AI News, Recent Reports Make Machine Learning Sound Like a Sport. It isn’t

Recent Reports Make Machine Learning Sound Like a Sport. It isn’t

News that Baidu, the Google of China, cheated to take the lead in an international competition for artificial intelligence technology has caused a storm among computer science researchers.

The Imagenet Challenge is a competition run by a group of American computer scientists which involves recognising and classifying a series of objects in digital images.

In their paper about the submission, Baidu themselves weren’t claiming anything more than an engineering advance: they built a large supercomputer that could handle more data than previous implementations.

A necessary advance, but very much a “scaling up” of existing solutions – one that would be financially outside the reach of a typical academic research group.

They produced the machine learning equivalent of the high jump’s “Frosbury Flop” to win the 2012 version of the competition with such a significant improvement that all leading entries are now derived from their model.

The “cheating scandal” was labelled as such by the very same prestigious technical publication that broadcast the initial results to its readers within two days of the e-print’s publication: MIT Technology Review.

Singling out MIT Technology Review in this case may be a little unfair, because this is part of a wider phenomenon where technical results are trumpeted in the press before they are fully tasted (let alone digested) by the scientific community.

Of those researchers involved in CIFAR, Hinton has been hired by Google, Yann LeCun leads Facebook’s AI Research team, Andrew Ng heads up research at Baidu and Nando de Freitas was recently recruited to Google DeepMind, the London start-up that Google lavished £400m on acquiring.

The technical press is becoming susceptible to tabloid sensationalism in this area, but who can blame them as companies and universities ramp up their claims of scientific advance?

But the very real danger is that expectations of significant advance or misunderstanding of the underlying phenomenon will bring about an AI bubble of the type we saw 30 years ago.

It did not merit a pre-publication announcement in MIT Technology Review and the pre-publication withdrawal should have been just a footnote to add to the diverse collection that keep all astute academics scientifically wary.

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