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It’s Only Common Sense: Why Should They Take Your Call?
We humans take for granted our remarkable ability to predict things that happen around us. For example, consider Rube Goldberg machines: One of the reasons we enjoy them is because we can watch a chain-reaction of objects fall, roll, slide and collide, and anticipate what happens next.
But how do we do it? How do we effortlessly absorb enough information from the world to be able to react to our surroundings in real-time? And, as a computer scientist might then wonder, is this something that we can teach machines?
That last question has recently been partially answered by researchers at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL), who have developed a computational model that is just as accurate as humans at predicting how objects move.
By training itself on real-world videos and using a “3-D physics engine” to simulate human intuition, the system — dubbed “Galileo” — can infer the physical properties of objects and predict the outcome of a variety of physical events.
While the researchers’ paper focused on relatively simple experiments involving ramps and collisions, they say that the model’s ability to generalize its findings and continuously improve itself means that it could readily predict a range of actions.
“From a ramp scenario, for example, Galileo can infer the density of an object and then predict if it can float,” says postdoc Ilker Yildirim, who was lead author alongside CSAIL PhD student Jiajun Wu. “This is just the first step in imbuing computers with a deeper understanding of dynamic scenes as they unfold.”
Researchers fed their physics prediction system videos of collisions, shown as screenshots in (a) and (b), which were then converted into simulations generated by the 3-D physics engine, shown in (c) and (d).
The paper, which was presented this past month at the Conference on Neural Information Processing Systems (NIPS) in Montreal, was co-authored by postdoc Joseph Lim and professor William Freeman, as well as professor Joshua Tenenbaum from the Department of Brain and Cognitive Sciences.
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