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 Pulkit Agrawal’s YouTube channel. Agrawal, 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 Deepak Pathak, Alexei A. Efros and Trevor Darrell at the Berkeley Artificial Intelligence Research Lab for a surprising purpose: to make a machine curious.—–

“You can think of curiosity as a kind of reward which the agent generates internally on its own, so that it can go explore more about its world,” Agrawal said. This internally generated reward signal is known in cognitive psychology as “intrinsic motivation.”

The feeling you may have vicariously experienced while reading the game-play description above — an urge to reveal more of whatever’s waiting just out of sight, or just beyond your reach, just to see what happens — that’s intrinsic motivation.

Humans also respond to extrinsic motivations, which originate in the environment. Examples of these include everything from the salary you receive at work to a demand delivered at gunpoint. Computer scientists apply a similar approach called reinforcement learning to train their algorithms: The software gets “points” when it performs a desired task, while penalties follow unwanted behavior……more here