AI pioneer sees Korea's role in developing world model - The Korea Times

Yann LeCun argues that current large language models are fundamentally limited because they excel at predicting text sequences but fail to understand the physical world. He emphasizes a critical gap between systems that manipulate language and those that can interact with reality, noting that we lack autonomous systems capable of navigating the unpredictability of real-world environments. This limitation highlights the need for AI to move beyond statistical pattern matching toward genuine causal understanding. To address this, LeCun advocates for the development of "world models" that learn physical laws and causal relationships. These models enable AI to predict the consequences of its own actions, ensuring safety and effectiveness in real-world applications. By focusing on predictive capabilities rather than just language generation, AI can achieve the level of autonomy required for complex tasks like robotics and self-driving cars, which current generative AI struggles to handle independently. This vision is highly relevant to open data because developing robust world models requires diverse, real-world data that is often siloed across industries and borders. LeCun supports federated learning approaches, such as Project Tapestry, which allow models to learn from distributed data without centralizing it, thereby preserving privacy while enabling collaboration. This underscores the importance of open, interoperable data ecosystems where countries and companies combine their unique strengths and datasets to build more capable and general-purpose AI systems.

Source: koreatimes.co.kr
Published on 2026-10-13

AI pioneer sees Korea's role in developing world model - The Korea Times

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