On 3 November 2026, Advanced Machine Intelligence (AMI) Executive Chairman Yann Lecun gave a talk, “World Models: Enabling the Next AI Revolution,” as part of the College of Computing and Data Science and IAS@NTU Lee Kong Chian Distinguished Professor Public Lecture series. Lecun, who is also a Jacob T. Schwartz Professor at NYU, discussed current challenges facing AI systems, particularly LLMs, in attaining human-like intelligence, and proposed that world models offer a promising path for developing truly “intelligent” systems. World models create a model of a given environment, aim to “learn” its rules, and can reason using these rules to predict outcomes or propose actions leading to desired outcomes; unlike LLMs trained on human language, a world model “learns” the world through its own sensors and inputs. Lecun closed with a slide titled “Recommendations to my fellow AI scientists,” suggesting generative models, probabilistic models, contrastive methods, and reinforcement learning should all be “abandoned,” and concluded, “If you are interested in human-level AI, don’t work on LLMs.” CCDS Nanyang Assistant Professor Zhang Mengmi agreed that language cannot be the foundation of human-like intelligence, citing developmental science and proposing AI agents that actively seek information, decide where to look, and learn through their own experiences, with biologically inspired attention, memory, and active learning.