AI's long, hot summer

13 September 2018

  • machine learning

Machine learning is fast becoming the leading paradigm in computer science. Even if the declinists are correct that we’re heading toward an AI winter,1,2 its fair to say the last few years are evidence of a major change in the research climate.

A few days ago Yan LeCun posted a tweet noting that he, Yoshua Bengio and Geoff Hinton now fill the top three ranks for citations per day in computer science.3 Hinton and Bengio also came in first and second respectively for the same metric in both 2017 and 2016.

There is plenty of debate around the accuracy of bibliometrics, but even taking these numbers skeptically it is clear that machine learning has made a mark in computer science that will not be easily erased.

Putting aside dubious predictions of the end of work; machine learning affords a kind of automation previously beyond the reach of computers. Machine learning is perhaps the first computing technology flexible enough to capture culture; this is revelealed most plainly when systems unexpectedly reflect human biases.

This capacity for cultural computing in combination with an increasingly data saturated society makes AI extraordinarly profitable for tech companies and valuable for governments.

Of course, machine learning cannot sustain this level of interest indefinitely but we seem set for the longest and hottest AI summer on record.


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