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35개정렬
0:32ENSam Altman on the Continuing Demand for Software WorkSam Altman, Matthew Berman · OpenAIIn a short interview excerpt, Altman says demand for software may be larger than expected even as AI changes how it is made.
12:49ENAlex Mashrabov: Higgsfield agents that choose models and launch their own GPUsAlex Mashrabov · HiggsfieldAt OpenAI DevDay 2026, Higgsfield's CEO walks through why a continuous media experience needs an agent that picks models, scores its own output, and provisions GPUs — including a live 10-hour AI stream small teams can now ship.
13:12ENAlexandra Lato on Building Stack Overflow for Agents and Stack Internal with CodexAarti Bagul, Alexandra Lato · OpenAIStack Overflow’s VP of Product explains how a small team used Codex with opposite constraints on two products—and why agent-native design means letting agents contribute knowledge, not just read it.
0:33ENAnthropic's IPO and AI Safety: A Question of ConsistencyMatthew Berman, Christian Catalini · Forward FutureIn a short interview excerpt, Matthew Berman and economist Christian Catalini question how a planned public offering relates to warnings about advanced AI risk.
4:41ENInside Jane Street’s Internship: Projects, Mentorship and LearningCody · Jane StreetInterns and employees describe collaborative interviews, learning finance from the basics, and working on projects used by the firm.
10:36ENForward Future Live: An Opera Visit and NVIDIA EarningsMatthew Berman, Nick Wentz · Forward FutureMatthew Berman and Nick Wentz open their November 21, 2025 show with an opera story and a brief look at NVIDIA.
13:30ENHow 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.
1:09ENA Discussion of GPT-4.5’s Scaling and TrainingA speaker offers an assessment of GPT-4.5’s cost, speed and alleged training challenges.
2:53ENJane Street Interns on Projects, Mentorship and LearningDylan · Jane StreetFormer interns and staff describe the learning, open-ended projects and relationships that shaped their internship experience.
0:57ENSundar Pichai on Preparing for AI in Knowledge WorkMatthew Berman, Sundar Pichai · Forward FutureIn a short interview clip, Google's CEO suggests learning to use AI as an everyday assistant while work changes.
0:49ENSundar Pichai on AI Glasses and Project Astra’s MemorySundar Pichai · GoogleGoogle’s CEO discusses why glasses fit everyday AI interaction and describes testing Astra’s memory of an object.
0:54ENSatya Nadella on Separating Personal and Work AI AgentsMatthew Berman, Satya Nadella · Forward FutureIn an interview excerpt, Microsoft CEO Satya Nadella discusses identity boundaries and the risk of data leakage between personal and corporate agents.
1:05ENSatya Nadella on AI, Productivity, and Cancer CareMatthew Berman, Satya Nadella · Forward FutureIn a short interview excerpt, the Microsoft CEO discusses the potential of cheaper AI to help coordinate healthcare work.
7:17ENErik and Martin Demaine on the Mathematics of Curved-Crease SculptureErik Demaine, Martin Demaine · MITThe MIT father-and-son collaborators turn paper folding into art and research, embedding puzzles and mathematical fonts in their sculpture Solve Me.
3:37ENMark Zuckerberg Announces Llama 3 Training and Meta’s AI PlansMatthew Berman, Mark Zuckerberg · Forward FutureIn January 2024, Matthew Berman reacts to Zuckerberg’s remarks on general intelligence, compute and AI devices.
3:08ENDavid Guetta’s AI-Generated Eminem Voice and the Questions It RaisesMatthew Berman, David Guetta · Forward FutureMatthew Berman discusses a synthetic vocal played during a DJ set and considers consent, creative use and possible legal disputes.
5:42ENJane Street’s Trading Interview: What to ExpectMichael, Megan · Jane StreetA trader and a recruiter explain the interview stages, how to approach open-ended questions and what the firm says it looks for.
11:26ENRichard Dawkins on Memes, the Internet and Why Wrong Ideas Spread게스트 Richard Dawkins · 진행자 Lex FridmanThe man who coined the word explains to Lex Fridman what makes a meme more than a metaphor, why the internet is a step change for ideas, and why an idea, like a peacock's tail, doesn't need to be true or useful to spread.
12:42ENThe 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.
14:55ENKasparov on Deep Blue, AlphaZero, and Why Machines Win Closed Systems but Humans Still Choose the Questions게스트 Garry Kasparov · 진행자 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.
10:20ENHow C++ Is Standardized: Votes, Consensus and the Move to a Three-Year Release Cycle게스트 Bjarne Stroustrup · 진행자 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.
11:11ENYann 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.
14:41ENTalking 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.
11:41ENTeaching 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.
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