Source: Technology Review
People who worry that we’re on course to invent dangerously intelligent machines are misunderstanding the state of computer science.
Yoshua Bengio leads one of the world’s preëminent research groups developing a powerful AI technique known as deep learning. The startling capabilities that deep learning has given computers in recent years, from human-level voice recognition and image classification to basic conversational skills, have prompted warnings about the progress AI is making toward matching, or perhaps surpassing, human intelligence.
Prominent figures such as Stephen Hawking and Elon Musk have even cautioned that artificial intelligence could pose an existential threat to humanity. Musk and others are investing millions of dollars in researching the potential dangers of AI, as well as possible solutions. But the direst statements sound overblown to many of the people who are actually developing the technology. Bengio, a professor of computer science at the University of Montreal, put things in perspective in an interview with MIT Technology Review’s senior editor for AI and robotics, Will Knight.
Should we worry about how quickly artificial intelligence is advancing?
There are people who are grossly overestimating the progress that has been made. There are many, many years of small progress behind a lot of these things, including mundane things like more data and computer power. The hype isn’t about whether the stuff we’re doing is useful or not—it is. But people underestimate how much more science needs to be done. And it’s difficult to separate the hype from the reality because we are seeing these great things and also, to the naked eye, they look magical.
Is there a risk that AI researchers might accidentally “unleash the demon,” as Musk has put it?
It’s not like somebody found some magical recipe suddenly. Things are much more complicated than the simple story some people would like to tell. Journalists would sometimes like to tell the story that someone in their garage will have this amazing idea, and then we have a breakthrough and have AI. Similarly, companies want to tell a nice little story that “Oh, we have this revolutionary technology that’s going to change the world—AI is almost here, and we are the company that’s going to deliver it.” That’s not at all how it works.
“We’re missing something big. We’ve been making pretty fast progress, but it’s still not at the level where we would say the machine understands. We are still far from that.”
What about the idea, central to these concerns, that AI could somehow start improving itself and then become difficult to control?
It’s not how AI is built these days. Machine learning means you have a painstaking, slow process of acquiring information through millions of examples. A machine improves itself, yes, but very, very slowly, and in very specialized ways. And the kind of algorithms we play with are not at all like little virus things that are self-programming. That’s not what we’re doing.
What are some of the big unsolved problems with AI?
Unsupervised learning is really, really important. Right now, the way we’re teaching machines to be intelligent is that we have to tell the computer what is an image, even at the pixel level. For autonomous driving, humans label huge numbers of images of cars to show which parts are pedestrians or roads. It’s not at all how humans learn, and it’s not how animals learn. We’re missing something big. This is one of the main things we’re doing in my lab, but there are no short-term applications—it’s probably not going to be useful to build a product tomorrow.
Another big challenge is natural language understanding. We’ve been making pretty fast progress in the past few years, so it’s very encouraging. But it’s still not at the level where we would say the machine understands. That would be when we could read a paragraph and then ask any question about it, and the machine would basically answer in a reasonable way, as a human would. We are still far from that.
What approaches beyond deep learning will be needed to create a true machine intelligence?
Traditional endeavors, including reasoning and logic—we need to marry these things with deep learning in order to move toward AI. I’m one of the few people who think that machine learning people, and especially deep learning people, should pay more attention to neuroscience. Brains work, and we still don’t know why in many ways. Improving that understanding has a great potential to help AI research.
And I think that neuroscience people would gain a lot from keeping track of what we do and trying to fit what they observe of the brain with the kinds of concepts we are developing in machine learning.
Did you ever think you’d have to explain to people that AI isn’t about to take over the world? That must be odd.
It’s certainly a new concern. For so many years, AI has been a disappointment. As researchers we fight to make the machine slightly more intelligent, but they are still so stupid. I used to think we shouldn’t call the field artificial intelligence but artificial stupidity. Really, our machines are dumb, and we’re just trying to make them less dumb.
Now, because of these advances that people can see with demos, now we can say, “Oh, gosh, it can actually say things in English, it can understand the contents of an image.” Well, now we connect these things with all the science fiction we’ve seen and it’s like, “Oh, I’m afraid!”
Okay, but surely it’s still important to think now about the eventual consequences of AI.
Absolutely. We ought to be talking about these things. The thing I’m more worried about, in a foreseeable future, is not computers taking over the world. I’m more worried about misuse of AI. Things like bad military uses, manipulating people through really smart advertising; also, the social impact, like many people losing their jobs. Society needs to get together and come up with a collective response, and not leave it to the law of the jungle to sort things out