What Are 'World Models'? The Key to the Next Big AI Leap (www.wsj.com)

Current AI systems are largely “book smart” — trained on text, images and videos — and lack the kind of embodied, predictive understanding of environments that humans and animals build. The industry term for what’s missing is “world models”: compact, internal simulations that encode objects, physics, time and causal dynamics so an agent can plan, act and predict consequences. Building world models typically means training agents inside realistic simulations (think Gran Turismo or Microsoft Flight Simulator) so they can learn forward models, latent state representations and policies that work across time and uncertainty. World models matter because they shift AI from passive pattern recognition to active, model-based reasoning. Technically, they enable sample-efficient learning, counterfactual and long-horizon planning, better generalization to new situations, and safer testing in simulated environments before real-world deployment. For AI/ML research this points toward tighter integration of predictive latent models, model-based reinforcement learning, multi-modal sensory grounding and interpretable dynamics — a pathway toward more robust robotics, autonomous systems and agents that can reason about “what if” scenarios rather than just predicting the next word.