Always interesting former IBMer Irving Wladawsky-Berger talks AI hype, cycles, and lots of supporting links and observations. Been through it all, and this second spin has delivered more, but still not close to the full expectations. And as we touched closer to the goal, more fear too.
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After Years of Promise and Hype, Is AI Once More Failing to Deliver?
The June 13 issue of The Economist included an in-depth look at the limits of AI, with seven articles on the subject. “There is no question that AI - or, to be precise, machine learning, one of its sub-fields - has made much progress,” notes The Economist in the issue’s overview article. “Computers have become dramatically better at many things they previously struggled with… Yet lately doubts have been creeping in about whether today’s AI technology is really as world-changing as it seems. It is running up against limits of one kind or another, and has failed to deliver on some of its proponents’ more grandiose promises.”
Transformative technologies, - remember the dot-com bubble, - are prone to hype cycles, when all the excitement and publicity accompanying their early achievements often lead to inflated expectations, followed by disillusionment if the technology fails to deliver. But AI is in a class by itself, as the notion of machines achieving or surpassing human levels of intelligence has led to feelings of both wonder and fear over the past several decades.
The article reminds us that AI has gone through two such major hype cycles since the field began in the mid-1950s. Early achievements, - like beating humans at checkers and proving logic theorems, - led researchers to conclude that machines would achieve human-level intelligence within a couple of decades. This early optimism collapsed leading to the first so-called AI winter from 1974-1980. The field was revived in the 1980s with the advent of commercial expert systems and Japan’s Fifth Generation project, but it didn’t last long, leading to the second AI winter from 1987-1993....."
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