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Ronald J. Brachman and Hector J. Levesque, Machines Like Us: Toward AI with Common Sense

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By Henry Kautz

Machines Like Us: Toward AI with Common Sense Ronald J. Brachman and Hector J. Levesque (2022)) 320pp., $US25(paperback), MIT Press, Cambridge MA, ISBN 9780262547321

Since its birth in the 1940s, the field of artificial intelligence has been divided into two camps, one focused on artificial neural networks and the other on reasoning with symbolic representations of knowledge. The symbolic representational approach firmly dominated the field until 2012, when a neural network named ‘AlexNet’ handily won an algorithm competition for recognizing objects in images (Krizhevsky et al., 2012). Further convincing successes of neural network algorithms for speech recognition, the game of Go (Silver et al., 2017), and other problems that had long eluded the KR approach soon followed. Today, neural networks, under the banner of ‘deep learning’, where ‘deep’ refers to the fact that the artificial neurons are arranged in many layers, dominate research and commercial applications. Most students studying AI learn little about knowledge representation, and the approach is rarely mentioned in news stories and popular accounts of AI.

page: 121 – 124
Prometheus: Critical Studies in Innovation
Volume 39, Issue 2
SKU: 390205

SKU: 390205 Category: Tag:

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By Henry Kautz

Machines Like Us: Toward AI with Common Sense Ronald J. Brachman and Hector J. Levesque (2022)) 320pp., $US25(paperback), MIT Press, Cambridge MA, ISBN 9780262547321

Since its birth in the 1940s, the field of artificial intelligence has been divided into two camps, one focused on artificial neural networks and the other on reasoning with symbolic representations of knowledge. The symbolic representational approach firmly dominated the field until 2012, when a neural network named ‘AlexNet’ handily won an algorithm competition for recognizing objects in images (Krizhevsky et al., 2012). Further convincing successes of neural network algorithms for speech recognition, the game of Go (Silver et al., 2017), and other problems that had long eluded the KR approach soon followed. Today, neural networks, under the banner of ‘deep learning’, where ‘deep’ refers to the fact that the artificial neurons are arranged in many layers, dominate research and commercial applications. Most students studying AI learn little about knowledge representation, and the approach is rarely mentioned in news stories and popular accounts of AI.

page: 121 – 124
Prometheus: Critical Studies in Innovation
Volume 39, Issue 2
SKU: 390205

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