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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. I've been in the field of AI for 60 years. I was 14. I met Marvin Minsky who was in his 30s. My grandson, Vlad, 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 the showing now that
this is really the past 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.
Great. Good morning. Good morning, Tia. 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 a lambda and a barred 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 chat GPT. 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's just introduced bar to think a few days ago. OpenAI has also introduced GPT-4. Without going into comparison with these LLMs, because it changes like every day.
I can write things in one style and ask it to be articulated 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 CUNY form 5,000 years ago. You're probably using CUNY form 5,000 years ago.
Almost say, this 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 processes and other means to help us.
But this latest breakthrough allows us to creatively create written language based on the LLMs 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 her various questions. How do my views of consciousness relate to those of Marvin Nitsky and how do they compare?
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 answer is 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, then gender 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 I 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 that 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. But the time comes out. It will 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?
Critics of AI, where from show how large language models may not be perfect. There was one reason it said, well, it can't. If you put mathematics inside language, it doesn't do that correctly. But now within a year of saying that that's not longer true. So one of my themes, and this is also true of Peter and Selene, has been the acceleration of progress and 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 it shows it 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.
Now people very often call this Moore's law. I really believe we shouldn't do that anymore because that's 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. If you go to the bottom left, the first programmable computer was the Zusa one 1941. It performed point 00007 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. 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 class as computer. And they use 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 D.A. 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 with 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 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 to anything to do with Intel. Now every five years, people were going around saying Moore's laws over.
You might remember that this started when the COVID pandemic started just a few years ago, people saying Moore's laws over. Because I went around saying, cave should not be called Moore's law, but regardless of that, whether Intel chips with 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 for 80 years from point 007 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 Minski who was in his 30s Frank Rosenblatt 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. A lot of stories about that. But in the early years, it was really not clear that neural nets could do anything successful.
And the 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. Then our mass mass stream 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 you can do brings up concerns about its effect on human employment, which you were just talking about. When appointments really not necessarily the best way to bring resources to human. And look at around the world. Francis 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 I think a difference will 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.
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 also 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 is also extremely exciting, which is simulated biology. This is 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 and actually took two days to create. It's been the most successful COVID vaccine.
And because then 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. 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 human 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 want to 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. 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 testing the turning test. I came out with that in 1999. People thought that was crazy. The Stanford had a conference. Actually, 80% of the people came, didn't think we would do it, but they thought it would take 100 years.
They keep holding people. And now the, the everybody actually thinks that we will actually pass the turning test by 2029. And actually the parents of turning test, meaning it's equivalent to humans, we're actually going to have to dumb them down. Because if it does everything that the computer could 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. Actually had an aunt who's 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 what do you talk about? And I said, longevity escape velocity. Oh, what's that? And I described it. The very last thing she said to me, the 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.
I'm going to take privilege and ask the first question. Ray, we've seen LLM's. What's the next major breakthrough that you expect to see on the on the road of evolution of AI? Well, LLM's, 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. It's been 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 five thousand 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 a way biology is going to be done. And we're going to see amazing progress starting really at 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.
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 Management, we're creating AI powered robots called Biamni. 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?
That's a very good comment. But very pleased to hear of your amazing progress. I mean, you have a robot that can actually take something in actually flip a cap or for 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 tests where you need to put it in the dishwasher and went 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. And so we're going to be using 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 it will be quite prevalent within a few years.
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Get started on your longevity journey with Prolon today. Now back to the episode. Name and where you're from and then question. I'm Samuel Smith from Tyler, Texas. I'm currently working on a way to help students learn using AI and putting them 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 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 available elements 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.
And then 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 use them together. And another big application of education and 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. We're going to completely use a lot of the language models that are coming out very soon to really revamp education. Thank you. Good morning, Ray. I'm Lee Shang Liu. I'm from Texas. I'm 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?
Because you help to shape it and then deliver it. Or you calculate the laws that other people don't. And then you can predict it. So which one is actively shaping it? 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 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 for 80 years, school, the Stern Schuhler 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. The school 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 interested in the book, but I was amazingly interested in the manual typewriter. I mean, he has a machine, had no electronics, manual typewriter. And it could take a blank piece of paper and turn it into something that looked like he came from a book.
And 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 ex 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.
And I saw little things from radios, broken bicycles. This was an era where you were allowed a 60 year old kid to go around the neighborhood and collect these things. You probably get arrested today. But. And I went around saying, I've 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 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 was 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 it, they actually created a virtual reality theater that was 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 capture 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. I began to study how technology evolves. And really about 40 years ago, I realized that computers were on this exponential rise.
And so that's so I didn't get into futures and for futures in itself. It was really to plan my own projects and that what I would get involved in. 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 can we do with the computers of this power and other types of things that we can keep that we can manage. But that's really been my plan is to figure out what is capable. And that you saw that chart. It's a 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 that with that type of power. So that's that's how I go.
Just to point out it's a straight line on a log scale, meaning it's going exponentially. Yes. Thank you sir. Mike. Hi, great to meet you Ray. Quick question. When do you think that quantum computing will break RSA encryption? Well, a little bit skeptical of quantum computing. I mean, people go around saying, oh, we've got this 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 a thousand, maybe even 10,000 qubits to create one qubit that's actually accurate. Now, let's how I checked 50 divided by a thousand was less than one. 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. Another people working on it. They've got some theories.
So 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. Thanks. Hello, Ray. My name's 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 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, virtual reality we're using right now is a little bit limited. There's actually some new 3D forms that have 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. 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 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.
Thank you, Ray. Mike. Hello, Ray. Mike, Juan, Lord 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 that. Yeah, I've actually done that. 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 an eye blinking and so on and regaining 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 I think things are moving in positive direction and 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. The bad news is true, but we completely ignore the good news. I mean, looking for life was like 50 years ago or 1900 human life expectancy was 48. It's 35 and 18 and 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 experts systems the way to go to general intelligence. Do you think that means that hint of how the brain or our brain really works? And if that's the case, does it mean that the more we understand the 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 it. 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. It sounds like a lot, but it actually didn't do very much. But I got to 10 billion and started to do things.
The recent ones started now with 100 billion going to trillion connections. And 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 Lisa Dal, 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 the computer. In my view, though, we're going to add this to ourselves and we'll all become master go. Play us a 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. And if you get trillion, seems to be way beyond what humans can do. We can be very intelligent. Thank you. Hi, Ray. I'm Annie Chahal-Honin. 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 for, 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? Well, something I think that Peter and also Salim would agree with. Is it failure? It's just a step towards success.
I mean, failure is really the late form of success. When Edison was trying out many thousands of different things that would create a light bulb. And he tried it out and it didn't work. His feeling is, OK, 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. I 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 is able to solve this problem. And in order to solve the problem, what we have to do this, this, and this. In order to do these, we have to do these other things. And I work backwards from the solutions to the 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 II twin. Also, 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 were training and Ray Kurzweil, LLM at the moment.
And if so, how many time you spend on it and how you do it. Yeah. Well, I mean, I write down lots of things. We came out with a product that you could actually search. A book and ask a question and we'll 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 lead all 200, 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. I didn't write quite as much as I did because we didn't have word processes then. But he wrote a number of things and put them in there. And then I used Talk to Books and asked some questions. And it was really like talking to him. I mean, I didn't know what answer would come out with.
We go through everything he'd written and said, OK, 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 our cells 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 when we could go back. Not really 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 search engine and you have to do it, you got to like 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, OK, 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 later, men with originally St. Louis, now pomp and obiach. And I had my original question kind of was on the direction of somewhat 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 were experiencing already and is just going to be accelerating. Gross 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. 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 to act like Shakespeare or E coming with some other poet and they'll actually act like that. And I can talk to them. 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 ready, very mixed up. I mean, you're looking at your phone there to see what your question was. We're going to have 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 and 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 the tree and say, oh, I could take that branch off. I could strip off the leaves and I could create a tool that just weren't able to do that.
So our brain plus the fact that we can actually manipulate the environment and allow 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 and a dish, a battery dish playing ping pong.
I thought, what about if we put these neurons on sensors and connect them to AI, quantum computers, 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 they come from, but maybe they will evolve into something greater and not just to have a machine embedded in our brain. So to 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 Minzki and he says, 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 intelligence you are on the more you can process things, the more feelings you have from it. I think that's where feelings come from. And yes, we could actually grow things that are outside of our cells that could be part of our feelings as well.
My idea was that who says unconsciousness 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 one second. Dagmar. Please go ahead. Where are you in the planet, Dagmar? Germany. In Germany, great.
Now, with the history of Germany, I 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 of learning?
Well, I was actually just in Germany a few months ago. And I think they've considered 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. 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 to ask these machines questions, we should know you and 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. Jason, are you on the planet?
Hey, Ray. I mean, 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 book actually has a apparel 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 a desk, put our hands behind our hands. Seems to work. We're all still making it. But these new technologies do have downsides. You can certainly imagine AI being in the power of somebody could be human or any other type of entity that wants to control us.
And it could happen. I was actually part of the Silamar 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 spread very easily, but they don't make it 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. 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 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, this way of these technologies being quite abusive. And that happens when I was born with the atomic age. Please, sir. Hi. My name is Yosin from the Netherlands. And as I was trying to think of a question, I wasn't sure. So I asked Chad, Dupiti, 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, their 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. It's ideal with human beings. And I can't always account for what they might be doing. Yes. So I think we have to actually export certain values. I try to associate with people who have somebody.
I mean, I'll 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 launched language models. I mean, even though people are concerned, they might say the wrong thing. Sometimes they do. I mean, there was a bunch of 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 its decisions it's made. We already can't predict what these large language models will do. But I think we are actually sharing our values with them. Let's go to the shallash on zoom. We're also monitoring upvoted questions in Slido here. Then we'll come back here.
Gotcha. 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 breed cleaner air and drink safer water? Well, if you look at some of the graphs in Petersburg and in my book, you see we definitely headed in that direction. We're not there. Alternative energy, for example, is actually expanding in 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 these homes, the cities and somebody actually takes out a cell phone and makes a call. So I mean, it is spreading quite rapidly. By 19, by 2029, computers will pass a training test. There certainly can do it in many ways already. Once it can actually do everything that humans can do, it will go, wait, pass 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, Shilash. Please.
Hi, Ray. My name is Ashisham representing chemical 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 LLM's 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 serviced 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.
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Thank you. Hi, my name is Pete Zachill. 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 and the 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.
Well, it's a lot of questions, but I think everything is moving to the cloud. And people say everything in the clouds or someone could blow up one of these cloud centers and we 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 still have that information.
In fact, it would be very if part of 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.
And so we don't want to lose that. I think it would 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 folds and so you could blow up. You know, 80% of it you'd still have everything that was there before. So redundancies actually a major advantage of cloud thinking. We used to have computers. I mean, I got access to IBM 1620, but I was 14.
14 year old using computers hardly amazing today, but there are only 12 computers in New York City at that time. And yet actually go to the computer. And if anything happened at the computer, that data would be lost. But now everything it's 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.
Maddie, please. Hi, Marie 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, Jones is actually a large section of my next book about jobs and what is that we'd like to to accomplish. And jobs have turned over many, many times. I mean, none of the jobs that people have in 1800 and it's almost true of 1900 to people who 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 that creativity. And 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 in a better so. 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 of future. It's always the same. But if you actually look at history, you see it's constantly changing. Thank you. Joe. Hi, right. Joe Honein from Baymar, giant Washington several years ago you 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 GTP for 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 don't be fascinated with that. Well, 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 could 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, 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, 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. You said welcome. Hi, this is Josef from Abu Wai. The question for you, Ray, but also for the audience. So if you have any thoughts, idea, 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 adapt, exponential thinking and abandon thinking into this. And what are these technologies that might help us to, you know, disrupt this type of activities?
Yeah, well, it doesn't 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. So I do spend a lot of time actually learning a lot. My daughter is actually a cartoonist from New Yorkers.
And she has very interesting ideas and that's, and she's actually collaborated with her on many projects. So how you parent, I think it's different. 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 than routine work that we'd rather not do. Thank you. I want to make a quick point here.
If you went back 50, 70 years ago, if you were a parent and something happened with the 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 in 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. I don't think I would have had the career I had. I 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 the 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 Zechronkov. Thank you. Great fan. Alex Zechronkov. I founded a company called Incelechal Medicine. And my question is maybe a little bit personal. So right now according to your bio, you are 75. And that's a very interesting age to be. I always like to talk to people in 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, getting to 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 uptime, 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, many more people are appreciative. And I appreciate the tools more than I did back then.
But we're really discovery that's still a lot we don't know about the world and we're going to continue to learn more and more about that. Thank you. And Gary, do you want to ask a 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 that's actually a little bit behind what we've done with language. I think within five or six years, it's a 2029.
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. 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. 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 commitment to book our free. On that note, everybody, please give it up for Ray Kurzweil and Selimis Mael.