Human-centered research and design to make AI partnerships productive, enjoyable, and fair.
About the team
The past few years have seen rapid advances in machine learning, with new technologies achieving dramatic improvements in technical performance. But we can go beyond optimizing objective functions. By building AI systems with users in mind from the ground up, we open up entire new areas of design and interaction.
PAIR is devoted to advancing the research and design of people-centric AI systems. We're interested in the full spectrum of human interaction with machine intelligence, from supporting engineers to understanding everyday experiences with AI.
Our goal is to do fundamental research, invent new technology, and create frameworks for design in order to drive a human-centered approach to artificial intelligence. And we want to be as open as possible: we’re building open source tools that everyone can use, hosting public events, and supporting academics in advancing the state of the art.
Can machines explain the "why" behind their decisions? We're investigating ways for people to understand more about ML models, starting with visualizations that look under the hood of complex systems. See this video: Visualizing High-Dimensional Space.
We are investigating how AI technology and design can help the practice of clinicians and medical researchers
How ML is changing the way we build experiences and interact with the world.
Read the "AI Outside In" blog by our writer-in-residence, independent tech journalist and researcher David Weinberger. David explains central concepts in the field of machine learning, within a broader context of social issues and ideas.
Policy tradeoffs in machine learning can be complex, but visualizations and interactive explanations can help people understand these critical issues.
Machine learning-based forecasts may one day help deploy emergency services and inform evacuation plans for areas at risk of an aftershock.
People who played the Quick, Draw! Game created a dataset that reflects a variety of perspectives around the globe. See visualizations of these 50 million drawings and a video about why diversity matters to machine learning.
TensorFlow.js is an open-source library for hardware-accelerated machine learning on the web. Train neural nets entirely in your browser, or run pre-trained models.
We are designing machine learning algorithms to incorporate human knowledge and be more controllable and interpretable, without sacrificing accuracy.
Training data can be seen as the raw ingredients for a machine learning system. A central focus of our group is helping engineers understand what goes into their models, as in the open-source Facets package.
Explore GAN training dynamics with this interactive visualization.
Education is central to our mission. The Playground is an open-source neural net you can play with in your browser, allowing users to experiment with different architectures through a graphical UI.
Daniel Smilkov, Nikhil Thorat, Charles Nicholson, Emily Reif, Fernanda B. Viégas, Martin Wattenberg
Daniel Smilkov, Shan Carter, D. Sculley, Fernanda B. Viégas, Martin Wattenberg
Martin Wattenberg, Fernanda B. Viégas, Ian Johnson
Fernanda B. Viégas, Martin Wattenberg
Brendan J. Meade, William T. Freeman, James Wilson, Fernanda B. Viégas, Martin Wattenberg
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, Martin Wattenberg
Brendan J. Meade, Phoebe M. R. DeVries, Jeremy Faller, Fernanda Viegas, Martin Wattenberg
Some of our people
Advancing the state of the art in machine learning means thinking beyond narrowly-defined objective functions. It requires weaving user needs and societal values into the design and evaluation of these systems.