Learning to teach to speed up learning
The first artificial intelligence programs to defeat the world’s best players at chess and the game Go received at least some instruction by humans, and ultimately, would prove no match for a new generation of AI programs that learn wholly on their own, through trial and error. A combination of deep learning and reinforcement learning algorithms are responsible for computers achieving dominance at challenging board games like chess and Go, a growing number of video games, including Ms. Pac-Man, and some card games, including poker. But for all the progress, computers still get stuck the closer a game resembles real life, with hidden information, multiple players, continuous play, and a mix of short and long-term rewards that make computing the optimal move hopelessly complex. To get past these hurdles, AI researchers are exploring complementary techniques to help robot agents learn, modeled after the way humans pick up new information not only on our own,...