Executive chairman of AMI Labs and professor at NYU
Yann LeCun
Yann LeCun is executive chairman of AMI Labs and a professor at NYU whose research spans neural networks, computer vision, and world models.
About Yann LeCun
Yann LeCun is executive chairman of Advanced Machine Intelligence Labs and the Jacob T. Schwartz Professor at New York University. His current research interests include machine learning, perception, robotics, and world models that represent how environments behave.
He previously founded and directed Meta's FAIR research organization and served as Meta's chief AI scientist through 2025. His work helped develop convolutional neural networks and their use in visual recognition. He shared the 2018 ACM A.M. Turing Award with Geoffrey Hinton and Yoshua Bengio.
Mentioned in our coverage
40 articles name Yann LeCun, excluding author credits.
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- Dario Amodei Warned Against GPT-2 in 2019. Now He's at the Centre of the Open-Source AI War. →
Dario Amodei helped decide not to release GPT-2 in full in 2019 because OpenAI feared misuse. Seven years later, he runs Anthropic, keeps Claude completely closed, and finds himself at the epicentre of the most heated open-source AI debate the industry has ever seen.
- The History of Artificial Intelligence: From Turing's 1950 Test to AGI in 2026 →
76 years. From a philosopher's thought experiment about whether a machine can think, to autonomous coding agents that ship production software on their own. This is the complete history of artificial intelligence: real names, real papers, real dates, and the ideas that changed everything.
- Top 10 AI Instructors and Educators in the United States for 2026 →
Discover the most influential AI instructors and educators shaping the future of artificial intelligence education in the United States in 2026, from global educators to academic pioneers.
- What Are World Models? The AI Systems That Simulate Reality (Starchild-1 and Beyond) →
World models represent a fundamental shift in AI—from systems that process text to ones that understand physics, space, and causality. This guide covers how they work, why they matter, and the leading examples shaping the field in 2026.