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1:50:09EN
Cognitive Revolution "How AI Changes Everything"· 2024년 2월 27일팟캐스트Nine Hundred Occupations, Twenty Years, and Still No AI Revolution진행자 Nathan LabenzEconomist Robin Hanson tells Cognitive Revolution why each new AI demo looks like the last one, and why wages and headcount are the tests that matter.기업과 전략AGI TimelinesAI Strategy+3
1:58:43EN
Cognitive Revolution "How AI Changes Everything"· 2024년 2월 7일팟캐스트A Worm With 302 Neurons Already Makes Trade-offs. AI Is Still on the Same Ladder.진행자 Nathan LabenzMax Bennett maps 600 million years of brain evolution onto five breakthroughs — and asks which rungs today's models have actually reached.모델과 학습AI HistoryNeuroscience & AI+3
1:25:50EN
Cognitive Revolution "How AI Changes Everything"· 2023년 4월 18일팟캐스트Prompt Engineering Became Programming, and Riley Goodside Is Fine With That진행자 Nathan LabenzScale AI’s first staff prompt engineer on tool use, fine-tuning without amnesia, generate-then-rank, and why the 2023 job-of-the-future story never arrived.기업과 전략Prompting & in-context learningStructured outputs+3
2:02:45EN
Lex Fridman· 2020년 12월 26일팟캐스트Steal the Code: You Get Zero Points for Making the Algorithm Run진행자 Lex FridmanCharles Isbell and Michael Littman tell Lex Fridman why machine learning is not just statistics, why struggle has to stay hopeful, and what a $6,600 online master's actually sells.머신러닝 기초AI in EducationAI research+3
46:25EN
Lex Fridman· 2019년 1월 17일팟캐스트Deep Learning at the Edge of 2019: BERT, Autopilot, and the Ideas That Broke Through진행자 Lex FridmanA tour of the architectural, applied, and automation breakthroughs that defined deep learning in 2018 and set up 2019.모델과 학습Deep LearningAI research+3
1:25:16EN
Lex Fridman팟캐스트From Hand-Built Features to ResNets: Andrej Karpathy's Crash Course in Convolutional Networks진행자 Lex FridmanA 2016 Deep Learning School talk covering how convnets work, how the ImageNet winners evolved, and how to apply them without being a hero.모델과 학습Deep LearningComputer Vision+3
12:16EN
Lex Fridman팟캐스트Rule 30 and Richard Feynman: Stephen Wolfram on Intuition Versus Computational Experiment진행자 Lex FridmanWolfram recalls working with Feynman at Thinking Machines, why Feynman hid his calculations, and how building a computer language changed his approach to science.AI HistoryFirst PrinciplesScience Communication+1
1:03:25EN
Lex Fridman팟캐스트Theano in an Hour: Symbolic Graphs, Automatic Gradients and a Compiler That Rewrites Your Math진행자 Lex FridmanPascal Lamblin of MILA walks through how Theano builds, differentiates, optimizes and runs computation graphs, then trains logistic regression, a convnet and an LSTM.모델과 학습Deep Learning머신러닝 기초+3
40:25EN
Lex Fridman팟캐스트A Language That Knows the World: Stephen Wolfram on Computable Knowledge진행자 Lex FridmanIn this clip from the Lex Fridman Podcast, Stephen Wolfram explains what makes the Wolfram Language different from ordinary programming languages and how it grew into a computable knowledge base, from Wolfram Alpha to computational contracts.Programming LanguagesSoftware engineeringAI History
1:20:58EN
Lex Fridman팟캐스트From Bag of Words to Attention: Quoc Le Teaches Sequence-to-Sequence Learning Through Email Replies진행자 Lex FridmanA Deep Learning School talk that builds sequence-to-sequence models step by step around one running example, automatic email reply, and then surveys memory and operation-augmented networks.모델과 학습Deep Learning머신러닝 기초+2
1:27:41EN
Lex Fridman팟캐스트Deep Learning in 2020: Lex Fridman's Tour of Transformers, Self-Play, Self-Driving and What Comes Next진행자 Lex FridmanAn MIT lecture surveys the biggest advances of 2017 to 2019 across language, reinforcement learning, deep learning theory and autonomous vehicles, and lists Fridman's hopes for the year ahead.모델과 학습Deep LearningAI research+3