Following is the full transcript of digital visionary Kevin Kelly’s talk titled “How AI Can Bring On a Second Industrial Revolution” at TED conference.
Kevin Kelly – TED Talk TRANSCRIPT
I’m going to talk a little bit about where technology is going. And often technology comes to us, we’re surprised by what it brings.
But there’s actually a large aspect of technology that’s much more predictable, and that’s because technological systems of all sorts have leanings, they have urgencies, they have tendencies.
And those tendencies are derived from the very nature of the physics, chemistry of wires and switches and electrons, and they will make reoccurring patterns again and again.
And so those patterns produce these tendencies, these leanings. You can almost think of it as sort of like gravity.
Imagine raindrops falling into a valley. The actual path of a raindrop as it goes down the valley is unpredictable. We cannot see where it’s going, but the general direction is very inevitable: it’s downward.
And so these baked-in tendencies and urgencies in technological systems give us a sense of where things are going at the large form. So in a large sense, I would say that telephones were inevitable, but the iPhone was not. The Internet was inevitable, but Twitter was not.
So we have many ongoing tendencies right now, and I think one of the chief among them is this tendency to make things smarter and smarter. I call it cognifying — cognification — also known as artificial intelligence, or AI. And I think that’s going to be one of the most influential developments and trends and directions and drives in our society in the next 20 years.
So, of course, it’s already here. We already have AI, and often it works in the background, in the back offices of hospitals, where it’s used to diagnose X-rays better than a human doctor. It’s in legal offices, where it’s used to go through legal evidence better than a human paralawyer.
It’s used to fly the plane that you came here with. Human pilots only flew it seven to eight minutes, the rest of the time the AI was driving. And of course, in Netflix and Amazon, it’s in the background, making those recommendations. That’s what we have today.
And we have an example, of course, in a more front-facing aspect of it, with the win of the AlphaGo, who beat the world’s greatest Go champion. But it’s more than that. If you play a video game, you’re playing against an AI.
But recently, Google taught their AI to actually learn how to play video games. Again, teaching video games was already done, but learning how to play a video game is another step. That’s artificial smartness.
What we’re doing is taking this artificial smartness and we’re making it smarter and smarter. There are three aspects to this general trend that I think are underappreciated. I think we would understand AI a lot better if we understood these three things.
I think these things also would help us embrace AI, because it’s only by embracing it that we actually can steer it. We can actually steer the specifics by embracing the larger trend. So let me talk about those three different aspects.
The first one is: our own intelligence has a very poor understanding of what intelligence is.
We tend to think of intelligence as a single dimension, that it’s kind of like a note that gets louder and louder. It starts like with IQ measurement. It starts with maybe a simple low IQ in a rat or mouse, and maybe there’s more in a chimpanzee, and then maybe there’s more in a stupid person, and then maybe an average person like myself, and then maybe a genius.
And this single IQ intelligence is getting greater and greater. That’s completely wrong. That’s not what intelligence is — not what human intelligence is, anyway. It’s much more like a symphony of different notes, and each of these notes is played on a different instrument of cognition.
There are many types of intelligences in our own minds. We have deductive reasoning, we have emotional intelligence, we have spatial intelligence. We have maybe 100 different types that are all grouped together, and they vary in different strengths with different people.
And of course, if we go to animals, they also have another basket — another symphony of different kinds of intelligences, and sometimes those same instruments are the same that we have. They can think in the same way, but they may have a different arrangement, and maybe they’re higher in some cases than humans, like long-term memory in a squirrel is actually phenomenal, so it can remember where it buried its nuts. But in other cases they may be lower.
When we go to make machines, we’re going to engineer them in the same way, where we’ll make some of those types of smartness much greater than ours, and many of them won’t be anywhere near ours, because they’re not needed. So we’re going to take these things, these artificial clusters, and we’ll be adding more varieties of artificial cognition to our AIs. We’re going to make them very, very specific.
So your calculator is smarter than you are in arithmetic already. Your GPS is smarter than you are in spatial navigation. Google, Bing, are smarter than you are in long-term memory. And we’re going to take, again, these kinds of different types of thinking and we’ll put them into, like, a car.