Yann LeCun discusses limitations of LLMs, bilingualism, video prediction, AGI prospects, and societal fears around AI progression. He highlights the necessity of human control over AI advancements, ethical considerations in open-source systems, and the gradual journey towards achieving AGI over a decade.

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?

Yann LeCun is the Chief AI Scientist at Meta, professor at NYU, Turing Award winner, and one of the seminal researchers in the history of machine learning. Please support this podcast by checking out our sponsors: – Public Goods: https://publicgoods.com/lex and use code LEX to get $15 off – Indeed: https://indeed.com/lex to get $75 credit – ROKA: https://roka.com/ and use code LEX to get 20% off your first order – NetSuite: http://netsuite.com/lex to get free product tour – Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 off EPISODE LINKS: Yann’s Twitter: https://twitter.com/ylecun Yann’s Facebook: https://www.facebook.com/yann.lecun Yann’s Website: http://yann.lecun.com/ Books and resources mentioned: Self-supervised learning (article): https://bit.ly/3Aau1DQ 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 (06:58) – Self-supervised learning (17:17) – Vision vs language (23:08) – Statistics (28:55) – Three challenges of machine learning (34:45) – Chess (42:47) – Animals and intelligence (52:31) – Data augmentation (1:13:51) – Multimodal learning (1:25:40) – Consciousness (1:30:25) – Intrinsic vs learned ideas (1:34:37) – Fear of death (1:42:29) – Artificial Intelligence (1:56:18) – Facebook AI Research (2:12:56) – NeurIPS (2:29:08) – Complexity (2:37:33) – Music (2:42:28) – Advice for young people

Yann LeCun, Chief AI Scientist at Meta and a pioneer in neural networks, engages in a lively discussion about open-source AI. He shares insights on the financial race in AI, where tech giants invest heavily in advancements. Yann tackles the risks of open-sourcing powerful models and advocates for guidelines to ensure ethical development. He also voices his thoughts on AI regulation and explains the necessity of transparency in research. Known for his candidness, he isn’t shy about sharing his views on figures like Elon Musk and Donald Trump.

Yann LeCun is one of the fathers of deep learning, the recent revolution in AI that has captivated the world with the possibility of what machines can learn from data. He is a professor at New York University, a Vice President & Chief AI Scientist at Facebook, co-recipient of the Turing Award for his work on deep learning. He is probably best known as the founder of convolutional neural networks, in particular their early application to optical character recognition. This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on iTunes or support it on Patreon.

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/craigssEye on A.I. Twitter: https://twitter.com/EyeOn_AI

Experts discuss the commoditization of AI models, the open vs closed debate in AI, the value accrual in the industry, the future of AI pricing models, and Apple's potential in the AI industry.

One of the core questions in AI and investing today; who wins, startups or incumbents? Startups have speed and innovation but incumbents have scale, resources, and distribution? Today we hear from 6 leading investors and founders discussing where they place their bets who has the advantage; startups or incumbents? Emad Mostaque is CEO @ StabilityAI, the parent company of Stable Diffusion. To date, Emad has raised over $110M with Stability with the latest round reportedly pricing the company at $4BN.  Yann LeCun is VP & Chief AI Scientist at Meta and Professor at NYU. He was the founding Director of FAIR and of the NYU Center for Data Science. Clem Delangue is the Co-Founder and CEO @ Hugging Face, the AI community building the future. Clem has raised over $160M from the likes of Sequoia, Coatue, Addition and Lux Capital to name a few. Sarah Guo is the Founding Partner @ Conviction Capital, a $100M first fund purpose-built to serve “Software 3.0” companies. Prior to founding Conviction, Sarah was a General Partner at Greylock. Vince Hankes is a Partner @ Thrive Capital where he has led the firm’s investments in OpenAI, Melio, and Airplane.dev. Prior to Thrive, Vince learned the craft of venture from Lee Fixel @ Tiger. Tomasz Tunguz is the Founder and General Partner @ Theory Ventures, a $230M fund that invests $1-25m in companies that leverage technology discontinuities into go-to-market advantages. The Question of the Day: Who wins? Startups or Incumbents?

This week Connor Shorten, Yannic Kilcher and Tim Scarfe reacted to Yann LeCun's keynote speech at this year's ICLR conference which just passed. ICLR is the number two ML conference and was completely open this year, with all the sessions publicly accessible via the internet. Yann spent most of his talk speaking about self-supervised learning, Energy-based models (EBMs) and manifold learning. Don't worry if you hadn't heard of EBMs before, neither had we! Thanks for watching! Please Subscribe! Paper Links: ICLR 2020 Keynote Talk: https://iclr.cc/virtual_2020/speaker_7.html A Tutorial on Energy-Based Learning: http://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf Concept Learning with Energy-Based Models (Yannic's Explanation): https://www.youtube.com/watch?v=Cs_j-oNwGgg Concept Learning with Energy-Based Models (Paper): https://arxiv.org/pdf/1811.02486.pdf Concept Learning with Energy-Based Models (OpenAI Blog Post): https://openai.com/blog/learning-concepts-with-energy-functions/ #deeplearning #machinelearning #iclr #iclr2020 #yannlecun

Join Yann LeCun, Meta’s Chief AI Scientist and Turing Award winner, as he discusses the future of AI. He explains the revolutionary Joint Embedding Predictive Architecture (JEPA) and its potential to enhance real-world modeling. LeCun dives into the limitations of current AI, comparing it to human understanding and instincts. He also stresses the importance of aligning AI with human values and preventing a loss of control over advanced systems. Tune in for insights into how AI might transform education and our daily lives!