Perception vs. Knowledge in artificial intelligence

“Perception” and “knowledge.” I believe I’ve been guilty of using these words in the past to communicate ideas of research, without being overly clear in what they mean to me. When borrowing from other disciplines words such as these and ones such as “memory,” one should use them as consistent to their original meaning as possible. But I don’t believe there is anything wrong with choosing words that are out of their original context, so long as you clearly articulate the meaning of the words you’re choosing.

“Perception” and “knowledge” were two such words I was using today when talking with a fellow graduate student at the University of Alberta. Part way through our conversation, it became clear that I wasn’t being overly clear with how I was defining either word. We were discussing the idea of “perception as prediction.” An idea I have blogged about briefly in the past. The basic premise of the idea is that what we “perceive” to be true, are actually predictions that we believe to be true if we were to take certain actions. A baby perceives that it’s in the presence of it’s mothers breast because it predicts that if it were to take certain actions, it would receive sensorimotor stimulants (breast milk). A golfer perceives that she is standing on the green because she predicts that if she were to strike the ball, it would roll evenly over the ground. This theory of perception could be taken further to suggest that I perceive that Donald Trump is the current President of the United States because I predict if I were to enter his name into Google, I would discover that he was indeed the president.

The first two examples (the baby with breast milk, and the golfer) seem to fit nicely into a “perception” framework. To me, perception seems to be grounded in making sense of the immediate sensorimotor stream of data. “Making sense” perhaps means making predictions of what future implications would be to the sensorimotor stream would be if certain actions were taken now. However, the example of perceiving that Donald Trump is the president, seems to state something beyond my immediate sensorimotor stream of data. It’s less about “perceiving” what these observations mean. And more like “knowledge” that can be drawn upon to make other predictions. Knowledge seems to be based on making sense of what WOULD happen to my sensorimotor stream if I took certain actions now. Is this differentiation arbitrary? Is perception just a special case of knowledge – it’s knowledge grounded in the immediate senses vs. future senses? I believe that most people separate knowledge from perception in this way. But how do we “draw upon” knowledge when it’s needed? Is this knowledge hidden somehow in the sensorimotor data? Or is it part of a recurrent layer in a network? Or perhaps, it really is no different than “perception” in that this knowledge regarding Donald Trump could be one of millions of general value functions in a network of general value functions. If this is the case, then couldn’t you, in addition to saying “perception as prediction,” also say “knowledge as prediction?” I believe this is, actually exactly what researchers have argued in papers such as “Representing knowledge as Predictions.”

To me, perception refers to an ability to make further, more abstract sense of my immediate senses. To make “further, more abstract sense” means to be able to know that I’m “in a car” if I see street signs, and hear the hum of a motor. This more abstract sense of being “in a car” could be modeled as a set of predictions that are true, given the current sensorimotor input. Knowledge, on the other hand, can be modeled the exact same way. Through a set of predictions grounded in immediate senses. The difference, perhaps, is that perception, deals with more subjective information. It’s about making more abstract sense of my immediate senses. But the mechanism to compute perception and knowledge, could be the same. If that is the case, and these computations take the same input, and whose output is used the same, then what is the difference? Perhaps nothing?


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