The Hype and Hope of Cognitive Computing

Much of the trouble here is caused by marketing writing checks that the technology can’t yet cash. For those who haven’t read Jennings Brown’s recent deep dive on the backlash around Watson in Gizmodo, it’s worth 20 minutes of time.

He notes that Watson’s hype has soared over the last decade, with Jeopardy appearances and bold marketing around medical applications, often stretching the limitations of what the technology could actually support at the time.

Perhaps the disconnect is just another example of the gap between potential and practical application, not unlike the enthusiasm demonstrated by those involved in the Telenor lab. But the terminology has soured many to the technology’s potential.

"'Cognitive' is marketing malarkey," Tom Austin, a vice president and fellow at Gartner, told ComputerWorld a few years back. "It implies machines think. Nonsense. Bad assumptions lead to bad conclusions."

It didn’t start out that way. “Cognitive computing” began as a retreat to a position of relative safety. Brown quotes Michael Karasick, vice president of cognitive computing at IBM research, a 27-year veteran at IBM, as saying, “when we did Watson back in the day, AI was a four-letter word. Well, we got to call it something. If we call it AI no one will take us seriously.”

And a lack of clarity around definitions may be leading to a lack of understanding—and therefore a lack of enthusiasm—among potential buyers. In a survey of professionals (most in the technology, media, and communications sectors) conducted by Deloitte in a recent webcast, 43% of respondents said they do not have a cognitive computing strategy in place, and another 40% do not know if they have a strategy in place. Only 8% said they currently do have a strategy in place, and other 5% said they have cognitive computing initiatives in place, but no strategy.

But there are many reasons to be enthusiastic about cognitive computing fulfilling its promise, over time, especially in the communications space, or any other industry that produces massive amounts of data.

As Bernard Marr points out in Forbes, big data is fuel for nascent AI technologies. The ability to chew through enormous amounts of data helps machines learn to better emulate neural process and learn more effectively.

This sentiment is echoed by Randy Bean at MIT Sloan Management Review. “The availability of greater volumes and sources of data is, for the first time, enabling capabilities in AI and machine learning that remained dormant for decades due to lack of data availability, limited sample sizes, and an inability to analyze massive amounts of data in milliseconds.” Sloan writes. “Digital capabilities have moved data from batch to real-time, online, always-available access.”

Bean bases his conclusion, in part, on the assessment of Pete Johnson, a Yale-trained data scientist who heads up big data and AI initiatives at massive insurer MetLife.

“We have now reached critical mass,” Johnson told Bean. “When you put these things — big data, AI, machine learning — together, we are starting to see better solutions for a number of classic problems. It will take longer for products with much longer tails involving health/wellness and life. But it’s coming.”

And it may well be. In addition to the countless hours and dollars spent by IBM in its thankless history of AI pioneering and all of the aforementioned AI work being done, there are working groups on cognitive computing at ETSI and hundreds of other rapidly maturing efforts to design systems that emulate human thought.

So where’s the balance between hope and hype? I, for one, am prepared to wait and see.