The AIhub coffee corner captures the musings of AI experts over a short conversation. This month we delve into world models. What are they, and what potential do they have? Joining the conversation this time are: Sanmay Das (Virginia Tech), Rina Dechter (University of California, Irvine), Tom Dietterich (Oregon State University), Sabine Hauert (University of […]
Insider Brief A study from Virginia Tech found that AI image generators are better at representing large cities than smaller communities, raising questions about geographic bias as generative AI tools increasingly shape travel, planning and public perception. The research, published in Technology in Society, found that images generated by OpenAI’s DALL·E 2 more accurately reflected […]
DeepMind's shift to 'world models' could redefine AI's role in robotics and scientific discovery, emphasizing causality over language processing.
The post Google DeepMind CEO Demis Hassabis says language models can’t understand reality, pushes for ‘world models’ appeared first on Crypto Briefing.
Listen to the session or watch below AI companies want to build systems that understand the external world and overcome the limitations of LLMs. Recent developments have brought world models to the forefront of the AI discussion. Watch a conversation with editor in chief Mat Honan, senior AI editor Will Douglas Heaven, and AI reporter…
AI video generation startup Runway is betting that video generation is the path to world models. And that being an AI outsider is an advantage, not a liability.
Insider Brief PRESS RELEASE — Origin Lab, the technology platform turning licensed game worlds into structured training data for world models and multimodal AI, announced an $8M seed round led by Lightspeed Venture Partners. The financing will accelerate Origin Lab’s software, capture, enrichment, QA, search, and delivery systems, while expanding its applied research work in […]
GRASP is a new gradient-based planner for learned dynamics (a “world model”) that makes long-horizon planning practical by (1) lifting the trajectory into virtual states so optimization is parallel across time, (2) adding stochasticity directly to the state iterates for exploration, and (3) reshaping gradients so actions get clean signals while we avoid brittle “state-input” gradients through high-dimensional vision models.
Large, learned world models are becoming increasingly capable. They can predict long sequences of future observations in high-dimensional visual spaces and generalize across tasks in ways that were difficult to imagine a few years ago. As these models scale, they start to look less like task-specific predictors and more like general-purpose simulators.
But having a powerful predictive model is not the same as being able to use it effectively for control/learning/planning. In practice, long-horizon planning with modern world models remains fragile: optimi
Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we meet PhD students and early-career researchers, find out how machine learning is used for particle physics discoveries, cast an eye over the latest AI Index […]
AI-generated video has gone from novelty to creative tool almost overnight, and Runway has a front row seat to the shift. The New York-based company has raised close to $860 million at a $5.3 billion valuation, and its models are going toe-to-toe with the most well-funded labs in the world, including Google and OpenAI. The technology goes way beyond […]