The evolution and future of AI
The original AI researchers were very interested in games because they were extremely complex. Huge numbers of possible positions and gains were available, yet they're simple in a certain way. They're simple in that the moves are well-defined, the goals are well-defined. So you don't have to solve everything all at once. With chess in particular, in the work on Deep Blue at IBM, what became apparent, what computers could do on our problem like that was bringing a massive amount of compute resource to do deeper searches, to investigate more options of moves in chess than was previously possible. Watson defeating jeopardy. So this was another crossover point, in the development of AI and cognitive computing. That the questions that IBM was able to answer with jeopardy were questions that weren't simply looking up in the database, and finding the answer somewhere. Rather it required information retrieval over lots of different information resources. Then the combining of these together using machine learning that could arrive at answers that went beyond what was simply written somewhere.
Now, our technology is so much better and so much more advanced that we're really ready to move on and to tackle much more challenging problems that have this ill-defined or messy nature.
Every industry from oil and gas, to healthcare, to media and entertainment, to retail are just being swamped by a tsunami of unstructured data. That can be multimedia, can be images, it can be video, it can be text. It's really the ability to understand that data that is becoming critical. One of the most valuable applications of cognitive computing is in the health domain. Medical providers, physicians, nurses, assistants face enormous challenges, leveraging all of the available information that's out there. The medical literature increases by about 700,000 articles every year.
There's already millions of journal articles out there, and today's imaging technologies produce very rich amount of information. In fact, a particular scan might have 5,000 images in it. You combine the image analysis with natural language understanding, and text analysis, leveraging the medical literature, leveraging the patient's medical history, the physician has got a lot more information and knowledge at their disposal to help them make the best diagnosis possible. Clearly, there is intersection of what the computer can do and what people are able to do. That gives you something that's better than each of them individually. What is going to be truly interesting is to see what is the best way for them to have really symbiotic type of interaction, taking advantage of each other's strengths to collectively solve a problem? Watson, it looks at another aspect of intelligence, and a much more difficult aspect of intelligence, that is language. You have to be able to interpret the questions and come up with the right answers no matter what the topic. So I think the ideal scenario for AI in the modern world is not to try and develop a system that completely autonomously handled every aspect of a problem but have a collaboration between machines doing what they do best and people doing what they do best. I can imagine that combination will do better than either one by themselves. We're constantly here looking for what's the next grand challenge problem we can take on? That's not just around the corner or a year away, but it's going to take a multi-year effort. When we get there, we'll have something that's valuable for the world.
Avinash C. Pillai
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