What the new artificial intelligence initiative does—and doesn't—mean
On February 11, U.S. President Donald Trump signed an executive order to launch the American Artificial Intelligence Initiative, which will focus federal resources on the development of AI. The executive order outlines five key areas of focus: research and development, availability of data and resources, ethical standards and governance, education, and international collaboration that also protects American interests.
“No advance has captured our imagination more than artificial intelligence,” Michael Kratsios, deputy U.S. chief technology officer, wrote in an opinion article for Wired in advance of the official signing. The field offers many promises addressing problems in defense, transportation, medicine, and more. But along with those promises come a slew of concerns—an issue this latest order is intended to address, according to Kratsios.
The U.S. “must act now to ensure this innovation generates excitement, rather than uncertainty,” he writes.
Much remains to be seen about what this executive order actually means for the future of AI. For one, the exact amount of funding the White House might request for such advancements remains unknown, Science magazine reports. Details about enacting any of these measures and tracking progress are also unclear, the New York Times reports. Ready or not, though, AI already pervades our world—from popup ads to bank loans.
“A lot of decisions are being made by these systems, and that's why people are concerned,” notes Janelle Shane, a researcher and AI-humor blogger at AIweirdness.com. But its current capacities are far from what you see in any science fiction movie.
You might have some questions about this new initiative and the technology involved. What does AI really do? Are robots coming after my job? We've got you covered.
What is artificial intelligence?
Artificial intelligence is a field of computer science focused on the development of systems that can “think” independently to solve problems and learn over time. This differs from, say, an automated factory in which assembly-line robots execute specific pre-programmed tasks.
Today's AI algorithms are trained on broad data sets, seeking patterns in the information to describe past data and predict information that's yet to be seen. One commonly described tool in this field is the artificial neural network, a framework to help machines learn that's loosely based on the human brain. Each node of the system is like a neuron, reacting to and processing input from other nodes to work toward an answer.
There are three basic ways to train AI systems, explains Kasia Kozdon, a Ph.D. student at University College London who specializes in bio-inspired AI. The first is supervised learning, in which a person knows—or thinks they know—the correct answer and feeds that to the AI system.
“Basically, you train the AI to agree with you,” she says.
The second type is unsupervised learning, in which the AI must find the answer for itself by identifying patterns and relationships in sets of data. Finally, there's reinforcement learning. One well-known example of this is Google's AI that was able to master the supremely difficult Atari gameMontezuma's Revenge. The AI was never told how to get points, but it was designed to be rewarded for curiosity, so that it worked out the game rules and how to advance all on its own.
How well does AI work?
Humans have yet to accomplish what's known as artificial general intelligence, or Strong AI, which would be machines that could think on a human level and problem-solve to accomplish a variety of tasks, Kozdon says: “Strong is the holy grail which companies would love to have.” (Though some experts would argue it's a good thing we're not there yet, as that could place millions of jobs at risk.)
Instead, modern AI is very good at narrow tasks, like targeted marketing or even beating humans at chess. Companies are currently using AI to perform a host of functions, such as Siri answering questions, Gmail filtering out spam, Neflix and Spotify suggesting new movies or music, and LinkedIn proposing new connections. (Learn about Sophia, the robot that looks almost human)
Shane uses AI for more light-hearted projects, the often hilarious results of which show the limits of modern machine learning, from generating pickup lines like “you look like a thing and I love you” to crafting knock-knock jokes about cows with no lips.
Working with AI can also be a bit like training a dolphin, Shane says. Give one of those bright and adaptable animals a goal or task that it can figure out how to accomplish, and it inevitably finds unusual ways around what you are asking. For instance, trainers at the Institute for Marine Mammal Studies in Mississippi attempted to train Kelly the dolphin to pick up trash in her pool in exchange for fish. But Kelly figured out a loophole: Whenever people drop paper in the tank, she squirrels it away under a rock and tears off tiny slivers to exchange with passing trainers for fish.