Erik Mueller was one of the original engineers behind IBM's Watson, who left last year to go out to start his own consulting company. I had the chance to ask Erik some questions about his views on A.I. related topics. The interview is published below.
1. You were part of the original Watson team - how did that project come together and what was your role on the project?
I joined the group working on Watson on February 12, 2010, a year before Watson beat the human Jeopardy champions. I contributed the UnitQuestionAnswerScorer and StoryProblemDetector components, helped improve the ESG parsing, and helped with LAT detection. In addition to helping Watson win Jeopardy, I was one of three researchers in charge of getting Watson ready for its application to medicine—the others were Tony Levas and Sugato Bagchi. After Jeopardy, several members of the group joined Tony, Sugato, and me to continue work on healthcare. We created two systems. Watson for Healthcare is described in our AI Journal article. My main contribution to this was the SKB structured medical knowledge base, which was mined from medical texts. WatsonPaths is described in an IBM technical report. I created custom models for specific types of medical subquestions, like going from a disease to a finding or the reverse.
2. What was the most difficult problem to solve when building Watson?
There wasn’t any single difficult problem to solve when building Watson. Once the basic architecture was in place, the main problem was driving up its performance to the desired level. This involved a repeated process of finding and fixing errors that the system or components of the system were making.
3. Deep learning is all the rage in A.I., but Watson is more of a "cognitive computing" approach. How would you explain such an approach to an audience of business executives?
I would urge them to be open-minded in considering both approaches and how they can be combined. Watson exploits a large body of documents, whereas deep learning exploits a large number of training examples. (Watson uses a smaller number of training examples to learn to rank answers.) Deep learning can be used to build better components within Watson.
4. When you and I last spoke, we talked about how humans reason symbolically. Yann LeCun at Facebook has implied that "thought vectors" will basically solve AI. You seem to be one of the few skeptics here. What do you think an eventual solution to general A.I. looks like, technically? Will it be deep learning? Symbolic? Or even something that we haven't solved yet?
That’s interesting. He may be right. I’m starting to think that there’s less difference between symbolic AI and neural nets than I originally thought. In my new book, Transparent Computers, I talk about the fact that we can implement reasoning—and the explanation capability that comes along with reasoning—a number of different ways. We can use symbolic inference rules to generate each step in a reasoning chain, and we can also use a neural network to do this. I think we’re still at the early stages of working all this out. There’s a lot that needs to be invented.
5. If you were going to start a PhD today, and were interested in A.I., what problem or technology would you choose to focus?
Right now, computers are ubiquitous, but they’re getting more and more annoying. I’m very interested in how we create intelligent systems that we can understand and that are transparent.
6. I wrote a post about OpenAI being a dangerous thing, actually causing the problem it is trying to solve. Do you agree or disagree?
I think your analysis is dead on.
7. Do you have any concerns about technology and the future of A.I.?
I’m keeping an eye on it. There have been some amazing results recently that seem to indicate AI may be progressing faster than we thought it would. On the other hand, the problem of narrative understanding is as far from being solved today as it ever was. All you have to do is take three sentences at random from a newspaper and think about what it would take for a computer to understand them. The training data needed to get a neural net to understand those three sentences is fairly large. Think about the training data needed to understand arbitrary sequences of sentences. I don’t think researchers have an appreciation for how large the space is.
8. What is the most interesting advancement/research you've seen in A.I. in the past 12 months?
I’m impressed with the work at Facebook on using memory networks to understand narratives. It’s amazing that this works at all. But the narratives are very simple and a far cry from naturally-occurring text.
9. What business opportunities do see for cognitive computing to have the most impact? Any areas where it isn't being applied that entrepreneurs should be considering?
There are lots of applications in healthcare. One area that I'm very interested in is increasing transparency in medicine. When you or your loved ones are in the hospital, it's hard to understand what's going on. You need to be able to see exactly where you are in diagnosis and treatment protocols using easy-to-understand diagrams. Domain-specific Siri-like intelligent assistants is another big area.
10. You left IBM last year to consult on your own, what kinds of work are you doing and what types of clients are you accepting?
I help clients use cognitive computing to solve a range of business problems. I’m seeing the most activity in the following areas: automotive applications, healthcare, Twitter analytics, entertainment, sports, advertising, data analytics, insight, quantitative trading, query expansion, education, tutoring systems, conversational agents, and highly-focused intelligent assistants. I really like it because there’s so much variety.