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

Episode Date: February 1, 2024

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

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now it's changed. I am optimistic, but I'm also worried about it. I've been in the field of AI for 60 years. I was 14. I met Marvin Minsky, who was 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. Ray, good morning. Good morning to you.

It's great to be with you, Peter, and also Salim. 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. 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 BART, I think, a few days ago. OpenAI has also introduced GPT-4. Without going into a 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. 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. 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 LLM'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. It can write code perfectly. It 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. So I actually took a couple of the top LLMs, and I asked various questions like, how do my views of consciousness relate to those of Marvin Minsky, 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 LLMs from Google and from OpenAI. 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 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 LLMs 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 MBA. 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, we can't come out with this without covering this. 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 how we got here and what will happen in the near future. 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 Salim, 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. It shows for each year the best 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. 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. 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 it was happening. If you go to the bottom left, the first programmable computer was the ZUSA-1, 1941.

It performed.00007 calculations per second per dollar. Zuse was a German, apparently 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. 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, 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 happened. That's the nature of exponential growth. 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 that I noticed 40 years ago. And as you can see, it's done that.

direction 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 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.

And of course, I went around saying, OK, it should not be called Moore's Law. But regardless of that, whether Intel 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. not for depressions or for any other reason. It's gone on for 80 years from 0.00007 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 believe that neural nets were effective decades ago did so really based on their inclination, not any evidence. I've been in the field of AI for 60 years. That's quite amazing. Like, where does the time go? I was 14. I met Marvin Minsky, who was in his 30s. Frank Rosenblatt, who created the Perceptron, the first popular neural net. 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. 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. Employment is really not necessarily the best way

to bring resources to human. I mean, look at around the world. France is now dealing with protests because they're adding a couple of years before people can access their retirement. It tells me that people really don't like the jobs they do for employment. So 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 and 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. already. This is not an alien invasion of intelligent machines coming from Mars. 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, 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 use biological simulation of humans to replace human testing. I mean, rather than spending a year or several years testing 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. 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.

It will be done a thousand times faster than conventional methods. times faster than conventional methods. And based on our ability to do this, we should be able to 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, 80% of the people who came did think we would do it, but they thought it would take 100 years. They keep polling people,

and now 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 going to have to dumb them down, because if it does everything that a computer can 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 10-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 97. She was a psychologist. And she actually was still meeting with her patients at 97. 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 well, what do you talk about? And I said, 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. Thank you, Ray. All right. I'm going to take privilege and ask the first question. Ray, we've seen LLMs. What's the next major breakthrough that you expect to see on the road of evolution of AI? Well, LLMs, I mean, they do remarkable things, but it's really just the beginning. I mean, the very first time I saw an LLM 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 5000 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. 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. 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 a hundred 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.

Great. 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 Beomni 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?

Yes, that's a very good comment. I've been 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. 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. 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. And I think that's another thing we're going to see really starting now

and will be quite prevalent within a few years. Over the years, I've experimented with many intermittent fasting programs. The truth is, I've given up on intermittent fasting as I've seen no real benefit when it comes to longevity. But this changed when I discovered something called Prolon's 5-Day Fasting Nutrition Program. It harnesses the process of autophagy. This is a cellular recycling process that revitalizes your body at a molecular level. And just one cycle of the 5-Day Prolon Fasting Nutrition Program can support healthy

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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? 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 when 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 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.

And another big application of education is socialization, getting to learn other people and make friends and so on. So we're going to have to actually do that as well. Computers can definitely help there. But we're going to completely use large language models that are coming out very soon to really revamp education. Thank you. education. Thank you. Good morning, Ray. I'm Yi Xiang Liu.

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

My great-grandmother actually started the first school that educated women to 14th grade. In 1850, if you were able to get an education at all, as a woman, it went through ninth 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. It's called the Sternschule 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. 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. 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, his 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 23 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. 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. 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. 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. I'll 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 10, and they were quite fascinated,

and they said, well, you have quite an imagination there. So other people were saying what they wanted to be, fighting fires, 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 was when do you approach a certain problem? Like I did character recognition in the 70s. I did speech recognition in the 80s. Why did I do it that way? It's because speech recognition requires actually more computation. So I began to study how technology evolves. And really about 40 years ago, I realized that computers were on this

exponential rise. And so I didn't get into futurism for futurism itself. It was really to plan my own projects and what I would get involved in. And so if I look forward five years, ten years we're now actually at a very fast pace of this exponential path as you can see I'll see what are the capabilities going to be and then you need to use a little bit of

imagination, you know, what can we do with computers of this power and other types of things that we can manage but that's really been my plan is to figure out what is capable power and other types of things that we can manage. But that's really been my plan, is to figure out what is capable. And you saw that chart. It's an absolutely straight line.

I had it 40 years ago and projected it as a straight line, and it's exactly where it should be. And then you can use imagination as to what you can do with that type of power. So that's how I go. Ray, just to point out, it's a straight line on a log scale, meaning it's going exponentially. Exactly. Thank you. Yes, sir.

Mike. Hi, great to meet you, Ray. Quick question. When do you think that quantum computing will break RSA encryption? Well, I'm a little bit skeptical of quantum computing. I mean, people go around saying, oh, we've got these 50-qubit computers. But it creates lots of errors. And we've actually figured out 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 1,000, maybe even 10,000 qubits to create one qubit that's actually accurate. The last time I checked, 50 divided by 1,000 was less than 1. 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 classical computing. Thank you. Next. 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? 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, 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. 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. 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 would be great to actually be able to hug each of you and so on.

That's all coming. So increasing communication, 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. Thank you, Ray. Mike. Hello, Ray. 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. So, Ray, this is recently they put some subjects in a functional MRI and then fed the output to stable diffusion. Yeah, I've actually done that uh this was maybe five years ago wasn't perfect uh but it was significant uh 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 It affects things that we don't usually notice, like our eye blinking and so on, and we're gaining more ability to do that.

We can do pretty good telling if people are telling the truth or not. So that's going to 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 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. But that 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, 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. Hi, Ray Sadok Cohen from Istanbul, Turkey. It looks like

LLMs are with the aid of some expert systems, the way to go to general intelligence. Do you think that means that's a hint of how the brain or our brain really works? And if that's the case, does it mean that the more we understand LLM models, the more we understand our brain and be able to hack it? And is that a hint that we are more deterministic than we thought we were? Well, it's a very good question. It uses a somewhat different technique. Neural nets, every phase, it's able to get itself closer to the truth.

We don't actually see anything in our brain that actually does that. It does it a different way. But somehow we have all these different connections and the large language models that are effective i mean we actually had large language models that had 100 million connections that sounds like a lot but it actually didn't do very much when i got to 10 billion it started to do things. The recent ones started now at 100 billion, going to a trillion connections. And it basically is able to look at all the different connections

between them. And that's exactly what our brain does. And these things are going to go way beyond what our brain does. We see that already. I mean, I can play Go. I'm hardly the best player. But Lee Sedol, who is the best human player, and that used to be significant because he could just look at the board and be able to do something that no one else could do. And he says he's not going to play Go anymore because he can't compete with a computer. In my view, though, we're going to play Go anymore because he can't compete with a computer.

In my view, though, we're going to add this to ourselves and we'll all become players of Go and everything else that we want. But yes, it's using the same ability to connect things and if you get enough of them, it seems to be basically a trillion seems to be way beyond what humans can do. We can be very intelligent. Thank you. Hi, Ray. I'm Annie Chahal-Honan. 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? to solve it or get to the next step? Well, something I think that Peter and also Salim 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.

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 idea. your own mission? Do you have to have some 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, we had to do this, this, and this. In order to do these, we had to do these other things. And I worked backwards from the solution 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. Thank you. Hi, Ray. I'm Dom 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 leverage my strengths but also balance my weaknesses and i was wondering if you are training and ray kersweil llm at the moment and if so how many time you spend on it and how you do it so would you yeah well i mean i write down lots of things uh we came out with a product that you could actually search a book and ask a question and it will find the best answer in what you've written.

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 every sentence in 200,000 books and give you an answer, which is quite remarkable. 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 him questions. And it was really like talking to him. I mean, I didn't know what answer would come out with it. We'd go through everything he'd 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. 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 does the time go. 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've got to turn the machine on, you've got to find the right place, you've got to put in the answer to the question, you should actually be listening

and say, okay, the actress you want is so-and-so, before you even ask it, because we'll 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. Great question, Tom. Howard. Hi, Ray. Howard Lederman, originally St. Louis, now Pompano Beach. And my original question kind of was on the direction 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. 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 are already pretty close to human. I mean, you can talk to them, and 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 said, okay, I want you to act like Shakespeare or E.E. Cummings or some other poet, and they'll actually act like that, and I can talk to them. or E.E. Cummings or some other poet, and they'll actually act like that, and I can talk to them. 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. Computers are going to help us get through the day, and so we're not going to be interacting just with humans or machines machines is part of who we are and that's actually the big difference between human beings there are other 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 have this thumb. So they can't like look at the 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. Thank you. 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 comes from, but maybe that will evolve into something greater. And not just to have the machine embedded in our brain, so to actually grow neurons and connect these sensors to the AI.

Yeah. actually grow neurons and connect these sensors to the AI. Yeah. Well, you bring 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, well, that's philosophical.

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 and 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 ourselves

that could be part of our feelings as well. 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 thank you we're going to pause and go to zoom uh one second uh dagmar please go everybody where are you in the planet dagmar Please go ahead. Hi, everybody. Where are you in the planet, Dagmar?

Germany. In Germany, great. 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? Well, I was actually just in Germany a few months ago,

and I think they've considered their past and how that happens and how we can avoid it happening, I think, more than any other country. And I really felt

that while I was there. 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. 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, good morning. Where are you on the planet? 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? Yeah, well, each of my books actually has an apparels 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 and we would actually get under our desk, put our hands behind our hands. Seemed to work.

We're all still here. 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. 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, in terms of recreation and everything else. But yes, there's ways of these technologies being quite abusive. And that happened when I was born with the atomic age.

Please, sir. Hi, my name is Yassine. 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? 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 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 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.

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 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. Thank you. Let's go to Shailesh on Zoom. We're also monitoring upvoted questions in Slido here. Then we'll come back here. Shailesh?

Go ahead, Shailesh. I'm in Mumbai, India. So 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? 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. That's not true today, but we're actually headed in that direction.

Not everybody has access to the Internet. 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 cell phone and makes a call. So, I mean, it is spreading quite rapidly. By 2029, computers will pass the Turing test. They certainly can do it in many ways already.

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. 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 the kinds of terrible lives that we see through poverty and lack of access to information.

So it's really just the next few decades that we need to get through, but we're already making a lot of progress. Thank you. Thank you, Shailesh. Please. Yeah. Hey, Ray. My name is Ashish. I'm representing chemicals and material 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? Well, as I said, my grandmother was actually the first person to get a PhD in chemistry in Europe. And I actually asked something like that.

She said, well, chemistry is really something that serves other industries, so we need to see what other industries need. What kind of products do we need to make LLMs more powerful? What kind of chemicals do we need to prevent certain types of diseases?

And so it's not any one particular type of thing it's really service every other industry that we're trying to advance hey everyone i want to take a quick break from this episode to tell you about a health product that i love and that i use every day in fact i use it twice a day it's seeds dso1 daily symbiot. Hopefully by now you understand that your microbiome and your gut health are one of the most important modifiable parts of your health. You know your gut microbiome is connected to everything.

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ownership of data? Thank you. Yeah, well, it's a lot of questions, but I think everything is moving to the cloud and people say moving to the cloud. And people say, everything in the cloud? So someone could blow up one of these cloud centers, we'd lose everything. But that'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 would be very, if part fact, ultimately, we're going to have our thinking is going to be in our brains and in the computer. The brain part is not going to grow, but the computer part will grow, and ultimately, most of our thinking will be in the computer part. in the computer part. And so we don't want to lose that.

I think it'd be actually very hard to actually exit the world because 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, 90% of it, you'd still have everything that was there before. So redundancy is actually a major advantage of cloud thinking. We used to have computers. I mean, I got access to IBM 1620 when I was 14. 14-year-old using computers

hardly amazing today, but there were only 12 computers in all of New York City at that time. And you had to actually go to the computer. And if anything happened to the computer, that data would be lost. But now everything is stored in the cloud. Everything on your phone

is stored in the cloud. So, and I think that's a good thing because I think information is extremely important. Hi Ray, Maddy from Houston, Texas. 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? Well, jobs is actually a large section of my next book about jobs and what it is that

we'd like to accomplish. And jobs have turned over many, many times. I mean, none of the jobs that people had in 1800, and it's almost true of 1900, do 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.

But we still see 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. 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. Thank you. Joe. Hi, Ray. Joe Honan from Bainbridge Island, Washington. Several years ago, I had asked you a question about, 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 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 surprised, 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. Well, I'll start with that. 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 experienced it, it surprises me and is quite delightful. And we're going to see that more and more. I mean, mean every six months it's going to be a whole new world

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, I just, 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. Thank you, Joe. Yousef. Welcome. Hi, Ray. This is Yousef 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 disrupt this type of activities? Yeah, well, I mean, it does make me think, what can we actually do with the extra time we have with 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 so I do spend a lot of time, actually learn a lot.

My daughter is actually a cartoonist for the New Yorkers, and she has very interesting ideas. And I've actually collaborated with her on many projects. So how you pair it, 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 as computers do more

of the routine work that we'd rather not do. Thank you. I want to make a quick point here. If we went back 50, 70 years ago, if you were a parent and something happened with a child, you had no idea what to do. 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 interesting wisdom beyond 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 that's not something you would allow a child to do then. But that actually got me on this path that I'm still on. Let's go to our final question here. A good one to close on, I'm sure, Dr. Alex Zagbrankov. Thank you.

Great fan, Alex Zagbrankov. I founded a company called InSilica Medicine. And my question is maybe a little bit personal. So right now, according to your bio, you're 75. And that's a very interesting age to be. I always like to talk to people, you know, of various ages to understand how to plan my own life. 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?

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? Well, first of all, Gaines is 200. So that would be 125 years from now. 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 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. 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.

Barry, do you want to ask your quick prediction? Ray, when do you think we're going to have our personal robot buddy like Rosie the Robot? Well, I mean, you're working on that. A lot of other people are working on it.

I think there's actually a little bit behind what we've done with language. I think within five or six years, let's say 2029, 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 look 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. But actually robots that do what humans do and 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. 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 the longevity world as well. That actually reminds me. 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 on a way that we can actually get you a copy of the book for free. On that note, everybody, please give it up for Ray Kurzweil and Salim Ismail.