Ray Kurzweil: Future of Intelligence | MIT 6.S099: Artificial General Intelligence (AGI)

Welcome to MIT Course 6S099 Artificial General Intelligence. Today we have Ray Kurzweil. He is one of the world's leading inventors, thinkers and futurists with a 30 year track record of accurate predictions. Called the restless Genius by the Wall Street Journal and the ultimate thinking machine by Forbes Magazine, he was selected as one of the top entrepreneurs by Inc. Magazine which described him as the rightful heir to Thomas Edison. PBS selected him as one of the 16 revolutionaries who made America. Ray was the principal investigator of the first CCD flatbed scanner, the first omnifont optical character recognition, the first point to speech reading machine for the blind, the first text to speech synthesizer, the first music synthesizer capable of recreating the grand piano and other orchestral instruments, and the first commercially marketed large vocabulary speech recognition. Among Ray's many honors, he received a Grammy Award for outstanding achievements in music technology. He's the recipient of the National Medal of Technology, was inducted into the National Inventors hall of Fame, holds 21 honorary doctorates and honors from three US presidents. Ray has written five national best selling books including the New York Times bestsellers the Singularity is Near from 2005 and how to Create a Mind from 2012. He is co founder and chancellor of Singularity University and a Director of Engineering at Google, heading up a team developing machine intelligence and natural language understanding. Please give Ray a warm welcome.

It's good to be back. I've been in this lecture hall many times and walked the infinite corridor. I came here as an undergraduate in 1965. Within a year of my being here, they started a new major called computer science. It did not get its own course number. 6 1. Even biotechnology recently got its own course number. But how many of you are CS majors? Okay. How many of you do work in deep learning? How many of you have heard of deep learning? I came here first in 1962 when I was 14. I became excited about artificial intelligence. It had only gotten its name six years earlier, the 1956 Dartmouth Conference by Marvin Minsky and John McCarthy. So I wrote Minsky a letter. There was no email back then. And he invited me up. He spent all day with me as if he had nothing else to do. He was a consummate educator. And the AI field had already bifurcated into two warring camps. The symbolic school which Minsky was associated with and the connectionist school was not widely known. In fact, I think it's still not widely known that Minsky actually invented the neural net in 1953. But he had become negative about it, largely because There was a lot of hype that these giant brains could solve any problem. So the first popular neural net, the Perceptron, was being promulgated by Frank Rosenblatt at Cornell. So Minsky said, oh, where are you going now? I said, to see Rosenblatt at Cornell. I said, don't bother doing that. And I went there and Rosenblatt was touting the Perceptron, that it ultimately would be able to solve any problem. So I brought some printed letters that had a camera and it did a perfect job of recognizing them. As long as they were carrier 10 different type style didn't work at all. And he said, but don't worry, we can take the output of the Perceptron and feed it as the input to another perceptron and take the output of that and feed it to a third layer. And as we add more layers, it'll get smarter and smarter and generalize. And I said, that's interesting. Have you tried that? Well, no, but it's high in our research agenda. Things did not move quite as quickly back then as they do now. He died nine years later, never having tried that idea. Turns out to be remarkably prescient. I mean, he never tried multilayer neural nets. And all the excitement we see now about deep learning comes from a combination of two things. Many layer neural nets and the law of accelerating returns, which I'll get to a little bit later, which is basically the exponential growth of computing so that we can run these massive nets and handle massive amounts of data. It would be decades before that idea was tried. Several decades later, three level neural nets were tried. They were a little bit better. They could deal with multiple type styles, still weren't very flexible. Now it's not hard to add other layers. It's a very straightforward concept. There was a math problem, the disappearing gradient or the exploding gradient, which I'm sure many of you are familiar with. Basically you need to take maximum advantage of the range of values in the gradients, not let them explode or disappear and lose the resolution. That's a fairly straightforward mathematical transformation. With that insight, we could now go to 100 layer neural nets. And that's behind sort of all the fantastic games that we've seen recently. AlphaGo trained on every online game and then became a fair go player. It then trained itself by playing itself and soared past the best human. AlphaGo. Zero started with no human input at all, with within hours of iteration, soared past AlphaGo also soared past the best chess programs. They had another innovation. Basically you need to evaluate the quality of the board at each point. And they used another hundred layer neural net to do that evaluation. So there's still a problem in the field, which is there's a motto that life begins at a billion examples. One of the reasons I'm at Google is we have a billion examples for examples of pictures of dogs and cats that are labeled. So you got a picture of a cat and it says cat and then you can learn from it. And you need a lot of them. AlphaGo trained on a million online moves. The that's how many we had of master games. And that only created a sort of fair go player. A good amateur could defeat it. So they worked around that. In the case of Go by basically generating an infinite amount of data by having the system play itself. Had a chat with Demis Hassifas. You know, what kind of situations can you do that with? You have to have some way of simulating the world. So go or chess are. Even though go is considered a difficult game, it's a, you know, the definition of it exists on one page, so you can simulate it. That applies to math. I mean, math axioms are. Can be contained on a page or two. It's not very complicated. It gets more difficult when you have real life situations like biology. So we have biological simulators, but the simulators aren't perfect. So learning from the simulators will only be as good as the simulators. That's actually the key to being able to do deep learning on biology, autonomous vehicles. You need real life data. So the Waymo systems have gone three and a half million miles. That's enough data to then create a very good simulator. The simulator is really quite realistic because they had a lot of real world experience and they've gone a billion miles in the simulator. But we don't always have that opportunity to either create the data or have the data around. Humans can learn from a small number of examples. Your significant other, your professor, your boss, your investor can tell you something once or twice and you might actually learn from that. Some humans have been reported to do that. And that's kind of the remaining advantage of humans. Now there's actually no back propagation in the human brain. It doesn't use deep learning. It uses a different architecture. That same year, in 1962, I wrote a paper how I thought the human brain worked. There was actually very little neuroscience to go on. There was one neuroscientist, Vernon Mountcastle, that had something relevant to say, which is he did. I mean, there was the common wisdom at the time and there's still a lot of neuroscientists that say this, that we have all these different regions of the brain. They do different things. They must be different. There's V1 in the back of the head where the optic nerve spills into that can tell that that's a curved line, that's a straight line. Does these simple feature extractions on visual images. It's actually a large part of the neocortex. There's a fusiform gyrus up here which can recognize faces. We know that because if it gets knocked out through injury or stroke, people can't recognize faces. They will learn it again. With a different region of the neocortex is the famous frontal cortex, which does language and poetry and music. So these must work on different principles. He did autopsies on the neocortex in all these different regions and found they all looked the same. They had the same repeating pattern, same interconnections. He said, neocortex is neocortex. So I had that hint. Otherwise I could actually observe human brains in action, which I did from time to time. And there's a lot of hints that you can get that way. For example, if I ask you to recite the Alphabet, you actually don't do it from A to Z. You do it as a sequence of sequences. A, B, C, D, E, F, G, H, I, J, K. So we learn things as forward sequences of sequences forward. Because if I ask you to recite the Alphabet backwards, you. You can't do it unless you learn that as a new sequence. So these are all interesting hints. I wrote a paper that I that the neocortex is organized as a hierarchy of modules, and each module can learn a simple pattern. And that's how I got to meet President Johnson. And that initiated a half century of thinking about this issue. I came to MIT to study with Marvin Minsky. Actually, I came for two reasons. One, that Minsky became my mentor, which was a mentorship that lasted for over 50 years. The fact that MIT was so advanced it actually had a computer, which the other colleges I considered didn't have. It was an IBM 7094,32K of 36 bit words. So it's 150K of core storage, two microsecond cycle time, two cycles for instruction. So a quarter of a MIP and thousands of students and professors shared that one machine. In 2012, I wrote a book about this thesis. It's now actually an explosion of neuroscience evidence to support it. The European Brain Reverse Engineering Project. Has identified a repeating module of about 100 neurons. It's repeated 300 million times. So it's about 30 billion neurons in the neocortex. The neocortex is the outer layer of the brain. That's part where we do our thinking. And they can see in each module axons coming in from another module. And then the output ax, the single output axon of that module, goes as the input to another module. So we can see it organized as a hierarchy. It's not a physical hierarchy. The hierarchy comes from these connections. The neocortex is a very thin structure. It's actually one module thick. There's six layers of neurons, but it constitutes one module. And we can see that it learns a simple pattern and various reasons I cite in the book. The pattern recognition model it's using is basically a hidden Markov model. How many of you have worked with Markov models? Okay, that's usually no hands go up when I ask that question. But a Markov model is not. It is learned, but it's not backpropagation. It can learn local features. So it's very good for speech recognition. And the speech recognition work I did in the 80s used these Markov models. I became the standard approach because it can deal with local variations. So the fact that a vowel is stretched, you can learn that in a Markov model. It doesn't learn long distance relationships. That's handled by the hierarchy. And something we don't fully understand yet is exactly how the neocortex creates that hierarchy. But we have figured out how it can connect this module to this module. Does it then grow? I mean, there's no virtual communication or wireless communication. It's an actual connection. So does it grow an axon, you know, from one place to another, which could be inches apart? Actually, they all. All these connections are there from birth, like the streets and avenues of Manhattan. There's vertical and horizontal connections. So if it decides and how it makes that decision is still not fully understood that it wants to connect this module to this module. There's already a vertical, horizontal, and a vertical connection. It just activates them. We can actually see that now. And I can see that happening in real time on non invasive brain scans. So there's a tremendous amount of evidence that in fact, the neocortex is a hierarchy of modules that can learn. Each module learns a simple sequential pattern. And even though the patterns we perceive don't seem like sequences, they may seem three dimensional or even more complicated, they are in fact represented as sequences. But the complexity comes in with the hierarchy. So the neocortex emerged 200 million years ago with mammals. All mammals have a neocortex. It's one of the distinguishing features of mammals. These first mammals were small, they were rodents, but they were capable of a new type of thinking. Other non mammalian animals had fixed behaviors, but those fixed behaviors were very well adapted for their ecological niche. But these new mammals could invent a new behavior. So creativity and innovation was one feature of the neocortex. So a mouse is escaping a predator. Its usual escape path is blocked. It will invent a new behavior to deal with. It probably wouldn't work, but if it did work, it would remember it and would have a new behavior, and that behavior could spread virally through the community. Another mouse watching this would say to itself, that was really clever going around that rock. I'm going to remember to do that. And it would have a new behavior. Didn't help these early mammals that much because, as I say, the non mammalian animals were very well adapted to their niches and nothing much happened for 135 million years. But then, 65 million years ago, something did happen. There was a sudden, violent change to the environment. We now call it the Cretaceous extinction event. There's been debate as to whether it was a meteor or an asteroid. I mean, a meteor or a volcanic eruption. The asteroid or meteor hypothesis is in the ascendancy. But if you dig down to an area of rock reflecting 65 million years ago, the geologists will explain that it shows a very violent, sudden change to the environment. And we see it all around the globe. So it was a worldwide phenomenon. The reason we call it an extinction event is that's when the dinosaurs went extinct. That's when 75% of all the animal and plant species went extinct. And that's when mammals overtook their ecological niche. So to anthropomorphize, biological evolution said to itself, this neocortex is pretty good stuff. And it began to grow it. Now, mammals got bigger, their brains got bigger at an even faster pace, taking up a larger fraction of their body. The neocortex got bigger, even faster than that, and developed these curvatures that are distinctive of a primate brain, basically to increase its surface area. But if you stretched it out, the human neocortex is still a flat structure. It's about the size of a table napkin, just as thin. And it basically created primates, which became dominant in their ecological niche. Then something else happened. Two million years ago, biological evolution decided to increase the neocortex further and increase the size of the enclosure and. And basically filled up the frontal cortex with our big skulls with more neocortex. And up until recently, it was felt, as I said, that this was. The frontal cortex was different because it does these qualitatively different things. But we now realize that it's really just additional neocortex. So remember what we did with it. We were already doing a very good job of being primates, so we put it at the top of the neocortical hierarchy. And we increased the size of the hierarchy. It was maybe 20% more neocortex, but it doubled or tripled the number of levels. Because as you go up the hierarchy, it's kind of like a pyramid. There's fewer and fewer modules. And that was the enabling factor for us to invent language and art, music. Every human culture we've ever discovered has music. No primate culture really has music. There's debate about that, but it's really true. Invention, technology, technology required another evolutionary adaptation, which is this humble appendage here. No other animal has that. If you look at a chimpanzee, it looks like they have a similar hand, but the thumb is actually down here. The. It doesn't work very well if you watch them trying to grab a stick. So we could imagine creative solutions. Yeah, I could take that branch and strip off the leaves and put a point on it, and we could actually carry out these ideas and create tools and then use tools to create new tools, and started a whole nother evolutionary process of tool making. And that all came with the neocortex. So Larry Page read my book in 2012 and liked it. So I met with him and asked him for an investment in a company I'd started actually a couple weeks earlier to develop those ideas commercially, because that's how I went about things as a serial entrepreneur. And he said, well, we'll invest, but let me give you a better idea. Why don't you do it here at Google? We have a billion pictures of dogs and cats and we got a lot of other data and lots of computers and lots of talent, all of which is true. I said, well, I don't know. I just started this company to develop this. We'll buy your company. And how are you going to value a company that hasn't done anything? Just started a couple weeks ago. And he said, we can value anything. So I took my first job five years ago, and I've been basically applying this model, this hierarchical model to understanding language, which I think really is the holy grail of AI. I think Turing was correct in designating basically text communication as what we now call a Turing complete problem. That requires there's no simple NLP tricks that you can apply to pass a valid Turing test, with an emphasis on the word valid. Mitch Kaper and I had a six month debate on what the rules should be. Because if you read Turing's 1950 paper, he describes this in a few paragraphs and doesn't really describe how to go about it. But if it's a valid Turing test, meaning it's really convincing you through interrogation and dialogue that it's a human that requires a full range of human intelligence. And I think that test has stood the test of time. We're making very good progress on that. I mean, just last week you may have read that two systems passed a paragraph comprehension test. It's really very impressive. When I came to Google, we were trying to pass these paragraph comprehension tests. We aced the first grade test, second grade test, we kind of got average performance. And the third grade test had too much inference already. You had to know some common sense knowledge, as it's called, and make implications of things that were in different parts of the paragraph. And there's too much inference and it really didn't work. So this is now adult level just slightly surpassed average human performance. But we've seen that once something an AI does something at average human levels, it doesn't take long for it to soar past average human levels. I think it'll take longer in language than it did in simple games like Go. But it's actually very impressive that it surpasses now average human performance. It's used in LSTM Long Short Temporal Memory. But if you look at the adult test, in order to answer these questions, it has to put together inferences and implications of several different things in the paragraph with some common sense knowledge that's not explicitly stated. So that's I think a pretty impressive milestone. So I've been developing, I've got a team of about 45 people and we've been developing this hierarchical model. We don't use Markov models because we can use deep learning for each module. And so we create an embedding for each word and we create embedding for each sentence. This we have a can talk about it because we have a published paper on it. It can take into consideration context. If you use Smart Reply, if you use Gmail on your phone, you'll see it gives you three suggestions for responses. That's called Smart Reply. They're simple suggestions, but it has to actually understand perhaps a complicated email. And the quality of the suggestions is really quite good. Quite on point. That's for my team, using this kind of hierarchical model. So instead of Markov models, it uses embeddings. Because we can use backpropagation, we might as well use it. But I think what's missing from deep learning is this hierarchical aspect of understanding, because the world is hierarchical. That's why evolution developed a hierarchical brain structure, to understand the natural hierarchy in the world. And there are several problems with big deep neural nets. One is the fact that you really do need a billion examples, and we don't. Sometimes we can generate them, as in the case of Go, or if we have a really good simulator, as in the case of autonomous vehicles. Not quite the case yet. In biology, very often you don't have a billion examples. We certainly have billions of examples of language, but they're not annotated. And how would you annotate it anyway? With more language that we can't understand in the first place. So it's kind of a chicken and an egg problem. So I believe this hierarchical structure is needed. Another criticism of deep neural nets is they don't explain themselves very well. It's a big black box that gives you pretty remarkable answers. I mean, in the case of these games Demis described, it's playing in both Go and chess is almost an alien intelligence, because it would do things that were shocking to human experts, like sacrificing a queen and a bishop at the same time, or in close succession, which shocked everybody, but then went on to win. Or early in a Go game, putting a piece at the corner of the board, which is kind of crazy to most experts because you really want to start controlling territory. And yet, on reflection, that was the brilliant move that enabled it to win that game. But it doesn't really explain how it does these things. If you have a hierarchy, it's much better at explaining it because you could look at the content of the modules in the hierarchy and they'll explain what they're doing. And just to end on the first application of applying this to health and medicine, this will get into high gear and we're going to really see us break out of the linear extension to longevity that we've experienced. I believe we're only about a decade away from longevity escape velocity. We're adding more time than is going by, not just the infant life expectancy, but to your remaining life expectancy. I think if someone is diligent, they can be there already. I think I'VE at longevity escape velocity. Now a word on what life expectancy means. It used to be assumed that not much would happen. So whatever your life expectancy is, with or without scientific progress, it really didn't matter. Now it matters a lot. So life expectancy really means how long would you live in terms of a statistical likelihood if there were not continued scientific progress? But that's a very inaccurate assumption that scientific progress is extremely rapid. I mean, just as an AI in biotech there are advances now every week. It's quite stunning. Now you could have a computed life expectancy, let's say 30 years, 50 years, 70 years from now. You could still be hit by the proverbial bus tomorrow. We're working on that with self driving vehicles. But we'll get to a point and I think if you're diligent, you can be there now in terms of basically advancing your own statistical life expectancy at least to keep pace with the passage of time. I think it will be there for most of the population at least if they're diligent, within about a decade. So if you can hang in there, we may get to see the remarkable century ahead. Thank you very much.

A question, Please raise your hand and we'll get you a mic.

Hi.

So you mentioned both neural network models and symbolic models. And I was wondering how far have you've been thinking about combining these two approaches, creating a symbiosis between neural models and symbolic ones?

I don't think we want to use symbolic models as they've been used. How many are familiar with the psych project that was a very diligent effort in Texas to define all of common sense reasoning and it kind of collapsed on itself and became impossible to debug because you'd fix one thing and it would break three other things. That complexity ceiling has become typical of trying to define things through logical rules. Now it does seem that humans can understand logical rules. We have logical rules written down for things like logic and game playing and so on. But you can actually define a connectionist system to have such a high reliability on a certain type of action that it looks like it's a symbolic rule even though it's represented in a connectionist way. And connection systems can both capture the soft edges. Because many things in life are not sharply defined. They can also generate exceptions. So you don't want to sacrifice your queen in chess except certain situations that might be a good idea. So you can capture that kind of complexity. So we do want to be able to learn from accumulated human wisdom that Looks like it's symbolic, but I think we'll do it with a connection system. But again, I think the connection systems should develop a sense of hierarchy and not just be one big massive neural net.

So I understand how we want to use the neocortex to extract useful stuff and commercialize that, but I'm wondering how our middle brain and the organs that are below the neocortex will be useful for turning that into if you want to do so.

Well, the cerebellum is an interesting case in point. It actually has more neurons than the neocortex and it's used to govern most of our behavior. Some things if you write a signature that's actually controlled by the cerebellum. So a simple sequence is stored in the cerebellum, but there's not any reasoning to it. It's basically a script. Most of our movement now has actually been migrated from the cerebellum to the neocortex. Cerebellum is still there. Some people, entire cerebellum is destroyed through disease. They still function fairly normally. Their movement might be a little erratic as our movement is largely controlled by the neocortex. But some of the subtlety is a kind of pre programmed script. And so they'll look a little clumsy, but they actually function okay. A lot of other areas of the brain control autonomic functions like breathing, but our thinking really is controlled by the neocortex. In terms of mastering intelligence, I think the neocortex is the brain region we want to study.

I'm curious what you think might happen after the singularity is reached in terms of this exponential growth of information. Yeah. Do you think it will continue or will there be a whole paradigm shift? What do you predict?

Well, in the singularities near, I talk about the atomic limits based on molecular computing as we understand it, and it can actually go well past 2045 and actually go to trillions of trillions of times greater computational capacity than we have today. So I don't see that stopping anytime soon and will go way beyond what we can imagine. And it becomes an interesting discussion what the impact on human civilization will be. So to take a maybe slightly more mundane issue that comes up is, oh, it's going to eliminate most jobs or all jobs. The point I make is it's not the first time in human history we've done that. How many jobs circa 1900 exist today? And that was the feeling of the Luddites, which was an actual society that formed in 1800 after the automation of the textile industry in England. They looked at all these jobs going away and felt, oh, employment's going to be just limited to an elite. Indeed, those jobs did go away, but new jobs were created. So if I were a PRUSSIAN FUTURIST In 1900, I would say, well, 38% of you work on farms and 25% work in factories. That's two thirds of the working force. But I predict by 2015, 115 years from now, it's going to be 2% on farms and 9% in factories. And everybody would go, oh, my God, we're going to be out of work. And I said, well, don't worry. For all these jobs we eliminate through automation, we're going to invent new jobs. And people say, oh, really? What new jobs? And I'd say, well, I don't know, we haven't invented them yet. That's the political problem. We can see jobs very clearly going away fairly soon, like driving a car or a truck. And the new jobs haven't been invented. I mean, just look at the last five or six years. Many a lot of the increase in employment has been through.