Ride the Lightning

Gartner Places Machine Learning at the Peak of the Hype Cycle

August 25, 2016

Gartner has published the 2016 edition of its Hype Cycle chart. The most fascinating part of the 2016 edition of the Hype Cycle is not where any one technology shows up. It's how multiple incarnations of one underlying technology — machine intelligence — are spread out across several points on the infamous trough-and-plateau chart.

Gartner's label for the rise of machine intelligence is "the perceptual smart machine age," and it predicts that such machines will be "the most disruptive class of technologies over the next 10 years."

The benefits of what Gartner calls "radical computational power, near-endless amounts of data, and unprecedented advances in deep neural networks" are on the rise, but none has yet ripened to the point where it is routinely useful. Furthermore, Gartner doesn't see any of them becoming mainstream before at least two years pass, with most of them in the "wait five years or more" category.

As an InfoWorld post speculated, the real issue — and the reason Gartner may be pushing machine learning's heyday ahead — is not that machine learning faces technical barriers. Rather, the primary obstacle is that machine learning isn't a cure-all, so it'll take time to reveal its genuinely beneficial applications. As the post notes, the inventors of the laser couldn't figure out what to do with it at first either.

Careening headfirst into the "Trough of Disillusionment" are self-driving cars (labeled as "autonomous vehicles") and "Natural-Language Question Answering." Both are examples of machines expected to act like humans but without mistakes. There's little point of having a self-driving car if it hops the curb and forgets to brake for pedestrians, and there's little reason to have a machine respond to plain-English questions unless it can produce precise, accurate and coherent answers.

As Gartner sees it, the reality of both projects is that they're messier and more complex than anyone could anticipate. It's one matter to make a car that can maintain speed and distance from other cars on the highway, and another to make a car that can parallel-park without getting a ticket – at least today. The same goes for natural language, especially since natural language is ambiguous by nature.

Reasonable discussions about machine intelligence see it as an augmenter, not a replacement, for human insight and understanding. That too may change, but not soon. Yet everywhere we turn AI and machine intelligence are hyped far beyond their current capabilities.

As my friend Ed Walters says, 95% of AI is hype. Most of what is called AI is simply smart programming. Thanks to Dave Ries for forwarding the story.

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