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2:00:19EN
Cognitive Revolution "How AI Changes Everything"· Jun 6, 2023Podcast1.5 Million GPT-4 Fairy Tales Let a 33-Million-Parameter Model Out-Reason GPT-2Host Nathan LabenzTry this fill-in: Lily likes cats and dogs. She asked her mom for a dog. Mom said no, so she asked for a…
10:16EN
Matthew Berman· May 21, 2023DarkBERT: Adapting a Language Model to Dark-Web TextMatthew Berman · Forward FutureMatthew Berman reviews research on a RoBERTa-based model and its cybersecurity evaluations.
1:13:52ENMatthew Berman· Apr 15, 2023Andriy Mulyar on GPT4All, Data Quality and Open Local ModelsMatthew Berman, Andriy Mulyar · Forward FutureThe Nomic AI co-founder explains how GPT4All emerged, why training data matters, and the tradeoffs between local and centralized AI.
8:02ENMatthew Berman· Apr 12, 2023Meta’s Segment Anything: Promptable Image Masks and the SA-1B DatasetMatthew Berman · Forward FutureIn April 2023, Matthew Berman reviews Meta’s Segment Anything project and demonstrates selecting image regions with clicks, boxes and automatic mask generation. The model segments pixels; it does not by itself name every object or guarantee accurate results on every image.
6:24ENMatthew Berman· Apr 6, 2023BloombergGPT: 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.
5:08ENMatthew Berman· Apr 3, 2023Chatting with a PDF in Colab Using LangChain and a Vector IndexMatthew Berman · Forward FutureMatthew Berman demonstrates a 2023 PDF question-answering notebook; its retrieval workflow does not fine-tune model weights and its revenue example is incorrect.
1:29:31ENCognitive Revolution "How AI Changes Everything"· Mar 30, 2023Podcast130,000 Teleop Demos: Google's GPT Playbook for RobotsHost Nathan LabenzKeerthana Gopalakrishnan of Google Robotics tells Nathan Labenz how RT-1, SayCan, and PaLM-E turn language-model scaling into kitchen-ready mobile manipulators.
1:03:29ZH → English
跟李沐学AI· Jan 12, 2023A 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.
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Lex Fridman· Dec 28, 2021Podcast · 2 speakersA Car That Cannot Drive Itself Will Be About as Useful as a HorseGuest Elon Musk · Host Lex FridmanIn a Lex Fridman conversation built around Tesla Autopilot, Elon Musk argues that every driver input is an error, that today’s hardware is already enough for full self-driving, and that watching the human will soon matter less than making the system safer than a person.
1:28:46ENLex Fridman· Jan 4, 2021Podcast · 2 speakersThe Speech Company That Gets Paid to Label Its Own Training DataGuest Dan Kokotov · Host Lex FridmanRev VP of engineering Dan Kokotov tells Lex Fridman why a $1.25-a-minute human transcript, a 14% machine word-error rate, and a two-sided freelancer market are the same flywheel.
1:12:03ENLex Fridman· May 5, 2020Podcast · 2 speakersMice Don't Get Alzheimer's: Daphne Koller on Disease in a DishGuest Daphne Koller · Host Lex FridmanThe Coursera co-founder and insitro CEO on why most diseases are not one disease, how to turn cells into data, and why overconfident neural nets can kill.
1:17:29ENLex Fridman· Sep 23, 2019Podcast · 2 speakersThe Algorithm Is Already Better. The Clinic Still Uses 1967.Guest Regina Barzilay · Host Lex FridmanMIT's Regina Barzilay on why deep learning already beats the breast-cancer 'density' score — and why data, liability, and incentives still will not move.
57:44ENLex Fridman· Aug 1, 2019Podcast · 2 speakersKevin Scott: Treat AI Like a Hash Table, and Measure Platforms by What Others EarnGuest Kevin Scott · Host Lex FridmanMicrosoft's CTO talks radical markets, data dignity, Dunbar's number, and why a platform fails if only a handful of companies decide what AI is for.
32:45ENLex Fridman· Apr 12, 2019Podcast · 2 speakersElon Musk: Every Driver Input Is an Error Tesla Autopilot Must EliminateGuest Elon Musk · Host Lex FridmanAfter MIT published a study of functional vigilance on Autopilot, Lex Fridman sat down with Musk to argue about cameras on drivers, fleet data, and whether humans still belong in the loop.
1:21:35EN
Lenny's PodcastPodcast · 2 speakersLook Outside the Building: Shaun Clowes on Why Most PMs Fall Short and How AI Changes the JobGuest Shaun Clowes · Host Lenny RachitskyConfluent's chief product officer explains how to become a far better product manager, why data matters more than models, and what AI will and won't do to SaaS.
1:03:59ENCognitive Revolution "How AI Changes Everything"PodcastBuilding a World Model of Life: Biohub's Alex Rives on Why Better Measurement Must Come Before Virtual CellsHost Nathan LabenzBiohub's head of science explains why today's virtual cell models fall short, how a $1.8 billion data push aims to fix it, and what a five-year information-level model of the cell could unlock.
1:07:08ENLenny's PodcastPodcast · 2 speakersIf the Model Is the Product, the Eval Is the PRD: Inside Mercor's RiseGuest Brendan Foody · Host Lenny RachitskyMercor CEO Brendan Foody explains why AI labs will pay almost anything for expert-written evals, how his company grew from $1 million to a $400 million run rate in 16 months, and what that means for jobs and founders.
1:10:32ENLenny's PodcastPodcast · 2 speakersA Billion Dollars, No VC, Under 100 People: Surge AI's Edwin Chen on Data Quality and Where AI Is HeadingGuest Edwin Chen · Host Lenny RachitskySurge AI's founder explains how a bootstrapped team built the leading human-data company, why he distrusts benchmarks, and how reinforcement learning environments are the next frontier.
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Unsupervised Learning: With Jacob EffronPodcastThe Robot That Folded the Laundry: Physical Intelligence’s Three FrontiersHost Jacob EffronKarol Hausman and Danny Driess explain how robot foundation models moved from coded primitives to web-scale pretraining, and why performance is now the hard gate.
1:07:35ENLex FridmanPodcastWhy AI Will Never Be Perfect: Lex Fridman's Case for Keeping Humans in the LoopHost Lex FridmanIn MIT 6.S093's opening lecture, Lex Fridman lays out five research areas of human-centered AI, from machine teaching to arguing machines that flag their own uncertainty.
1:11:50ENLex FridmanPodcastData Is Everything: Teaching Self-Driving Cars to Understand the Human Behind the WheelHost Lex FridmanIn an MIT lecture, Lex Fridman shows how deep learning can sense pedestrians, driver posture, gaze, emotion and cognitive load, and why that matters for human-centered autonomy.
1:24:01ENLenny's PodcastPodcastFrom Sandwich Math to Expert Data: Scale AI's CEO on What Actually Makes Models SmarterHost Lenny RachitskyJason Droege explains what really happened with Meta's investment in Scale AI, how data labeling turned into expert work, and what Uber Eats taught him about building businesses.
15:43ENLex FridmanPodcast · 2 speakersInside RIDER: The Rugged Black Box Behind MIT's Large-Scale Driving StudyGuest MIT-AVT · Host Lex FridmanA walkthrough of the data-collection device MIT-AVT uses to record drivers and vehicles in semi-autonomous cars month after month.
1:19:56ENLenny's PodcastPodcast · 2 speakersAnalytics With a Seat at the Table: How DoorDash Builds a Centralized Data TeamGuest Jessica Lachs · Host Lenny RachitskyDoorDash analytics chief Jessica Lachs explains why she keeps data scientists in one central org, how she hires for curiosity, and how she picks metrics that actually move a business.Load moreShowing 24 of 96