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4:48ENPoisonGPT: A Demonstration of Model Supply-Chain RiskMatthew Berman · Forward FutureMatthew Berman discusses Mithril Security’s 2023 demonstration of an edited GPT-J model and a misleading repository name. The experiment illustrates a supply-chain risk, not a breach of the original model provider.
54:00ENDouwe Kiela on Data, Retrieval and Enterprise AIInvité : Douwe Kiela · Anime : Harry StebbingsThe Contextual AI CEO discusses language model trade-offs, evaluation, open models and the hurdles to enterprise use.
51:22ENThree IPOs Later, Frank Slootman Still Thinks the Job Is UrgencyAnime : Sarah Guo, Elad GilSnowflake's CEO tells No Priors how he picked the right elevator, turned a 15-person backup startup into an EMC acquisition, and why a data lake is a landfill you should not train on.
6:49ENReddit’s API Changes, AI Training Data and the Open InternetMatthew Berman · Forward FutureMatthew Berman argues that the 2023 Reddit API dispute exposed tensions between AI data demand, third-party developers and the people who create online content.
3:15:07ENWikipedia Worked Once Writing Stopped Feeling Like Grad SchoolInvité : Jimmy Wales · Anime : Lex FridmanJimmy Wales tells Lex Fridman how Nupedia's seven-stage review died, why Wikipedia still refuses ads and governments, and what large language models should actually do with an encyclopedia.
59:28ENFrom $500,000 to $50,000: MosaicML Made Training Your Own Model a Priced ProductAnime : Nathan LabenzJonathan Frankle and Abhi Venigalla explain who is training custom LLMs, why extraction beats chat, and how ALiBi stretches a 65k context window.
2:00:19EN1.5 Million GPT-4 Fairy Tales Let a 33-Million-Parameter Model Out-Reason GPT-2Anime : 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…
10:16ENDarkBERT: Adapting a Language Model to Dark-Web TextMatthew Berman · Forward FutureMatthew Berman reviews research on a RoBERTa-based model and its cybersecurity evaluations.
1:13:52ENAndriy 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.
8:02ENMeta’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.
6:24ENBloombergGPT: 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.
5:08ENChatting 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.
1:29:31EN130,000 Teleop Demos: Google's GPT Playbook for RobotsAnime : 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多模态论文串讲:BLIP、CoCa 与 BEiT-3从图文数据清理到统一预训练,梳理三种视觉语言模型的结构、目标函数与相互联系。
2:31:48ENA Car That Cannot Drive Itself Will Be About as Useful as a HorseInvité : Elon Musk · Anime : 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.
1:28:46ENThe Speech Company That Gets Paid to Label Its Own Training DataInvité : Dan Kokotov · Anime : 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.
1:12:03ENMice Don't Get Alzheimer's: Daphne Koller on Disease in a DishInvité : Daphne Koller · Anime : 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.
1:17:29ENThe Algorithm Is Already Better. The Clinic Still Uses 1967.Invité : Regina Barzilay · Anime : 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.
57:44ENKevin Scott: Treat AI Like a Hash Table, and Measure Platforms by What Others EarnInvité : Kevin Scott · Anime : 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.
32:45ENElon Musk: Every Driver Input Is an Error Tesla Autopilot Must EliminateInvité : Elon Musk · Anime : 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.
1:21:35ENLook Outside the Building: Shaun Clowes on Why Most PMs Fall Short and How AI Changes the JobInvité : Shaun Clowes · Anime : 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.
1:03:59ENBuilding a World Model of Life: Biohub's Alex Rives on Why Better Measurement Must Come Before Virtual CellsAnime : 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.
1:07:08ENIf the Model Is the Product, the Eval Is the PRD: Inside Mercor's RiseInvité : Brendan Foody · Anime : 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.
1:10:32ENA Billion Dollars, No VC, Under 100 People: Surge AI's Edwin Chen on Data Quality and Where AI Is HeadingInvité : Edwin Chen · Anime : 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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