Yann LeCun, Chief AI Scientist at Meta and Turing Award winner, dives into the fascinating world of self-supervised learning. He discusses how this approach mimics human learning, distinguishing it from traditional methods. LeCun explores the complexities of machine intelligence, emphasizing the blend of causal reasoning and background knowledge. The conversation also touches on the evolution of intelligence across species, the philosophical implications of AI and mortality, and the future of human-machine interaction, making for an enlightening dialogue on the nature of knowledge and learning.

Yann LeCun, VP & Chief AI Scientist at Meta and recipient of the 2018 ACM Turing Award, shares his journey through the world of AI. He dismisses fears of AI dominance, asserting that digital assistants will play a crucial role in the future. Yann discusses the shift from closed to open AI models, emphasizing collaboration for innovation. He reflects on humanity's fascination with job loss due to AI, arguing that new opportunities will arise. Ultimately, he sees a promising future where AI enhances human creativity and intelligence.

Des Traynor, Co-founder of Intercom, and Yann LeCun, AI pioneer at Meta and NYU, dive into the future of AI. They dissect the fight between open-source and closed models, emphasizing the rise of collaborative approaches. The duo also tackles the commoditization of large language models and the critical importance of data diversity. They explore the shift in AI business models towards outcome-based pricing and debate the roles of intelligent assistants—highlighting Apple's unique position against giants like Google and Amazon.

Yann LeCun, a leading figure in deep learning and the Chief AI Scientist at Facebook, shares his groundbreaking insights on AI. He discusses the critical need for ethical frameworks in AI, navigating the challenges of value misalignment. LeCun explores the history of neural networks, addressing past declines and the potential for collaboration in tech advancements. He delves into self-supervised learning's role in AI, critiques misconceptions, and emphasizes the importance of real-world interactions for developing intelligent systems, particularly in autonomous driving.

We are now sponsored by Weights and Biases! Please visit our sponsor link: http://wandb.me/MLST Patreon: https://www.patreon.com/mlst Yann LeCun thinks that it's specious to say neural network models are interpolating because in high dimensions, everything is extrapolation. Recently Dr. Randall Balestriero, Dr. Jerome Pesente and prof. Yann LeCun released their paper learning in high dimensions always amounts to extrapolation. This discussion has completely changed how we think about neural networks and their behaviour. [00:00:00] Pre-intro [00:11:58] Intro Part 1: On linearisation in NNs [00:28:17] Intro Part 2: On interpolation in NNs [00:47:45] Intro Part 3: On the curse [00:48:19] LeCun [01:40:51] Randall B YouTube version: https://youtu.be/86ib0sfdFtw