Machine learning: How to go from theory to reality

A lack of skilled people continues to stymie the AI revolution. That’s why smart companies invest as much in cultural change as technology adoption—and Google shows how

Machine learning: How to go from theory to reality
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We’re awash in increasingly sophisticated machine learning technologies. Too bad so few people know how to use them.

As recent 451 Research survey data indicates, a lack of skilled people continues to stymie the AI revolution. Indeed, people—not tech—have been the cause of slower-than-expected uptake of everytechnological revolution. This is why smart companies invest as much in cultural change as technology adoption.

Machine learning’s hype and hope

Not that anyone is allowing the abilityto make use of machine learning to get in the way of claiming benefits therefrom. Just look at the ongoing rise in mentions of AI and machine learning on public earnings calls. All executives want to pretend their company is going big on machine learning. Yes, AI mentions dipped briefly in the latest quarter, but the long-term trend is up and to the right—without commensurate investments to make that cheap talk pay rich dividends.

To wit, when 451 Research asked the biggest barriers to machine learning, 36 percent said they don’t know where the On button is, with another 32 percent saying they can’t afford it or don’t really know what to do with the data, as Figure 1 shows.

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Figure 1: Responses to the question “What is your organization's most significant barrier to using machine learning?” from 209 respondents, from 451 Research’s “Voice of the Enterprise: AI & Machine Learning, Adoption, Drivers & Stakeholders 2018” report.

Even so, this didn’t stop 49 percent of those surveyed from claiming “competitive advantage” from their mostly nonexistent roll-out of machine learning, as Figure 2 shows.

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Figure 2: Responses to the question “What are the most significant benefits your organization has realized or expects to realize from its use of machine learning?” from 207 respondents, from 451 Research’s “Voice of the Enterprise: AI & Machine Learning, Adoption, Drivers & Stakeholders 2018” report.

This isn’t much different from early surveys on big data, when enterprises claimed huge benefits from big data in one breath but in the next admitted that they had no clue what they were doing.

No one wants to be the machine learning or big data noob, but getting it right isn’t as easy as vendor press releases would suggest. The 451 Research survey data finds roughly 50 percent of companies having deployed or planning to deploy machine learning algorithms in the next year, but I’d be shocked if those many of that 50 percent are very advanced in their machine learning adoption. Most are still feeling their way, hoping it will all pay off.

Small wonder, then, that McKinsey & Co.’s analysis indicates that it will take another ten years for machine learning/AI to get absorbed into common use. Change takes time.

Winning in machine learning: It’s a culture thing

For companies that want to win big in machine learning, the right strategy probably isn’t to sit around, waiting for the world to change. In the cloud, Pivotal sells software to enable companies to build out cloud services, as well as professional services designed to enable the cultural change necessary to embrace those cloud services. IBM, HPE, SAP, Oracle, and so on have long done the same in their markets when embracing or driving similar technology shifts. These companies clearly recognize that selling technology is only half the story. Unless there are people equipped to use that software, it’s going to end in tears.

On the machine learning side, the company doing most to enable cultural change is Google, but in a different way from Pivotal.

Google isn’t running “dojos” where machine learning masters train aspirants, Pivotal-style. Instead, Google has open-sourced key technologies like TensorFlow that let those aspirants learn by doing. Dubbed “an open source machine learning framework for everyone,” TensorFlow paves the way for a generation of AI practitioners to grow up doing machine learning/AI “the Google way.” This, in turn, promises to coax many of those same practitioners to run their machine learning workloads on Google’s cloud, where they’re optimized to run best.

But even if they don’t, open-sourcing TensorFlow is helping to lower the bar to machine learning success by improving access to powerful machine learning technology at zero cost.

Through open source libraries like TensorFlow, machine learning will go from important-but-complex to important-and-accessible, one developer download at a time. It’s a genius strategy, because it reflects the need to change the people who use technology before that technology can impact the world.

Copyright © 2018 IDG Communications, Inc.