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IBM Watson: 75 Industries Have Watson Apps. What about Revenue from Watson?

Was it just four years ago? How PR time flies. I read “Boyhood.” Now here’s the subtitle, which is definitely Google-licious:

Watson was just 4 years old when it beat the best human contestants on Jeopardy! As it grows up and goes out into the world, the question becomes: How afraid of it should we be?

I am not too afraid. If I were the president of IBM, I would be fearful. Watson was supposed to be well on its way north of $1 billion in revenue. If I were the top wizards responsible for Watson, I would be trepedatious . If I were a stakeholder in IBM, I would be terrified.

But Watson does not frighten me. Watson, in case you do not know, is built from:

1. Open source search
2. Acquired companies’ technology
3. Home brew scripts
4. IBM bit iron

The mix is held together with massive hyperbole-infused marketing.

The problem is that the revenue is just not moving the needle for the Big Blue bean counters. Please, recall that IBM has reported dismal financial results for three years. IBM is buying back its stock. IBM is selling its assets. IBM is looking at the exhaust pipes of outfits like Amazon. IBM is in a pickle.

The write up ignores what I think are important factoids about IBM. The article asserts:

The machine began as the product of a long-shot corporate stunt, in which IBM engineers set out to build an artificial intelligence that could beat the greatest human champions at Jeopardy!, one that could master language’s subtleties: rhymes, allusions, puns….It has folded so seamlessly into the world that, according to IBM, the Watson program has been applied in 75 industries in 17 countries, and tens of thousands of people are using its applications in their own work. [Emphasis added]

How could I be skeptical? Molecular biology. A cook book. Jeopardy.

Now for some history:

Language is the “holy grail,” he said, “the reflection of how we think about the world.” He tapped his head. “It’s the path into here.”

And then the epiphany:

Watson was becoming something strange, and new — an expert that was only beginning to understand. One day, a young Watson engineer named Mike Barborak and his colleagues wrote something close to the simplest rule that he could imagine, which, translated from code to English, roughly meant: Things are related to things. They intended the rule as an instigation, an instruction to begin making a chain of inferences, each idea leaping to the next. Barborak presented a medical scenario, a few sentences from a patient note that described an older woman entering the doctor’s office with a tremor. He ran the program — things are related to things — and let Watson roam. In many ways, Watson’s truest expression is a graph, a concept map of clusters and connective lines that showed the leaps it was making. Barborak began to study its clusters — hundreds, maybe thousands of ideas that Watson had explored, many of them strange or obscure. “Just no way that a person would ever manually do those searches,” Barborak said. The inferences led it to a dense node that, when Barborak examined it, concerned a part of the brain…that becomes degraded by Parkinson’s disease. “Pretty amazing,” Barborak said. Watson didn’t really understand the woman’s suffering. But even so, it had done exactly what a doctor would do — pinpointed the relevant parts of the clinical report, discerned the disease, identified the biological cause. To make these leaps, all you needed was to read like a machine: voraciously and perfectly.

I have to take a break. My heart is racing. How could this marvel of technology be used to save lives, improve the output of Burger King, and become the all time big winner on the the Price Is Right?

Now let’s give IBM a pat on the back for getting this 6.000 word write up in a magazine consumed by those who love the Big Apple without the New Yorker’s copy editors poking their human nose into reportage.

From my point of view, Watson needs to deliver:

1. Sustainable revenue
2. Demonstrate that the system can be affordable
3. Does not require human intermediaries to baby sit the system
4. Process content so that real time outputs are usable by those needing “now” insights
5. Does not make egregious errors which cause a human using Watson to spend time shaping or figuring out if the outputs are going to deliver what the user requires; for example, a cancer treatment regimen which helps the patient or a burrito a human can enjoy.

Hewlett Packard and IBM have managed to get themselves into the “search and content processing” bottle. It sure seems as if better information outputs will lead to billions in revenue. Unfortunately the realty is that getting big bucks from search and content processing is very difficult to do. For verification, just run a query on Google News with these terms: Hewlett Packard Autonomy.

The search and content processing sector is a utility function. There are applications which can generate substantial revenue. And it is true that these vendors include search as a utility function.

But pitching smart software spitballs works when one is not being watched by stakeholders. Under scrutiny, the approach does not have much of a chance. Don’t believe me? Take your entire life savings and buy IBM stock. Let me know how that works out.

Stephen E Arnold, May 25, 2015

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