Nick Bostrom on What Happens When Our Computers Get Smarter Than We Are? at TED Talks – Transcript
Nick Bostrom – Philosopher
I work with a bunch of mathematicians, philosophers and computer scientists, and we sit around and think about the future of machine intelligence, among other things. Some people think that some of these things are sort of science fiction-y, far out there, crazy. But I like to say, okay, let’s look at the modern human condition. This is the normal way for things to be.
But if we think about it, we are actually recently arrived guests on this planet, the human species. Think about if Earth was created one year ago, the human species, then, would be 10 minutes old. The industrial era started two seconds ago.
Another way to look at this is to think of world GDP over the last 10,000 years, I’ve actually taken the trouble to plot this for you in a graph. It looks like this. It’s a curious shape for a normal condition. I sure wouldn’t want to sit on it.
Let’s ask ourselves, what is the cause of this current anomaly? Some people would say it’s technology. Now it’s true, technology has accumulated through human history, and right now, technology advances extremely rapidly — that is the proximate cause, that’s why we are currently so very productive. But I like to think back further to the ultimate cause.
Look at these two highly distinguished gentlemen: We have Kanzi — he’s mastered 200 lexical tokens, an incredible feat. And Ed Witten unleashed the second superstring revolution. If we look under the hood, this is what we find: basically the same thing. One is a little larger, it maybe also has a few tricks in the exact way it’s wired. These invisible differences cannot be too complicated, however, because there have only been 250,000 generations since our last common ancestor. We know that complicated mechanisms take a long time to evolve. So a bunch of relatively minor changes take us from Kanzi to Witten, from broken-off tree branches to intercontinental ballistic missiles.
So this then seems pretty obvious that everything we’ve achieved pretty much, and everything we care about, depends crucially on some relatively minor changes that made the human mind. And the corollary, of course, is that any further changes that could significantly change the substrate of thinking could have potentially enormous consequences.
Some of my colleagues think we’re on the verge of something that could cause a profound change in that substrate, and that is machine superintelligence. Artificial intelligence used to be about putting commands in a box. You would have human programmers that would painstakingly handcraft knowledge items. You build up these expert systems, and they were kind of useful for some purposes, but they were very brittle, you couldn’t scale them. Basically, you got out only what you put in. But since then, a paradigm shift has taken place in the field of artificial intelligence.
Today, the action is really around machine learning. So rather than handcrafting knowledge representations and features, we create algorithms that learn, often from raw perceptual data. Basically the same thing that the human infant does. The result is AI that is not limited to one domain — the same system can learn to translate between any pairs of languages, or learn to play any computer game on the Atari console.
Now of course, AI is still nowhere near having the same powerful, cross-domain ability to learn and plan as a human being has. The cortex still has some algorithmic tricks that we don’t yet know how to match in machines.
So the question is, how far are we from being able to match those tricks? A couple of years ago, we did a survey of some of the world’s leading AI experts, to see what they think, and one of the questions we asked was, “By which year do you think there is a 50% probability that we will have achieved human-level machine intelligence?”
We defined human-level here as the ability to perform almost any job at least as well as an adult human, so real human-level, not just within some limited domain. And the median answer was 2040 or 2050, depending on precisely which group of experts we asked. Now, it could happen much, much later, or sooner, the truth is nobody really knows.
What we do know is that the ultimate limits to information processing in a machine substrate lie far outside the limits in biological tissue. This comes down to physics. A biological neuron fires, maybe, at 200 hertz, 200 times a second. But even a present-day transistor operates at the Gigahertz. Neurons propagate slowly in axons, 100 meters per second, tops. But in computers, signals can travel at the speed of light.