Our new paper analyzes the important ways AI systems organize the visual world differently from humans.
Summary generated by The Flow from the publisher's feed. The full article lives at DeepMind Blog.
We’ve designed a method that encourages AIs to teach each other with examples that also make sense to humans.
World models -- internal simulators that learn the structure and dynamics of an environment -- have become one of the most actively debated concepts in AI. From model-based reinforcement learning and video generation to embodied robotics and ultimately, physical AI, researchers across AI subfields are building systems that they call "world models", yet there is no consensus on what a world model fundamentally is, what it should predict, or how it should be built.
Using AI to perceive the universe in greater depth
We’re extending Gemini to become a world model that can make plans and imagine new experiences by simulating aspects of the world.
AI models can help map species, protect forests and listen to birds around the world
arXiv:2607. 06401v1 Announce Type: new Abstract: World models -- internal simulators that learn the structure and dynamics of an environment -- have become one of the most actively debated concepts in AI.
