To reach "common sense," AI might need to take a step back

In recent years, deep learning has taken great strides in some of the most challenging areas of artificial intelligence, including computer vision, speech recognition, and natural language processing.

However, some problems remain unsolved. Deep learning systems are poor at handling novel situations, they require enormous amounts of data to train, and they sometimes make weird mistakes that confuse even their creators.

Some scientists believe these problems will be solved by creating larger and larger neural networks and training them on bigger and bigger datasets. Others think that what the field of AI needs is a little bit of human “common sense.”

In their new book Machines Like Us, computer scientists Ronald J. Brachman and Hector J. Levesque discuss their view and potential solution to this missing piece of the AI puzzle, which has eluded researchers for decades. In an interview with TechTalks, Brachman discussed what common sense is and isn't, why machines don't have it, and how “knowledge representation,” a concept that has been around for decades but has fallen by the wayside during the deep learning craze, can steer the AI community in the right direction.

Read the full review of Machines Like Us and interview with Ron Brachman on TechTalks.

For more AI book reviews:

• A framework for enterprise AI adoption

• Why lifeless AI is not intelligent

• How algorithms come into being

• Why neural networks aren’t fit for natural language understanding