The project called PAIR will focus on developing the "human side" of AI

If artificial intelligence is the future, then we as humans had better learn to get along with our AI counterparts. That’s something Google is looking into with the People + AI Research initiative (PAIR), which brings together researchers across Google to study and redesign the ways in which people interact with AI systems.

PAIR’s research is divided into three areas, based on different user needs: engineers and researchers, domain experts, and everyday users. The goal is to help develop the “human side” of AI, from making AI easier to program for engineers, to using machine learning to make work easier in a variety of professions, to ensuring AI is inclusive so that everyone can benefit.

Accompanying the announcement of PAIR, Google is releasing two open source visualization tools, Facets Overview and Facets Dive, aimed at AI engineers. The tools are made to address the very beginning of the machine learning process, giving engineers a clear view of the data they use to train AI systems.

“Training data is a key ingredient in modern AI systems, but it can often be a source of opacity and confusion,” said the company in a blog post. “With Facets, engineers can more easily debug and understand what they’re building.”

PAIR also acknowledges that Google is far from the only company working on improving AI, and has made it part of its mission to include collaborations with other AI researchers. Right now PAIR is working with a pair of visiting academics—Prof. Brendan Meade of Harvard and Prof. Hal Abelson of MIT—who are focusing on education and science in the age of AI.

Google

If artificial intelligence is the future, then we as humans had better learn to get along with our AI counterparts. That’s something Google is looking into with the People + AI Research initiative (PAIR), which brings together researchers across Google to study and redesign the ways in which people interact with AI systems.

PAIR’s research is divided into three areas, based on different user needs: engineers and researchers, domain experts, and everyday users. The goal is to help develop the “human side” of AI, from making AI easier to program for engineers, to using machine learning to make work easier in a variety of professions, to ensuring AI is inclusive so that everyone can benefit.