Eric Siegel, Ph.D. is the founder of the Predictive Analytics World conference series—which includes events for business, government, healthcare, workforce, manufacturing, and financial services—the author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die—Revised and Updated Edition (Wiley, January 2016), executive editor of The Predictive Analytics Times, and a former computer science professor at Columbia University. For more information about predictive analytics, see the Predictive Analytics Guide.
Eric Siegel: So I’ve been asked periodically for a couple of decades whether I think artificial intelligence is possible. And I taught the artificial intelligence course at Columbia University. I’ve always been fascinated by the concept of intelligence. It’s a subjective word. I’ve always been very skeptical. And I am only now newly a believer. Now this is subjective. This is sort of an aesthetic thing but my opinion is that IBM’s Watson computer is able to answer questions, in my subjective view, that qualifies as intelligence. I spent six years in graduate school working on two things. One is machine learning and that’s the core to prediction – learning from data how to predict. That’s also known as predictive modeling. And the other is natural language processing or computational linguistics.
Working with human language because that really ties into the way we think and what we’re capable of doing and does turn out to be extremely hard for computers to do. Now playing the TV quiz show Jeopardy means your answering questions – quiz show questions. The questions on that game show are really complex grammatically. And it turns out that in order to answer them Watson looks at huge amounts of text, for example, a snapshot of all the English speaking Wikipedia articles. And it has to process text not only to look at the question it’s trying to answer but to retrieve the answers themselves. Now at the core of this it turns out it’s using predictive modeling. Now it’s not predicting the future but it’s predicting the answer to the question, you know. It’s the same in that it’s inferring an unknown even though someone else may already know the answer so there’s no sort of future thing. But will this turn out to be the answer to the question.
The core technology is the same. In both cases it’s learning from examples. In the case of Watson playing the TV show Jeopardy it takes hundreds of thousands of previous Jeopardy questions from the TV show having gone on for decades and learns from them. And what it’s learning to do is predict is this candidate answer to this question likely to be the correct answer. So it’s gonna come up with a whole bunch of candidate answers, hundreds of candidate answers, for the one question at hand at any given point in time. And then amongst all these candidate answers it’s going to score each one. How likely is it to be the right answer. And, of course, the one that gets the highest score as the highest vote of confidence – that’s ultimately the one answer it’s gonna give. It’s correct, I believe, about 90 or 92 percent of the time that it actually buzzes in to intentionally answer the question.
You can go on YouTube and you can watch the episode where they aired the, you know, the competition between the IBM’s computer Watson and the all time two human champions of Jeopardy. And it just rattles off one answer after another. And it doesn’t matter how many years you’ve been looking at – in fact, maybe the more years you’ve studied the ability or inability of computers to work with human language, the more impressive it is. It’s just rattling one answer after another. I never thought that in my lifetime I would have cause to experience that the way I did which was, “Wow, that’s anthropomorphic. This computer seems like a person in that very specific skill set. That’s incredible. I’m gonna call that intelligent.”
Directed/Produced by Jonathan Fowler, Elizabeth Rodd and Dillon Fitton