First there was “open washing,” the marketing strategy for dressing up proprietary software as open source. Next came “cloud washing,” whereby datacenter-bound software products masqueraded as cloud offerings. The same happened to big data, with petabyte-deprived enterprises pretending to be awash in data science.
Now we’re into AI-washing — an attempt to make dumb products sound smart.
Judging by the , the entire Fortune 500 went from bozo status to the Mensa society. Not to rain on this parade, but it’s worth remembering that virtually all so-called AI offerings today should be defined as “artificially inflated” rather than “artificially intelligent.”
Everybody’s so smart
As , global director of economic research and chief economist, Bloomberg Intelligence, the number of mentions of artificial intelligence on earnings calls has exploded since mid-2014:
It’s possible that in the last three years, the state of AI has accelerated incredibly fast so that nearly every enterprise now has something worthwhile to say on the subject. More likely, everyone wants on the AI bandwagon, and in the absence of mastery, they’re marketing.
AI is, after all, incredibly difficult. Yann LeCun, director of AI research at Facebook, at a recent O’Reilly conference that “machines need to understand how the world works, learn a large amount of background knowledge, perceive the state of the world at any given moment, and be able to reason and plan.”
Most companies have neither the expertise on staff nor the scale to pull this off. Or, at least, not to an extent worthy of talking about AI initiatives on earnings calls.
Developers recognize this even if their earnings-touting executives don’t. For example, as an extensive, roughly 8,500-strong uncovers, less than one quarter of developers think AI-driven chatbots are currently worthwhile. While chatbots aren’t the only expression of AI, they’re one of the most visible examples of hype getting out in front of reality.
“) that explored the current and future state of AI as applied to messaging and chatbots. Executives from Google, PayPal, and Sprint joined me, and it quickly became clear that the promise of AI has yet to be realized and won’t be for some time. Instead of overpromising a near-term AI future, the session seemed to conclude, it would be best for enterprises to focus on small-scale AI projects that deliver simple but effective consumer value.
For example, machine learning/AI can be used to interpret patterns in X-rays, as Dr. Ziad Obermeyer of Harvard Medical School and Brigham and Women’s Hospital and Ezekiel Emanuel, Ph.D., of the University of Pennsylvania, . Deep, mind-blowing AI? Nope. Effective (and likely to render a big chunk of the radiologist population under-employed)? Likely.
The trick to making AI work well is data: lots and lots of data. Most companies simply aren’t in a position to gather, create, or harness that data. , and yet anyone who has used Amazon’s Echo or Apple’s Siri knows that the output of their mountains of data is still relatively basic. Each of these companies sees the potential, however, and is ramping up efforts to collect and annotate data. Amazon, for example, has 15,000 to 20,000 low-paid people working behind the scenes on labeling snippets of data. Those people are building toward an AI-driven future, but it’s still the future.
So let’s not get ahead of ourselves. Everyone may be talking about AI, but it’s mostly artificial with precious little intelligence. That’s OK, so long as we recognize it as such and build simple services that deliver on their promise.
In sum, we don’t need an AI revolution. Evolution will do nicely.