The evolution and future of AI | 1st | week 4 |.
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.