Click For Photo: https://www.sciencedaily.com/images/2019/05/190515144017_1_540x360.jpg
Most AI agents -- computer systems that could endow robots or other machines with intelligence -- are trained for very specific tasks -- such as to recognize an object or estimate its volume -- in an environment they have experienced before, like a factory. But the agent developed by Grauman and Ramakrishnan is general purpose, gathering visual information that can then be used for a wide range of tasks.
"We want an agent that's generally equipped to enter environments and be ready for new perception tasks as they arise," Grauman said. "It behaves in a way that's versatile and able to succeed at different tasks because it has learned useful patterns about the visual world."
Scientists - Learning - Type - Machine - Learning
The scientists used deep learning, a type of machine learning inspired by the brain's neural networks, to train their agent on thousands of 360-degree images of different environments.
Now, when presented with a scene it has never seen before, the agent uses its experience to choose a few glimpses -- like a tourist standing in the middle of a cathedral taking a few snapshots in different directions -- that together add up to less than 20 percent of the full scene. What makes this system so effective is that it's not just taking pictures in random directions but, after each glimpse, choosing the next shot that it predicts will add the most new information about the whole scene. This is much like if you were in a grocery store you had never visited before, and you saw apples, you would expect to find oranges nearby, but to locate the milk, you might glance the other way. Based on glimpses, the agent infers what it would have seen if it had looked in all the...
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