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The US Administration has been trying to foster efforts to stay ahead in artificial intelligence technology, as many of the algorithms now considered AI were developed many years ago, and they are fundamentally limited. “We are harvesting the intellectual fruit that was planted decades ago,” says John Everett, deputy director of the Information Innovation Office within DARPA (the US Defense Advanced Research Projects Agency). “That’s why we’re looking at far forward challenges — challenges that might not come to fruition for a decade.”

A new five-year, $2 billion plan, launched by President Trump in February, could help achieve this goal.

DARPA is one of the agencies recruited for the task. Through its AI Next program, DARPA has launched nine major research projects meant to tackle those limitations. They include a major effort to teach AI programs common sense, a weakness that often causes today’s systems to fail. Giving AI a broader understanding of the world — something that humans take for granted — could eventually make personal assistants more helpful and easier to chat with, and it could help robots navigate unfamiliar environments, according to .

Training data is the lifeblood of machine learning. Another DARPA project will seek to develop AI programs that learn using less data.

Other projects being funded focus on designing more efficient AI chips; exploring ways to explain the decision-making of opaque machine-learning tools; and making AI programs more secure.

The US has long been focused on funding emerging research through academia and agencies like DARPA. And that, in turn, has shaped the technological landscape in ways that weren’t always evident at first. Take self-driving cars, for example. A decade ago, DARPA organized a series of driverless-vehicle contests in desert and urban settings. The competitions triggered a wave of excitement about the potential for automated driving, and a huge wave of investment followed. Many researchers who took part went on to start Google’s driverless-car effort.