Why We Are in Danger of Overestimating AI

Data-heavy computers have ways to go to catch up with human thought and common sense

By Richard Waters

Artificial intelligence is one of the important technological advances of the early 21st century. Already it has meant that machines can read medical images as well as a radiologist, and enabled the auto industry to develop autonomous cars.

The technology is in danger of being overrated, however, and considerably more work is needed before we can reach the long-dreamt-of moment when machine intelligence matches the human variety.

When we discuss AI today we are mainly referring to just one facet of it: deep learning. This technology has its limitations, says Dave Ferrucci, a former AI expert at IBM. The Watson project he led there contributed to the rise of interest in cognitive systems when seven years ago it beat the best human players at Jeopardy, the US television quiz show.

However, Mr Ferrucci, co-founder and chief executive of Elemental Cognition, stresses that deep learning is simply a statistical technique for finding patterns in large amounts of data. It has predictive value but no true understanding in the sense that a human does. Having a computer simply spew out an answer “is not sufficient in the long term,” he says. “You want to say: ‘Here’s why.’”

The case against deep learning was put forcefully at the start of this year in a paper by Gary Marcus, a psychology professor at New York University and a persistent sceptic. His list of complaints extends from its heavy reliance on large data sets to its susceptibility to machine bias and its inability to handle abstract reasoning. Mr Marcus’s conclusion was that “one of the biggest risks in the current overhyping of AI is another AI winter”.

He was referring to the period in the 1970s when over-optimism about the technology was followed by a period of…

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