Topic: world models

Workshop bringing together researchers working on AI for learning dynamics from data, with a special focus on information-theoretic methods. Topics include predictive information, the information bottleneck, latent-variable models, entropy and mutual-information estimation, world models of biological systems, and connections to dynamical systems and control theory. The workshop is part of the six-week NITMB program “Image-Based Scientific Machine Learning for Theories of Biological Dynamics Across Scales” (February 1–March 12, 2027). Program: AI in Biological Dynamics Scientific Focus

Supports synthetic training data for models that understand everyday human activity. Topics include scene-conditioned text-to-simulation motion and interaction, causally coherent audio-visual synthesis, hours-scale multimodal episode generation with persistent world state, and action-conditioned scenario synthesis using world models. Research should improve activity coverage, cross-modal consistency, episode duration and control over generated scenarios. Use the ARA proposal template; four pages excluding appendices are encouraged. Decisions are expected in February 2027. Applications close o

Ming-Yu Liu, NVIDIA vice president of research and head of the Cosmos Lab, joins Practical AI hosts Daniel Whitenack and Chris Benson to discuss AI systems that interact with the physical world. The conversation examines open models, world models and simulation as foundations for robotics and autonomous vehicles, and how research and deployment can advance physical AI. Published 1 October 2026; publisher runtime 47 minutes 19 seconds.

Permanent, full-time reader appointment in the School of Informatics, with hybrid working. Research should advance AI methods at the intersection of perception and action. Relevant areas include continual learning, agentic and world models, embodied intelligence, reinforcement learning, decision-making under uncertainty and long-duration autonomy. Responsibilities include original research, undergraduate and postgraduate teaching, and student supervision. The advertised closing time is 23:59 UK time on 20 October 2026.

Andrew Dai joins The Information Bottleneck to discuss language-model pre-training, next-token prediction, training-data quality and visual reasoning. The conversation examines counting and image understanding, world models and JEPA. Hosted by Ravid Shwartz-Ziv and Allen Roush.

Justin Johnson joins Sam Charrington to discuss world models and spatial artificial intelligence. Topics include representations of three-dimensional environments, simulation and the role of such models in enabling systems to reason about and act within the physical world.

MIT professor Phillip Isola discusses what makes learned representations useful and why independently trained models can develop similar internal structure. He explores the platonic representation hypothesis, local clustering versus global geometry, and the neural thickets account of why pretrained networks can adapt readily to downstream tasks. Other topics include language models as world models, recurrent architectures, biological comparisons, and studying autonomous language-model agents as artificial life. Hosted by Ravid Shwartz Ziv and Allen Roush; watch the full research conversation o