Yann LeCunEvery podcast appearance, updated as new ones drop

Yann LeCun, Turing Award winner and former Chief AI Scientist at Meta, joins Jacob Effron. The conversation centers on Yann's contrarian thesis that LLMs are a dead-end on the path to human-level intelligence, despite being useful products — because they can't predict the consequences of their actions, can't plan, and fundamentally can't model the messy, high-dimensional real world. He unpacks his alternative architecture, JEPA (Joint Embedding Predictive Architecture), which learns abstract representations rather than generating pixel-level predictions, and explains why this approach is essential for robotics, industrial applications, and any system that needs to operate beyond the substrate of language. Yann also reveals the real story behind his departure from Meta (he had zero technical influence on Llama, contrary to public narrative), the genesis of his Tapestry project for sovereign open-source AI, why he believes LLMs are intrinsically unsafe, where he diverges from his fellow Turing laureates Hinton and Bengio, and why he predicts the industry will recognize the paradigm shift by early 2027. Throughout, he offers candid reflections on the tension between research and product at major labs, and why he intentionally headquartered AMI Labs in Paris with zero Silicon Valley VC money. (0:00) Introduction (01:45) Why LLMs Aren't the Path to Intelligence (07:51) AMI and World Models (12:07) The JEPA Architecture Explained (15:55) Problems with Robotics Models Today (20:37) Silicon Valley Herd Behavior (28:18) Tapestry: Sovereign AI for the Rest of the World (35:49) OpenAI Is the Next Sun Microsystems (40:51) Why Yann's Views Diverged from Hinton & Bengio (44:32) LLMs Are Intrinsically Unsafe (58:00) Why Yann Left Meta (1:00:26) Reflections on FAIR (1:12:11) Advice for PhD Students LeWorldModel Paper: https://arxiv.org/abs/2603.19312 With your host: @jacobeffron - Partner at Redpoint With your host: @jacobeffron - Managing Director at Redpoint

Tom sits down with Yann LeCun, the Jacob T. Schwartz Professor of Computer Science at NYU, and Executive Chairman of Advanced Machine Intelligence Labs. Yann is co-winner of the 2018 ACM Turing Award for his research in neural network learning. Yann takes us from his days as a postdoc working with Geoffrey Hinton, through his days as Chief AI Scientist at Facebook/Meta. His simultaneous roles as a Professor at NYU and Chief AI Scientist at a large AI provider give Yann a unique perspective on how technological advances and commercial forces combined to get us to today's state of the art.

Yann LeCun is one of the most influential figures in artificial intelligence. Alex LeBrun is the founder of Nabla and newly announced CEO of AMI Labs , a new AI research company he and Yann are building around a bold idea: large language models aren’t enough for medicine. In this special episode of How I Doctor , Dr. Graham Walker sits down in-person with Alex and Yann to explore the next frontier of AI in healthcare - world models. While today’s AI systems excel at predicting the next word, Yann argues that real clinical intelligence requires something deeper: models that can imagine, simulate, and plan. From the limitations of LLMs in high-stakes environments to the concept of building a “patient model” that can predict the consequences of treatment decisions, this episode dives into what it would actually take to build AI that reasons more like a physician. They discuss why documentation was the first breakthrough use case, how 80% accuracy fails in clinical settings, and why reliability, and not hype, will determine who wins in healthcare AI. This isn’t about replacing doctors. It’s about amplifying them. If AI is going to meaningfully change medicine, it won’t be through better chatbots. It will be through systems that understand the world. Watch or Listen 🎥 Watch the full video conversation now — exclusively on https://www.offcall.com/learn/podcast/ai-world-models-medicine-yann-lecun-alex-lebrun 🔊 Or stream the audio version on your favorite podcast platform. What You’ll Learn How predicting the next word isn’t the same as clinical reasoning and where LLMs fall short in medicine. What “world models” are and how they differ fundamentally from today’s large language models. Why 80% accuracy isn’t acceptable in healthcare and what reliability really means in clinical AI. Why medical coding may be one of the next frontiers for AI in clinical workflows. How AI assistants could amplify doctors the way a research lab amplifies a professor, by making clinicians smarter, not obsolete. 🩺 Offcall is more than a platform — it’s a community. Join today ! 📝 For a full transcript of this episode click HERE 🎧 Subscribe to receive new How I Doctor episodes directly in your feed here: https://episodes.fm/1767429315 👨‍⚕️Follow Dr. Graham Walker on LinkedIn https://www.linkedin.com/in/graham-walker-md/ IG https://www.instagram.com/ubergraham/ Blu

Yann LeCun declares Meta AI and LLMs fundamentally flawed dead ends blocking true intelligence paths radically. Architectural limits curse transformers chasing scale absent reasoning world models potently. Visionary slams trillion-parameter illusion demanding hierarchical abstraction revolution disruptively. Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.ai AI Chat YouTube Channel: https://www.youtube.com/@JaedenSchafer Join my AI Hustle Community: https://www.skool.com/aihustle See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info .

El experto Yann LeCun, galardonado con un Premio Turing y pionero del reconocimiento de imágenes, sostiene que los modelos de lenguaje actuales han alcanzado un límite insuperable porque carecen de una comprensión verdadera de la realidad física. A través de su nueva iniciativa, propone sustituir la arquitectura de los chats convencionales por sistemas denominados JEPA, los cuales aprenden mediante la observación de videos y representaciones abstractas. Estos modelos de mundo permiten que la inteligencia artificial comprenda conceptos básicos como la gravedad o la permanencia de objetos, habilidades que un niño adquiere de forma natural pero que el texto no puede transmitir. La propuesta enfatiza la investigación abierta y el desarrollo de capacidades de planificación interna en lugar de la simple predicción de palabras. En última instancia, el autor argumenta que la verdadera inteligencia general solo se logrará cuando las máquinas puedan simular y predecir consecuencias dentro del entorno físico real.

Yann LeCun – Why LLMs Will Never Get Us to AGI "The path to superintelligence - just train up the LLMs, train on more synthetic data, hire thousands of people to school your system in post-training, invent new tweaks on RL-I think is complete bullshit. It's just never going to work." After 12 years at Meta, Turing Award winner Yann LeCun is betting his legacy on a radically different vision of AI. In this conversation, he explains why Silicon Valley's obsession with scaling language models is a dead end, why the hardest problem in AI is reaching dog-level intelligence (not human-level), and why his new company AMI is building world models that predict in abstract representation space rather than generating pixels. Timestamps (00:00:14) – Intro and welcome (00:01:12) – AMI: Why start a company now? (00:04:46) – Will AMI do research in the open? (00:06:44) – World models vs LLMs (00:09:44) – History of self-supervised learning (00:16:55) – Siamese networks and contrastive learning (00:25:14) – JEPA and learning in representation space (00:30:14) – Abstraction hierarchies in physics and AI (00:34:01) – World models as abstract simulators (00:38:14) – Object permanence and learning basic physics (00:40:35) – Game AI: Why NetHack is still impossible (00:44:22) – Moravec's Paradox and chess (00:55:14) – AI safety by construction, not fine-tuning (01:02:52) – Constrained generation techniques (01:04:20) – Meta's reorganization and FAIR's future (01:07:31) – SSI, Physical Intelligence, and Wayve (01:10:14) – Silicon Valley's "LLM-pilled" monoculture (01:15:56) – China vs US: The open source paradox (01:18:14) – Why start a company at 65? (01:25:14) – The AGI hype cycle has happened 6 times before (01:33:18) – Family and personal background (01:36:13) – Career advice: Learn things with a long shelf life (01:40:14) – Neuroscience and machine learning connections (01:48:17) – Continual learning: Is catastrophic forgetting solved? Music: "Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0. "Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0. Changes: trimmed About The Information Bottleneck is hosted by Ravid Shwartz-Ziv and Allen Roush, featuring in-depth conversations with leading AI researchers about the ideas shaping the future of machine learning.

Yann LeCun, deep learning pioneer and Meta’s AI heavyweight, is out and he's not leaving quietly. In this episode host Emily Laird unpacks his philosophical split with Meta over the limits of large language models, his obsession with world models, and why he thinks real intelligence means predicting your kitchen layout, not just auto-completing your emails. Join the AI Weekly Meetups Connect with Us: If you enjoyed this episode or have questions, reach out to Emily Laird on LinkedIn . Stay tuned for more insights into the evolving world of generative AI. And remember, you now know more about Yann LeCun's next move. Connect with Emily Laird on LinkedIn

Join us as Turing Award recipient Yann LeCun, Chief Scientist at Meta, critiques the state of AI, arguing that current systems, including Large Language Models (LLMs), are nowhere near matching the learning efficiency observed in humans and animals. LeCun proposes a major architectural shift, advocating that AI must abandon generative models for training and instead focus on building internal "World Models" to enable reasoning and planning. Discover how the Joint Embedding Predictive Architecture (JEPA) uses self-supervised learning to train machines to acquire robust, abstract representations of reality, a crucial step toward achieving common sense and human-level intelligence.

AI is at the forefront of technological advances and is also reshaping creativity, ownership and societal interactions. In episode 7 of Penn Engineering’s Innovation & Impact podcast, host Vijay Kumar , Nemirovsky Family Dean of Penn Engineering and Professor in Mechanical Engineering and Applied Mechanics, speaks with Meta’s Chief AI Scientist and Turing Award winner, Yann LeCun , about the journey of AI, how we define intelligence and the possibilities and challenges it presents. With a bachelor’s in Electrical Engineering and a Ph.D. in Computer Science, LeCun’s academic background brought him to the AT&T Bell Laboratories after his first postdoctoral position in 1988, right at the cusp of the Internet Age. The work he began in his doctorate and later expanded on at Bell Labs would eventually become the foundational work for what we now know as AI. As testament to this work, LeCun was awarded the 2018 ACM Turing Award (with Geoffrey Hinton and Yoshua Bengio) for "conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing," a lifetime accomplishment in the field of computer science. Listen to the full episode to hear more on everything from the intelligence of the neural nets that inspired LeCun’s original AI research to computers learning to play chess and how much longer we will have to wait to see machines reach a human level of intelligence. Subscribe to Penn Engineering’s Innovation & Impact podcast on Apple Music , Spotify or your favorite listening platforms, or find all the episodes on our Penn Engineering YouTube channel . Follow Penn Engineering: https://www.youtube.com/@pennengineering https://www.instagram.com/pennengineering https://www.linkedin.com/school/penn-engineering https://twitter.com/PennEngineers https://bsky.app/profile/pennengineering.bsky.social https://www.facebook.com/PennEngineering Hosted on Acast. See acast.com/privacy for more information.

From early inspirations to groundbreaking AI achievements, Yann's journey chronicles the rise of deep learning, the struggles for recognition, and the revolution that changed computing forever. 00:09- About Yann LeCun Yann is the Chief AI Scientist for Facebook AI Research (FAIR). He is also a Silver Professor at New York University on a part-time basis, mainly affiliated with the NYU Center for Data Science, and the Courant Institute of Mathematical Sciences.

Yann LeCun, Meta’s chief AI scientist and Turing Award winner, joins us to discuss the limits of today’s LLMs, why generative AI may be hitting a wall, what’s missing for true human-level intelligence, the real meaning of AGI, Meta’s open-source strategy with Llama, the future of AI assistants in smart glasses, why diversity in AI models matters, and how open models could shape the next era of innovation Support the show on Patreon! http://patreon.com/aiinsideshow Subscribe to the YouTube channel! http://www.youtube.com/@aiinsideshow Note: Time codes subject to change depending on dynamic ad insertion by the distributor. CHAPTERS: 0:00:00 - Podcast begins 0:01:40 - Introduction to Yann LeCun, Chief AI Scientist at Meta 0:02:11 - The limitations and hype cycles of LLMs, and historical patterns of overestimating new AI paradigms. 0:05:45 - The future of AI research, and the need for machines that understand the physical world, can reason and plan, and are driven by human-defined objectives 0:14:47 - AGI Timeline, human-level AI within a decade, with deep learning as the foundation for advanced machine intelligence 0:21:35 - Why true AI intelligence requires abstract reasoning and hierarchical planning beyond language capabilities, unlike today's neural networks that rely on computational tricks 0:30:24 - Meta's open-source LLAMA strategy, empowering academia and startups, and commercial benefits 0:36:10 - The future of AI assistants, wearable tech, cultural diversity, and open-source models 0:42:52 - The impact of immigration policies on US technological leadership and STEM education 0:44:26 - Does Yann have a cat? 0:45:19 - Thank you to Yann LaCun for joining the AI Inside podcast Learn more about your ad choices. Visit megaphone.fm/adchoices

Yann LeCun is the chief AI scientist at Meta. He joins Big Technology Podcast to discuss the strengths and limitations of current AI models, weighing in on why they've been unable to invent new things despite possessing almost all the world's written knowledge. LeCun digs deep into AI science, explaining why AI systems must build an abstract knowledge of the way the world operates to truly advance. We also cover whether AI research will hit a wall, whether investors in AI will be disappointed, and the value of open source after DeepSeek. Tune in for a fascinating conversation with one of the world's leading AI pioneers. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. For weekly updates on the show, sign up for the pod newsletter on LinkedIn: https://www.linkedin.com/newsletters/6901970121829801984/ Want a discount for Big Technology on Substack? Here’s 40% off for the first year: https://tinyurl.com/bigtechnology Questions? Feedback? Write to: bigtechnologypodcast@gmail.com Learn more about your ad choices. Visit megaphone.fm/adchoices

How important is open source to the future of AI and are we at human-level intelligence yet? Patrick Moorhead and Daniel Newman are joined by Meta 's Yann LeCun , VP & Chief AI Scientist for a conversation on the latest AI developments and insights from WEF25 in this segment of The View From Davos. Get their take on: - The importance of open source for accelerating AI development and - Going beyond LLMs: LeCun imagines future AI systems will understand the physical world, reason, plan, and have persistent memory - The role of AI in addressing global challenges - Insights into future AI projects at Meta - Yann LeCun's perspective on ethical AI and its governance

Please join my mailing list here 👉 https://briankeating.com/list to win a meteorite 💥 What are the current limitations of AI? What advancements do we need to achieve human-like intelligence? And how can we develop AI safely to align with our values? Here today to offer us an astounding look behind the scenes of AI development is Meta’s chief AI scientist, Yann LeCun! Yann is a pioneer in AI and a Turing Award winner who has been at the forefront of major breakthroughs in machine learning and neural networks. As the architect behind transformative AI technologies, Yann joins us to demystify the path toward human-level intelligence and the challenges that lie ahead. He also introduces the Joint Embedding Predictive Architecture (JEPA), a potential game-changer for enabling AI to model and predict complex real-world scenarios. Tune in for a look into the future of AI! — Key Takeaways: 00:00:00 Intro 00:04:11 Is AI barely as smart as a cat? 00:08:50 Joint-Embedding Predictive Architecture (JEPA) 00:30:25 Comparing self-supervised learning and dark matter 00:39:23 AGI and human-level intelligence 00:51:50 Preventing loss of control over powerful AI 01:02:45 Future of AI and education 01:10:56 What was Yann wrong about? 01:12:14 Outro Additional resources: ➡️ Learn more about Yann LeCun: 📱 Website: https://yann.lecun.com/ ✖️ Twitter: https://x.com/ylecun ➡️ Follow me on your fav platforms: ✖️ Twitter: https://twitter.com/DrBrianKeating 🔔 YouTube: https://www.youtube.com/DrBrianKeating?sub_confirmation=1 📝 Join my mailing list: https://briankeating.com/list ✍️ Check out my blog: https://briankeating.com/cosmic-musings/ 🎙️ Follow my podcast: https://briankeating.com/podcast — Into the Impossible with Brian Keating is a podcast dedicated to all those who want to explore the universe within and beyond the known. Make sure to follow

We're bringing you a special episode of On With Kara Swisher! Kara sits down for a live interview with Meta's Yann LeCun, an “early AI prophet” and the brains behind the largest open-source large language model in the world. The two discuss the potential dangers that come with open-source models, the massive amounts of money pouring into AI research, and the pros and cons of AI regulation. They also dive into LeCun’s surprisingly spicy social media feeds — unlike a lot of tech employees who toe the HR line, LeCun isn’t afraid to say what he thinks of Elon Musk or President-elect Donald Trump. This interview was recorded live at the Johns Hopkins University Bloomberg Center in Washington, DC as part of their Discovery Series. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Kara sits down for a live interview with Yann LeCun, an “early AI prophet” and the brains behind the largest open-source large language model in the world. The two discuss the potential dangers that come with open-source models, the massive amounts of money pouring into AI research, and the pros and cons of AI regulation. They also dive into LeCun’s surprisingly spicy social media feeds — unlike a lot of tech employees who toe the HR line, Yann isn’t afraid to say what he thinks of Elon Musk or President-elect Donald Trump. This interview was recorded live at the Johns Hopkins University Bloomberg Center in Washington, DC as part of their Discovery Series. Questions? Comments? Email us at on@voxmedia.com or find us on Instagram and TikTok @onwithkaraswisher Learn more about your ad choices. Visit podcastchoices.com/adchoices

A lot of us have heard conjectures around A.I., edge cases of the positive and negative side of A.I., and a lot of us are trying to predict what’s next. Most of my understanding of A.I. comes only from viewing what is apparent today.. reinforcement learning spaces like chat gpt becoming a go-to.. In this episode of People by WTF, we uncover the basics of this mystery of artificial intelligence with one of the founding fathers of A.I., Yann LeCun. We spoke about popular AI myths and broke down complex concepts that can perhaps help the next generation of builders build in this space. To learn more about Machine Learning, we’ve collated some sources for your benefit and growth in this industry. (https://www.notion.so/Machine-Learning-Resource-Document-14ba9e22882a8022a878ee25a3738267?pvs=21) #NikhilKamath Co-founder of Zerodha, True Beacon and Gruhas Host of #wtfiswithnikhilkamath Twitter: https://x.com/nikhilkamathcio/ Instagram: https://www.instagram.com/nikhilkamathcio/ LinkedIn: https://www.linkedin.com/in/nikhilkamathcio?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=ios_app Facebook: https://www.facebook.com/nikhilkamathcio/ #YannLeCun Professor at NYU, VP & Chief AI Scientist at Meta Linkedin: https://www.linkedin.com/in/yann-lecun/ Instagram: https://www.instagram.com/yannlecun/ Facebook: https://www.facebook.com/yann.lecun Twitter: https://x.com/ylecun **Timestamps:** 00:00 Yann’s Intro 01:50 Difference between an Engineer and a Scientist 03:15 Yann’s interest in AI and Mathematics 04:05 Godfather of AI | Yann’s feelings about it 05:22 Teaching & fame at NYU 06:00 Heroes in Science 07:46 Three problems with the world - Yann’s lens 10:18 What is AI and how did we get here? 13:13 What is intelligence? | The Elephant Analogy 15:00 AI - perception & understanding 16:20 The two branches of AI - solving & learning 17:30 Emergence of classical computer science | Heuristic programming 20:15 Is A.I. inspired from biology? 26:36 Is building authentic models for finance possible through AI? 28:36 Different parts of A.I | GOFAI, Machine learning 30:18 What is GOFAI? 31:14 Different types of Machine Learning 32:22 What is Reinforcement learning? 33:19 What is Self supervised learning? Up & Coming 35:14 Is AI telling you what you want to hear? 38:00 What is a transformer? 40:24 What is a back propagation algorithm? 42:58 What’s happening in the reinforcement learning space? 48:06 What is a convolutional neural network ? 49:08 What is a Neuron - the Machine Learning perspective 50:00 What is a neural network language model & how does it work? 58:00 The AI tree | LLMs 59:55 The next challenge of AI 1:01:40 - Pictures/ Videos - what's happening there? 01:03:20 LLM’s limited memory | Types of memory 01:04:45 AI’s path to human like learning 01:10:26 What is JEPA ? 01:11:58 How far in the future can you predict through JEPA ? 01:14:10 AI’s future prediction - Utopian or Dystopian ? 01:16:30 The LLM Loop | What needs to change 01:18:50 Building data centers in India - Yann’s thoughts 01:21:09 What should a 25 y/o build in the AI space? | Careers in the AI space 01:26:18 What should an investor

Artificial Intelligence has undeniably transformed our world. Whether it's for better or worse, one thing is certain: it's here to stay. I often find myself debating - will AI eventually replicate human intelligence, or will it remain a powerful tool, an execution engine for humanity? The deeper you dive, the clearer it becomes that the reality is far more nuanced than it seems. So, I decided to go back to the basics to figure out how the concept of A.I. even began & finally understand what the systems are that could potentially define our future.. Stay tuned for a masterclass with than #YannLeCun#NikhilKamath Co-founder of Zerodha, True Beacon and Gruhas Host of #wtfiswithnikhilkamath Twitter: https://x.com/nikhilkamathcio/ Instagram: https://www.instagram.com/nikhilkamathcio/ LinkedIn: https://www.linkedin.com/in/nikhilkamathcio?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=ios_app Facebook: https://www.facebook.com/nikhilkamathcio/ #YannLeCun Professor at NYU, VP & Chief AI Scientist at Meta Linkedin: https://www.linkedin.com/in/yann-lecun/ Instagram: https://www.instagram.com/yannlecun/ Facebook: https://www.facebook.com/yann.lecun Twitter: https://x.com/ylecun

In this episode, our host, Leslie meets the "Godfather of AI," Yann LeCun, to discuss AI's rapid adoption in India and the challenges that lie ahead. LeCun explains that AI is evolving into a common infrastructure, much like how Linux powers the internet, but India still faces significant obstacles. He emphasizes the need for AI to learn from real-world interactions, moving beyond the current limits of large language models. LeCun expresses optimism about AI’s future, predicting human-level intelligence could be achievable soon, provided the right foundational work is done. Shifting the focus to the geopolitical landscape, the discussion turns to the relationship between India and China. Our hosts Shouvik Das and Leslie D'Monte highlight China’s position as a tech powerhouse, emphasizing that if India wants to strengthen its component manufacturing capabilities, collaboration with China is key. The "China plus one" strategy has its limitations, as much of the tech industry’s IP, designs, and component ecosystems are built around Chinese standards. To truly make a mark in component manufacturing, India will likely need China’s support. Interestingly, China doesn’t seem opposed to closer ties; it views India not only as an important export market but also as a strategic location to sell its services globally, an encouraging start for potential collaboration. Tune in for an insightful episode! Learn more about your ad choices. Visit megaphone.fm/adchoices

Welcome to the AI Daily Podcast , your go-to source for the most current insights and debates surrounding artificial intelligence technologies. In today's episode, we delve into groundbreaking discussions with two preeminent figures in the AI community: Yann LeCun and Dario Amodei. In the first part of our episode, Yann LeCun , a Turing Award winner and a luminary in the field of artificial intelligence, offers a candid assessment of the present and future states of AI technologies. Contrary to popular belief, LeCun argues that AI is far from achieving or surpassing human intelligence. He points out critical limitations in current systems, particularly large language models, which lack essential capabilities like persistent memory, reasoning, and understanding of the physical world. He stresses that manipulating language does not equate to genuine intelligence and discusses the need for innovative approaches to edge closer to Artificial General Intelligence (AGI). His commentary is a call to action, urging a reevaluation of where AI technology truly stands and where it is headed. In the second segment, Dario Amodei , CEO of Anthropic, introduces a compelling alternative to the term "artificial general intelligence" with his concept of "powerful AI." Amodei envisions an AI that surpasses the intellect of a Nobel laureate across various disciplines, capable of handling diverse communication modes and executing complex tasks independently. Projected to surface by 2026, this powerful AI promises to be a transformative force across all societal domains, tantamount to "a country of geniuses in a datacenter." Furthermore, Amodei invites a crucial dialogue on the ethical dimensions of such advanced technologies, highlighting the need for a balanced and responsible approach to AI development and its integration into society. This episode is a must-listen for anyone involved in or interested in the future of AI development—from academic researchers and tech industry leaders to policymakers and everyday tech enthusiasts. Tune in to gain a deeper understanding of the current capabilities of artificial intelligence, the innovative paths forward, and the thoughtful consideration of ethical implications in AI advancements. Links: Meta’s Yann LeCun says worries about A.I.’s existential threat are ‘complete B.S.’ Super Micro Computer Shares Surge on Shipment News. Can the Stock Continue to Rebound? Can AI Help Figure Out the Complex Interactions of Precision Nutrition? Here's how Anthropic CEO Dario Amodei defines artificial general intelligence Did Musk Try To Stop Government Workers From Discussing AI Supercomputer?

Enregistrement (sur iPhone) du discours de Yann LeCun lors de la conférence de l’UFE le 1er juillet 2024 à New York. M. LeCun y parle de vision de sa vision pour l’avenir de l’IA, et met l’accent sur le rôle crucial des modèles open source et la nécessité de développer au-delà des grands modèles de langages (LLMs). Points clés: • L’importance de l’IA open source pour maintenir la souveraineté nationale • Les limitations des systèmes d’IA actuels et la nécessité de nouvelles architectures • Le potentiel transformateur de l’IA en médecine et dans d’autres domaines • Les implications éthiques et sociétales des avancées de l’IA

My guest is Yann LeCun, a pioneering French-American computer scientist, known for his groundbreaking work in machine learning, computer vision, and neural networks. Yann is the Silver Professor at the Courant Institute of Mathematical Sciences at New York University and serves as the Vice President and Chief AI Scientist at Meta. Yann is one of the world’s most influential computer scientists. He has accumulated over 350,000 citations on Google Scholar, he is one of the founding figures in the field of deep learning thanks to its contribution to convolutional neural networks and backpropagation algorithms, and he is a vocal proponent of open source. In recognition of his significant contributions to artificial intelligence, he was awarded the Turing Award in 2018, often referred to as the “Nobel Prize of Computing.” Our conversation is structured into three distinct parts. We begin by discussing the overarching dynamics in the AI space, then narrow our focus to the firm level, and finally, we conclude with an exploration of the challenges that lie ahead. By the end of this discussion, you will learn whether open source has a chance to make it in AI, the key factors for scaling an AI foundation model, the role ecosystems play in market dynamics, Meta long term strategy in the space, how concentration among chip manufacturers impacts AI companies, the current effect of the European AI Act on AI companies, what Yann would like to see regulators doing, and more. I hope you enjoy the conversation.

👋 来互动 🫸 Flow_第三浪 @Linktree 🫸 Flow_第三浪 @即刻 🫸 Flow_第三浪 @X | Twitter 🫸 Flow_第三浪 @豆瓣 🕵️ 英文原声 & 节目频道 Yann Lecun: Meta AI, Open Source, Limits of LLMs, AGI & the Future of AI | Lex Fridman Podcast #416 Lex Fridman 🎸 背景音乐 Moonlight - Red Dead Redemption 2 That's The Way It Is - Red Dead Redemption 2 👫🏽 本期对谈人 & 发言人 杨立昆（Yann LeCun）：Meta（前Facebook）的首席人工智能科学家，纽约大学教授，图灵奖得主，是人工智能领域的重要人物。 Lex Fridman：播客主持人，与各领域专家进行深入对话的科技界人士。 📝 目录 大型语言模型的局限性 双语主义与思考 视频预测 联合嵌入预测架构（JEPA） JEPA与大型语言模型的比较 DINO与I-JEPA V-JEPA 分层规划 自回归大型语言模型 人工智能幻觉 人工智能中的推理 强化学习 觉醒人工智能 开源 人工智能与意识形态 马克·安德森 Llama 3 人工通用智能（AGI） 人工智能末日论者 约斯查·巴赫 人形机器人 对未来的希望 💁🏻 本期（不完全）提及人物 & 事物 汉斯莫拉维克 ：机器人学先驱，提出了莫拉维克悖论，探讨了人工智能在不同任务上的难易程度，尤其是在高级认知任务与低级感知和运动任务之间的差异。 马克扎克伯格 ：Meta（前Facebook）的创始人兼CEO，推动了公司在人工智能和虚拟现实领域的发展，包括投资于大型语言模型和其他AI技术 穆斯塔法西塞 ：前Facebook人工智能研究院的研究员，后来在非洲设立了谷歌的研究实验室，并启动了Co-Kera项目，旨在创建能使用塞内加尔当地语言的大型语言模型。 Joscha Bach ：在人工智能和认知科学领域有影响力的人物，提出了关于人工智能和人类未来合作的见解，包括对人工智能系统可能的社会影响的讨论。 Marc Andreessen ：硅谷著名的风险投资家和企业家，对科技产业有深刻的见解，提出了关于大型科技公司在人工智能领域面临的挑战和机遇的观点。 ✂️ 自回归大型语言模型 ：一种人工智能模型，通过预测文本中的下一个单词来生成连贯的语言输出，通常用于聊天机器人、文本生成等应用。 联合嵌入预测架构（JEPA） ：一种用于视频处理的人工智能架构，通过预测视频的未来帧来学习视频内容的表示，旨在提高模型对视频数据的理解能力。 DINO和I-JEPA ：Facebook 人工智能研究院开发的技术，用于训练神经网络处理图像和视频数据，通过对比学习和自监督学习的方法提高模型的性能。 V-JEPA ：视频联合嵌入预测架构的简称，是JEPA在视频领域的应用，用于处理和理解视频内容。 模型预测控制（MPC） ：一种控制策略，用于在不确定的环境中进行决策和规划，通过模拟未来可能的状态来指导当前的行动。 人工通用智能（AGI） ：指的是一种具有广泛认知能力的人工智能系统，能够在各种领域和任务中理解、学习和应用知识，与人类智能相似。 莫拉维克悖论 ：机器人学先驱莫拉维克提出的观点，指出高级认知任务（如下棋）对计算机来说相对容易，而低级感知和运动任务（如抓取物体）却难以实现。 开源人工智能 ：指将人工智能系统的设计、代码和数据集公开，以便任何人都可以使用和改进，促进技术的民主化和创新。 HAL 9000 ：是电影《2001太空漫游》中的一个虚构的人工智能角色，代表了对人工智能可能失控的担忧。 元宇宙 ：一个虚拟现实空间的概念，用户可以在其中与他人互动、工作和娱乐，通常被视为互联网的下一代发展。 Meta（前Facebook） ：是一家全球性的社交媒体公司，拥有多个流行的社交平台，如Facebook、Instagram和WhatsApp。Meta致力于技术创新，包括人工智能和虚拟现实，旨在连接人们并构建社区。 纽约大学 ：是一所位于美国纽约市的私立研究型大学，以其学术研究和教育质量而闻名，特别是在科学、艺术和商业领域。 DeepMind ：是一家专注于人工智能研究和应用的公司，以其开发先进的机器学习算法和系统而知名，包括AlphaGo，这是一个击败人类围棋世界冠军的程序。 UC Berkeley ：加州大学伯克利分校，是一所位于美国加州的公立研究型大学，以其在计算机科学、工程学和其他技术领域的创新和研究而著称。 谷歌 ：是一家全球领先的互联网技术公司，提供广泛的服务，包括搜索引擎、云计算、广告技术和各种移动和互联网应用程序。 Infosys ：是一家总部位于印度的全球性信息技术服务和咨询公司，提供业务咨询、信息技术和外包服务。 Unitree ：是一家致力于开发高性能机器人和人工智能解决方案的公司，旨在推动机器人技术的发展和应用。 波士顿动力 ：是一家知名的机器人设计和制造公司，以其创新的四足机器人和其他先进机器人技术而闻名。 Facebook 人工智能研究院 ：是Facebook下属的一个研究机构，专注于人工智能的研究，包括机器学习、计算机视觉和自然语言处

👋 来互动 🫸 Flow_第三浪 @Linktree 🫸 Flow_第三浪 @即刻 🫸 Flow_第三浪 @X | Twitter 🫸 Flow_第三浪 @豆瓣 🕵️ 英文原声 & 节目频道 Yann Lecun: Meta AI, Open Source, Limits of LLMs, AGI & the Future of AI | Lex Fridman Podcast #416 Lex Fridman 🎸 背景音乐 Moonlight - Red Dead Redemption 2 That's The Way It Is - Red Dead Redemption 2 👫🏽 本期对谈人 & 发言人 杨立昆（Yann LeCun）：Meta（前Facebook）的首席人工智能科学家，纽约大学教授，图灵奖得主，是人工智能领域的重要人物。 Lex Fridman：播客主持人，与各领域专家进行深入对话的科技界人士。 📝 目录 大型语言模型的局限性 双语主义与思考 视频预测 联合嵌入预测架构（JEPA） JEPA与大型语言模型的比较 DINO与I-JEPA V-JEPA 分层规划 自回归大型语言模型 人工智能幻觉 人工智能中的推理 强化学习 觉醒人工智能 开源 人工智能与意识形态 马克·安德森 Llama 3 人工通用智能（AGI） 人工智能末日论者 约斯查·巴赫 人形机器人 对未来的希望 💁🏻 本期（不完全）提及人物 & 事物 汉斯莫拉维克 ：机器人学先驱，提出了莫拉维克悖论，探讨了人工智能在不同任务上的难易程度，尤其是在高级认知任务与低级感知和运动任务之间的差异。 马克扎克伯格 ：Meta（前Facebook）的创始人兼CEO，推动了公司在人工智能和虚拟现实领域的发展，包括投资于大型语言模型和其他AI技术 穆斯塔法西塞 ：前Facebook人工智能研究院的研究员，后来在非洲设立了谷歌的研究实验室，并启动了Co-Kera项目，旨在创建能使用塞内加尔当地语言的大型语言模型。 Joscha Bach ：在人工智能和认知科学领域有影响力的人物，提出了关于人工智能和人类未来合作的见解，包括对人工智能系统可能的社会影响的讨论。 Marc Andreessen ：硅谷著名的风险投资家和企业家，对科技产业有深刻的见解，提出了关于大型科技公司在人工智能领域面临的挑战和机遇的观点。 ✂️ 自回归大型语言模型 ：一种人工智能模型，通过预测文本中的下一个单词来生成连贯的语言输出，通常用于聊天机器人、文本生成等应用。 联合嵌入预测架构（JEPA） ：一种用于视频处理的人工智能架构，通过预测视频的未来帧来学习视频内容的表示，旨在提高模型对视频数据的理解能力。 DINO和I-JEPA ：Facebook 人工智能研究院开发的技术，用于训练神经网络处理图像和视频数据，通过对比学习和自监督学习的方法提高模型的性能。 V-JEPA ：视频联合嵌入预测架构的简称，是JEPA在视频领域的应用，用于处理和理解视频内容。 模型预测控制（MPC） ：一种控制策略，用于在不确定的环境中进行决策和规划，通过模拟未来可能的状态来指导当前的行动。 人工通用智能（AGI） ：指的是一种具有广泛认知能力的人工智能系统，能够在各种领域和任务中理解、学习和应用知识，与人类智能相似。 莫拉维克悖论 ：机器人学先驱莫拉维克提出的观点，指出高级认知任务（如下棋）对计算机来说相对容易，而低级感知和运动任务（如抓取物体）却难以实现。 开源人工智能 ：指将人工智能系统的设计、代码和数据集公开，以便任何人都可以使用和改进，促进技术的民主化和创新。 HAL 9000 ：是电影《2001太空漫游》中的一个虚构的人工智能角色，代表了对人工智能可能失控的担忧。 元宇宙 ：一个虚拟现实空间的概念，用户可以在其中与他人互动、工作和娱乐，通常被视为互联网的下一代发展。 Meta（前Facebook） ：是一家全球性的社交媒体公司，拥有多个流行的社交平台，如Facebook、Instagram和WhatsApp。Meta致力于技术创新，包括人工智能和虚拟现实，旨在连接人们并构建社区。 纽约大学 ：是一所位于美国纽约市的私立研究型大学，以其学术研究和教育质量而闻名，特别是在科学、艺术和商业领域。 DeepMind ：是一家专注于人工智能研究和应用的公司，以其开发先进的机器学习算法和系统而知名，包括AlphaGo，这是一个击败人类围棋世界冠军的程序。 UC Berkeley ：加州大学伯克利分校，是一所位于美国加州的公立研究型大学，以其在计算机科学、工程学和其他技术领域的创新和研究而著称。 谷歌 ：是一家全球领先的互联网技术公司，提供广泛的服务，包括搜索引擎、云计算、广告技术和各种移动和互联网应用程序。 Infosys ：是一家总部位于印度的全球性信息技术服务和咨询公司，提供业务咨询、信息技术和外包服务。 Unitree ：是一家致力于开发高性能机器人和人工智能解决方案的公司，旨在推动机器人技术的发展和应用。 波士顿动力 ：是一家知名的机器人设计和制造公司，以其创新的四足机器人和其他先进机器人技术而闻名。 Facebook 人工智能研究院 ：是Facebook下属的一个研究机构，专注于人工智能的研究，包括机器学习、计算机视觉和自然语言处理等领域。

Yann LeCun is the Chief AI Scientist at Meta, professor at NYU, Turing Award winner, and one of the most influential researchers in the history of AI. Please support this podcast by checking out our sponsors: – HiddenLayer : https://hiddenlayer.com/lex – LMNT : https://drinkLMNT.com/lex to get free sample pack – Shopify : https://shopify.com/lex to get $1 per month trial – AG1 : https://drinkag1.com/lex to get 1 month supply of fish oil Transcript: https://lexfridman.com/yann-lecun-3-transcript EPISODE LINKS: Yann’s Twitter: https://twitter.com/ylecun Yann’s Facebook: https://facebook.com/yann.lecun Meta AI: https://ai.meta.com/ PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/lexfridman – Twitter: https://twitter.com/lexfridman – Instagram: https://www.instagram.com/lexfridman – LinkedIn: https://www.linkedin.com/in/lexfridman – Facebook: https://www.facebook.com/lexfridman – Medium: https://medium.com/@lexfridman OUTLINE: Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) – Introduction (09:10) – Limits of LLMs (20:47) – Bilingualism and thinking (24:39) – Video prediction (31:59) – JEPA (Joint-Embedding Predictive Architecture) (35:08) – JEPA vs LLMs (44:24) – DINO and I-JEPA (45:44) – V-JEPA (51:15) – Hierarchical planning (57:33) – Autoregressive LLMs (1:12:59) – AI hallucination (1:18:23) – Reasoning in AI (1:35:55) – Reinforcemen

In Boz To The Future’s sixteenth episode, Meta CTO and Head of Reality Labs Andrew “Boz” Bosworth talks to Meta’s VP of AI Research Joelle Pineau and Chief AI Scientist Yann LeCun about FAIR, Meta’s Fundamental AI Research Lab, and the future ahead. Now in its tenth year, FAIR celebrates a decade of significant breakthroughs and contributions to the AI community, thanks to Meta’s continued commitment to open research and science. They talk about how some of this early work laid the foundation for the progress we’re seeing today and why it’s important to take a long view of things given how unpredictable this progress can be. For feedback and suggestions, drop Boz a message @boztank on Instagram , Threads , or Twitter .

This episode is sponsored by Oracle. AI is revolutionizing industries, but needs power without breaking the bank. Enter Oracle Cloud Infrastructure (OCI): the one-stop platform for all your AI needs, with 4-8x the bandwidth of other clouds. Train AI models faster and at half the cost. Be ahead like Uber and Cohere. If you want to do more and spend less like Uber, 8x8, and Databricks Mosaic - take a free test drive of OCI at https://oracle.com/eyeonai Welcome to episode 150 of the 'Eye on AI' podcast. In this episode, host Craig Smith sits down with Yann LeCun, a Turing Award winner who has been instrumental in advancing convolutional neural networks and whose work spans machine learning, computer vision, and more. Tune is as Craig and Yann explore the intricacies of AI, world models, and the challenges of continuous learning. In this episode, Yann delves deep into the concept of a "world model" - systems that can predict the world's future states, allowing agents to make informed decisions. The discussion transitions to the challenges of training these models, particularly when dealing with diverse data like text and images. We then discuss the computational demands of modern AI models, with Yann highlighting the nuances between generative models for videos and language. He also touches upon the idea of the "Embodied Turing Tests" and how augmented language models can bridge the gap between human-like behavior and computational efficiency.The spotlight then shifts to pressing concerns surrounding the open-source nature of AI models, with Yann articulating the legal ramifications and the future of open-source AI. Drawing from global perspectives, including China's stance on open-source, Yann underscores the imperative for a collaborative approach in the AI space, ensuring it's reflective of diverse global needs. Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI (00:00) Preview, Oracle and Introduction (02:42) Decoding The World Model and Gaia 1 (07:43) Energy and Computational Demands of AI (08:06) Video vs. Text Processing & True AI Capabilities (11:17) Embodied Turing Test & Augmented LLMs (15:38) Is AI a Threat To Society? (25:04) Where is AI Development Headed? (31:06) Interplay of Neuroscience and AI** (33:33) Yann's Vision, JEPA, and Learning Challenges (39:05) Yann's Career, AI Progress, and Challenges (44:47) The Open Source Debate in AI (55:30) Oracle Cloud Infrastructu

Yann LeCun is VP & Chief AI Scientist at Meta and Silver Professor at NYU affiliated with the Courant Institute of Mathematical Sciences & the Center for Data Science. He was the founding Director of FAIR and of the NYU Center for Data Science. After a postdoc in Toronto he joined AT&T Bell Labs in 1988, and AT&T Labs in 1996 as Head of Image Processing Research. He joined NYU as a professor in 2003 and Meta/Facebook in 2013. He is the recipient of the 2018 ACM Turing Award for "conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing". Huge thanks to David Marcus for helping to make this happen. In Today's Episode with Yann LeCun: 1.) The Road to AI OG: How did Yann first hear about machine learning and make his foray into the world of AI? For 10 years plus, machine learning was in the shadows, how did Yan not get discouraged when the world did not appreciate the power of AI and ML? What does Yann know now that he wishes he had known when he started his career in machine learning? 2.) The Next Five Years of AI: Hope or Horror: Why does Yann believe it is nonsense that AI is dangerous? Why does Yann think it is crazy to assume that AI will even want to dominate humans? Why does Yann believe digital assistants will rule the world? If digital assistants do rule the world, what interface wins? Search? Chat? What happens to Google when digital assistants rule the world? 3.) Will Anyone Have Jobs in a World of AI: From speaking to many economists, why does Yann state "no economist thinks AI will replace jobs"? What jobs does Yann expect to be created in the next generation of the AI economy? What jobs does Yann believe are under more immediate threat/impact? Why does Yann expect the speed of transition to be much slower than people anticipate? Why does Yann believe Elon Musk is wrong to ask for the pausing of AI developments? 4.) Open or Closed: Who Wins: Why does Yann know that the open model will beat the closed model? Why is it superior for knowledge gathering and idea generation? What are some core historical precedents that have proved this to be true? What did Yann make of the leaked Google Memo last week? 5.) Startup vs Incumbent: Who Wins: Who does Yann believe will win the next 5 years of AI; startups or incumbents? How important are large models to winning in the next 12 months? In what ways does regulation and legal stop incumbents? How has he seen this at Meta? Has his role at Meta ever stopped him from being impartial? How does Yan deal with that?

In this episode, Yann LeCun , a renowned computer scientist and AI researcher, shares his insights on the limitations of large language models and how his new joint embedding predictive architecture could help bridge the gap. While large language models have made remarkable strides in natural language processing and understanding, they are still far from perfect. Yann LeCun points out that these models often cannot capture the nuances and complexities of language, leading to inaccuracies and errors. To address this gap, Yann LeCun introduces his new joint embedding predictive architecture - a novel approach to language modelling that combines techniques from computer vision and natural language processing. This approach involves jointly embedding text and images, allowing for more accurate predictions and a better understanding of the relationships between original concepts and objects. Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI

Yann LeCun is the chief AI scientist at Meta, a professor of computer science at NYU, and a pioneer of deep learning. He joins Big Technology Podcast to put Generative AI in context, discussing whether ChatGPT and the like are a step toward human-level artificial intelligence, or something completely different. Join us for a fun, substantive discussion about this technology, the makeup of OpenAI, and where the field heads next. Stay tuned for the second half, where we discuss the ethics of using others' work to train AI models. Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. For weekly updates on the show, sign up for the pod newsletter on LinkedIn: https://www.linkedin.com/newsletters/6901970121829801984/ Questions? Feedback? Write to: bigtechnologypodcast@gmail.com Learn more about your ad choices. Visit megaphone.fm/adchoices