AI News, BOOK REVIEW: Artificial intelligence helps reveal how people process abstract thought

Artificial intelligence helps reveal how people process abstract thought

'As we rely more and more on these systems, it is important to know how they work and why,' said Cameron Buckner, assistant professor of philosophy and author of a paper exploring the topic published in the journal Synthese.

These neural networks -- multi-layered artificial neural networks, with nodes replicating how neurons process and pass along information in the brain -- demonstrate how abstract knowledge is acquired, he said, making the networks a useful tool for fields including neuroscience and psychology.

Buckner set out to fill that void, considering the use of AI for abstract reasoning, ranging from strategy games to visual recognition of chairs, artwork and animals, tasks that are surprisingly complex considering the many potential variations in vantage point, color, style and other detail.

'Computer vision and machine learning researchers have recently noted that triangle, chair, cat, and other everyday categories are so dif?cult 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,' Buckner wrote.

To overcome the challenges, the systems have to control for so-called nuisance variation, or the range of differences that commonly affect a system's ability to identify objects, sounds and other tasks -- size and position, for example, or pitch and tone.

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.

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.

“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,”

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.

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