Let us call that new level robust artificial intelligence: intelligence that, while not 
necessarily superhuman or self-improving, can be counted on to apply what it knows to 
a wide range of problems in a systematic and reliable way, synthesizing knowledge from 
a variety of sources such that it can reason flexibly and dynamically about the world, 
transferring what it learns in one context to another, in the way that we would expect of 
an ordinary adult. 

In a certain sense, this is a modest goal, neither as ambitious or as unbounded as 
"superhuman" or "artificial general intelligence" but perhaps nonetheless an important, 
hopefully achievable, step along the way—and a vital one, if we are to create artificial 
intelligence we can trust, in our homes, on our roads, in our doctor's offices and 
hospitals, in our businesses, and in our communities.  

Quite simply, if we cannot count on our AI to behave reliably, we should not trust it.1
The Next Decade in AI-