You probably can’t remember what it feels like to play Super Mario Bros. for the very first time, but try to picture it. An 8-bit game world blinks into being: baby blue sky, tessellated stone ground, and in between, a squat, red-suited man standing still — waiting. He’s facing rightward; you nudge him farther in that direction. A few more steps reveal a row of bricks hovering overhead and what looks like an angry, ambulatory mushroom. Another twitch of the game controls makes the man spring up, his four-pixel fist pointed skyward. What now? Maybe try combining nudge-rightward and spring-skyward? Done. Then, a surprise: The little man bumps his head against one of the hovering bricks, which flexes upward and then snaps back down as if spring-loaded, propelling the man earthward onto the approaching angry mushroom and flattening it instantly. Mario bounces off the squished remains with a gentle hop. Above, copper-colored boxes with glowing “?” symbols seem to ask: What now?
This scene will sound familiar to anyone who grew up in the 1980s, but you can watch a much younger player on . , a computer science researcher at the University of California, Berkeley, is studying how innate curiosity can make learning an unfamiliar task — like playing Super Mario Bros. for the very first time — more efficient. The catch is that the novice player in Agrawal’s video isn’t human, or even alive. Like Mario, it’s just software. But this software comes equipped with experimental machine-learning algorithms designed by Agrawal and his colleagues , and at the for a surprising purpose: to make a machine curious.
To read more, click here.