Teaching AI to see the world more like we do

The promise and peril of using visual AI to study cities

In their new book, “How AI Sees the City,” the leaders of MIT’s Senseable City Lab examine the technology’s implications for researching urban life.

Interpretable machine learning through teaching

We’ve designed a method that encourages AIs to teach each other with examples that also make sense to humans.

A Definition and Roadmap for World Models

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.

Our contribution to a global environmental standard for AI

Using AI to perceive the universe in greater depth

Using AI to perceive the universe in greater depth

Our vision for building a universal AI assistant

We’re extending Gemini to become a world model that can make plans and imagine new experiences by simulating aspects of the world.

Mapping, modeling, and understanding nature with AI

AI models can help map species, protect forests and listen to birds around the world

A Definition and Roadmap for World Models

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.

AI agents create virtual playgrounds to help robots get crucial training data

“SceneSmith” system uses collaborative AI agents to create realistic 3D environments of places like kitchens, hotels, and living rooms, where robots can simulate everyday chores.

Collective alignment: public input on our Model Spec

OpenAI surveyed over 1,000 people worldwide on how AI should behave and compared their views to our Model Spec. Learn how collective alignment is shaping AI defaults to better reflect diverse human values and perspectives.

AI in abundance

Playing ZendoWorld: Challenging AI Agents on Active Visual Concept Induction

arXiv:2607. 08233v1 Announce Type: new Abstract: A central challenge in building intelligent systems is enabling agents to jointly perceive complex inputs, form hypotheses about hidden patterns, and design informative experiments to test them.