compile-Time Constraints on Templatesゲスト Bjarne Stroustrup · ホスト Lex FridmanBjarne Stroustrup explains what C++ concepts are, why they are real and shippable today, and why it took decades to get them right.Programming LanguagesSoftware engineeringCoding Culture
11:11EN
Lex Fridmanポッドキャスト · 2 人Filling the Empty Quadrants: Jim Gates on Supersymmetry and the Beauty of Broken Symmetryゲスト Jim Gates · ホスト Lex FridmanTheoretical physicist Jim Gates explains supersymmetry with a four-slice pie, recalls how he chose it for his thesis in the 1970s, and connects it to the way physics moves from mathematics to experiment.PhysicsMathematicsScience Communication
12:52EN
Lex Fridmanポッドキャスト · 2 人Roger Penrose on a Universe That Restarts: When the Far Future Becomes the Next Big Bangゲスト Roger Penrose · ホスト Lex FridmanPenrose explains conformal cyclic cosmology, in which the remote future of one cosmic eon is mathematically the same as the Big Bang of the next.CosmologyPhilosophy of ScienceBlack Holes+1
14:47EN
Lex Fridmanポッドキャスト · 2 人Build Your Own Types: What Bjarne Stroustrup Learned from Simula, Fortran and Lispゲスト Bjarne Stroustrup · ホスト Lex FridmanThe creator of C++ traces how Simula's user-defined types shaped his language, and why he builds for constrained, long-lived systems.Programming LanguagesSoftware engineeringCoding Culture
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
11:59EN
Lex Fridmanポッドキャスト · 2 人From a Side Project to TensorFlow's Core: François Chollet on How Keras Came to Beゲスト François Chollet · ホスト Lex FridmanThe creator of Keras recalls why he built it in 2015 around recurrent networks, how it moved onto TensorFlow, and what TensorFlow 2.0 means for its design.オープンソースとコミュニティDeep LearningOpen-source ecosystems+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