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2 diapositives4:48EN
Matthew Berman· 12 juil. 2023PoisonGPT: 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.AI securityData quality & curationModel Interpretability+1
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20VC with Harry Stebbings· 30 juin 2023Podcast · 2 voixDouwe 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.IA en entrepriseGénération augmentée par récupérationData quality & curation+2
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No Priors: AI, Machine Learning, Tech, & Startups· 29 juin 2023Podcast · 2 voixThree 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.Entreprise et stratégieCompany BuildingLeadership+3
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Matthew Berman· 21 juin 2023Reddit’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.Open-source ecosystemsData quality & curationPrivacy & data governance+2
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Lex Fridman· 18 juin 2023Podcast · 2 voixWikipedia 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.Open source et communautéOpen-source ecosystemsFree Speech+3
59:28EN
Cognitive Revolution "How AI Changes Everything"· 16 juin 2023PodcastFrom $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.Modèles et entraînementFine-tuningPretraining & midtraining+3
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Cognitive Revolution "How AI Changes Everything"· 6 juin 2023Podcast1.5 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…Modèles et entraînementSynthetic dataData quality & curation+3
4 diapositives10:16EN
Matthew Berman· 21 mai 2023DarkBERT: Adapting a Language Model to Dark-Web TextMatthew Berman · Forward FutureMatthew Berman reviews research on a RoBERTa-based model and its cybersecurity evaluations.AI securityPretraining & midtrainingBenchmarks+1
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Matthew Berman· 15 avr. 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.Local inferenceOpen-weight modelsData quality & curation+2
5 diapositives8:02EN
Matthew Berman· 12 avr. 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.Computer VisionVision et vidéoDeep Learning+1
5 diapositives6:24EN
Matthew Berman· 6 avr. 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.IA en financeModèles et entraînementÉvaluations+1
3 diapositives5:08EN
Matthew Berman· 3 avr. 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.Génération augmentée par récupérationDocument understanding & OCRWorkflows de développement et tests+1
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Cognitive Revolution "How AI Changes Everything"· 30 mars 2023Podcast130,000 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.AgentsRoboticsMultimodal AI+3
8 diapositives1:03:29ZH
跟李沐学AI· 12 janv. 2023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。Multimodal AIModèles et entraînementData quality & curation
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Lex Fridman· 28 déc. 2021Podcast · 2 voixA 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.SécuritéAutonomous DrivingRobotics+3
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Lex Fridman· 4 janv. 2021Podcast · 2 voixThe 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.Multimodal et médiasIA vocaleValidation avec humain dans la boucle+3
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Lex Fridman· 5 mai 2020Podcast · 2 voixMice 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.AI in healthcareComputational BiologyAI for science+3
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Lex Fridman· 23 sept. 2019Podcast · 2 voixThe 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.AI in healthcareAI for scienceData quality & curation+3
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Lex Fridman· 1 août 2019Podcast · 2 voixKevin 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.Entreprise et stratégieAI StrategyProduct Strategy+3
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Lex Fridman· 12 avr. 2019Podcast · 2 voixElon 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.SécuritéAutonomous DrivingValidation avec humain dans la boucle+3
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Lenny's PodcastPodcast · 2 voixLook 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.Produit et UXProduct ManagementAI Product Management+3
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Cognitive Revolution "How AI Changes Everything"PodcastBuilding 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.Modèles et entraînementAI for scienceComputational Biology+3
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Lenny's PodcastPodcast · 2 voixIf 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.Évaluations et observabilitéÉvaluationsData quality & curation+3
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Lenny's PodcastPodcast · 2 voixA 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.Modèles et entraînementCompany BuildingData quality & curation+3
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