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AI helps discover how people process abstract thought

A philosopher has deconstructed the neural networks behind machine learning to show how humans process abstract learning.

The research by Cameron Buckner, assistant professor of philosophy at the University of Houston and author of the paper published in the journal Synthese, is based on Deep Convolutional Neural Networks (DCNNs), which are shaped on the concept that human knowledge stems from experience.

These neural networks replicate neurons’ ability to process and pass along information in the brain. This process of knowledge acquisition could, therefore, prove a useful tool for fields including neuroscience and psychology, Buckner says.

In the paper, Buckner highlights that sometimes the success of neural networks at complex tasks resulting in perception and discrimination has gone beyond the ability of scientists to understand the way the work.

The philosophy professor believes this could be due to the fact that scientists focus has been on results rather than understanding how the networks relate with traditional philosophical accounts of human perception.

Buckner says he intends to fill that void by analysing the use of AI for abstract reasoning in strategy games and visual recognition of chairs, artwork and animals, for example. Tasks that are particularly complex due to the many potential variations in perspective, colour, style and other detail.

“Computer vision and machine learning researchers have recently noted that triangle, chair, cat, and other everyday categories are so difficult to recognize because they can be encountered in a variety of different poses or orientations that are not mutually similar in terms of their low-level perceptual properties,” he wrote, “… a chair seen from the front does not look much like the same chair seen from behind or above; we must somehow unify all these diverse perspectives to build a reliable chair-detector.”

Less than a decade ago, Buckner says, scientists believed advances in machine learning would be unable to reach the human ability to produce abstract knowledge. Now that machines are beating humans at strategic games, driverless cars are being tested around the world and facial recognition systems are deployed everywhere from cell phones to airports, finding answers has become more urgent.

“These systems succeed where others failed,” he said, “because they can acquire the kind of subtle, abstract, intuitive knowledge of the world that comes automatically to humans but has until now proven impossible to program into computers.”


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