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1:11:09EN
Lex Fridman· 2020년 3월 3일팟캐스트 · 2명Ava smiled when nobody was watching게스트 Alex Garland · 진행자 Lex FridmanAlex Garland talks with Lex Fridman about Ex Machina, Annihilation, Devs, and how you tell whether anything is really in there.Consciousness & IntelligencePhilosophy of MindScience Communication+3
1:12:48EN
Lex Fridman· 2020년 2월 29일팟캐스트 · 2명John Hopfield's Physics of Mind: Glitches Become Features게스트 John Hopfield · 진행자 Lex FridmanThe Princeton physicist walks Lex Fridman from a wobbly London bridge to Hopfield networks, consciousness-as-story, and why his famous model still does not explain learning.모델과 학습Neuroscience & AI머신러닝 기초+3
1:39:56EN
Lex Fridman· 2020년 2월 26일팟캐스트 · 2명Why Marcus Hutter Treats Compression as Intelligence Itself게스트 Marcus Hutter · 진행자 Lex FridmanThe DeepMind researcher behind AIXI tells Lex Fridman why a Wikipedia compression contest, Solomonoff induction, and an incomputable formula belong in the same conversation.에이전트AGI(범용 인공지능)AI research+3
1:45:49EN
Lex Fridman· 2020년 2월 24일팟캐스트 · 2명Prediction is not a decision: Michael I. Jordan on markets and the unfinished AI revolution게스트 Michael I. Jordan · 진행자 Lex FridmanThe Berkeley statistician tells Lex Fridman that today's systems are a proto-engineering field, and that the hard work is connecting producers and consumers — not imitating minds.기업과 전략머신러닝 기초Recommendation systems+3
16:03EN
Lex Fridman· 2020년 2월 22일팟캐스트 · 2명Quantum Supremacy Is a Well-Defined Speedup, Not a Useful Task게스트 Scott Aaronson · 진행자 Lex FridmanScott Aaronson explains what Google's 53-qubit experiment actually demonstrated, how it was verified, and why complexity theory can offer evidence but not proof against classical spoofing.BenchmarksScience Communication
1:29:10EN
Lex Fridman· 2020년 2월 20일팟캐스트 · 2명Andrew Ng: The Hardest Part of AI Is Rarely the Algorithm게스트 Andrew Ng · 진행자 Lex FridmanIn a conversation with Lex Fridman, Ng traces a path from late-night Coursera recordings to factory floors, arguing that messy data, habits, and starting small matter more than hype.기업과 전략Deep LearningScaling Laws+3
슬라이드 4장58:03EN
Jane Street· 2020년 2월 18일Matt Might on an Algorithm for Precision MedicineMatt Might · University of Alabama at BirminghamA computer scientist describes how genetic data, experimental models and logic programming can help clinicians investigate difficult cases.Computational BiologyAI in healthcareSoftware engineering
1:33:42EN
Lex Fridman· 2020년 2월 17일팟캐스트 · 2명Ask One Question of Every Quantum Headline: Is It Faster Than Classical?게스트 Scott Aaronson · 진행자 Lex FridmanScott Aaronson walks Lex Fridman from Turing's method of replacing unanswerable riddles with workable ones to Google's quantum supremacy experiment, and explains why useful chemistry simulation is closer than breaking RSA.Free WillPhilosophyScience Communication
1:44:55EN
Lex Fridman· 2020년 2월 14일팟캐스트 · 2명If You Need 60,000 Digits, You Have Not Found Intelligence게스트 Vladimir Vapnik · 진행자 Lex FridmanVladimir Vapnik tells Lex Fridman that intelligence is the art of shrinking a sea of functions with a few good predicates — and that MNIST, solved with sixty examples per class, is the test.모델과 학습머신러닝 기초AI research+3
7:45EN
Lex Fridman· 2020년 2월 10일팟캐스트 · 2명Jim Keller on Recipes, Understanding and Redesign게스트 Jim Keller · 진행자 Lex FridmanWhy an efficient procedure can become a constraint, and when computer architects may need to rethink the problem.SemiconductorsSoftware engineeringLeadership
26:02EN
Lex Fridman· 2020년 2월 9일팟캐스트Moore’s Law Is Not Dead—It Is a Cascade of S-Curves진행자 Lex FridmanJim Keller explains why transistor scaling keeps outrunning its obituary, and how exponential hardware changes the mathematics AI can attempt.인프라와 배포SemiconductorsScaling Laws+3
1:34:44EN
Lex Fridman· 2020년 2월 5일팟캐스트 · 2명Moore's Law Has Been About to Die for Forty Years게스트 Jim Keller · 진행자 Lex FridmanChip architect Jim Keller tells Lex Fridman why a cascade of diminishing-return curves still looks exponential, why you rewrite the CPU every five years, and why driving a car is not a super-hard problem.SemiconductorsFirst PrinciplesAutonomous Driving+1
슬라이드 2장1:23:49EN
Jane Street· 2020년 1월 31일Steve Lee on PowerShell 7: Compatibility, New Features and the RoadmapSteve Lee · MicrosoftA historical look at the move from Windows PowerShell to cross-platform PowerShell, including demonstrations of the version 7 preview and its compatibility trade-offs.Software engineeringDeveloper productivity개발 워크플로와 테스트
1:38:49EN
Lex Fridman· 2020년 1월 29일팟캐스트 · 2명Even a simulation would still be real게스트 David Chalmers · 진행자 Lex FridmanDavid Chalmers tells Lex Fridman why a simulated world would still count as reality, and why the hard problem of consciousness still will not go away.Consciousness & IntelligencePhilosophy of MindSimulation Hypothesis+3
1:30:52EN
Lex Fridman· 2020년 1월 25일팟캐스트 · 2명Cristos Goodrow: There Is No YouTube Algorithm, Only People Moving Through Video게스트 Cristos Goodrow · 진행자 Lex FridmanYouTube’s head of search and discovery tells Lex Fridman why collaborative filtering learned language on its own, why watch time is not satisfaction, and why nobody can predict a viral hit.제품과 UXRecommendation systemsProduct engineering+3
1:03:12EN
Lex Fridman· 2020년 1월 21일팟캐스트 · 2명Paul Krugman: America's Cruelty Is a Choice, Not a Law of Technology게스트 Paul Krugman · 진행자 Lex FridmanThe Nobel economist tells Lex Fridman why productivity data undercut the robot-job panic, why markets fail in health care, and why universal programs beat a premature basic income.Political Philosophy
1:13:51EN
Lex Fridman· 2020년 1월 19일팟캐스트Answering Questions Without Seeing the Data: Andrew Trask on the Privacy-Preserving AI Stack진행자 Lex FridmanA tour of remote execution, differential privacy, secure multiparty computation, and the societal consequences of making private data usable.보안과 안전Privacy & data governanceOpen-source ecosystems+3
1:39:57EN
Lex Fridman· 2020년 1월 17일팟캐스트 · 2명People Say They Want Perfect Robots. They Trust the Ones That Push Back게스트 Ayanna Howard · 진행자 Lex FridmanAyanna Howard on human-robot interaction, the ethics of safety-critical code, and why a little imperfection is what actually earns trust.보안과 안전안전과 거버넌스Robotics+3
1:18:41EN
Lex Fridman· 2020년 1월 14일팟캐스트 · 2명The SS Soldier Who Hugged a Jewish Boy, and the Systems That Still Fool Us게스트 Daniel Kahneman · 진행자 Lex FridmanDaniel Kahneman on System 1, deep learning that does not know what it is saying, and why a machine that must call a human is almost as hard as a machine that never needs one.머신러닝 기초Deep LearningHuman-AI Collaboration+3
1:02:45EN
Lex Fridman· 2020년 1월 7일팟캐스트 · 2명Aliens Would Count. They Probably Wouldn't Write e^x.게스트 Grant Sanderson · 진행자 Lex FridmanGrant Sanderson of 3Blue1Brown talks with Lex Fridman about notation, infinity, and why a visualization has to pick a single arrow.MathematicsPhilosophy of ScienceScience Communication+3
20:44EN
Lex Fridman· 2020년 1월 5일팟캐스트 · 2명The Staff Job That Became a Dictatorship: Stalin's Improbable Road to Power게스트 Stephen Kotkin · 진행자 Lex FridmanStephen Kotkin traces how war, revolution, contingency, and a new administrative post let Stalin build a personal dictatorship inside the Bolshevik regime.Political PhilosophyLeadershipGeopolitics+2
1:37:22EN
Lex Fridman· 2020년 1월 3일팟캐스트 · 2명Lenin Wanted an Assistant. Stalin Built a Dictatorship게스트 Stephen Kotkin · 진행자 Lex FridmanPrinceton historian Stephen Kotkin talks with Lex Fridman about Stalin’s rise, Putin’s popularity, and why checks on power are a feature, not a bug.GeopoliticsPolitical PhilosophyLeadership+2
1:45:56EN
Lex Fridman· 2019년 12월 30일팟캐스트 · 2명Point eight is enough: Donald Knuth on algorithms, TeX, and a life in one chair게스트 Donald Knuth · 진행자 Lex FridmanLex Fridman visits Donald Knuth at home for a conversation about the IBM 650, geek brains, literate programming, The Art of Computer Programming, TeX, P versus NP, and a happiness budget of 80 percent.Coding CulturePhilosophyProgramming Languages+2
1:52:40EN
Lex Fridman· 2019년 12월 28일팟캐스트 · 2명Without Analogies, There Can Be No Concepts게스트 Melanie Mitchell · 진행자 Lex FridmanMelanie Mitchell talks with Lex Fridman about why analogy sits at the core of thought, why scaled-up learning still fails at common sense, and why she thinks human-level AI is more than a century away.Deep Learning CritiqueAGI TimelinesAI History+3
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