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42:18ENTen Percent of Search Is Health — and the Doctor Is Googling TooHost Sarah Guo, Elad GilGoogle researcher Karan Singhal on Med-PaLM 2, the real bar for medical language models, and why medicine is a harder alignment lab than chatbots.
11:07ENTesting MosaicML MPT-7B Chat and StoryWriter in 2023Matthew Berman · Forward FutureMatthew Berman examines MPT-7B model variants, their licenses and a mixed set of local tests.
5:24ENGoogle I/O 2023: Bard, PaLM 2 and AI Across Google's ProductsMatthew Berman · Forward FutureMatthew Berman reviews Google's 2023 AI announcements and weighs its focus on improving established products.
4:14:33ENThe Next Token Is Not a Proof: Stephen Wolfram on ChatGPT and Computational TruthGuest Stephen Wolfram · Host Lex FridmanIn his fourth conversation with Lex Fridman, Stephen Wolfram contrasts large language models with Wolfram|Alpha, then follows computational irreducibility from ChatGPT through observers, thermodynamics, and the three great theories of twentieth-century physics.
9:58ENHuggingChat’s 2023 Debut: Testing Writing, Reasoning and Python CodeMatthew Berman · Forward FutureMatthew Berman tries the early OpenAssistant-powered HuggingChat on writing, arithmetic, logic and a Snake game, then compares a few outputs with GPT-3.5 and GPT-4. The results illustrate both promise and limitations in this small historical test.
5:47ENSam Altman on Scaling AI Models: Matthew Berman Examines OpenAI’s 2023 DirectionMatthew Berman · Forward FutureA 2023 discussion of model scaling, possible ways to improve GPT-4 and the safety priorities OpenAI described at the time.
40:25ENA 6-Billion-Parameter Clone of ChatGPT’s Core TrickHost Sarah Guo, Elad GilDatabricks CTO Matei Zaharia tells Sarah Guo and Elad Gil how Dolly learned instruction-following on fifty times less data than GPT-3 — and why he would rather look facts up than bake them into a trillion-parameter network.
6:24ENBloombergGPT: Mixing Financial and General Data for a Language ModelMatthew Berman · Forward FutureMatthew Berman examines the 2023 finance-focused research model, its training corpus, evaluation and decision not to release weights.
7:53ENHuggingGPT: Using a Language Model to Coordinate AI ToolsMatthew Berman · Forward FutureMatthew Berman explains a research system that routes multimodal tasks to specialist models using a language model as controller.
8:05ENA 2023 Guide to Running the Original GPT4All Model LocallyMatthew Berman · Forward FutureMatthew Berman demonstrates an early CPU-based GPT4All release, its terminal installation and the resources available for fine-tuning.
6:50ENRunning LLaMA and Alpaca Locally: A 2023 Dalai TutorialMatthew Berman · Forward FutureMatthew Berman demonstrates an early local-model setup on a MacBook Air and explains the settings that control generation.
7:55ENHow to Train a Custom Image Model in Leonardo.aiMatthew Berman · Forward FutureMatthew Berman walks through Leonardo.ai’s 2023 model galleries, training-set guidance, and custom model workflow, then shares results from several experiments.
1:03:29ZH → EnglishA Multimodal Paper Review: BLIP, CoCa, and BEiT-3From cleaning image–text data to unified pretraining, a guide to the architectures, objectives, and connections behind three vision–language models.
1:07:10ZH → EnglishInstructGPT: From Human Feedback to Language Model AlignmentA guide to InstructGPT’s training process, results, and limitations, from supervised fine-tuning and reward modeling to PPO.
3:28:47ENKarpathy Calls Trained Neural Nets Complicated Alien ArtifactsGuest Andrej Karpathy · Host Lex FridmanOn Lex Fridman's podcast, Andrej Karpathy refuses the brain analogy, walks the Fermi paradox through Nick Lane's chemistry, and treats the Transformer as a general-purpose differentiable computer.
2:10:08ENGato Is Only the BeginningGuest Oriol Vinyals · Host Lex FridmanDeepMind's Oriol Vinyals talks with Lex Fridman about generalist agents, thrown-away weights, and whether human-level AGI arrives in his lifetime.
2:10:39ENDemis Hassabis: Solve Intelligence, Then Solve Everything ElseGuest Demis Hassabis · Host Lex FridmanThe DeepMind CEO tells Lex Fridman why the Turing Test is the wrong exam, how AlphaFold rewrote biology, and why the quiet radio sky suggests we are probably alone.
2:45:10ENA Teenager Learns to Drive in Twenty Hours. A Car Still Cannot.Guest Yann Lecun · Host Lex FridmanYann LeCun tells Lex Fridman why self-supervised world models are the missing dark matter of intelligence, and why machines are not even at the level of a house cat.
2:31:57ENJay McClelland on How Thought Emerges From Neural NetworksGuest Jay McClelland · Host Lex FridmanThe Stanford cognitive scientist talks with Lex Fridman about connectionism, backpropagation, David Rumelhart, and why intuition discovers what logic later proves.
2:51:33ENThe Self Is a GPT Prompt: Wojciech Zaremba on Consciousness, Codex, and Fermi's SilenceGuest Wojciech Zaremba · Host Lex FridmanOpenAI co-founder Wojciech Zaremba tells Lex Fridman he thinks we might be alone in the universe — and that GPT-3, Codex, and a one-handed robot already hint at how intelligence is built.
2:18:29ENYour Cortex Is 150,000 Brains Voting on a Coffee CupGuest Jeff Hawkins · Host Lex FridmanJeff Hawkins tells Lex Fridman that intelligence is learning a model of the world, the neocortex is tens of thousands of independent maps that vote, and the fastest path to machine intelligence is reverse-engineering that sheet.
2:30:30ENThe Fallacy of the Successful First StepGuest Ishan Misra · Host Lex FridmanBerkeley's Jitendra Malik explains why computer vision still looks easy, why fully autonomous driving is not near, and what children know that today's networks do not.
1:56:17ENIf You Reran Earth a Million Times, Would Anything Like Us Appear?Guest Risto Miikkulainen · Host Lex FridmanRisto Miikkulainen on neuroevolution, hyena raids, basil that never sleeps, and why variation-plus-selection still finds solutions backpropagation cannot see.
2:01:07ENMichael Littman isn’t waiting for a runaway god. He’s watching us.Guest Michael Littman · Host Lex FridmanThe Brown reinforcement-learning researcher tells Lex Fridman why accidental superintelligence feels like a 747 assembling itself — and why social media, rote language, and theory of mind worry him more.
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