Insights from Ray Kurzweil on AI Future

All right, I'm so excited to be here with you, Ray. It's great to be here. Great to see everybody together. Yeah. Beautiful audience. So my favorite thing in that introduction of you is that you have been working in AI longer than any other human alive, which means if you live forever... and we'll get to that, you will always have that distinction. I think that's right. Marvin Minsky was actually my mentor. If he were alive today, he would actually be more than 61 years. And we're going to bring him back also. So I'm not sure how we'll count the distinction then. All right, so we're going to fix the audio, but this is what we're going to do with this conversation. I'm going to start out asking Ray some questions about where we are today. We'll do that for a few minutes, then we'll get into what has to happen to reach the singularity, so the next 20 years. get into a discussion about what the singularity is, what it means, how it changed our lives, and then at the end we'll talk a little bit about how, if we believe this vision of the future, what it means for us today. Ask your questions, they'll come in, I'll ask them as they go in the different sections of the conversation, but let's get cracking. Can you hear me? You can't hear Ray. Well, This will be recorded. You guys are going to all live forever. There will be plenty of time. It will be fine. I'm just going to get started. I assume the audio will get worked out. They do a fabulous job here at South By. I think they should be able to hear me. All right. We got this over on the right. Audio engineers, are we good to go? We're good to go. All right. All right, first question, Ray. So, you've been working in AI for 61 years. Wait, can you hear me? Let's not... So everybody in the front can hear you, but nobody in the back can hear you. Can you hear me now? All right. I'll speak louder. First question. So you've been living in the AI revolution for a long time. You've made lots of predictions, many of which have been remarkably accurate. We've all been living in a remarkable two-year transition. transformation with large language models a year and a half. What has surprised you about the innovations in large language models and what has happened recently? Well, I did finish this book a year ago and didn't really cover large language models, so I delayed the book to cover that. And I'm not sure if you can see it. I was expecting this that to happen like a couple of years later. I mean I made a prediction in 1999 that would happen by 2029 and we're not quite there yet but we will but it looks like it's maybe a year or two ahead of schedule. So there was maybe a bit of a surprise. Wait, you predicted back in 1999 that a computer would pass the Turing test in 2029. Are you revising that to something more closer to today? No, I'm still saying 2029. The definition of the Turing test is not precise. We're going to have people claiming that the Turing test has been solved, and people are saying that GPT-4 actually passes it, some people. So it's going to be like maybe two or three years where people start claiming, and then they continue to claim, and finally everybody will accept it. So it's not like it happens in one day. But you have a very specific definition of the Turing test. When do you think we'll pass that definition? Well, the Turing test is actually not that significant because that means that you can... A computer will pass for a human being. And what's much more important is age. AGI, automatic general intelligence, which means it can emulate any human being. So you have one computer, and it can do everything that any human being can do. And that's also 2029. It all happens. at the same time. But nobody can do that. I mean, just take an average large language model today. You can ask it anything, and it will answer you pretty convincingly. No human being can do all of that, and it does it very quickly. It will write a very nice essay in 15 seconds, and then you can ask it again, and it will write another essay, and no human being can actually perform at that level. Right, so you have to dumb it down to actually do it. actually have a convincing Turing test. To have a Turing test, you have to dumb it down. Yeah. Let me ask the first question from the audience, since I think it's quite relevant to where we are, which is Brian Daniel. Is the Kurzweil curve still accurate? Is the Kurzweil curve still accurate? Yes, in fact, can I see that? Let's pull the slides up. First slide. So this is an 80-year track record. This is an exponential growth. A straight line on this curve means exponential curvature. If it was sort of exponential, but... Not quite. It would curve. This is actually a straight line. It started out with a computer that did 0.0000007 calculations. calculations per second per constant dollar. That's the lower left-hand corner. At the upper right-hand corner, it's 65 billion calculations per second for the same amount of money. So that's why large language models have only been feasible for two years. We actually had large language models before that, but it didn't work very well. And this is an exponential curve. Technology moves in an exponential curve. We see that, for example, having renewable energy come from the sun and wind. That's actually an exponential curve. It's increased. It's gone. We've decreased the price by 99.7%. We've multiplied the amount of energy coming from solar energy a million-fold. So this kind of curve really directs all kinds of technology. And this is the reason that we're making progress. I mean, we knew how to do large language models years ago, but we're dependent on this curve. And it's pretty amazing. It started out increasing relay speeds, then vacuum tubes, then integrated circuits, and each year it makes the same amount of progress, approximately, regardless of where you are on this curve. We just added the last point. And it's, again, we basically multiply this by two every 1.4 years. And this is the reason that computers are exciting, but it actually affects every type of technology. And we just added the last point like two weeks ago. All right, so let me ask you a question. You know, you wrote a book about how to build a mind. You have a lot about how the human mind is constructed. A lot of the progress in AI, AI systems are being built on what we understand about neural networks, right? So clearly our understanding... of this helps with AI. In the last two years, by watching these large language models, have we learned anything new about our brains? Are we learning about the insides of our skulls as we do this? It really has to do with the amount of connection The brain is actually organized fairly differently. The things near the eye, for example, deal with vision. And we have different ways of implementing different parts of the brain that remember different things. We actually don't need that. In a large language model, all the connections are the same. We have to get the connections up to a certain point. If it approximately matches what the brain does, which is about a trillion times more trillion connections. It will perform kind of like the brain. We're kind of almost at that point. Wait, so you think? The GPT-4 is 400 billion. The next ones will be a trillion or more. So the construction of these models, they are more efficient in their construction than our brains are. We make them to be as efficient as possible, but it doesn't really matter how they're organized. And we can actually create certain software that will actually expand the amount of connections more for the same amount of computation. But it really has to do with how many connections are... A particular computer is responsible for. So as we approach AGI, we're not looking for a new understanding of how to make these machines more efficient. The transformer architecture was clearly very important. We can really just... get there with more computing. But the software and the learning is also important. I mean, you could have a trillion connections, but if you didn't have something to learn from, it wouldn't be very effective. So we actually have to be able to collect all this. this data, so we do it on the web and so on. I mean, we've been collecting stuff on the web for several decades. That's really what we're depending on, to be able to train these large language models. And we shouldn't actually call them large language models, because they deal with much more than language. I mean, it's language, but you can add pictures. You can add things that affect disease that have nothing to do with language. In fact, we're using now simulated biology to to be able to simulate different ways to affect disease and that has nothing to do with language. So they really should be called large event models. Do you think there's anything that happens inside of our brains that cannot be captured by computation and by math? No, I mean what would that be? I mean Okay, quick poll of the audience. Raise your hand if you think there's something in your brain that cannot be captured by computation or math like a soul all right so convince them that they're wrong right I mean consciousness is very important but it's actually not scientific There's no way I could slide somebody in and the light will go on. Oh, this one's conscious. No, this one's not. It's not scientific, but it's actually extremely important. And another question, why am I me? How come what happens to me I'm conscious of and I'm not conscious of what happens to you? These are deeply mysterious things, but they're really not. not conscious. So Marvin Minsky, who was my mentor for 50 years, he said, it's not scientific and therefore we shouldn't bother with it. And any discussion of consciousness he would kind of dismiss. But he actually did, his reaction to people was totally dependent on whether he felt they were conscious or not. So he actually did use that. But it's not something that we're ignoring because there's no way to tell to tell whether something's conscious. And that's not just something that we don't know and we'll discover. There's really no way to tell whether or not something's conscious. What do you mean? Like, this is not conscious, and the gentleman sitting right there is conscious. I'm pretty confident. Well, how do you prove that? I mean we kind of agree with humans, that humans are conscious. Some humans are conscious, not all humans. But how about animals? We have big disagreements. Some people say animals are not conscious, and other people think animals are conscious, maybe some animals are conscious and others are not. There's no way to prove that. Okay, I want to run down this consciousness question. we do that I want to make sure I understood your previous answer correctly. So the feeling I get of being in love or the feeling any emotion that I get could eventually be represented in math in a large language model. Yeah I mean certainly the behavior the feelings that you have if you're with somebody that you love is definitely dependent on what the connections do you can tell within or not that's happening. All right. And back to... Is everybody here convinced? Not entirely. All right, well, close enough. So you don't think that it's worth trying to define consciousness. I mean, you spend a fair amount in your book giving different arguments about what consciousness means, but it seems like you're arguing on stage that we shouldn't try to define it. There's no way to actually prove it. I mean, we have certain agreements. I agree that all of you are conscious. You actually made it into this room, so that's a pretty good indication that you're conscious. But that's not a proof. And there may be human beings that don't seem quite conscious at the time. Are they conscious or not? And animals, I mean, I think elephants and whales are conscious, but not everybody agrees with that. So at what point can we then essentially, how long will it be until we can essentially download the entire contents of your brain and express it through some kind of a machine? That's actually an important question because we're going to talk about longevity. We're going to get to a point where we have longevity escape velocity. And it's not that far away. I think if you're diligent, you'll be able to achieve that by 2029. That's all. only five or six years from now. So right now, you go through a year, use up a year of your longevity, but you get back from scientific progress, right now, about four months. But that scientific progress is on an exponential curve. It's going to speed up every year. And by 2029, if you're diligent, you'll use up a year of your longevity with a year passing, but you'll get back a full year. And past 2029, you'll get back more. than a year, so you'll actually go backwards in time. Now that's not a guarantee of infinite life because You could have a 10 year old and you could compute his longevity. It's many, many decades and he could die tomorrow. But what's important about actually capturing everything in your brain, we can't do that today. And we won't be able to do that in five years. But you will be able to do that by the singularity, which is 2045. And so at that point, you can actually go inside the brain and capture everything in there. Now, your thinking is going to... to be a combination of the amount you get from computation which will add to your thinking and that's automatically captured. I mean right now anything that you have in a computer is automatically captured today and the kind of additional thinking we'll have by adding to our brain, that will be captured. But the connections that we have in the brain are not necessarily the same. that we have in the brain that we start with, we'll still have that. That's not captured today, but that will be captured in 2045. We'll be able to go inside the brain and capture that as well. And therefore, we'll actually capture the entire brain, which will be backed up. So even if you get wiped out, you walk into a bomb and it explodes, we can actually recreate everything that was in your brain. by 2045. That's one of the implications of the singularity. Now, that doesn't absolutely guarantee, because, I mean, the world could blow up and all the... Computer, all the things that contain computers could blow up and so you wouldn't be able to recreate that. So we never actually get to a point where we absolutely guarantee that... you live forever, but most of the things that right now would upset capturing that will be overcome by that time. There's a lot there, Ray. Let's start with escape velocity. So do you think that anybody in this audience in their current biological body will live to be 500 years old? You're asking me? Yeah. Absolutely. I mean if you're gonna be alive in five years, and I imagine all of you will be alive in five years. If they're alive for five years they will likely live to be 500 years old. If they're diligent and I think the people in this audience will be diligent. Wow, all right, well you can drink whatever you want as long as you don't get run over tonight because you don't have to worry about decline. All right, so let me ask a question. I want to get, we're going to spend a lot of time on what the singularity is, what it means, and what it'll be like, but I want to ask some questions that'll lead us up there. So I'm going to take this question from Mark Sternberg and modify it slightly. In the time frame, AI will be able to do, or sufficiently sophisticated computers, in your argument, can do everything that the human brain can do. What will they not be able to do in the next 10 years? Well. One thing has to do with being creative. Some people will be able to do everything a human can do, but they're not going to be able to create new knowledge. That's actually wrong, because we can simulate, for example, And the Moderna vaccine, for example, we didn't do it the usual way, which is somebody sits down and thinks, well, I think this might work. And then they try it out. It takes years to try it out on multiple people. one person's idea about what might work, they actually listed everything that might work. And there was actually several billion different mRNA sequences. And they said, let's try them all. And they tried every single one by simulating biology. And that took two days. So one weekend, they tried out several billion different possibilities. And then they picked the one that turned out to be the best. And that actually was the... Moderna vaccine up until today. Now, they did actually test it on humans. We'll be able to overcome that as well because we'll be able to test using simulated biology as well. They actually decided to test it. It's a little bit hard to give up testing on humans. humans, we will do that. So you can actually try out every single one, pick the best one, and then you can try out that by testing on a million simulated humans and do that in a few days as well. And that's actually the future of how we're going to create medications for diseases. And there's lots of things going on now with cancer and other diseases that are using that. So that's a whole new method. It's actually starting now. It started right here with the Moderna vaccine. We did another cure for a mental disease. It's actually now in stage three trials. That's going to be how we create medications from now on. But what are the frontiers? What can we not do? So that's the question. where a computer is being creative. And it's not just actually trying something that occurs to it. It makes a list of everything that's possible and tries it all. Is that creativity or is that just brute force with maximum capability? It's much better than any other form of creativity. And yes, it's creative, because you're trying out every single possibility, and you're doing it very quickly, and you come up with something that we didn't have before. I mean, what else would create... creativity be. All right. So we're going to cross the frontier of creativity. What will we not cross? What are the challenges that will be outstanding the next ten years? Well, we don't know everything, and we haven't gone through this process. It does require some creativity to make it happen. imagine what might work. And we have to also be able to simulate it in a biochemical simulator. So we actually have to figure that out. And we'll be using people for a while to do that. So we don't know everything. I mean, to be able to do everything a human being can do is one thing, but there's so much we don't know that we want to find out. And that requires creativity. That will require... some kind of human creativity working with machines. All right, let's go back to what's going to happen to get us to the singularity. So clearly, we have the chart that you showed on the power of compute. It's been very... very steady, moving straight up on a logarithmic scale on a straight line. There are a couple of other elements that you think are necessary to get to the singularity. One is the rise of nanobots, and the other is the rise of brain machine interfaces. And both of those have gone more slowly than AI. So convince the audience that. Well, it would be slow, because any time you affect the human body, a lot of people are gonna be concerned about it. If we do something with computers, we have a new algorithm, or we increase the speed of it, nobody really is concerned about it. You can do that. Nobody cares about any dangers in it. I mean, that's the reality. There's some dangers that people care about. Yeah, but it goes very far. very quickly. That's one of the reasons it goes so fast. But if you're affecting the body, we have all kinds of concerns that it might affect it negatively. So we want to actually try it on people. But the reason brain-machine interfaces haven't moved in an exponential curve isn't just because you know, lots of people are concerned about the risks to humans. I mean, as you explain in the work, in the book, they just don't work as well as they could. Um, if we could try things out without having to test it, it would go a lot faster. I mean, that's the reason it goes slowly. Um, but, um, There's some thought now that we could actually figure out what's going on inside the brain and put things into the brain without actually going inside the brain. We would need something like Brain Link. We could just... I mean, there's some tests where we can actually tell what's going on in the brain without actually putting something inside the brain. And that might actually be a way to do this much more quickly. But your prediction about the singularity depends, maybe I'm reading it wrong, not just on the continued exponential growth of compute, but on solving this particular problem too, right? Yes, because we want to increase the amount of intelligence that humans can command. So we have to be able to marry the best computers with our actual brain. And why do we have to do that? Because like... right now, here I go, I have my phone. In some ways, this augments my intelligence. It's wonderful. Yeah, but it's very slow. I mean, if I ask you a question, you're going to have to type it in or speak it, and it takes a while. I mean, I ask a question, and then people fool around with their computer. It might take 15 seconds or 30 seconds. It's not like it just goes right into your brain. I mean these are very useful. These are brain extenders. We didn't have these a little while ago. Generally in my talks I ask people who here has their phone. I'll bet here maybe there's one. two people but everybody here has their phone. That wasn't true five years ago, definitely wasn't true ten years ago. And it is a brain extender but it does have some speed problems. So we want to increase that speed. A question could just come up while we're talking and the computer would instantly tell you what the answer is without you having to fool around with an external device. And that's almost feasible today. And something like that would be helpful to do this. But could you not get a lot of the good that you talk about if we just kept, the problem with connecting our brains to the machines is suddenly you're in this whole world. these complicated privacy issues where stuff is being injected in my brain, stuff in my brain is going elsewhere. You're opening up a whole host of ethical, moral, existential problems. Can't you just make the phones a lot better? Well, that's the idea, that we can do that without having to go inside your brain but be able to tell what's going on in your brain externally without going inside the brain. you know with some kind of device all right well let's keep moving into the future so we're moving into the future we have exponential growth computer we solve a way of you know ideally figuring out how to communicate directly with your brain to speed things up explain why nanobots are essential to your vision of where we're going well if you really want to tell what's going on inside the brain you've got to be able to go at the level of the particles in the brain so we can actually tell what they're doing And that's feasible. We can't actually do it, but we can show that it's feasible. And that's one possibility. We're actually hoping that you could do this without actually affecting the brain at all. OK. All right, so we're pushing ahead. We've got nanobots that run around inside of our brain. They're understanding our head. They're extracting thoughts. They're inputting thoughts. Let's go to this nice question, which fits in lovely, from Luis Candreva. What are the five main ethical questions that we will face as that happens? Is four enough? Four is fine. There might even be six, Ray, but you can give us four. I mean, we're going to have a lot more power if we can actually, with our own brain, control computers. Does that give people too much power? Also, I mean, right now we talk about having a certain amount of value based on your... ...on your talent. This will give talent to people who otherwise don't have talent. And talent won't be as important because you'll be able to gain talent just by merging with the right kind of large language model, or whatever we call them. It also seemed kind of arbitrary why we would give more power to somebody who has more talent, because they didn't create that talent, they just happened to have it. But everybody says we should give somebody who has talent in an area more power. This way you'd be able to gain talent. Just as in the Matrix, you could learn to fly in a helicopter just by downloading the right software as opposed to spending a lot of time doing that. Is that fair or unfair? I mean, I think that would fall into the ethical challenge area. And it's not like we get to the end of this and say, okay, this is finally what the singularity is all about, and people can do certain things, and they can't do other things, but it's over. We'll never get to that point. I mean, this curve is going to continue, the other curve, is going to continue indefinitely. And we've actually shown, for example, with nanotechnology, we can create a computer where a one liter computer would actually match the amount of power that all human beings today have. like 10 to the 10th persons would all fit into one leader computer. Does that create ethical problems? So I mean a lot of the implications kind of run against what we've been assuming about human beings. Wait, on the talent question which is super interesting, do you feel like everybody when we get to 2040 will have equal capacities? I think we'll be more different because we'll have different interests. You might be into some fantastic type of music and I might be into some kind of literature or something else. We're going to have different interests and so we'll excel at certain things. depending on what your interests are. So it's not like we all have the same amount of power, but we'll all have fantastic power compared to what we have today. And if you're in Texas where there are no regulations, you'll probably get it first instead of you in Massachusetts. Exactly, yeah. Let me ask you another ethical question while we're on this one. So about a few minutes ago, you mentioned the capacity to replicate someone's brain and bring them back. So let's say I do that with my father, passed away six years ago, sadly. I bring him back, and I'm able to create a mind and a body just like my father's. It's exact, perfect replica, all of his thoughts. What happens to all the bills that he owed when he died? Because that's a lot of money, and a lot of bill collectors call me. Do we have to pay those off? off or are we good? Well, we're doing something like that with my daughter and you can read about this in her book and it's also in my book. We collected everything my father had written. He died when I was 22 so he's been dead for more than 50 years. And we fed that into a large language model and basically asked him the question, of all the things he ever wrote, what best answers this question? And then you can put any question you want. and then you could talk to him. You'd say something, you'd then go through everything he ever had written and find the best answer that he actually wrote to that question. And it actually was a lot like talking to him. You could ask him what he liked about music. He was a musician. He actually liked Brahms the best. And it was very much like talking to him. And I reported on this in my book and Amy talks about this in her book. And Amy actually asked the question, could I fall in love with this person even though I've never met him? And she does a pretty good job. I mean, you really do fall in love with this character that she creates even though she never met him. So we can actually, with today's technology, do something where you can actually emulate somebody else. And I think as we get further on, we can actually do that more and more responsibly and more and more... that really would match that person and actually emulate the way he would move and so on, his tone of voice. Well, you know, my dad, he loved Brahms, too, particularly those piano trios. So if we can solve the back taxes problem, we'll get my dad and your dad's bots to hang out. It would be great. Well, yeah, that would be cool. All right. All right, we've got 20 minutes left. I want to get to the thing that I most want to understand. Because it's something that's, by the way, this book is wonderful. I think you guys are all going to get signed copies of it when it comes out. It's truly remarkable, as are all of Ray's books. Whether you agree or disagree, they'll definitely make you think more. One of the things that I don't think you do in this book is describe what A day will be like in 2045 when we're all much more intelligent. So it's 2045. We're all a million times as intelligent. I wake up. Do I have breakfast or do I not have breakfast? Well, the answer to that question is kind of the same as it is now. First of all, the reason it's called a singularity is because we don't really fully understand that question. Singularity is borrowed from physics. Singularity in physics is where you have a black hole and no light can escape, and so you can't actually tell what's going on inside the black hole, and so we call it a singularity. singularity. So this is a historical singularity where we're borrowing that term from physics and call it a singularity because we can't really answer the question. If we actually multiply our intelligence a million fold, what's that like? It's a little bit like asking a mouse, gee, what would it be like if you had the amount of intelligence of this person? The mouse wouldn't really even understand the question. It does have intelligence, has a fair amount of intelligence, but it couldn't understand that question, it couldn't articulate an answer. That's a little bit what it would be like for us to take the next step in intelligence by adding all the intelligence that singularity would provide. But I'll give you one answer. I said if you're diligent you'll achieve longevity escape velocity in five or six years. If we want to actually emulate everything that's going on inside the brain, let's go out a few more years, say 2040, 2045. Now there's a lot, you talk to a person, they've got all the connections that they had originally, plus all this additional connections that we add through... Having them access computers and that becomes part of their thinking. So can you, suppose that person like blows up or something happens to their mind. you definitely can recreate everything that's of a computer origin. Because we do that now. Anytime we create anything with a computer, it's backed up. So if the computer goes away, you've got... to back up and you can recreate it. Maybe it says, okay, but what about the thinking in their normal brain that's not done with computers? We don't have some ways of backing that up. When we get to the singularity with 2045, we'll be able to back that up as well because we'll be able to figure out, we'll have some ways of actually figuring out what's going on in that. sort of non biological, non mechanical brain. And so we'll be able to back up both the normal brain as well as the computer edition. And I believe that's feasible by 2045. In your vision of it... So you can back up their entire brain. That doesn't guarantee, I mean, the whole world could blow up and lose all the data centers. So it's not absolute guarantee. I'd be ashamed. What I don't understand is will we even be fully distinct people? If we're sharing memories and we're all uploading our brains to the cloud and we're getting all this information coming back directly into our neocortex, are we still distinct? Yes, but we could also find new ways of communicating. So the computers that extend my brain interact with computers that extend your brain. We could create something that's like a hybrid or not. And it would be up to our own decision as to whether or not to do that. So there'll be some new ways of communicating. Let me ask another question about this. This is what, when I was reading the book, this is where I kept getting stuck. You were extremely optimistic. You were optimistic about where we are today. You're optimistic that technology has been a massive force for good. You're optimistic that it will continue to be a massive force for good. Yet there is a lot of uncertainty in the future you were describing. Well, first of all, I'm not necessarily optimistic. There are things that can go wrong. We had things that can go wrong before we had computers. When I was a child, atomic weapons were... were created and people were very worried about an atomic war. We would actually get under our desk and put our hands behind our head to protect us against an atomic war. It seemed to work, actually. here, but if you would ask people, we had actually two weapons that went off in anger and killed a lot of people within a week, and if you would ask people, what's the chance that we're going to go another 80 years and this will never happen again, nobody would say that that was true, but it has happened. That doesn't mean it's not going to happen next week. But anyway, that's a great danger. And I think that's a much greater danger than computers are. Yes, there are dangers, but the computers will also be more intelligent to avoid kinds of dangers. Yes, there's some bad people in the world, but I mean, go back 80, 90 years, we had 100 million people die in Asia and Europe from World War II. We don't have wars like that anymore. We could. We certainly have the atomic weapons to do that. And you could also imagine computers could be involved with that. But if you actually look, and this goes right through one piece. First of all, if you look at my lineage of computers, going from a tiny fraction of one calculation... to 65 billion. That's a 20 quadrillion fold increase that we've achieved in 80 years. And look at this, U.S. personal income. This is done in constant dollars. So this has nothing to do with inflation. And this is the average income in the United States. It's multiplied by about 100-fold. And we live far more successfully if you actually people say, oh, things were great 100 years ago. They weren't. And you can look at this chart, and lots of, I've got 50 charts in the book that show the kind of progress we've made. The number of people that live in dire poverty has gone down dramatically. We actually did a poll where they asked people, people that live in poverty, has it gone up or down? 80% said it's gone up. But the reality is it's actually fallen by 50%. in the last 20 years. So what we think about the past is really the opposite of what's happened. Things have gotten far better than they have, and computers are going to make things even better. I mean, just the kind of things you can do now with a large language model didn't exist two years ago. Do you ever worry that... Take it as a given, if peers have made things better, take it as a given that personal income will keep going up. Do you ever worry it's just coming too quickly and it'll be better if maybe the slope of the Kurzweil curve was a little less steep? That's a big difference in the past. talk about what effect did the railroad have. Lots of jobs were lost, or even the cotton ginny. That happened 200 years ago. And people were quite happy making money with the cotton ginny, and suddenly the cotton ginny that was gone and machines were doing that and people say well wait till this gets going all jobs will be lost and that's actually what was said at that time but actually income went up more and more people worked we created and if you say well what are they gonna do you couldn't answer that question because it was in industries that nobody had a clue of like for example all of electronics were going to be gone Things are getting better even if jobs are lost. Now you can certainly point to jobs. Take computer programming. Google has, I don't know, 60,000 people that program computers, and lots of other companies do. At some point, that's not going to be a feasible job. They can already code, large language models can write code. Not quite the way an expert programmer can do, but how long is that going to take? It's measured in years, not in decades. Nonetheless, I believe that things will get better, because we wipe out jobs, but we create other ways of having an income. And if you actually point to something... say this machine, and this is being worked on, can wash dishes. You just have a bunch of dishes, it'll pick the ones that have to go in the dishwasher and clean everything else up, and that will wash dishes for you. Would we want that not to happen? Would we say, well, this is kind of upsetting things, let's get rid of it? It's not going to happen. And no one would advocate that. So, we'll find things to do, we'll have other methods of distributing money, and it will continue these kinds of curves that we've seen already. It's kind of remarkable that we've got large language models before we've got robotic dishwashers. You have grandchildren. You know, what would you tell a young person? You know, they buy in, they agree, or, you know. How would you tell them to best prepare themselves for what will be a, if you're correct, a remarkably different future? I'd be less concerned about what will make money and much more concerned about what turns them on. They love video games, so they should learn about that. They should read literature that turns them on. Some of those literature in the future will be created by computers. and find out what in the world has a positive effect on their mental being. And if you know that your child or your grandchild, this gets to one of the questions that is asked. on the screen here, if you know that someone is going to live for hundreds of years, as you predict, how does that affect the way, certainly means they shouldn't retire at 65, but what else does it change about the way they should think about their lives? Well I talk to people and say well I wouldn't want to live past 100 or maybe they're a little more ambitious to say I don't want to live past 110. But if you actually look at when people decide they've had enough and they don't want to live anymore, that never ever happens unless these people are in some kind of dire pain. They're in physical pain or emotional pain and spiritual pain or whatever and they just cannot bear to be alive anymore. Nobody takes their lives other than that. And if we can actually overcome many kinds of physical problems, cancers wiped out and so on, which I expect to happen, people will be even that much more happy to live and they'll want to continue to experience tomorrow. And tomorrow's going to be better and better. These kinds of progress, it's not going to go away. So people will want to live. unless they're in dire pain, but that's what the whole sort of medical profession is about, which is going to be greatly amplified by tomorrow's computers. Let me ask you a great question that has popped on the screen. This is from Colin McCabe. AI is a black box. Nobody knows how it was built. How do you show that AI is trustworthy to users who want to trust it, adopt it, and accept it, particularly if you're going to upload it directly into your brain? Well, it's not true that nobody knows how they work. Right. Most people who are using a large language model don't know what data sense went into it. There are things that happen in the transformer layer that even the architects don't understand. Right, but we're going to learn more and more about that. And, in fact, how computers work will be, I think, a very common type of talent that people want to gain. And ultimately we'll have more trust of computers. I mean, large language models aren't perfect, and you can ask it a question and it can give you something that's incorrect. I mean, we've seen that just recently. The reason we have these computers give you incorrect information is it doesn't have the information to begin with and it actually doesn't know what it doesn't know and that's actually something we're working on. so that it knows, well, I don't know that. That's actually very good if it can actually say that because right now it will find the best thing it knows. And if it's never trained on that information and there's nothing in there that tells you, it will just give you the best guess, which could be very incorrect. And we're actually learning to be able to figure out when it knows and when it doesn't know. But ultimately, we'll have pretty good confidence when it knows and when it doesn't know, and we can actually rely on what it says. So your answer to the question is, A, we will understand more, and B, they'll be much more trustworthy, so it won't be as risky to not understand them? Right. Okay. You've spent your life making predictions, some of which, like the Turing test, you've held on to and been remarkably accurate. As you move from an overwhelming optimist to now slightly of a pessimist, what is a prediction? Well, my books have always had a chapter on how these things can go wrong and parents. Tell me a prediction that you are chewing over right now, but you're not sure whether you want to make it or whether you don't want to make it. I mean there's well-known dangers in nanotechnology. If someone were to create a nanotechnology that replicates, well known as... replicates everything into paper clips and turn the entire world into paper clips that would not be positive unless you're staples but then and that's feasible take somebody who's A little bit mental to do that, but it could be done. And we actually will have something that actually avoids that. So we'll have something that can detect that this is actually turning everything into paper clips and destroy it before it does that. But I mean I have a chapter in this new book, The Singularity is Nearer, that talks about the kinds of things that could happen. Oh, the most remarkable part of this book is he does exactly the mathematical calculations on how long it would take nanobots to turn the world into grey goo and how long it would take the blue goo to stop the grey goo. It's remarkable. The book will be out soon. You definitely need to read it. until the end. But this leads to a, maybe, let me try and answer the question I asked before is what should young people think about and be working on, you said their passions and what turns them on, shouldn't they be thinking through how to design and architect these future systems so they are less likely to turn us into Grey Goo or paper clips? Absolutely. I don't know if everybody wants to work on that. But folks in this room, right, technologically minded, you guys should all be working on not turning us into Grey Goo, right? Yes. That would be on the list. But then that leads to another question, which is what will the role of humans be in thinking through that problem when they're only a millionth or a billionth or a trillionth as intelligent as they are? machines say that again so we're gonna have these really hard problems to solve you know right right now we are along with our machines you know we can be extremely intelligent but ten years from now fifteen years from now there will be machines that will be so much more intelligent than us. What will our role, what will the role of humans be in trying to solve these problems? Well, first of all, I see those as extensions of humans. And we wouldn't have them if we didn't have humans to begin with. And humans have a brain that can think. these things through and we have this thumb. It's not really very much appreciated but like whales and elephants actually have a larger brain than we have and they can probably think deeper thoughts but they don't have a thumb and so they don't create technology. A monkey can create, it actually has a thumb but it's actually down an inch or so and therefore it really can't grab very well. So it can create a little bit of technology but the thumb is like my own thing. It creates, cannot create other technology. So the fact that we have a thumb means we can create integrated circuits that can become a large language model that comes from the human brain. And it's actually trained with everything that we've ever thought. Anything that human beings have thought has been documented and it can go into these large language models. And everybody can work on these things. And it's not true only certain wealthy people will have it. I mean, how many people here have phones? If it's not 100%, it's like 99.9%. And you don't have to be kind of from a wealthy group. I mean, I see people who are homeless who have their own phone. It's not that expensive. And so that represents the distribution of these capabilities. It's not something you have to be fabulously wealthy to afford. So you think that we're headed into a future where we're going to live much longer and we'll be much more equal? Say again? We think we're heading into a society where we'll live much longer, be wealthier, but also much more equality. Yes, absolutely. And we've seen that already. All right. Well, we're out of time, but Ray and I will be back in 21, 24. 2224 and 2324. So thank you for coming today. Thank you so much. He is an American treasure. Thank you, Ray Kurzweil. Thank you.

All right, I'm so excited to be here with you, Ray. It's great to be here. Great to see everybody together.

Yeah. Beautiful audience. So my favorite thing in that introduction of you is that you have been working in AI longer than any other human alive, which means if you live forever... and we'll get to that, you will always have that distinction. I think that's right.

Marvin Minsky was actually my mentor. If he were alive today, he would actually be more than 61 years. And we're going to bring him back also.

So I'm not sure how we'll count the distinction then. All right, so we're going to fix the audio, but this is what we're going to do with this conversation. I'm going to start out asking Ray some questions about where we are today. We'll do that for a few minutes, then we'll get into what has to happen to reach the singularity, so the next 20 years. get into a discussion about what the singularity is, what it means, how it changed our lives, and then at the end we'll talk a little bit about how, if we believe this vision of the future, what it means for us today.

Ask your questions, they'll come in, I'll ask them as they go in the different sections of the conversation, but let's get cracking. Can you hear me? You can't hear Ray.

Well, This will be recorded. You guys are going to all live forever. There will be plenty of time.

It will be fine. I'm just going to get started. I assume the audio will get worked out. They do a fabulous job here at South By. I think they should be able to hear me.

All right. We got this over on the right. Audio engineers, are we good to go? We're good to go. All right.

All right, first question, Ray. So, you've been working in AI for 61 years. Wait, can you hear me? Let's not... So everybody in the front can hear you, but nobody in the back can hear you.

Can you hear me now? All right. I'll speak louder. First question. So you've been living in the AI revolution for a long time.

You've made lots of predictions, many of which have been remarkably accurate. We've all been living in a remarkable two-year transition. transformation with large language models a year and a half. What has surprised you about the innovations in large language models and what has happened recently? Well, I did finish this book a year ago and didn't really cover large language models, so I delayed the book to cover that.

And I'm not sure if you can see it. I was expecting this that to happen like a couple of years later. I mean I made a prediction in 1999 that would happen by 2029 and we're not quite there yet but we will but it looks like it's maybe a year or two ahead of schedule. So there was maybe a bit of a surprise. Wait, you predicted back in 1999 that a computer would pass the Turing test in 2029. Are you revising that to something more closer to today?

No, I'm still saying 2029. The definition of the Turing test is not precise. We're going to have people claiming that the Turing test has been solved, and people are saying that GPT-4 actually passes it, some people. So it's going to be like maybe two or three years where people start claiming, and then they continue to claim, and finally everybody will accept it.

So it's not like it happens in one day. But you have a very specific definition of the Turing test. When do you think we'll pass that definition?

Well, the Turing test is actually not that significant because that means that you can... A computer will pass for a human being. And what's much more important is age. AGI, automatic general intelligence, which means it can emulate any human being.

So you have one computer, and it can do everything that any human being can do. And that's also 2029. It all happens. at the same time. But nobody can do that. I mean, just take an average large language model today.

You can ask it anything, and it will answer you pretty convincingly. No human being can do all of that, and it does it very quickly. It will write a very nice essay in 15 seconds, and then you can ask it again, and it will write another essay, and no human being can actually perform at that level. Right, so you have to dumb it down to actually do it. actually have a convincing Turing test.

To have a Turing test, you have to dumb it down. Yeah. Let me ask the first question from the audience, since I think it's quite relevant to where we are, which is Brian Daniel. Is the Kurzweil curve still accurate?

Is the Kurzweil curve still accurate? Yes, in fact, can I see that? Let's pull the slides up.

First slide. So this is an 80-year track record. This is an exponential growth. A straight line on this curve means exponential curvature. If it was sort of exponential, but...

Not quite. It would curve. This is actually a straight line.

It started out with a computer that did 0.0000007 calculations. calculations per second per constant dollar. That's the lower left-hand corner.

At the upper right-hand corner, it's 65 billion calculations per second for the same amount of money. So that's why large language models have only been feasible for two years. We actually had large language models before that, but it didn't work very well. And this is an exponential curve. Technology moves in an exponential curve.

We see that, for example, having renewable energy come from the sun and wind. That's actually an exponential curve. It's increased.

It's gone. We've decreased the price by 99.7%. We've multiplied the amount of energy coming from solar energy a million-fold. So this kind of curve really directs all kinds of technology. And this is the reason that we're making progress.

I mean, we knew how to do large language models years ago, but we're dependent on this curve. And it's pretty amazing. It started out increasing relay speeds, then vacuum tubes, then integrated circuits, and each year it makes the same amount of progress, approximately, regardless of where you are on this curve. We just added the last point.

And it's, again, we basically multiply this by two every 1.4 years. And this is the reason that computers are exciting, but it actually affects every type of technology. And we just added the last point like two weeks ago. All right, so let me ask you a question. You know, you wrote a book about how to build a mind.

You have a lot about how the human mind is constructed. A lot of the progress in AI, AI systems are being built on what we understand about neural networks, right? So clearly our understanding...

of this helps with AI. In the last two years, by watching these large language models, have we learned anything new about our brains? Are we learning about the insides of our skulls as we do this? It really has to do with the amount of connection The brain is actually organized fairly differently. The things near the eye, for example, deal with vision.

And we have different ways of implementing different parts of the brain that remember different things. We actually don't need that. In a large language model, all the connections are the same.

We have to get the connections up to a certain point. If it approximately matches what the brain does, which is about a trillion times more trillion connections. It will perform kind of like the brain.

We're kind of almost at that point. Wait, so you think? The GPT-4 is 400 billion. The next ones will be a trillion or more. So the construction of these models, they are more efficient in their construction than our brains are.

We make them to be as efficient as possible, but it doesn't really matter how they're organized. And we can actually create certain software that will actually expand the amount of connections more for the same amount of computation. But it really has to do with how many connections are... A particular computer is responsible for.

So as we approach AGI, we're not looking for a new understanding of how to make these machines more efficient. The transformer architecture was clearly very important. We can really just...

get there with more computing. But the software and the learning is also important. I mean, you could have a trillion connections, but if you didn't have something to learn from, it wouldn't be very effective. So we actually have to be able to collect all this.

this data, so we do it on the web and so on. I mean, we've been collecting stuff on the web for several decades. That's really what we're depending on, to be able to train these large language models. And we shouldn't actually call them large language models, because they deal with much more than language.

I mean, it's language, but you can add pictures. You can add things that affect disease that have nothing to do with language. In fact, we're using now simulated biology to to be able to simulate different ways to affect disease and that has nothing to do with language. So they really should be called large event models.

Do you think there's anything that happens inside of our brains that cannot be captured by computation and by math? No, I mean what would that be? I mean Okay, quick poll of the audience.

Raise your hand if you think there's something in your brain that cannot be captured by computation or math like a soul all right so convince them that they're wrong right I mean consciousness is very important but it's actually not scientific There's no way I could slide somebody in and the light will go on. Oh, this one's conscious. No, this one's not.

It's not scientific, but it's actually extremely important. And another question, why am I me? How come what happens to me I'm conscious of and I'm not conscious of what happens to you?

These are deeply mysterious things, but they're really not. not conscious. So Marvin Minsky, who was my mentor for 50 years, he said, it's not scientific and therefore we shouldn't bother with it.

And any discussion of consciousness he would kind of dismiss. But he actually did, his reaction to people was totally dependent on whether he felt they were conscious or not. So he actually did use that. But it's not something that we're ignoring because there's no way to tell to tell whether something's conscious. And that's not just something that we don't know and we'll discover.

There's really no way to tell whether or not something's conscious. What do you mean? Like, this is not conscious, and the gentleman sitting right there is conscious. I'm pretty confident. Well, how do you prove that?

I mean we kind of agree with humans, that humans are conscious. Some humans are conscious, not all humans. But how about animals?

We have big disagreements. Some people say animals are not conscious, and other people think animals are conscious, maybe some animals are conscious and others are not. There's no way to prove that. Okay, I want to run down this consciousness question.

we do that I want to make sure I understood your previous answer correctly. So the feeling I get of being in love or the feeling any emotion that I get could eventually be represented in math in a large language model. Yeah I mean certainly the behavior the feelings that you have if you're with somebody that you love is definitely dependent on what the connections do you can tell within or not that's happening.

All right. And back to... Is everybody here convinced?

Not entirely. All right, well, close enough. So you don't think that it's worth trying to define consciousness.

I mean, you spend a fair amount in your book giving different arguments about what consciousness means, but it seems like you're arguing on stage that we shouldn't try to define it. There's no way to actually prove it. I mean, we have certain agreements.

I agree that all of you are conscious. You actually made it into this room, so that's a pretty good indication that you're conscious. But that's not a proof.

And there may be human beings that don't seem quite conscious at the time. Are they conscious or not? And animals, I mean, I think elephants and whales are conscious, but not everybody agrees with that. So at what point can we then essentially, how long will it be until we can essentially download the entire contents of your brain and express it through some kind of a machine? That's actually an important question because we're going to talk about longevity.

We're going to get to a point where we have longevity escape velocity. And it's not that far away. I think if you're diligent, you'll be able to achieve that by 2029. That's all.

only five or six years from now. So right now, you go through a year, use up a year of your longevity, but you get back from scientific progress, right now, about four months. But that scientific progress is on an exponential curve.

It's going to speed up every year. And by 2029, if you're diligent, you'll use up a year of your longevity with a year passing, but you'll get back a full year. And past 2029, you'll get back more.

than a year, so you'll actually go backwards in time. Now that's not a guarantee of infinite life because You could have a 10 year old and you could compute his longevity. It's many, many decades and he could die tomorrow.

But what's important about actually capturing everything in your brain, we can't do that today. And we won't be able to do that in five years. But you will be able to do that by the singularity, which is 2045. And so at that point, you can actually go inside the brain and capture everything in there. Now, your thinking is going to...

to be a combination of the amount you get from computation which will add to your thinking and that's automatically captured. I mean right now anything that you have in a computer is automatically captured today and the kind of additional thinking we'll have by adding to our brain, that will be captured. But the connections that we have in the brain are not necessarily the same.

that we have in the brain that we start with, we'll still have that. That's not captured today, but that will be captured in 2045. We'll be able to go inside the brain and capture that as well. And therefore, we'll actually capture the entire brain, which will be backed up.

So even if you get wiped out, you walk into a bomb and it explodes, we can actually recreate everything that was in your brain. by 2045. That's one of the implications of the singularity. Now, that doesn't absolutely guarantee, because, I mean, the world could blow up and all the...

Computer, all the things that contain computers could blow up and so you wouldn't be able to recreate that. So we never actually get to a point where we absolutely guarantee that... you live forever, but most of the things that right now would upset capturing that will be overcome by that time. There's a lot there, Ray.

Let's start with escape velocity. So do you think that anybody in this audience in their current biological body will live to be 500 years old? You're asking me? Yeah.

Absolutely. I mean if you're gonna be alive in five years, and I imagine all of you will be alive in five years. If they're alive for five years they will likely live to be 500 years old.

If they're diligent and I think the people in this audience will be diligent. Wow, all right, well you can drink whatever you want as long as you don't get run over tonight because you don't have to worry about decline. All right, so let me ask a question. I want to get, we're going to spend a lot of time on what the singularity is, what it means, and what it'll be like, but I want to ask some questions that'll lead us up there. So I'm going to take this question from Mark Sternberg and modify it slightly.

In the time frame, AI will be able to do, or sufficiently sophisticated computers, in your argument, can do everything that the human brain can do. What will they not be able to do in the next 10 years? Well. One thing has to do with being creative.

Some people will be able to do everything a human can do, but they're not going to be able to create new knowledge. That's actually wrong, because we can simulate, for example, And the Moderna vaccine, for example, we didn't do it the usual way, which is somebody sits down and thinks, well, I think this might work. And then they try it out.

It takes years to try it out on multiple people. one person's idea about what might work, they actually listed everything that might work. And there was actually several billion different mRNA sequences.

And they said, let's try them all. And they tried every single one by simulating biology. And that took two days.

So one weekend, they tried out several billion different possibilities. And then they picked the one that turned out to be the best. And that actually was the...

Moderna vaccine up until today. Now, they did actually test it on humans. We'll be able to overcome that as well because we'll be able to test using simulated biology as well.

They actually decided to test it. It's a little bit hard to give up testing on humans. humans, we will do that.

So you can actually try out every single one, pick the best one, and then you can try out that by testing on a million simulated humans and do that in a few days as well. And that's actually the future of how we're going to create medications for diseases. And there's lots of things going on now with cancer and other diseases that are using that. So that's a whole new method.

It's actually starting now. It started right here with the Moderna vaccine. We did another cure for a mental disease. It's actually now in stage three trials.

That's going to be how we create medications from now on. But what are the frontiers? What can we not do? So that's the question. where a computer is being creative.

And it's not just actually trying something that occurs to it. It makes a list of everything that's possible and tries it all. Is that creativity or is that just brute force with maximum capability?

It's much better than any other form of creativity. And yes, it's creative, because you're trying out every single possibility, and you're doing it very quickly, and you come up with something that we didn't have before. I mean, what else would create...

creativity be. All right. So we're going to cross the frontier of creativity.

What will we not cross? What are the challenges that will be outstanding the next ten years? Well, we don't know everything, and we haven't gone through this process. It does require some creativity to make it happen.

imagine what might work. And we have to also be able to simulate it in a biochemical simulator. So we actually have to figure that out. And we'll be using people for a while to do that. So we don't know everything.

I mean, to be able to do everything a human being can do is one thing, but there's so much we don't know that we want to find out. And that requires creativity. That will require...

some kind of human creativity working with machines. All right, let's go back to what's going to happen to get us to the singularity. So clearly, we have the chart that you showed on the power of compute.

It's been very... very steady, moving straight up on a logarithmic scale on a straight line. There are a couple of other elements that you think are necessary to get to the singularity.

One is the rise of nanobots, and the other is the rise of brain machine interfaces. And both of those have gone more slowly than AI. So convince the audience that.

Well, it would be slow, because any time you affect the human body, a lot of people are gonna be concerned about it. If we do something with computers, we have a new algorithm, or we increase the speed of it, nobody really is concerned about it. You can do that. Nobody cares about any dangers in it. I mean, that's the reality.

There's some dangers that people care about. Yeah, but it goes very far. very quickly.

That's one of the reasons it goes so fast. But if you're affecting the body, we have all kinds of concerns that it might affect it negatively. So we want to actually try it on people. But the reason brain-machine interfaces haven't moved in an exponential curve isn't just because you know, lots of people are concerned about the risks to humans. I mean, as you explain in the work, in the book, they just don't work as well as they could.

Um, if we could try things out without having to test it, it would go a lot faster. I mean, that's the reason it goes slowly. Um, but, um, There's some thought now that we could actually figure out what's going on inside the brain and put things into the brain without actually going inside the brain.

We would need something like Brain Link. We could just... I mean, there's some tests where we can actually tell what's going on in the brain without actually putting something inside the brain.

And that might actually be a way to do this much more quickly. But your prediction about the singularity depends, maybe I'm reading it wrong, not just on the continued exponential growth of compute, but on solving this particular problem too, right? Yes, because we want to increase the amount of intelligence that humans can command. So we have to be able to marry the best computers with our actual brain. And why do we have to do that?

Because like... right now, here I go, I have my phone. In some ways, this augments my intelligence.

It's wonderful. Yeah, but it's very slow. I mean, if I ask you a question, you're going to have to type it in or speak it, and it takes a while.

I mean, I ask a question, and then people fool around with their computer. It might take 15 seconds or 30 seconds. It's not like it just goes right into your brain.

I mean these are very useful. These are brain extenders. We didn't have these a little while ago. Generally in my talks I ask people who here has their phone.

I'll bet here maybe there's one. two people but everybody here has their phone. That wasn't true five years ago, definitely wasn't true ten years ago. And it is a brain extender but it does have some speed problems. So we want to increase that speed.

A question could just come up while we're talking and the computer would instantly tell you what the answer is without you having to fool around with an external device. And that's almost feasible today. And something like that would be helpful to do this.

But could you not get a lot of the good that you talk about if we just kept, the problem with connecting our brains to the machines is suddenly you're in this whole world. these complicated privacy issues where stuff is being injected in my brain, stuff in my brain is going elsewhere. You're opening up a whole host of ethical, moral, existential problems. Can't you just make the phones a lot better?

Well, that's the idea, that we can do that without having to go inside your brain but be able to tell what's going on in your brain externally without going inside the brain. you know with some kind of device all right well let's keep moving into the future so we're moving into the future we have exponential growth computer we solve a way of you know ideally figuring out how to communicate directly with your brain to speed things up explain why nanobots are essential to your vision of where we're going well if you really want to tell what's going on inside the brain you've got to be able to go at the level of the particles in the brain so we can actually tell what they're doing And that's feasible. We can't actually do it, but we can show that it's feasible.

And that's one possibility. We're actually hoping that you could do this without actually affecting the brain at all. OK.

All right, so we're pushing ahead. We've got nanobots that run around inside of our brain. They're understanding our head. They're extracting thoughts.

They're inputting thoughts. Let's go to this nice question, which fits in lovely, from Luis Candreva. What are the five main ethical questions that we will face as that happens? Is four enough? Four is fine.

There might even be six, Ray, but you can give us four. I mean, we're going to have a lot more power if we can actually, with our own brain, control computers. Does that give people too much power?

Also, I mean, right now we talk about having a certain amount of value based on your... ...on your talent. This will give talent to people who otherwise don't have talent. And talent won't be as important because you'll be able to gain talent just by merging with the right kind of large language model, or whatever we call them.

It also seemed kind of arbitrary why we would give more power to somebody who has more talent, because they didn't create that talent, they just happened to have it. But everybody says we should give somebody who has talent in an area more power. This way you'd be able to gain talent. Just as in the Matrix, you could learn to fly in a helicopter just by downloading the right software as opposed to spending a lot of time doing that.

Is that fair or unfair? I mean, I think that would fall into the ethical challenge area. And it's not like we get to the end of this and say, okay, this is finally what the singularity is all about, and people can do certain things, and they can't do other things, but it's over.

We'll never get to that point. I mean, this curve is going to continue, the other curve, is going to continue indefinitely. And we've actually shown, for example, with nanotechnology, we can create a computer where a one liter computer would actually match the amount of power that all human beings today have. like 10 to the 10th persons would all fit into one leader computer. Does that create ethical problems?

So I mean a lot of the implications kind of run against what we've been assuming about human beings. Wait, on the talent question which is super interesting, do you feel like everybody when we get to 2040 will have equal capacities? I think we'll be more different because we'll have different interests.

You might be into some fantastic type of music and I might be into some kind of literature or something else. We're going to have different interests and so we'll excel at certain things. depending on what your interests are.

So it's not like we all have the same amount of power, but we'll all have fantastic power compared to what we have today. And if you're in Texas where there are no regulations, you'll probably get it first instead of you in Massachusetts. Exactly, yeah.

Let me ask you another ethical question while we're on this one. So about a few minutes ago, you mentioned the capacity to replicate someone's brain and bring them back. So let's say I do that with my father, passed away six years ago, sadly. I bring him back, and I'm able to create a mind and a body just like my father's. It's exact, perfect replica, all of his thoughts.

What happens to all the bills that he owed when he died? Because that's a lot of money, and a lot of bill collectors call me. Do we have to pay those off? off or are we good? Well, we're doing something like that with my daughter and you can read about this in her book and it's also in my book.

We collected everything my father had written. He died when I was 22 so he's been dead for more than 50 years. And we fed that into a large language model and basically asked him the question, of all the things he ever wrote, what best answers this question? And then you can put any question you want. and then you could talk to him.

You'd say something, you'd then go through everything he ever had written and find the best answer that he actually wrote to that question. And it actually was a lot like talking to him. You could ask him what he liked about music.

He was a musician. He actually liked Brahms the best. And it was very much like talking to him.

And I reported on this in my book and Amy talks about this in her book. And Amy actually asked the question, could I fall in love with this person even though I've never met him? And she does a pretty good job. I mean, you really do fall in love with this character that she creates even though she never met him.

So we can actually, with today's technology, do something where you can actually emulate somebody else. And I think as we get further on, we can actually do that more and more responsibly and more and more... that really would match that person and actually emulate the way he would move and so on, his tone of voice. Well, you know, my dad, he loved Brahms, too, particularly those piano trios. So if we can solve the back taxes problem, we'll get my dad and your dad's bots to hang out.

It would be great. Well, yeah, that would be cool. All right.

All right, we've got 20 minutes left. I want to get to the thing that I most want to understand. Because it's something that's, by the way, this book is wonderful.

I think you guys are all going to get signed copies of it when it comes out. It's truly remarkable, as are all of Ray's books. Whether you agree or disagree, they'll definitely make you think more.

One of the things that I don't think you do in this book is describe what A day will be like in 2045 when we're all much more intelligent. So it's 2045. We're all a million times as intelligent. I wake up. Do I have breakfast or do I not have breakfast? Well, the answer to that question is kind of the same as it is now.

First of all, the reason it's called a singularity is because we don't really fully understand that question. Singularity is borrowed from physics. Singularity in physics is where you have a black hole and no light can escape, and so you can't actually tell what's going on inside the black hole, and so we call it a singularity. singularity. So this is a historical singularity where we're borrowing that term from physics and call it a singularity because we can't really answer the question.

If we actually multiply our intelligence a million fold, what's that like? It's a little bit like asking a mouse, gee, what would it be like if you had the amount of intelligence of this person? The mouse wouldn't really even understand the question. It does have intelligence, has a fair amount of intelligence, but it couldn't understand that question, it couldn't articulate an answer. That's a little bit what it would be like for us to take the next step in intelligence by adding all the intelligence that singularity would provide.

But I'll give you one answer. I said if you're diligent you'll achieve longevity escape velocity in five or six years. If we want to actually emulate everything that's going on inside the brain, let's go out a few more years, say 2040, 2045. Now there's a lot, you talk to a person, they've got all the connections that they had originally, plus all this additional connections that we add through...

Having them access computers and that becomes part of their thinking. So can you, suppose that person like blows up or something happens to their mind. you definitely can recreate everything that's of a computer origin.

Because we do that now. Anytime we create anything with a computer, it's backed up. So if the computer goes away, you've got... to back up and you can recreate it.

Maybe it says, okay, but what about the thinking in their normal brain that's not done with computers? We don't have some ways of backing that up. When we get to the singularity with 2045, we'll be able to back that up as well because we'll be able to figure out, we'll have some ways of actually figuring out what's going on in that. sort of non biological, non mechanical brain. And so we'll be able to back up both the normal brain as well as the computer edition.

And I believe that's feasible by 2045. In your vision of it... So you can back up their entire brain. That doesn't guarantee, I mean, the whole world could blow up and lose all the data centers. So it's not absolute guarantee. I'd be ashamed.

What I don't understand is will we even be fully distinct people? If we're sharing memories and we're all uploading our brains to the cloud and we're getting all this information coming back directly into our neocortex, are we still distinct? Yes, but we could also find new ways of communicating. So the computers that extend my brain interact with computers that extend your brain. We could create something that's like a hybrid or not.

And it would be up to our own decision as to whether or not to do that. So there'll be some new ways of communicating. Let me ask another question about this.

This is what, when I was reading the book, this is where I kept getting stuck. You were extremely optimistic. You were optimistic about where we are today.

You're optimistic that technology has been a massive force for good. You're optimistic that it will continue to be a massive force for good. Yet there is a lot of uncertainty in the future you were describing. Well, first of all, I'm not necessarily optimistic.

There are things that can go wrong. We had things that can go wrong before we had computers. When I was a child, atomic weapons were...

were created and people were very worried about an atomic war. We would actually get under our desk and put our hands behind our head to protect us against an atomic war. It seemed to work, actually.

here, but if you would ask people, we had actually two weapons that went off in anger and killed a lot of people within a week, and if you would ask people, what's the chance that we're going to go another 80 years and this will never happen again, nobody would say that that was true, but it has happened. That doesn't mean it's not going to happen next week. But anyway, that's a great danger. And I think that's a much greater danger than computers are.

Yes, there are dangers, but the computers will also be more intelligent to avoid kinds of dangers. Yes, there's some bad people in the world, but I mean, go back 80, 90 years, we had 100 million people die in Asia and Europe from World War II. We don't have wars like that anymore.

We could. We certainly have the atomic weapons to do that. And you could also imagine computers could be involved with that.

But if you actually look, and this goes right through one piece. First of all, if you look at my lineage of computers, going from a tiny fraction of one calculation... to 65 billion.

That's a 20 quadrillion fold increase that we've achieved in 80 years. And look at this, U.S. personal income. This is done in constant dollars.

So this has nothing to do with inflation. And this is the average income in the United States. It's multiplied by about 100-fold.

And we live far more successfully if you actually people say, oh, things were great 100 years ago. They weren't. And you can look at this chart, and lots of, I've got 50 charts in the book that show the kind of progress we've made. The number of people that live in dire poverty has gone down dramatically.

We actually did a poll where they asked people, people that live in poverty, has it gone up or down? 80% said it's gone up. But the reality is it's actually fallen by 50%.

in the last 20 years. So what we think about the past is really the opposite of what's happened. Things have gotten far better than they have, and computers are going to make things even better.

I mean, just the kind of things you can do now with a large language model didn't exist two years ago. Do you ever worry that... Take it as a given, if peers have made things better, take it as a given that personal income will keep going up.

Do you ever worry it's just coming too quickly and it'll be better if maybe the slope of the Kurzweil curve was a little less steep? That's a big difference in the past. talk about what effect did the railroad have. Lots of jobs were lost, or even the cotton ginny.

That happened 200 years ago. And people were quite happy making money with the cotton ginny, and suddenly the cotton ginny that was gone and machines were doing that and people say well wait till this gets going all jobs will be lost and that's actually what was said at that time but actually income went up more and more people worked we created and if you say well what are they gonna do you couldn't answer that question because it was in industries that nobody had a clue of like for example all of electronics were going to be gone Things are getting better even if jobs are lost. Now you can certainly point to jobs.

Take computer programming. Google has, I don't know, 60,000 people that program computers, and lots of other companies do. At some point, that's not going to be a feasible job. They can already code, large language models can write code.

Not quite the way an expert programmer can do, but how long is that going to take? It's measured in years, not in decades. Nonetheless, I believe that things will get better, because we wipe out jobs, but we create other ways of having an income.

And if you actually point to something... say this machine, and this is being worked on, can wash dishes. You just have a bunch of dishes, it'll pick the ones that have to go in the dishwasher and clean everything else up, and that will wash dishes for you. Would we want that not to happen?

Would we say, well, this is kind of upsetting things, let's get rid of it? It's not going to happen. And no one would advocate that.

So, we'll find things to do, we'll have other methods of distributing money, and it will continue these kinds of curves that we've seen already. It's kind of remarkable that we've got large language models before we've got robotic dishwashers. You have grandchildren.

You know, what would you tell a young person? You know, they buy in, they agree, or, you know. How would you tell them to best prepare themselves for what will be a, if you're correct, a remarkably different future? I'd be less concerned about what will make money and much more concerned about what turns them on.

They love video games, so they should learn about that. They should read literature that turns them on. Some of those literature in the future will be created by computers. and find out what in the world has a positive effect on their mental being. And if you know that your child or your grandchild, this gets to one of the questions that is asked.

on the screen here, if you know that someone is going to live for hundreds of years, as you predict, how does that affect the way, certainly means they shouldn't retire at 65, but what else does it change about the way they should think about their lives? Well I talk to people and say well I wouldn't want to live past 100 or maybe they're a little more ambitious to say I don't want to live past 110. But if you actually look at when people decide they've had enough and they don't want to live anymore, that never ever happens unless these people are in some kind of dire pain. They're in physical pain or emotional pain and spiritual pain or whatever and they just cannot bear to be alive anymore.

Nobody takes their lives other than that. And if we can actually overcome many kinds of physical problems, cancers wiped out and so on, which I expect to happen, people will be even that much more happy to live and they'll want to continue to experience tomorrow. And tomorrow's going to be better and better. These kinds of progress, it's not going to go away. So people will want to live.

unless they're in dire pain, but that's what the whole sort of medical profession is about, which is going to be greatly amplified by tomorrow's computers. Let me ask you a great question that has popped on the screen. This is from Colin McCabe.

AI is a black box. Nobody knows how it was built. How do you show that AI is trustworthy to users who want to trust it, adopt it, and accept it, particularly if you're going to upload it directly into your brain? Well, it's not true that nobody knows how they work.

Right. Most people who are using a large language model don't know what data sense went into it. There are things that happen in the transformer layer that even the architects don't understand.

Right, but we're going to learn more and more about that. And, in fact, how computers work will be, I think, a very common type of talent that people want to gain. And ultimately we'll have more trust of computers. I mean, large language models aren't perfect, and you can ask it a question and it can give you something that's incorrect. I mean, we've seen that just recently.

The reason we have these computers give you incorrect information is it doesn't have the information to begin with and it actually doesn't know what it doesn't know and that's actually something we're working on. so that it knows, well, I don't know that. That's actually very good if it can actually say that because right now it will find the best thing it knows. And if it's never trained on that information and there's nothing in there that tells you, it will just give you the best guess, which could be very incorrect. And we're actually learning to be able to figure out when it knows and when it doesn't know.

But ultimately, we'll have pretty good confidence when it knows and when it doesn't know, and we can actually rely on what it says. So your answer to the question is, A, we will understand more, and B, they'll be much more trustworthy, so it won't be as risky to not understand them? Right. Okay.

You've spent your life making predictions, some of which, like the Turing test, you've held on to and been remarkably accurate. As you move from an overwhelming optimist to now slightly of a pessimist, what is a prediction? Well, my books have always had a chapter on how these things can go wrong and parents.

Tell me a prediction that you are chewing over right now, but you're not sure whether you want to make it or whether you don't want to make it. I mean there's well-known dangers in nanotechnology. If someone were to create a nanotechnology that replicates, well known as... replicates everything into paper clips and turn the entire world into paper clips that would not be positive unless you're staples but then and that's feasible take somebody who's A little bit mental to do that, but it could be done.

And we actually will have something that actually avoids that. So we'll have something that can detect that this is actually turning everything into paper clips and destroy it before it does that. But I mean I have a chapter in this new book, The Singularity is Nearer, that talks about the kinds of things that could happen. Oh, the most remarkable part of this book is he does exactly the mathematical calculations on how long it would take nanobots to turn the world into grey goo and how long it would take the blue goo to stop the grey goo.

It's remarkable. The book will be out soon. You definitely need to read it.

until the end. But this leads to a, maybe, let me try and answer the question I asked before is what should young people think about and be working on, you said their passions and what turns them on, shouldn't they be thinking through how to design and architect these future systems so they are less likely to turn us into Grey Goo or paper clips? Absolutely. I don't know if everybody wants to work on that. But folks in this room, right, technologically minded, you guys should all be working on not turning us into Grey Goo, right?

Yes. That would be on the list. But then that leads to another question, which is what will the role of humans be in thinking through that problem when they're only a millionth or a billionth or a trillionth as intelligent as they are? machines say that again so we're gonna have these really hard problems to solve you know right right now we are along with our machines you know we can be extremely intelligent but ten years from now fifteen years from now there will be machines that will be so much more intelligent than us.

What will our role, what will the role of humans be in trying to solve these problems? Well, first of all, I see those as extensions of humans. And we wouldn't have them if we didn't have humans to begin with. And humans have a brain that can think. these things through and we have this thumb.

It's not really very much appreciated but like whales and elephants actually have a larger brain than we have and they can probably think deeper thoughts but they don't have a thumb and so they don't create technology. A monkey can create, it actually has a thumb but it's actually down an inch or so and therefore it really can't grab very well. So it can create a little bit of technology but the thumb is like my own thing. It creates, cannot create other technology. So the fact that we have a thumb means we can create integrated circuits that can become a large language model that comes from the human brain.

And it's actually trained with everything that we've ever thought. Anything that human beings have thought has been documented and it can go into these large language models. And everybody can work on these things. And it's not true only certain wealthy people will have it.

I mean, how many people here have phones? If it's not 100%, it's like 99.9%. And you don't have to be kind of from a wealthy group.

I mean, I see people who are homeless who have their own phone. It's not that expensive. And so that represents the distribution of these capabilities.

It's not something you have to be fabulously wealthy to afford. So you think that we're headed into a future where we're going to live much longer and we'll be much more equal? Say again? We think we're heading into a society where we'll live much longer, be wealthier, but also much more equality. Yes, absolutely.

And we've seen that already. All right. Well, we're out of time, but Ray and I will be back in 21, 24. 2224 and 2324. So thank you for coming today.

Thank you so much. He is an American treasure. Thank you, Ray Kurzweil.

Thank you.

Transcript for:Insights from Ray Kurzweil on AI Future

Transcript for:
Insights from Ray Kurzweil on AI Future