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“Interestingly, both the computer model and human subjects perform this task at chance and have a bias at saying that the object will move,” Yildirim says. "This suggests not only that humans and computers make similar errors, but provides further evidence that human scene understanding can be best described as probabilistic simulation.”
What’s next
The team members say that they plan to extend the research to more complex scenarios involving fluids, springs, and other materials. Continued progress in this line of work, they say, could lead to direct applications in robotics and artificial intelligence.
“Imagine a robot that can readily adapt to an extreme physical event like a tornado or an earthquake,” Lim says. “Ultimately, our goal is to create flexible models that can assist humans in settings like that, where there is significant uncertainty.”
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