Cognitive Computing – Latest and Greatest?

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Cognitive Computing

Cognitive Computing – Latest and Greatest?

Now we have cognitive search. I have lost track of whether it comes before or after machine learning, artificial intelligence (AI), natural language processing (NLP), or insight engines. Don’t hear much about it, except when good ole Watson is playing games.

Did you know that Watson has been in training for the past 7 years? Not exactly a quick project turnaround. According to Jerome Pesenti, Vice President of the Watson team at IBM, “When it comes to neural networks, we don’t entirely know how they work and what’s amazing is that we’re starting to build systems we can’t fully understand. The math and the behavior are becoming very complex and my suspicion is that as we create these networks that are ever larger and keep throwing computing power to it, it creates some interesting methodological problems.” Ok, IBM is now building systems they can’t understand?

Cognitive computing and machine learning were originally designed to make sense of large amounts of data. So much so that the only companies suitable for cognitive computing would be the IBMs of the world – small and mid-size companies just don’t have the critical mass. This spills over to system training. Organizations need not only sufficient data sets but also enough data scientists required to tune and test outputs – again a barrier to entry.

Applications where cognitive computing shows the most promise are content curation, search and discovery, expertise location, lessons learned analytics, data visualization, and intelligent personal assistants. Despite how great an end application is, end user adoption is an issue that will never go away. Proponents of cognitive computing focus on the personal assistant aspect of cognitive computing.

Not so fast. When Dutch company MotivAction conducted research on the issue of having a computer tell you what to do, it found that 75 percent of people wanted to remain in control of all decisions, 20 percent were comfortable allowing computers to decide occasionally, and only 5 percent would leave all decisions to computers. Moving to a cognitive computing-powered world may require more user hand-holding than programmers and early adopters realize. End users have more than once derailed the adoption of new applications.

The research by MotivAction is very telling. If a machine makes a catastrophic decision, and sooner or later one will, the machine won’t be blamed, eventually a human will. What none of these technologies can do is analyze risk, no matter how much we would like to believe they can. Unfortunately, risk remains in our hands, at least for the time being. When that changes, I’d suggest packing it all in and going home. Suffice to say, I think we are safe for the time being.

If your organization can’t wait years for a solution, it can always fall back on multi-term metadata generationclassification, and taxonomy solutions. With the ability to understand content in context, we are leaps and bounds ahead of the competition. Oops, we don’t have any competition. Give us a chance – we’ll show you why we are different.

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