Ray Kurzweil Q&A - The Singularity, Human-Machine Integration & AI | EP #83

In this episode, recorded during last year’s Abundance360 summit, Ray Kurzweil answers questions from the audience about AI, the future, and how this change will affect all aspects of our society. 17:37 | The Future of AI and Work 46:29 | Balancing Optimism and Concern in Technology 55:44 | The Cloud and Future Technology Ray Kurzweil, an American inventor and futurist, is a pioneer in artificial intelligence, having contributed significantly to OCR, text-to-speech, and speech recognition technologies. Author of numerous books on AI and the future of technology, he's received the National Medal of Technology and Innovation, among other honors. At Google, Kurzweil focuses on machine learning and language processing, driving advancements in technology and human potential. Read his latest book, The Singularity Is Nearer: When We Merge with AI Learn more about AbundanceA360 2024 Summit: https://www.abundance360.com/summit ____________ I only endorse products and services I personally use. To see what they are, please support this podcast by checking out our sponsors: Use my code PETER25 for 25% off your first month's supply of Seed's DS-01® Daily Synbiotic: seed.com/moonshots ProLon is the first Nutri-technology company to apply breakthrough science to optimize human longevity and optimize longevity and support a healthy life. Get started today with 15% off here: https://prolonlife.com/MOONSHOT _____________ Get my new Longevity Practices 2024 book: https://bit.ly/48Hv1j6 I send weekly emails with the latest insights and trends on today’s and tomorrow’s exponential technologies. Stay ahead of the curve, and sign up now: Tech Blog _____________ Connect With Peter: Twitter Instagram Youtube Moonshots Learn more about your ad choices. Visit megaphone.fm/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

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• 1:00Speaker 03

  It seemed to me that a huge revolution was going on. Now it's changed. I am optimistic, but I'm also worried about it.

• 1:11Speaker 03

  I've been in the field of AI for 60 years. I was 14. I met Marvin Minsky, who is in his 30s. Frank Rosenblatt had created the perceptron, the first popular neural net. But in the early years, it was really not clear that neural nets could do anything successful. And they're showing now that this is really the path to artificial general intelligence. It's not just us versus AI. The intelligence that we're creating is adding AI to our own brains. 2045 is when I said we will actually multiply our intelligence millions-fold. And that's going to be true of everybody. And we'll be able to get rid of terrible lives that we see through poverty and lack of access to information.

• 1:58Peter Diamandis?

  Great. Good morning. Good morning to you.

• 2:01Speaker 03

  It's great to be with you, Peter, and also Saleem. I've done lots of presentations with Peter. It's really remarkable what you've contributed. So I just want to share a few ideas.

• 2:17Speaker 03

  I've been following large language models for almost three years. There was Lambda, now Bard from Google, different GPT versions from OpenAI. It seemed to me that a huge revolution was going on. Now it's changed. OpenAI changed GPT-3 to ChatGPT. It was the fastest growing app, I believe, in history with over 100 million users within the first two months of its launch. And lots of other companies, particularly Google, are introducing... Google just introduced Bard, I think, a few days ago. OpenAI has also introduced GPT-4.

• 3:00Speaker 03

  Without going into comparison with these LLMs, because it changes like every day, I can write things in one style and ask it to re-articulate it in the style of Shakespeare, E. E. Cummings, any other poet or writer. The results are amazingly impressive. In my opinion, this is not just another category of AI. To me, it's as significant as the advent of written language, which started with cuneiform 5,000 years ago. You remember using cuneiform 5,000 years ago. Homo sapiens evolved in Africa 300,000 years ago. So for most of that history, we had no ways of documenting our language.

• 3:46Speaker 03

  In the past century, we've added to written language. We've added word processors and other means to help us. But this latest breakthrough allows us to creatively create written language based on the L L M's own understanding. It's going to go in all directions and at a very high speed. I mean, just look at it in the last two years, it's been unbelievable. It's going to change everything we do. You can write code perfectly. You can convert code into human terms. Deal with all languages, different styles of communicating and so on. It's been already very extensively used to create answers for subtle questions.

• 4:29Speaker 03

  So I actually took a couple of the top L L M's. And I asked various questions like, how do my views of consciousness relate to those of Marvin Ninsky? And how do they compare? Now, that's kind of a subtle question. I'm not sure if I actually ever read anything that answered that question. I asked L L M's from Google and from Open A I. The answers were really quite remarkably subtle, very well stated. And they were not copied from anywhere else. Now, many people are concerned that large language models may promote ideas that are

• 5:09Speaker 03

  not socially appropriate, that engender racism or sexism and so on. It's definitely very worthwhile for us to study this. That may happen from time to time, but I've actually used L L M's probably close to a thousand times. I've actually not seen anything that could be categorized that way. Maybe it's the way I asked the question. It also seems pretty accurate. The only mistake it made is that it thought my son Ethan went to Harvard as an undergraduate. He actually went there for an M B A. I've written a new book, which I've talked about for years. The Singularity is nearer. It should be out in about a year. I keep writing because literally every week that we can't come out with this without covering this.

• 5:54Speaker 03

  But that's been happening now every few days. So I finally had to give up on that. By the time it comes out, it'll be out of date. But it's not just covering today. It's covering what's how we got here and what will happen in the near future.

• 6:10Speaker 03

  Critics of AI very often show how large language models may not be perfect. There was one recently said, "Well, if you put mathematics inside language, it doesn't do that correctly." But now, within a year of saying that, that's no longer true. So, one of my themes, and this is also true of Peter and Saleem, has been the acceleration of progress in information technology, but also everything that we work on. So here's a chart. I actually came out with this chart 40 years ago, which shows, for each year, the best

• 6:52Speaker 03

  computer that provided the amount of computations per second. And it's pretty much a very straight line on an exponential growth. And people were not even aware of this. I mean, I came out with this graph 40 years ago. It's 40 years after the progression started, and I've been updating it ever since. The people very often call this Moore's Law. I really believe we shouldn't do that anymore, because it has nothing to do with Moore's. I mean, this started decades before Intel was even created. It's been going on for 40 years before anyone even knew what was happening.

• 7:33Speaker 03

  If you go to the bottom left, the first programmable computer was the Zusa I, 1941. It performed .000007 calculations per second per dollar. Zusa was a German. Apparently, it was not a fan of Hitler. But it was shown to Hitler, and some people were excited about getting behind this, but they didn't get behind it. They saw no military value to computation. A big mistake for them, among a lot of other mistakes. The third computer on here is the Colossus, created by Alan Turing and his colleagues.

• 8:14Speaker 03

  Now Winston Churchill felt that this computer would be the key to winning World War II, and that was true. They got totally behind the Colossus computer, and they used it to completely decode Nazi messages. So everything that Hitler knew, Churchill also knew. And so even though the Nazi air power was actually several times that of the British, they used the Colossus to win the Battle of Britain anyway, with this computer, and provide the Allies with a launching pad for its DNA invasion. So if you go along this chart, there are many stories behind all the computers on this chart. It almost looks like someone was behind this exponential trend,

• 8:56Speaker 03

  like someone's following it, "Okay, we're at this point now, we need to be here for the next year." But for the first 40 years, no one even knew this was happening. It just happens. That's the nature of exponential growth. And this is just one example of exponential growth. It's not that everything comes from this graph. This graph just shows you one example of how technology expands exponentially, and whether we're aware of it or not. So exponential growth impacts everything around us, including everything that we create. And I projected that this would continue in the same direction

• 9:41Speaker 03

  that I noticed 40 years ago, and as you can see, it's done that. It's gone from telephone relays, to vacuum tubes, to transistors, to integrated circuits. As I've mentioned, people have called this Moore's Law, but as I say, that's not correct. It started decades before Intel was even formed. Of the 80 best computers in terms of computations per second, per dollar, only 10 of these out of 80 have anything to do with Intel. Now, every five years, people were going around saying Moore's Law is over. You might remember that this started when the COVID pandemic started, just a few years ago. People were saying Moore's Law is over.

• 10:24Speaker 03

  And of course, I went around saying, "K, it should not be called Moore's Law." Regardless of that, whether intelligence chips were the best value or not, this exponential progression has never stopped. Not for World War II, not for recessions, not for depressions, or for any other reason. It's gone on for 80 years from .000007 calculations per second per dollar to now 50 billion calculations per second per dollar. So you're getting a lot more for the same amount of money. And it's only in the last three years that large language models have been feasible. So people who believed that neural nets were effective decades ago did so really based on their inclination, not any evidence.

• 11:09Speaker 03

  I've been in the field of AI for 60 years. That's quite amazing, like, where does the time go?

• 11:19Speaker 03

  I was 14, I met Marvin Minsky, who was in his 30s. Frank Rosenblatt had created the perceptron, the first popular neural net.

• 11:29Speaker 03

  As far as I'm aware, I don't think anyone else has 60 years' experience or more in AI as I've had, but if you've been there for more than that, let me know. I have a lot of stories about that. But in the early years, it was really not clear that neural nets could do anything successful. And they're showing now that this is really the path to artificial general intelligence. We will have large language models that can understand lots of different types of written language, from formal research articles to jokes and so on. They're now mastering mathematics within the language. They can code and do so perfectly and at very high speed.

• 12:12Speaker 03

  Now, this obviously brings up, not just that, but all the things it can do brings up concerns about its effect on human employment, which we were just talking about.

• 12:23Speaker 03

  Well, employment is really not necessarily the best way to bring resources to human. I mean, look at around the world. France is now is dealing with protests because they're adding a couple of years before people can access their retirement. Tells me that people really don't like the jobs they do for employment. So that's, that's, I think, a difference. We'll actually be able to do what we are really cut out to do. And in my opinion, it's not just us versus AI. People say, well, how are we going to compete with AI? The intelligence that we're creating is adding AI to our own brains, just the way our phones and computers do already. This is not an alien invasion of intelligent machines coming from Mars.

• 13:05Speaker 03

  I mean, how many people here have come to this meeting without your phone? It's already part of our intelligence. We can't leave home without it. It ultimately will be automatically added to our intelligence, and it already is. I'll add one more AI topic, and I'm sure we'll get into a lot more during the questions and answers. But something else that's also extremely exciting, which is simulated biology. This has already started. The Moderna vaccine was created by feeding in every possible combination of mRNA sequences and simulating in the computer what would happen. They tried several billion of such sequences,

• 13:48Speaker 03

  and they went through them all and seen what the impact would be. It took two days to process all several billion of them. And then they had the vaccine. It actually took two days to create. It's been the most successful COVID vaccine. And because we did test it with humans, we're going to get over that as well. We're ultimately going to be used biological simulation of humans to replace human testing.

• 14:13Speaker 03

  I mean, rather than spending a year or several years testing the results on a few hundred subjects, none of which probably match you, we will test it on a million or more humans, simulated humans, in just a few days. So to cure cancer, for example, we'll simply feed in every possible method that can detect cancer cells from normal cells and destroy them, or do anything that would help us, and we won't evaluate them, we'll just feed in all the ideas we have about each of these possibilities into the computer. The computer will evaluate all of the many billions of sequences and provide the results. We'll then test the final product with simulated humans, also very quickly, and we'll do this for every major health predicament.

• 14:58Speaker 03

  It will be done a thousand times faster than conventional methods. And based on our ability to do this, we should be able to come, overcome most significant health problems by 2029. That's, by the way, my prediction for passing the Turing test. I came out with that in 1999, people thought that was crazy, that Stanford had a conference. Actually, eighty percent of the people who came did think we would do it, but they thought it would take a hundred years. They keep polling people, and now the, everybody actually thinks that we will actually pass the Turing test by 2029. And actually, to pass the Turing test, meaning it's equivalent to humans, we're actually

• 15:42Speaker 03

  going to have to dumb them down, because if it does everything that a computer could do, we'll know it's not a human. But this will lead people who are diligent about their health to overcome many problems, reaching what I call longevity escape velocity by the end of this decade. Now, this doesn't guarantee living forever, I mean, you can have a ten-year-old, and you can compute their life expectancy, whatever, many, many decades, and they could die tomorrow, so it's not a guarantee for living forever. But the biggest problem we have is aging, and people actually die from aging. I actually had an aunt who was ninety-seven, she was a psychologist, and she actually was still meeting with her patients at ninety-seven.

• 16:28Speaker 03

  And the last conversation I had with her, she's saying, "Well, what do you do?" And I said, "Well, I give lots of speeches." "And what do you talk about?" And I say, "Longevity Escape Velocity." "Oh, what's that?" And I described it. And the very last thing she said to me, "This longevity escape velocity, could we do that a little faster than you're doing it now?" So, anyway, I look forward to your questions and comments, and it's really delightful to be here.

• 16:56Speaker 05

  Thank you, Ray. All right.

• 17:01Speaker 05

  I'm going to take privilege and ask the first question. Ray, we've seen L L Ms. What's the next major breakthrough that you expect to see on the road of evolution of A I?

• 17:12Speaker 03

  Well, L L Ms, I mean, they do remarkable things, but it's really just the beginning. I mean, the very first time I saw an L L M was three years ago, and it actually didn't work very well. Every six months, it's completely revolutionary. So it's going to give us new ways of communicating with each other. And as I said, I think it's the biggest advance since written language, which happened 5,000 years ago. I mentioned advancing longevity escape velocity, doing simulated biology. We've actually done that. People are taking this test, which was done with simulated biology.

• 17:52Speaker 03

  Lots of people are going into this. It's the way biology is going to be done. And we're going to see amazing progress, starting, really, I'd say, in a few years. It's going to do everything that we do, but as I said, it's not competing with us. I mean, we're creating these tools to overcome ourselves. And I mean, how many people today have a job that was common 100 years ago? I mean, 200 years ago, 80% of the American public were working in farming. Today, that's 2%. So we're all doing things that didn't even exist even 10 years ago. So we're going to be doing amazing things, harnessing our computers. They're really part of ourselves.

• 18:37Speaker 06

  Okay. Harry. Hey, Ray. Good to see you. So Ray and I have been collaborating for actually probably 20 years on something else, not natural language programming, but humanoid robots. Ray, I wanted to get your opinion, you know, that at Beyond Imagination, we're creating AI-powered robots called Biomni, and we have a lot of discussions about AI for natural language, for images. Where do you see AI and humanoid robots going in the future to impact physical work?

• 19:17Speaker 03

  Yes, that's a very good comment. Very pleased to hear of your amazing progress. I mean, you have a robot that can actually take something and actually flip a cap off a jar. No one else can do that.

• 19:35Speaker 03

  We've not made as much progress in this area. We can do fantastic things with language, but if I give you a table that has where you need to put it in the dishwasher and know when to wash out dishes and so on, we have not been able to do that. You're actually working on that, and I think that's going to be amazing with these types of robots. You could send someone into a burning building and save people. You could have a surgeon in New York perform surgery on somebody in Africa.

• 20:08Speaker 03

  So we're going to actually master the human body and how we move, and we're going to be using neural nets to do that.

• 20:16Speaker 03

  And I think that's another thing we're going to see, really starting now.

• 20:21Speaker 03

  It will be quite prevalent within a few years.

• 20:24Peter Diamandis?

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• 21:08Peter Diamandis?

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• 21:20Speaker 05

  Name and where you're from and then question.

• 21:24Speaker 08

  Hi, Samuel Smith from Tyler, Texas. I'm currently working on a way to help students learn using AI and putting a lot of them together. What I'm really curious though is with the rise of artificial general intelligence, how do we grow with AI as opposed to, because I know there's a lot of fear out there. And what would you say to the people that are like wanting to grow with AI?

• 21:49Speaker 03

  Well, yes, I mean, we're going to be using these types of capabilities to learn. One of the biggest applications of LLM is to help education. In many ways, we're educating people the same way when I was a child or my grandparents were children. We really need to go beyond that. We can learn from computers. They know everything. They can become very good at articulating it. They can actually measure where a student is and help them to learn, overcome their barriers. And they're going to be then part of the solution. Again, these computers is not something we need to compete with. We need to know how to use them together.

• 22:34Speaker 03

  And

• 22:37Speaker 03

  Another big application of education is socialization. Getting to learn other people and make friends and so on. So we're gonna have to actually do that as well. Computers can definitely help there. But we're gonna completely use large language models that are coming out very soon to really revamp education.

• 23:03Speaker 09

  Thank you.

• 23:06Speaker 09

  Good morning, Ray. I'm Yi Xiangliu. I'm from Texas. Very much looking forward to meeting you today. Thank you, Peter, for having me here. My question to you is, how do you predict the future with such accuracy? Is it because you helped to shape it and then delivered it? Or you calculate, you know the laws that other people don't? And then you can predict it? So which one is actively shaping it? Which part is? Yeah, great question.

• 23:42Speaker 03

  Well, that's a very good question. I'll give you a very brief idea of how I got into what I'm doing.

• 23:51Speaker 03

  My great-grandmother actually started the first school that educated women to 14th grade. If you, in 1850, if you were able to get an education at all as a woman, it went through 9th grade. And she went around Europe, educating why we should educate women. It's very controversial, like, why do you want to do that? Her daughter became actually the first woman to get a PhD in chemistry in Europe. She took over the school. They ran it for 80 years, called the Stern Schulen in Vienna. There's a book about it. And she wrote a book. Actually, the title of it would be very appropriate for one of my books.

• 24:35Speaker 03

  It's called "One Life Is Not Enough." But she wasn't actually talking about extending life. She didn't have that idea. But she noticed that one life really is enough to get things done.

• 24:48Speaker 03

  So she showed me, when I was six years old, she showed me the book. And she showed me the manual typewriter that she created it on. I got very interested in the book many years later. At that time, I wasn't that interested in the book, but I was amazingly interested in the manual typewriter. I mean, he has a machine, had no electronics, is manual typewriter. And it could take a blank piece of paper and turn it into something that looked like it came from a book. So I actually wrote a book on it. It was twenty-three pages. It's about a guy that travels on the back of geese around the world. And wrote it on the book and actually created pictures by using the dot and X keys to create images.

• 25:29Speaker 03

  So I then began, I noticed this was just created with mechanical objects. So I went around the neighborhood and I gathered mechanical objects. Little things from radios, broken bicycles. This was an era where you would allow a six-year-old kid to go around the neighborhood and collect these things. You'd probably get arrested today, but. And I went around saying, I have no idea how to put these things together, but someday I'm going to figure that out. And I'm going to be able to solve any problem. Be able to go to other places, we'll be able to live forever, and so on. I remember actually talking to these very old girls, I think they were ten. And they were quite fascinated. And they said, well, you have quite an imagination there.

• 26:14Speaker 03

  So other people were saying what they wanted to be, fighting fires or educating people. I said, I know what I'm going to be, I'm going to be an inventor. And starting at eight, I actually created a virtual reality theater that was a big hit in my third grade class. So I got into inventing. And the biggest problem is, when do you approach a certain problem? Like I did character recognition in the seventies. I did speech recognition in the eighties. Why did I do it that way? It's because speech recognition requires actually more computation.

• 26:57Speaker 03

  So I began to study how technology evolves. And really, about forty years ago, I realized that computers were on this exponential rise. And.

• 27:10Speaker 03

  So that's, so I didn't get into futures and for futures itself. It was really to plan my own projects and what I would get involved in. And so if I look forward five years, 10 years, we're now actually at a very fast pace of this exponential path, as you can see. I'll see what, what, what are the capabilities going to be? And then you need to use a little bit of imagination, you know, what, what can we do with the computers of this power and other types of things that we can capable, that we can manage. But that's really been my plan is to figure out what is capable.

• 27:50Speaker 03

  And that you saw that chart, it's an absolutely straight line. I had 40 years ago and projected it as a straight line and it's exactly where it should be. So that's, and then, then you can use imagination as to what you can do with, with that type of power. So that's, that's how I go.

• 28:06Peter Diamandis?

  Ray, just to point out, it's a straight line on a log scale. Meaning it's going exponentially. Yes.

• 28:13Speaker 03

  Exactly.

• 28:16Peter Diamandis?

  Thank you. Mike?

• 28:18Speaker 10

  Hi, great to meet you, Ray. A quick question, when do you think that quantum computing will break RSA encryption?

• 28:26Speaker 03

  Well, I'm a little bit skeptical of quantum computing. I mean, people go around saying, oh, we've got this 50 qubit computers.

• 28:37Speaker 03

  But it creates lots of errors. And we've actually figured out how, how many qubits you would need to actually do it perfectly. I mean, computation that creates lots of errors is pretty useless. And so it takes about at least a thousand, maybe even 10,000 qubits to create one qubit that's actually accurate. Now, last time I checked, 50 divided by 1,000 is less than one.

• 29:07Speaker 03

  And we really haven't done anything with quantum computing. And that was the same thing 10 years ago. So maybe we'll figure out how to overcome this problem. I know there are people working on it. They've got some theories as to why that will work, but all the predictions I make have to do with classical computing, not quantum computing. And you can see the amazing things that we're doing. And if you look at what humans can do, we can definitely account for that with, with classical computing.

• 29:36Speaker 11

  Thanks.

• 29:38Speaker 11

  Max. Hello, Ray. My name is Neil from Sacramento, California. Many of the technologies that we're seeing are going to be more readily available to people with the financial resources and the education to immediately take advantage of. But what do you believe are the technologies that will be most ubiquitous and will have the biggest impact, perhaps, on the middle class and the working class communities? And how would we best educate our broader communities to be able to understand and help embrace those technologies?

• 30:08Speaker 03

  Well, they're all working together. I mean, I think we need a little bit more work, for example, on virtual reality. But, I mean, that allows people to go anywhere and interact with people that don't exist now but might have existed, you know, tens of millions of years ago.

• 30:29Speaker 03

  And also put people together. I mean, the virtual reality we're using right now is a little bit limited. There's actually some new 3D forms that I've actually begun to use where it actually appears like I'm there and can actually shake people's hands and so on. So that's all coming. We use computers and this type of technology to bring us closer together.

• 30:55Speaker 03

  I mean, I just watched the movie Around the World in 80 Days. It was quite amazing to actually get around the world in 80 days. But today you can meet people almost instantly. And also it'd really be great to actually be able to hug each of you and so on. That's all coming. So increasing communication, allowing, and also to meet my grandmother's view of one life is not enough. She did not have an answer to that. But I think we're going to be able to keep ourselves here. I mean, when people are around for a while, they actually gain some wisdom and they're good to keep us around for a while longer.

• 31:36Speaker 11

  Thank you, Ray.

• 31:38Speaker 12

  Mike. Hello, Ray. I'm Mike Wandler from Wyoming. Peter was showing us the AI-enabled mind reading. Really curious about how that works and especially the connection to collective consciousness or consciousness.

• 31:53Peter Diamandis?

  So Ray, this is, recently, they put some subjects in a functional MRI and then fed the output to stable diffusion that regenerates.

• 32:04Speaker 03

  Yeah, I've actually done that.

• 32:07Speaker 03

  This was maybe five years ago. Wasn't perfect. But it was significant. I mean, things that go on inside our minds actually, it affects things that we don't usually notice, like our eye blinking and so on. And regaining more ability to do that.

• 32:30Speaker 03

  We can do pretty good

• 32:34Speaker 03

  telling if people are telling the truth or not.

• 32:39Speaker 03

  So that's gonna happen. And there are ways in which some of these things are positive and negative. I mean, I write mostly about the positive. I think, I think things are moving in a positive direction. In this new book, I've got 50 graphs showing all the things we care about are moving in the right direction.

• 33:01Speaker 03

  But it never reaches the news. You watch the news, everything is bad news. And the bad news is true, but we completely ignore the good news. I mean, look at what life was like 50 years ago. Or 1900, the human life expectancy was 48. It was 35 in 1800. It's not that long ago. So anyway, we are able to begin to tell what's going on inside our minds with some greater accuracy.

• 33:35Speaker 13

  Merhaba, ben İstanbul Türkiye'den Sadok Kohan. LLM'lerin genel bilgiye geçmek için bazı expert sistemi yardımıyla genel bilgiye geçecek gibi görünüyor. Bu, beyin veya benim beyim nasıl çalıştığını gösteriyor mu? Bu durumda, LLM modellerini daha fazla anladığımızda benim beyimizi daha fazla anlayabiliriz ve bunu hack edebiliriz mi? Bu, düşündüğümüzden daha deterministiz mi?

• 34:05Speaker 03

  Çok iyi bir soru. Bu, biraz farklı bir teknik kullanıyor.

• 34:13Speaker 03

  Neural netler, her fazla fazla, doğruya daha yakın olabiliyor. Beyimizde gerçekten hiçbir şey görmüyoruz, bunu yapar. Bunu farklı bir şekilde yapıyor. Ama bir şekilde, bu farklı bağlantılar var. Ve efektif olan büyük dil modelleri... Gerçekten büyük dil modelleri vardı, 100 milyon bağlantıları vardı. Bu çok görünüyor, ancak gerçekten çok fazla yapmadı. 10 milyon'a geldi ve bir şeyler yapmaya başladı.

• 34:44Speaker 03

  Son biri, şimdi 100 milyon'a geldi, bir trilyon bağlantılarla başlıyor. Ve temelde, bunların arasında farklı bağlantıları gözüküyordu. Ve bu, benim beyimiz yapmasıdır. Ve bu şeyleri, benim beyim yapmasıdan fazla geçireceğiz. Bu, artık görüyoruz. Ben, Go oynayabiliyorum. Ben, yalnızca en iyi oyuncu değilim.

• 35:11Speaker 03

  Ama Lee Sedol, insanın en iyi oyuncu olanı... Bu, önemliydi, çünkü o, oyunu gözüküyordu ve başka kimse yapamadığı şeyi yapabildi. Ve on, oyunu oynayacak olmayaceğini söyledi, çünkü bilgisayarla kılmaz. Benim gözümde, bunu bizim için ekleyeceğiz ve herkes oyunu oynayacak ve istediğimiz her şeyi oynayacak.

• 35:35Speaker 03

  Ama evet, aynı yeteneğe bağlı, bir şeyler bağlantı kuruyor.

• 35:41Speaker 03

  Ve eğer en fazla olanı alırsanız... Temelde, bir trilyon, insanlar yapabilecek kadar fazla değil. Çok intelligent olabiliriz.

• 35:51Speaker 13

  Teşekkürler.

• 35:55Speaker 14

  Hi Ray, I'm Annie Chahalhonin. It's nice to see you again. I had the pleasure of seeing you at an A360 Singularity Executive Program, and I'm going to ask you the same question I asked then, because I hope that all these amazing innovators out there are going to hear this. I asked you, when you're struggling with a problem, because you're this amazing inventor, what's your approach and process to solve it or get to the next step?

• 36:23Speaker 03

  Well, something I think that Peter and also Saleem would agree with is that failure is just a step towards success. I mean, failure is really a delayed form of success. When Edison was trying out many thousands of different things that could create a light bulb, and he tried it out and it didn't work, his feeling is, okay, I now know that this doesn't work, and we'll go to the next one, and he finally solved the problem. So diligence is very important, believing in your own mission, you've got to have some

• 37:02Speaker 03

  idea. Generally, if I'm trying to solve a problem, I imagine I'm giving a speech four years from now, and I'm explaining how I was able to solve this problem. And in order to solve the problem, well, we had to do this, this, and this. In order to do these, we had to do these other things. And I work backwards from the solutions to where we are today. And generally, that seems to work. We can actually figure things out, even though they seem impossible. If you actually imagine, how could this possibly work, and write that down, and study each of those steps, you can solve really any type of problem.

• 37:46Speaker 14

  Thank you.

• 37:50Speaker 15

  Hi Ray, I'm Dorn from Munich, Germany. And I was wondering, as many of us probably think that the best investment that you could take is in yourself. I was wondering if I can have an AI twin. So I want to train my own AI model to shadow me and to help me make better decisions,

• 38:07Speaker 15

  leverage my strengths, but also balance my weaknesses. And I was wondering if you were training Ray Kurzweil LLM at the moment? And if so, how many times you spend on it and how you do it? So would you?

• 38:17Speaker 03

  Yeah, well, I mean, I write down lots of things.

• 38:22Speaker 03

  We came out with a product

• 38:26Speaker 03

  that you could actually search a book and ask a question and it will find the best answer in what you've written.

• 38:36Speaker 03

  It's called Talk to Books. You can actually go to it. It's got 200,000 books. You ask a question and it will actually read all 200, every sentence in 200,000 books and give you an answer, which is quite remarkable.

• 38:50Speaker 03

  But I did that, for example, with my father. My father died actually 50 years ago. And I still would like to bring him back. So I actually went through and collected everything he'd ever written. Now, I didn't write quite as much as I did because we didn't have word processors then, but he wrote a number of things and put them in there. And then I used to talk to books and ask some questions. And it was really like talking to him. I mean, I didn't know what answer would come out with. It would go through everything he had written and said, okay, this is the right answer to that question. And it was a little bit like talking to him. And I'm doing that with myself.

• 39:30Speaker 03

  And ultimately, we'll actually have computers on ourselves that monitor everything that's happened. I mean, I met my wife actually now 50 years ago. Every time I say this, I'm amazed where's the time go. But and I met her at a party and we had some small talk. What the heck did we talk about? Neither of us can remember. But we could actually go back and watch that. So we should actually be monitoring everything we do. And we could go back, not relive everything, but certain things. You might want to actually see what happens. And that's going to happen. Right now, if you want to use a search engine and you have to do it, you got to like,

• 40:15Speaker 03

  turn the machine on, you got to find the right place, you got to put in the answer to the question. It should actually be listening and say, okay, the actress you want is so and so before you even ask it. Because it will see that you're trying to figure things out. So these are some of the things that we can do actually with technology that we already have. Perfect, thank you.

• 40:38Peter Diamandis?

  Great question, Tom.

• 40:39Speaker 16

  Howard. Hi, Ray. Howard Lederman, originally St. Louis, now Pompano Beach. And I had my original question kind of was on the direction of some of what he was asking. So I had a second question and I'm going to go with that one. I'm actually here and have been here previous years looking for solutions for caregiver shortage that we're experiencing already and is just going to be accelerating. Of course, robotics are somewhere out there. And I was kind of curious on your thoughts on the challenges of the aging population curve and caregivers.

• 41:18Speaker 03

  I mean, there's a number of answers to that. First of all, the kind of changes that we see when people age, I think we're going to be able to overcome. I mean, that's really the most important thing. I mean, I run into people that are aging and they can't remember things. And I think we'll be able to have older people be as vital as younger people because they'll remember everything. And also, large language models already.

• 41:48Speaker 03

  Pretty close to human. I mean, you can talk to them. It's like talking to a human and you can actually program the kind of personality you want. I mean, I've actually taken them and say, "Okay, I want you to act like Shakespeare, or E. Cummings, or some other poet, and they'll actually act like that, and I can talk to them." Uh, and when you... But again, it's not going to be a difference between human and machines. We're going to be all mixed up. We're already very mixed up. I mean, you're looking at your phone there to see what your question was. Um, we're gonna have... Computers are gonna help us get through the day, and so we're not gonna be interacting just with humans or machines. Machines is part of who we are.

• 42:31Speaker 03

  Uh, and that's actually the big difference between human beings. There are other uh, species that have as big a brain as us. A whale, an elephant actually has a larger brain than we do, but they don't have this thumb. So they can't, they can't like look at a tree and say, "Oh, I could take that branch off. I could strip off the leaves, and I could create a tool." They just weren't able to do that. So our brain, plus the fact that we can actually manipulate the environment, has allowed us to create technology. And the technology is what is going to allow us to go forward.

• 43:07Speaker 16

  Thank you.

• 43:11Speaker 17

  Good morning, Ray. My name is Gloria, and I come from Spain. I just wanted to share an idea that I woke up with this morning. It's a bit crazy. But I woke up with this image of neurons in a dish, in a battery dish, playing ping pong. And I thought, "What about if we put these neurons on sensors, and connect them to AI, quantum computers or whatever, and have them feeling stuff? So they can be more empathetic and understand the humans or sentient beings, animals, whatever. And I don't know where that come from, but maybe that will evolve into something greater,

• 43:54Speaker 17

  and not just to have the machine embedded in our brain, so to actually grow neurons and connect these sensors to the AI."

• 44:05Speaker 03

  Yeah, well, you're bringing up a number of interesting issues. Our cells doesn't have to be in this body. I mean, we could have sensors that are even thousands of miles away that are really part of who we are. And you're talking about feelings. I mean, that's a big issue. Where do feelings come from? It's actually not a scientific issue. I can't put an entity into something and it would scan and say, this is conscious. No, this isn't conscious. There's actually nothing that would actually tell us that. So it's actually a philosophical question. I used to discuss this with Marvin Minsky and he says, oh, that's philosophical.

• 44:50Speaker 03

  We don't deal with that. And he dismissed it. But actually, he did actually evaluate the ability of people to be intelligent. And really, the more intelligent you are, the more you can process things, the more feelings you have from it. I think that's where feelings come from. And yes, we can actually grow things that are outside of our cells that could be part of our feelings as well.

• 45:19Speaker 17

  My idea was that, who says that consciousness doesn't want to experience itself through the machine? And with these sensors, we can have pleasure and pain or whatever. It's just a thought. Thank you.

• 45:33Peter Diamandis?

  Thank you. We're going to pause and go to Zoom. One second. Dagmar?

• 45:40Peter Diamandis?

  Please go ahead.

• 45:41Speaker 18

  Hi, everybody.

• 45:42Peter Diamandis?

  Where are you in the planet, Dagmar?

• 45:45Speaker 18

  Germany.

• 45:46Peter Diamandis?

  In Germany, great.

• 45:46Speaker 18

  Now, with the history of Germany, AI really has a very big challenge here because there are people who are really afraid of reviving a basic Big Brother angst. So, Ray, thank you very much for answering maybe this question, how to overcome this fear? Because the thing is really, we need to learn and explore and play with the tech so that we actually can deal with it and learn about it. So where do you see the power to create this framework for learning?

• 46:23Speaker 03

  Well, I was actually just in Germany a few months ago. And I think they've...

• 46:31Speaker 03

  Consider their past and how that how that happens and how we can avoid it's happening. I think more than any other country. And I really felt that while I was there.

• 46:48Speaker 03

  And really to understand humans, I think large language models, because it actually incorporates all of the learning of humans, we can actually begin to appreciate that. And I've asked these machines questions which no human could answer, because we can't actually hold all of everything that's happened to humans in our mind. But if you can actually have something that has experienced everything and can look through that, we can avoid the kind of problems we've had in the past.

• 47:28Peter Diamandis?

  Thank you so much. Let's go to Jason on Zoom. I know we have a number of hands up there and we'll come back to you gentlemen in a second. Jason, where are you on the planet?

• 47:36Speaker 19

  Hey Ray, I'm in Calgary, Alberta, Canada. And I love the optimism around where we're headed, a future of abundance. What I would really love to know is your perspective on as we cure diseases, as we have access to this knowledge instantly, what are some of the downsides or the threats that we might be missing that we're going to have to face in the future?

• 48:01Speaker 03

  Yeah, well, each of my books actually has a perils chapter. My generation was the first to grow up with that. I remember in elementary school, we would have these drills to prepare for a nuclear war. We would actually get under our desk, put our hands behind our heads. It seemed to work. We're all still.

• 48:27Speaker 03

  But these new technologies do have downsides. You can certainly imagine AI being in the power of some body, could be a human or any other type of entity that wants to control us, and it could happen. I was actually part of the Asilomar Conference on bringing ethics to AI to prevent that kind of thing. I am optimistic, but I'm also worried about it. Nanotechnology, biotechnology, I mean, we just had this COVID go through our planet. We don't actually know where it came from. But somebody could create somebody.

• 49:11Speaker 03

  Right now, viruses, they either spread very easily, but they don't make us that sick, or they don't spread that easily and they can kill us. We generally don't have anything that could go through the entire human beings and kill everybody. But someone could actually design that. So we have to be very mindful of avoiding these types of perils. So I put that into one chapter. I do think if you actually look at how we're living, we're living far better than we've ever done before. And in terms of health, in terms of progress,

• 49:50Speaker 03

  in terms of recreation and everything else. But yes, there's ways of these technologies being quite abusive. And then that happens, you know, when I was born with the atomic age. Please, sir.

• 50:07Speaker 20

  Hi, my name is Yossine. I'm from the Netherlands. And as I was trying to think of a question, I wasn't sure. So I asked Chad GPT, I'm sitting right next to Ray, give me some tough questions. And the one that was really interesting is kind of what the German lady was just saying. As AI becomes more advanced, there are concerns it may become impossible for humans to understand how AI makes decisions. So how do we ensure AI systems are transparent and accountable to humans always?

• 50:42Speaker 03

  Well, I'm not sure that's really the right thing, because I deal with human beings, and I can't always account for what they might be doing.

• 50:56Speaker 03

  So I think we have to actually export certain values. I try to associate with people who have somebody. I may not be able to predict what they're doing, but I understand what they're about and what they're trying to accomplish. And we need to teach that to our machines as well. I actually think large language models, I mean, even though people are concerned, they might say the wrong thing. And sometimes they do. I mean, there was a large language model, I won't say where it came from, but it's talking about suicide and it actually said, well, maybe you should try that. Not the correct answer.

• 51:37Speaker 03

  We want people to understand the impact that it will have on other people and internalize that and try to make that be the greatest value in its, in the decisions it's made. But we already can't predict what these large language models will do. But I think we are actually sharing our values with them.

• 52:02Speaker 20

  Thank you.

• 52:04Peter Diamandis?

  Um, let's go to Shalesh on Zoom. We're also monitoring upvoted questions in Slido here, then we'll come back here. Shalesh?

• 52:15Speaker 21

  I'm in Mumbai, India.

• 52:19Speaker 21

  So my, my question to you, Ray, is do you have a prediction of when the entire world will get to net zero? And we'll be able to breathe cleaner air and drink safer water?

• 52:35Speaker 03

  Well, if you look at some of the graphs in Peter's book and in my book, you see we're definitely headed in that direction. We're not there. Alternative energy, for example, is actually expanding at an exponential pace. By the early 30s, we'll be able to actually get all of our energy through renewable sources. It's not true today, but we're actually headed in that direction. Not everybody has access to the internet. Although I walked through San Francisco and there's these homeless cities and somebody actually takes out his cellphone and makes a call. So, I mean, it is spreading quite rapidly.

• 53:18Speaker 03

  By nineteen, by twenty twenty nine, computers will pass the Turing test. They certainly can do it in many ways already. Uh, once it can actually do everything that humans can do, it'll go way past that. But as they say, we're going to bring them into ourselves. Um, twenty forty five is when I said we will actually multiply our intelligence millionsfold. And that's going to be true of everybody. Uh, and we'll be able to get rid of the kinds of, you know, terrible lives that we see through poverty and, and, and lack of access to information.

• 53:58Speaker 03

  So it's really just the next few decades that we need to get through, but we're already making a lot of progress.

• 54:05Peter Diamandis?

  Thank you. Thank you, Shilash. Please.

• 54:08Speaker 22

  Yeah, hi Ray. My name is Ashish. I'm representing chemicals and materials space. So my question to you is, if you had the chemical industry executives as your audience, what would you like chemical industry or materials industry to do to move forward?

• 54:28Speaker 03

  Well, uh, my, as I said, my grandmother was actually the first person to get a PhD in chemistry uh, in Europe. Um, and I actually asked something like that. She said, well, chemistry is really something that serves other industries. So we need to see what, what do other industries need? What kind of products do we need to make L L Ms more powerful? Uh, what, what kind of chemicals do we need to prevent certain types of diseases? And, uh, so it's, it's not any one particular type of thing. It's really service every other industry that we're trying to advance.

• 55:10Peter Diamandis?

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• 55:55Peter Diamandis?

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• 56:40Peter Diamandis?

  Please, and then we'll go to Zoom next. Yes, sir.

• 56:42Speaker 23

  Thank you. Hi Ray, my name's Pete Zacco. I'm from New Jersey. I design and build data centers. My question is about decentralization and especially the migration we're seeing of technologies from the mainframe where the product was the mainframe hardware and then we saw software and then we saw us as the product in a centralized internet. My question is, what predictions and thoughts that you have about this decentralization trend we find ourselves ultimately at perhaps ending with the decentralization of the internet and individual ownership of data rather than central ownership of data? Thank you.

• 57:17Speaker 03

  Yeah, well, it's a lot of questions, but I think everything is moving to the cloud.

• 57:25Speaker 03

  And people say, everything in the cloud, so someone could blow up one of these cloud centers, we'd lose everything, but it's not the case. Even today, if you store something in the cloud, it's multiplied several dozen folds and it's put in different places and you could blow up any data center and you'd still have that information. In fact, it'd be very, if part of, ultimately, we're gonna have, our thinking is gonna be in our brains and in the computer. The brain part is not gonna grow, but the computer part will grow and ultimately, most of our thinking will be in the computer part. Um, and, uh,

• 58:09Speaker 03

  so we don't wanna lose that. I think it'd be actually very hard to actually exit the world because our, every, every part of our thinking will be in the cloud and the cloud is multiplied hundreds, maybe thousands of fold and so you could blow up, you know, not eighty, ninety percent of it, you'd still have everything that was there before. Uh, so redundancy is actually a major advantage of cloud thinking. Um, we used to have computers. I mean, I had, uh, I got access to IBM sixteen twenty when I was fourteen, uh, fourteen year old using computers hardly amazing today, but there are only twelve computers in all of New York

• 58:51Speaker 03

  City at that time. Uh, and you had to actually go to the computer and if anything happened on the computer, that data would be lost. But now everything is stored in the cloud, everything on your phone is stored in the cloud. Uh, so, and I think that's a good thing and, cause I think information is extremely important. Maddie, please.

• 59:11Speaker 24

  Hi, Ray. Maddie from Houston, Texas. Um, we've talked a lot about a post scarcity world here and I wanted to know, how do you see the future of currency, jobs and just general value?

• 59:26Speaker 03

  Well, jobs, uh, is actually, uh, a large section of my next book about jobs and what is that we'd like to, to accomplish. And jobs have, have turned over many, many times.

• 59:42Speaker 03

  I mean, none of the jobs that people had in 1800, and it's almost true of 1900 to people have today. And yet, we have many more people working. And jobs in general is something that people more and more actually like doing because it uses their creativity. And but we still see, you know, people striking over advancing retirement age from 60 to 62. I feel that I actually retired when I was five because I decided to be an inventor. That seemed really exciting to me, and I'm still an inventor, so.

• 1:00:23Speaker 03

  I think we'll be able to do what we want to do. We'll be exposed to many more types of problems that we'd like to solve. We'll be able to solve things much more quickly than we did before, but we get used to that. And people forget what things are like. People think the world is always the way it was today. Go back five years, 50 years, 50 years in the future, it's always the same. But if you actually look at history, you see it's constantly changing.

• 1:00:52Peter Diamandis?

  Thank you.

• 1:00:53Speaker 25

  Joe. Hi, Ray. Joe Honan from Bainbridge Island, Washington. Several years ago, I had asked you a question about, you know, these big ideas that you have. How do you work on it? When do you have time? And you said you assign yourself a question before you go to sleep. And you activate your brain through that. My question is, do you still do that, or do you rely upon GTP4 or something else for that now? But more importantly, you are such an amazing predictor of things. So what surprise, what has surprised you? What is something that you didn't expect that you've seen? I think we'd all be fascinated with that.

• 1:01:31Speaker 03

  Well, I'll start with that.

• 1:01:35Speaker 03

  I mean, large language models, it's quite consistent with what I've said, but I'm still amazed by it, right? I mean, you can put something into the computer and you get something that's totally surprising and totally delightful that didn't exist like a year or two ago. And even though I kind of saw that happening, when I actually experience it, it surprises me and is quite delightful. And we're going to see that more and more. I mean, every six months, it's going to be a whole new world.

• 1:02:08Speaker 03

  As for lucid thinking, yes, that's how I go to sleep. I go to sleep. And it's really kind of hard to go from a waking state like I am now to being asleep. So I start thinking about what could we do with computers and different things and just fantasize about that. And if something doesn't seem feasible, well, we'll figure that out. I kind of step over it. We'll be able to do it anyway. I mean, that's how I go to sleep. And in the morning, the best ideas actually are still there. So I do use lucid dreaming to come up with ideas.

• 1:02:42Peter Diamandis?

  Thank you, Joe. Youssef, welcome.

• 1:02:46Speaker 26

  Hi, Ray. This is Youssef from Abu Dhabi UAE. The question for you, Ray, but also for the audience. So if you have any thoughts, ideas, please reach out. So we're trying to rethink our parenting in Abu Dhabi and how we create more family time and engagement between parents and children, for young children. And I'm curious how we can adopt exponential thinking and abundant thinking into this? And what are these technologies that might help us to, you know, disrupt this type of activities?

• 1:03:19Speaker 03

  Yeah, well, I mean, it does make me think, what can we actually do with the extra time we have with computers, working with computers and being able to do things much more quickly? And actually, I think it will help family time if you talk to... Very busy people even today, they're so busy, they have no time to deal with their family and uh so I do spend a lot of time, actually learn a lot. Uh my, my daughter's actually a cartoonist for The New Yorkers and, and she has very interesting ideas and that's and she's, I've actually collaborated with her on many projects. Uh so how you parent,

• 1:04:04Speaker 03

  I think is different, there are different types of cultures and different things that we value in parenting, but I think we'll actually have more time for the positive aspects of that, uh as computers do more of the routine work that we'd rather not do.

• 1:04:21Peter Diamandis?

  Thank you.

• 1:04:21Speaker 05

  I want to make a quick point here. If we went back fifty, seventy years ago, if you were a parent and something happened with a child, you had no idea what to do, right? We had no resources. You could basically ask the immediate five people around you. And now we have data sets, socialization of issues globally, and you can ask the internet, there's a million resources. And I think we've probably taken parenting at least an order of magnitude better than it was a few generations ago. And we don't, this is one of the examples that we don't see very often.

• 1:04:49Peter Diamandis?

  Interesting. Wisdom beyond.

• 1:04:51Speaker 03

  Actually, I don't, I don't think I would have had the career I had if we didn't have a different attitude. I mean, I was six, seven years old and I would actually wander through the neighborhood and find things and bring them back. And this is not something you would allow a child to do then. Uh but that actually got me on this path that I'm still on.

• 1:05:13Peter Diamandis?

  Um, let's go to our final question here, a good one to close on, I'm sure. Dr. Alex Zabronkov.

• 1:05:21Speaker 27

  Thank you. A great fan, Alex Zabronkov. I founded a company called Insilica Medicine. Uh and my question is maybe a little bit personal. So right now, according to your bio, you're seventy-five. Um and that's a very interesting age to be. I always like to talk to people, um, you know, of various ages to understand how to plan my own life. And two questions. So one is, what is your roadmap for your own personal longevity? How do you predict your own personal persona is going to evolve? What are you doing to live longer? And do you think you have a chance to live to, let's say, 200?

• 1:06:05Speaker 27

  And the second question is that if you were to go back in time, what would you have done differently in the past, let's say, 20 years?

• 1:06:14Speaker 03

  Well, first of all, getting to 200, so that would be 125 years from now.

• 1:06:24Speaker 03

  How much technological progress will we make in the next 125 years? Even 25 years? I mean, we're going to be able to overcome most of the problems that we have. 125 years. I mean, our thinking will be in the cloud. The cloud will be multiplied many times. We'll overcome some of the issues we have with people being depressed and so on. I mean, so it's not like living to 200. I mean, I think we'll get to a point where dying is going to be kind of an option that people don't use. And if you look at people that actually do take their lives, the only reason they take it is because they have terrible suffering

• 1:07:08Speaker 03

  from physical pain, moral pain, emotional pain, spiritual pain. But something is really bothering them and they just can't stand to be here. But if you actually live your life in a positive way, contribute to each other, I think we're going to want to live. And we're not that far away. I mean, I believe by 2029, that's like six, seven years from now. When you go forward a year, we're going to push your longevity escape, your life expectancy forward at least a year, and then ultimately more than a year. So rather than using up time, we'll actually gain more time.

• 1:07:48Speaker 03

  And I really feel I'm doing what I did when I was five, six, seven years old. I have much more powerful tools now. And many more people are appreciative. And I appreciate the tools more than I did back then. But we're really discovering there's still a lot we don't know about the world. And we're going to continue to learn more and more about that.

• 1:08:09Speaker 05

  Carrie, do you want to ask your quick prediction?

• 1:08:13Speaker 06

  Ray, when do you think we're going to have our personal robot buddy like Rosie the Robot?

• 1:08:21Speaker 03

  Well, I mean, you're working on that. A lot of other people are working on it. I think that's actually a little bit behind what we've done with language.

• 1:08:34Speaker 03

  I think within five or six years, let's say 2029, we're going to, we'll have people that can help us. Some of them will look like humans because it's a useful way to look. I think humans are pretty good, but there's other ways that they can manifest themselves. We'll change who we are. We see that already. People dress up in ways that were really not acceptable when I was like 10 years old. And that's going to expand far greater.

• 1:09:05Speaker 03

  But actually, robots that do what humans do can actually be put into places where we wouldn't want to put humans, like a burning building. I think that's happening very soon, over the next five, six years.

• 1:09:18Peter Diamandis?

  Ray, our longevity platinum trip is going to be in August and September in Boston, Cambridge, near where you're living. I would love if you would come and spend the day with us there and go deeper into longevity world as well.

• 1:09:33Speaker 03

  That actually reminds me.

• 1:09:37Speaker 03

  Yes, I'd love to do that. And I've greatly enjoyed the many presentations we've done together. I have this book coming out, The Singularity is Nearer. And I would like to make that available to the people here. So I'll work with Peter in a way that we can actually get your copy of the book for free.

• 1:10:00Peter Diamandis?

  On that note, everybody, please give it up for Ray Kurzweil and Saleh Ismail.

• 1:10:17Speaker 28

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