Sunday, July 21, 2019

Infants inspire computers (sorta')

From the article:

Those clever machines that "learn" stuff from piles of examples require hundreds of hours to achieve what humans do in hours. So...
Recent studies have highlighted two key contributors to humans' ability to acquire knowledge so quickly—namely, intuitive physics and intuitive psychology.

These intuition models, which have been observed in humans from early stages of development, might be the core facilitators of future learning. Based on this idea, researchers at the Korea Advanced Institute of Science and Technology (KAIST) have recently developed an intrinsic reward normalization method that allows AI agents to select actions that most improve their intuition models. In their paper, pre-published on arXiv, the researchers specifically proposed a graphical physics network integrated with deep reinforcement learning inspired by the learning behavior observed in human infants.

"Imagine human infants in a room with toys lying around at a reachable distance," the researchers explain in their paper. "They are constantly grabbing, throwing and performing actions on objects; sometimes, they observe the aftermath of their actions, but sometimes, they lose interest and move on to a different object. The 'child as a scientist' view suggests that human infants are intrinsically motivated to conduct their own experiments, discover more information, and eventually learn to distinguish different objects and create richer internal representations of them."

Psychology studies suggest that in their first years of life, humans are continuously experimenting with their surroundings, and this allows them to form a key understanding of the world. Moreover, when children observe outcomes that do not meet their prior expectations, which is known as expectancy violation, they are often encouraged to experiment further to achieve a better understanding of the situation they're in.

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