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1:02EN
Matthew Berman· 2026년 10월 15일From Punched Cards to AI Software FactoriesMatthew Berman · Forward FutureMatthew Berman contrasts early programming with an AI-assisted workflow and promotes Factory’s development tools.소프트웨어 팩토리코딩 에이전트Software engineering+1
슬라이드 5장10:16EN
Matthew Berman· 2026년 1월 14일Matthew Berman Reviews Disclosures in the Musk–OpenAI LawsuitMatthew Berman · Forward FutureEmails, messages and journal excerpts illuminate disputes over OpenAI’s early direction and the 2023 leadership crisis, while the underlying legal claims remain contested.OpenAIAI History안전과 거버넌스+1
슬라이드 3장8:16EN
Matthew Berman· 2026년 1월 12일AI and Erdős Problem 397: A Verified Proof, Prior Work and the Prospect of Self-ImprovementMatthew Berman · Forward FutureMatthew Berman discusses AI-assisted mathematics and argues that algorithmic discoveries could accelerate AI development, while the history of problem 397 adds important context.MathematicsAI for scienceAI research+2
슬라이드 10장15:09EN
Matthew Berman· 2024년 9월 21일AI News in September 2024: ChatGPT, Nuclear Power and New ModelsMatthew Berman · Forward FutureMatthew Berman reviews a week of AI and technology news, from the Three Mile Island power agreement to new reasoning, voice and open-weight models.AI HistoryFoundation ModelsAI cost & performance+2
슬라이드 5장12:03EN
Matthew Berman· 2024년 9월 6일Reflection 70B: Self-Correction Claims and the Limits of a 2024 DemoMatthew Berman · Forward FutureMatthew Berman reviews a model trained to generate reflection steps. Its initial benchmark claims later proved difficult for independent evaluators to reproduce.AI HistoryFoundation Models평가(Evals)+2
슬라이드 6장22:27EN
Matthew Berman· 2024년 3월 6일OpenAI’s 2024 Response to Elon Musk’s Lawsuit: Funding, Control and OpennessMatthew Berman · Forward FutureMatthew Berman examines OpenAI’s published account and emails while weighing contested claims about its founding mission, financing and whether AI research should remain open.OpenAIAI History안전과 거버넌스+2
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
슬라이드 3장17:51EN
Matthew Berman· 2023년 11월 20일OpenAI’s 2023 Leadership Crisis and What Happened NextMatthew Berman · Forward FutureMatthew Berman reviews Sam Altman’s abrupt dismissal and the employee response as events stood on November 20, 2023; subsequent developments changed the outcome.OpenAIAI HistoryCompany Building+2
슬라이드 5장7:09EN
Matthew Berman· 2023년 7월 28일AI News in July 2023: SDXL, Worldcoin, and Biological ComputingMatthew Berman · Forward FutureMatthew Berman reviews a week of AI developments, from Google’s Gemini project and new generative models to biometric identity and experiments with cultured neurons.Generative media모델과 학습Brain-Computer Interfaces+2
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
슬라이드6:00EN
Matthew Berman· 2023년 2월 13일Nothing, Forever: AI-Generated Comedy and the Future of TelevisionMatthew Berman · Forward FutureMatthew Berman examines the procedurally generated Seinfeld homage, its awkward comedy, moderation challenges and implications for future media.Generative mediaCreativityAI Safety+2
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