What's coming up in AI in 10 years?

An interview with Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence in Seattle.

On the limitations of AI:

AI systems can make limited black and white distinctions. Understanding is more difficult. Allen asked me at first, “Is it possible to give an artificial intelligence a reference book to read and then ask it questions?” It is presumably a simple activity, but the answer was no. We have been working on it and there’s progress but it is still a difficult problem.

The problem of common sense reasoning is one aspect, and a very important one, of this limitation. In the near term:

Q: Okay, so without being overly optimistic or pessimistic: Where are we going in ten years?

A: The best way to think ten years ahead is to look ten years back. During this time, in the micro, things changed like we have moved past the iPhone 3. But on the macro scale, not much has changed. In ten years, we are still going to be building AI systems that are narrow, that can play Go, for example, and win. Maybe they will also recognize faces and diagnose certain diseases. AI will be able to carry out those tasks in a superhuman way. But wider capabilities, the ones we think of as intelligence, such as understanding a situation or context, will be much harder to achieve. In 1996, the computer system Deep Blue beat Garry Kasparov in a game of chess. It can play the best chess game in the world, all the while the room is on fire, and not notice a thing. Today we have a program that can be the world champion of Go, which is a much more complicated game, while the room is on fire.

Q: Meaning that AI still cannot tell what is happening around it.

A: Yes. There has been no progress in its ability to understand what is happening around it. I expect that ten years from now, maybe there will be a program that beat the best Minecraft player in the world but it still won’t notice that the room is on fire. That’s where it needs us. That is why we need to aim for intelligence that enhances human capabilities, that works in tandem with people. [...] There’s a paradox that people tend to miss: things that are difficult for people are easy for machines and things that are difficult for machines are easy for people. The real world, real people, real speech, books—these are a lot harder than Go.