JUNE 21, 2012

Raymond Kurzweil, famous scientist, inventor, author of Singularity theory, head of Singularity University


It's a pleasure to be with you and it's exciting to have a congress like this, particularly here in  Moscow. It's my first trip, but I'm delighted to be here. I went to a school named MIT in the  Boston area; it's a leading technology school in the United States. I actually went there because  MIT was so advanced in 1965 that it actually had a computer. Most schools didn't have one. This  computer I carry around is actually several billion times more powerful per dollar or per ruble  than that computer. It's a million times cheaper, it's several thousand times more powerful, and  that's typical of information technology. I'm probably the only person you'll ever meet who has a dual degree from MIT, in computer science and creative writing, because that was very unusual.

In fact, there were very few people who majored in creative writing or literature at MIT. Most of, I'd say half of the authors we studied were Russian authors, particularity from the 19th century, but also some modern ones. And a lot of the mathematicians that we studied in computer science were also Russian; like, for example, Markov, in the late 19th century created a theory of Markov chains and hidden Markov models. That is actually the number one method, or algorithm, used in artificial intelligence today. I began using them in speech recognition in the early 1980's, and then we applied them to natural language understanding. There was recently an IBM computer that won the Jeopardy game, which is a game in English. I don't know if there is a Russian equivalent, but it's a language game, it's a popular television show, and the computer was able to defeat the best two human Jeopardy players. Those models are largely based on Markov models. And I'm writing a book on the brain now, and I have a theory as to how the neocortex works. And there's basically one module that recognizes a pattern, and that is repeated 300 million times, by my estimate. And they are organized in hierarchies. So at the very bottom you have little recognizers that recognize, you know, the little parts of letter shapes, a crossbar and a capital "A" and so on. At a high level you have recognition of things like humor, and irony, jealousy. They are actually the same pattern recognizers, but they exist at different levels on the conceptual hierarchy. And this very complex wiring we see in the neocortex, which is 80% of the human brain, that's where we do our thinking, it's not actually - it doesn't come pre-designed, that is actually wired by these little modules themselves based on what they learn. So, depending on what you actually think about and learn, it's actually creating wiring to generate this elaborate hierarchy of patterns. And it enables us to actually understand information in a hierarchical fashion, and the world is inherently made up of hierarchies. We have forests which are made up of trees, and trees are made up of branches, and branches are made up of little branches, and little branches are made up of leaves, and so on. There's a natural hierarchy to the world. We have a neocortex that can understand that. Only mammals have that. And in my book I describe actually the method, the algorithm that's used by these 300 million pattern recognizers.  Some people actually looked up on Google - there're 4 million quotes, quotations, on Google, saying how incredibly complex the human brain is.

But this book I'm writing is not adding to that collection of quotations. I really explain a fairly simple approach, relatively simple, that is repeated 300 million times. It's able to self organize  into these elaborate hierarchies. So by the time you get to be our age, and you've experienced life, the neocortex is complex. But it actually starts out fairly simple. This method actually turns out to be what I call "hidden Markov  models", which was invented, or discovered, by this Russian mathematician in the late 19th century. So this full and elaborate way of saying I'm happy to be here in Moscow, and in Russia, where there is a great tradition of learning both in culture and science and math, and I think that positions Russia very well for the future world. Because the future world is going to be based on creating new knowledge, and one of the things that children need in order to enter that world is an appreciation of the knowledge of the past, and also, an awareness of that  knowledge, so that we can build  our future knowledge. I was asked yesterday, we had a panel - what would I recommend for Russian education. It's really the same thing that I recommend everywhere, all throughout the world. It's certainly true in my country, in the United States. We have an educational system that's still based on the 19th century, which is teaching kids a lot of knowledge and information. Some of that is useful, so that we can appreciate the cultural foundation that we inherit from the past; we can't create the future without it. But we really do carry our knowledge with us. We can access all of the human knowledge with a few keystrokes. A kid in Africa with a Smartphone has access to more knowledge than the president of the United States or Russia did 15 years ago. These are very powerful tools. What we really need to teach children is how to create new knowledge. And the best way to do that is actually doing their own projects. This is a mission that I have, to bring this kind of entrepreneurial spirit, a spirit of actually learning by doing, into the schools. I started  a university called Singularity University, I co-founded it with Peter Diamandis of the X PRIZE Foundation that's backed by Google. NASA, the American space agency, provided us a campus in Silicon Valley in California. We have about 60 faculty, and we've 80 students from all around the world, including Russia, and half of the curriculum is actually doing projects. These students self organize into teams, they take on some world challenge: hunger, availability of water, applying three-dimensional printing to printing out modules to create new housing, and actually invent technologies of the future. Those projects may very well work out and change the world.

Even if they don’t, it's really the best way to learn. You know, what I remember from my youth, and from my education, it's really from my own projects. Because I was doing projects since I was 5, it's really the best way to learn. We should bring that spirit of leaning by doing into high schools, junior high schools, elementary schools, and I think we have an outstanding foundation here in Russia, but I will say that it's really one world now. I happened to be traveling around the world in October of 2008, when the current financial crisis, you  know, started the current worldwide recession. And literally within one week, every industry, in every country, I happened to be in 5 different continents that month, was suddenly affected.  So it's not just a poetic sentiment that we're all connected, it really is one world economy; it's increasingly one world culture with many traditions feeding into it. You know, a country like Russia or the United States are not islands. People ask me in the United States, well, how is the Unites States going to compete with emerging countries like China, and you know, it's really not a zero sum game. An engineer in Russia or China or Africa, who creates, let's say, a breakthrough in solar panels - that benefits everyone. It's one world economy. But education, particularly learning by doing, I think, is the key to the future.

It was alluded to earlier that the pace of change is getting faster and faster. That's something that I've studied and that I'm going to talk to you about. I happened to give a speech on the 500th anniversary of the University of Basel about a year ago. And this university was started 20 years after Gutenberg invented the printing press, not too far away from where he made that invention. I said, well, that's exciting, you must've had some of Gutenberg's books when you opened your doors in something like 1440, and they said, yes, we got them very quickly, it was only a hundred years later. And that is how technology moved in those days. It actually took 400 years for the printing press to reach a large audience. The telephone did that, it reached a quarter of the population in the United States, Europe and Russia, in about 50 years. Cell phones did that in about 7 years. Social networks, wikis, blogs, some other recent technologies have done that in 3 years. So the pace of change is getting faster and faster. And what's driving that is what I call the Law of Accelerating Returns, which is the exponential growth of information technology. Now, the way I encountered that - I decided I wanted to be an inventor when I was 5, as I mentioned - and I realized 30 years ago, in 1981 - I started to think about the fact that if I looked around at the inventors who were successful, their timing was perfect. They did things at exactly the right time. Turns out that timing is important for just about anything, whether it's a business investment, or romance, you've got to be in the right place at the right time. And most inventors actually get their projects to work, but usually inventors are too early, or they're too late. So I wanted to study technology turns - could I anticipate where the world would be by the time I finished the project? For a project that takes, let's say 3 or 4 years, it will be a different world 3 or 4 years later; that's certainly true today, but it was also true in 1981. So, to think, you know, you can't shoot at the target where it is, it's like skeet shooting, you have to figure out where it's going to be. Can I anticipate where technology would be? And so being an engineer, I gathered a lot of data, I did not expect, really, to find anything very predictable. The common wisdom was, and still in most quarters is, that you cannot predict the future. The future is unpredictable. And there are certainly many good examples of people who've made bad predictions in the past about the future. But I made a very surprising discovery. If you measure the underlying properties of information technology, for example, the power of computers per unit currency, instructions per second per constant dollar, for example, or the number of bits we're moving around wirelessly in the whole world, or the number of base pairs of DNA we are sequencing, and I could mention a hundred other measures like  that. They follow very predictable trajectories.  And what that predictable trajectory is - is an exponential progression that basically doubles every period of time. That doubling time, actually, itself gets faster and faster. The  world of computation, of computers, started with the collection of the United States Census in 1890, it's been growing exponentially ever since. The power of  computers for the same price doubled every 3 years in around 1900, every 2 years around 1950, it was 12 months in the year 2000, it's now down to 11 months, but it's a very smooth, a very predictable exponential trajectory. So, I had this curve, and I'll show it to you, up through 1980. In 1981, I continued the curve through 2050, and now we are at 2012 and we're exactly right on that curve. The power of computation has continued to flow exactly on that curve. And, so not only is it predictable, but what's predictable, is that it grows exponentially, and exponentials are very surprising.

They're also not our intuition. We have an intuition about the future. The reason we have a brain is, in fact, to predict the future. That's why we have intelligence. So, when I walk through the fields a thousand years ago, I would be walking and say, ok, there is an animal going that way, I'm going this way, and I'd make a prediction: we're going to cross paths in about two minutes, I'm going to go this other way. And that turned out to be good for survival. And I was making a prediction about where I would be, where the animal would be, and then made a decision - that's why intelligence is useful. Those predictions about the future, though, are linear. I assumed I was going to just keep going at the same speed, I assumed the animal was going to do the same thing. That actually is correct, and that works out quite well when it comes to the kinds of problems we needed to solve a thousand years ago. It turns out to be very inaccurate when it comes to predicting the future of information technology. Not any technology. In fact, technologies, before they become information technologies, progress linearly. Like health and medicine - I'll talk about that in  a moment  - but once it becomes an information technology, it progresses exponentially. And that's not our intuition. So one of the differences between my own predictions, of which I now have a 30 year track record, which have been very accurate, and my critics', is that they are using their intuition, which is hardwired in our brains, which is a linear prediction of the future. But it's simply  not accurate when it comes to information technology.

Now, you might think, well, what difference does that make, is it really that different, between a linear and an exponential projection? Well, it makes a huge - it doesn't make much difference in the short term, it makes huge difference in the long term. If you look at most models that governments use, for example, to predict the future - in the United States we have debates about social security, which is our old age retirement plan for the nation, and the prediction is that in 27 years it will run out of money. They are all based on linear predictions of the future. Those linear predictions work actually quite well for a year or two, they are totally ridiculous when it comes to 10 or 20 years. If I take 30 steps linearly - that's our intuition about the future - 1, 2, 3, 4, at step 30 I'm at 30.  If I take 30 steps exponentially, 2, 4, 8, 16, that is the reality of information technology. I get to a billion. It makes a huge difference in the reality of what will happen, and this is not just an idle speculation about the future. As I mentioned, this is several billion times more powerful per dollar, or per ruble, than the computer that thousands of us shared when I was a student. It's also a hundred thousand times smaller  - that's another exponential projection. And it's… that will continue, in 25 years from now this will again be a billion times more powerful for the same price, it'll be a hundred thousand times smaller again, it'll be the size of a blood cell, and will begin to integrate with them. That's not a completely new phenomenon, there are people walking around today with computers connected into their brains.

For example, if you are a Parkinson's patient or even someone who has a Cochlear implant for the deaf. There are computerized organs, like computerized pancreases that are being implanted in people. So this is already happening. It requires surgery today, but when it becomes the size of a blood cell, we'll just introduce them through the blood stream, and that's one way in which we will integrate with machines. Now, I want to show you some examples, but before I do, I just want to mention one other thing. Which is this exponential growth of information technology, which is ultimately very transformative, it does not just apply to these gadgets we carry around; it ultimately will apply to everything we care about.  Take, for example, health and medicine, which didn't use to be an information technology, it was just hit or miss. We would find things that happened to work: oh, here is something that lowers blood pressure, here's something that kills the HIV virus. They were discovered accidentally, we would systematically go through 10 thousand compounds to find something that worked. That's called drug discovery. That's not an information technology. But with the collection of the genome, which is basically the software of life, we actually now are beginning to treat biology as the information process which it truly is.

Genes are little software programs. How long do you go without updating the software on your phone? This is probably updating itself now as I speak. But I have software running in my body which hasn't been updated for thousands of years. And it's not just a metaphor, it's literally true. One of the genes, for example, the fat insulin receptor gene,  says hold on to every calorie, because the next hunting season may not work out so well. And that was a very good idea a thousand years ago. A thousand years ago there was very little food, and you didn't know when you'd get your next meal.

So you would store every calorie in your body, so that you could last until the next meal came along. I would like to be able to tell my fat insulin receptor gene: you don't need to do that any more. And that's exactly what was done in animal experiments. We turned that gene off, and these animals ate enormous amount and remained slim. And it wasn't a fake slimness, they didn't get diabetes, they didn't get heart disease, they lived 20% longer, they got the benefits of caloric restriction while doing the opposite. And there's pharmaceutical companies rushing to bring that to the human market, and know that scientists who did that, and that's just one example of hundreds of projects, where people are turning off or adding new genes. I'm involved with a project where we take cells out of the body, lung cells, add a new gene - this is for people that have a disease called pulmonary hypertension, which is caused by the lack of a certain gene. If you are missing a particular gene, you will get this disease. The disease typically is fatal within 6 to 12 month. Children at 5 or 6 years old who don't have this gene get this disease and generally don't last more than a year. We took these lung cells out of the body, added the new gene that they were missing, replicated that a million fold, injected back in the body, it went through the blood stream, ended up back in the lungs and actually cured this disease. And this is…

Sorry for my lack of Russian. So we added this gene that was missing, replicated these cells, injected it back in the body, it went through the blood stream, and actually cured this disease. And this is now undergoing human trials. So this is just one  example of many, where we are actually reprogramming biology. Biology is inherently an information process. By the way, the Genome project itself was a good example of this exponential growth I'm talking about. Halfway through the project the critics were saying, this isn't working, you've only collected 1% of the genome after 7 years, which is half of the 15 years that was projected. One percent, seven years, it's going to take 700 years, which is what we said originally. My reaction was, no, it's almost done, because it's an exponential trajectory. It's doubling every year, after 7 years, had it been doubling every time, 1% is only seven doublings away from a hundred percent. It continued to double every year and the project was finished on time. And every other aspect of biology has continued to scale up exponentially, now that we're treating it as an information technology. So, these technologies, the ability to reprogram our genes, for example,  are in an early stage, but these technologies are going to double in power every year, and they will be a thousand times more powerful than they are in 10 years, they'll be a million times more powerful than they are today in 20 years, and it will be a very different era. So let me show you just a few examples of how this works. I'll skip some of these slides, since we have discussed a lot of these issues. Here is a good example, this graph over here. By the way, these are logarithmic scales, meaning every level on this graph is a thousand times greater than the level below it, so a straight line is exponential growth. So that line actually represents trillions fold increase over a century. Trillions fold in what? This is actually measuring the number of bits we move around wirelessly in the world, so the power of communication technologies, wireless communication. A hundred years ago that was Morse code over Am radio; today it's 4G networks, but look at how smooth a trajectory that is. You would think that given all the vagaries of news, headlines - a billion dollar company goes bankrupt, you know, new products come out, one company takes over from another company, countries accused of dumping products  - that this would be very erratic. I mean, look at how smooth a trajectory that is!

So let me show you the one that I first discovered, which is this one. This is the power of computers going back to the 1890 American Census, and every level on this  - this is a logarithmic scale, every level on this graph is a hundred thousand times greater than the level below it, so this represents trillions fold increase over - a little over a century. Several billion fold just since I was an undergraduate. And a lot of people say, well, you know, you can't just take exponentials and project them out indefinitely. Because, well, take these exponentials and project them out and every exponential comes to an end. Well, in information technology, a particular method will come to an end, but it leads to research pressure to create the next method. Moore's law, which is the shrinking of component sizes on a chip, was not the first paradigm to bring exponential growth to computing, this started decades before Gordon Moore was even born. Chips was the fifth paradigm, not the first. In the 1950's we were shrinking vacuum tubes, making them smaller and smaller. In 1952, CBS  predicted the election of Eisenhower, the American president, first time the American networks did that. Then they were making the vacuum tubes smaller and smaller every year, and finally, they couldn't shrink them anymore and keep the vacuum, and that was the end of the shrinking of vacuum tubes. It was not the end of this exponential growth; it just went to another method, to transistors and finally, to chips. But again, look at how smooth a trajectory to that is. It's not affected by anything. It was not affected by the Great Depression that swept the world in the 1930's. It was not affected by World War One or World War Two, or by the cold war, or any of the events of the 20th century or 21st century.  People say, well, the recent recession must have slowed it  down, no, it's continued without stopping at all, through the recent economic problems. And it's a very  - it's really remarkable how smooth a trajectory that is. So I don't want to dwell on these examples of electronics, but up there, the cost of a transistor - you could buy one transistor for a dollar in 1968. I remember being in college and being very excited: wow, I can get a whole transistor for only one dollar! Today, you can get several billion for a dollar, and they are actually better, because they are smaller, and therefore, they are faster. The cost of a transistor cycle has come down by half in less than a year. That represents a 50% deflation rate. Economists actually worry about deflation. The concern is that as everything becomes information technology, you can get the same stuff a year later for half the price. You will buy more, that's an economic reality, but you're not going to double your consumption. So, therefore, the size of the economy, as measured in rubles or dollars, will shrink. And for actually a variety of good reasons that would not be a good thing. That's actually not what we see. This is bits of memory shipped, but I could show you 50 other graphs like this. We actually more than double our consumption every year. Because as price performance reaches certain levels, whole new applications explode.

People didn't buy iPods for 10 thousand dollars each, which is what it would've cost 15 years ago. When the price performance is there, whole new capabilities, search engines, fax machines, digital cameras, social networks, take off. And that actually represents 18% growth in constant currency, even though we can get twice as much capability for the same price. So, I mentioned the biotechnology revolution. This is, again, a very smooth doubling every year, and the cost has come down by half every year,  and many other aspects of biology are also scaling up in this exponential manner. Communication technologies - this little graph here I had in the early 80's. I had just a few points on that graph. And I projected out when I said it would be a worldwide web of communication, connecting hundreds of millions of people around the world to each other and to vast knowledge resources, emerging by the late 90's. And people thought that was crazy, when in the early 80's the entire American defense budget could only tie together 2000 scientists. But that's the power of exponential growth. That's what happened. This graph on the right is the same data, but seen on a linear scale, rather than a logarithmic scale. So it's the same information but it looks like, to the casual observer, that the World Wide Web came out of nowhere in the  mid-1990's.
But you could see it coming if you look to this exponential trajectory. With shrinking technology, I mentioned that this computer in my pocket is a hundred thousand times smaller than the one I used as a student, we can now actually manufacture things just from information, using three-dimensional printers and very inexpensive input materials. So, if I want to send you a book, or a music album, or a movie, I can send you an email attachment, and then you can read a book, or a movie, or a sound recording. But I can also send you a violin. And if you have a threedimensional printer, you can print it out. Now, three-dimensional printers have been expensive, they've just a few years ago cost tens of thousands of dollars, they are now in the thousands of dollars, there are a few recent announcements that they are in hundreds of dollars, I'm not sure those are real, but it's coming down in price. The scale of precision is getting finer and finer at an exponential pace. Right now it's in microns, millionths of a meter; it will be in billionths of a meter, nanometers, which will be true nanotechnology within 20 years. But that's not an artist's conception, that's an actual picture of a violin that was printed out on a three-dimensional printer.

Somebody printed out an airplane in modules, and snapped them together and flew in it, on a three-dimensional printer. Today with a three-dimensional printer you can print out 70% of the parts you need to build another three-dimensional printer. And that will be a hundred percent within 10 years. So ultimately, this is going to revolutionize manufacturing. We ultimately will be able to print out cloths. You can have your avatar in your three-dimensional virtual reality environment, and then dress it with different fashions, and then actually print out the cloths right on your desktop three-dimensional printer. This is going to revolutionize manufacturing, which is why I say that the future is not in manufacturing jobs, in fact, already in the United States, for example, one third of the population worked in factories in 1900, it's down to 3% today. And it's really going to be in creating the intellectual property and the intellect that goes into all of these industries. This is actually where "2045" comes from; this is a cover story in the American magazine "Time Magazine", about this idea - my idea of the Law of Accelerating Returns. They wanted to print that graph, and they said, hey, we want you to put this one computer on it, that we covered a few weeks ago. So we put it on there, and it's right on the curve. This is a curve that I laid out in 1981, and it's still exactly correct, in terms of the power of computation. And it’s an exponential graph. So this represents exponential growth that's going to continue well into the future.  The last important area I want to cover, because I want to leave some time for questions, is on the brain. I've been thinking about thinking for 50 years, and I'm writing a book that's coming out, actually, this fall, called "How to create a mind - the secret of human thought revealed". It really describes what I mentioned earlier, which is this  key algorithm in the neocortex that's repeated 300 million times, and it’s self organizing. And that the connections from one conceptual level to the next are made by these modules themselves. So not only does our brain create our thoughts, but our thoughts actually create our brain, quite literally. And we can actually see this now. And this is all scaling up exponentially. There are many different projects, like the Blue Brain project, that simulate the neocortex. And this one's System Watson which can actually deal very intelligently with human language. It's able to deal with questions in Jeopardy, which are very subtle uses of language, using humor, and puns, and metaphors, and requires accessing all of the human knowledge. And this system actually read in natural language all of Wikipedia in English, and several other encyclopedias. It actually read 200 million pages and mastered it all, and can come up with any information from those 200 million pages within 3 seconds. And it's based on models that are very similar to what I'm describing.

Markov models, actually, or the mathematical equivalent. But this is a simulation of a slice of the neocortex. All of this is scaling up at an exponential pace. There are working simulations of the human auditory cortex, where we process sound, the visual cortex, the cerebellum, where we do our skill formation. For example: catching a flyball - a kid does that without thinking about it, although she has to learn how to do it. I always wondered, how does that work? A child is relating - it's seeing the ball go up in the air, and then moves her hand and catches the ball. In order to do that you have to solve a dozen simultaneous differential equations in two seconds, and most ten year olds haven't taken calculus. So we wondered how that worked. The cerebellum actually solves those equations, that's how it works, and we've actually figured that out, and there are working models and simulations of the cerebellum. So let me address one more thing, and then we will have time for questions, which is, is this a good thing or a bad thing? There's actually a very popular, or influential public perception, or movement, that the world is getting worse. That the world is getting more violent, the poor are getting poorer, the environment is getting worse, the world in general is getting poorer. Turns out not to be the case, I have 50 different graphs like this, which… I'll show you this one, just to make the point. The world is actually getting better. The reason we think the world is getting worse is that we have much better information about what's wrong with the world. So if there's a battle in Fallujah, it's not far away. It's right… we're there, it's right on our palmtops. We experience it. So when there are problems with poverty, or health, or conflict, or war somewhere in the world, we hear about it, and we can't… very often we can't solve it right away. So we are frustrated, because we are empathetic creatures, and we want to solve problems that we hear about. And so people get frustrated and think things are getting worse. It's not like these problems didn't exist, in fact, they were much worse. There's a recent book by Steven Pinker that actually, human conflict and violence has been steadily coming down, despite all appearances. Because you hear about violence all the time, but actually there's much less of it in the world than there's ever been. The world is healthier, human longevity  was 20 a thousand years ago, it was 37 in 1800, it's now pushing 80.

So, this graph, it's a moving graph, this is 1800, and these are all countries. The big red circle is China. Actually, we'll watch China, because it does some interesting things. And this is on two different scales. On the X axis it's the wealth of nations, income per person, actually, GDP per capita. And it was in the hundreds of dollars, on average, in 1800. On the Y axis it’s life expectancy, which was in the twenties and thirties, depending on where you were.  The worldwide average is 37. So let's see what happened. This is now the early industrial revolution; a few countries are experimenting with new industry and are making some progress. China is bouncing around.  And as we get to the 20th century, this picks up pace, and while there is a have/have not divide, a gap, there's a wind that carries all of these nations towards the upper right hand corner of the graph, towards greater longevity and greater wealth. And this is not stopping, it's not slowing down. Here is a snapshot of 2009, but this is going to continue, in fact, it's going to go into even higher gear,  because of  this acceleration of the pace of change, this ongoing exponential growth of information technology, the greater purview of information technology and its influence on everything. So there's still a divide, the rich nations are still better off than the poor nations, but the poorest nations in the world are actually much better off than the richest nations were at the beginning of this process. So, I'll show you one last… well let me show you just a couple of things. People say, well, you know, if we live longer, we're going to run out of resources, we're already running out of energy. It's not true. We are running out of energy only if we limit ourselves to 19th century technologies. For example, solar energy  - we have 10 thousand times more sunlight than we need to meet a hundred percent of our energy needs. In other words, we only have to capture one part in 10 thousand of the sunlight falling on the Earth to meet all of our energy needs. The cost per Watt of solar energy on this graph here is coming down very quickly. As a result, the total amount of solar energy… anyway, I had a graph, showing smooth exponential growth of the total amount of solar energy. For the last 25 years it has doubled every two years. Very smooth exponential growth, doubling every two years, and it's only 7 doublings away from meeting 100% of the world's energy needs. Which means that right now, it's meeting 1% of the world's energy needs.  So people say, one percent, that's insignificant, it's a fringe player, they dismiss it, but they ignore this exponential growth. Which is the same thing we did with the internet, or with the genome project, but it's only 7 doublings at two years each, to meet 100% of our energy needs. And there are many other exponentially growing energy technologies as well. There are similar stories for clean water, for food, that are coming from new technologies.

So here is the progress we've made on longevity so far. This is when health and medicine was not an information technology. It was hit or miss, but it was still very useful. And this is going to go into high gear now that health and medicine has become and information technology, and we have the means of literally reprogramming our biology away from disease, and away from aging. And now many of these technologies are really gaining fruition. There was just… the day before yesterday, a major announcement that if you have a heart attack, half of the survivors have a damaged heart, and are therefore very weak. My father had this condition, he could hardly walk.
You can now get that fixed with stem cell therapies that actually re-grow your heart. That's just one example; we're going to be able to re-grow every organ in the body to be younger and free of disease. So, hang in there, it'll be, I think, a very interesting future for all of us. Thank you.

I've actually written 3 health books, the last two are with a medical doctor. We talk about three bridges to radical life extension. Bridge one is what you can do right now, based on today's nowledge. Bridge two is what I was talking about, when we get really to the mature phase of the biotechnology revolution. Phase three is nanotechnology, for example, blood cell sized devices that can go inside the blood stream and keep you healthy from inside. That will really provide very dramatic extensions to human longevity. So the goal right now is actually to get to these future points in good shape, alive and doing well. So you don't have to deploy today's methods to live hundreds of years, you just have to get to, actually, by our estimate, 15 years from now will be the mature part of the biotechnology revolution. So, a lot of these books that I've written are about how to do that, what to  practice today.  And it involves not… there's nothing fattish about it. It's eating healthy fats, rather than unhealthy fats. So, healthy fats, for example, are Omega 3, anti-inflammatory fats, or extra virgin olive oil, which is a monounsaturated fat. Unhealthy fats are eating too much meat products. For example,  eating healthy carbs, like vegetables, fruits, to some extent, avoiding unhealthy carbs like sodas and pastries. So, I follow these kinds of recommendations,  I take quite a few supplements to reprogram my biochemistry. I was diagnosed, actually, with type two diabetes 30 years ago, and I completely got rid of it  through  using this approach, and I've had no indication of type two diabetes because of my own diet. I also inherited my father's disposition to heart disease, but I don't have heart disease, again, because of this diet. So, it's not a one size fits all, depends on what your issues are. If you are a person like myself, I'm 64, although I come out younger on biological aging tests, and I have some genes that are not healthy that dispose me to type two diabetes or heart disease, then there's actually a fair amount that you need to do, which I do, and I'm able to stay healthy. If you are a twenty five year old without any health problems, there's not a lot you need to do other than wear your seatbelt and eat a reasonably healthy diet.

Well, we certainly can see already dramatic changes in the political process affected by the tremendous increase in social communication. We see that in the Unites States, we see that everywhere in the world, this social communication, where everybody can communicate with one another. It is very democratizing, not just politically, but in many other spheres of society. You know, 25 years ago you went to the doctor, the doctor was… really commanded all of the knowledge in medicine. Now, if a woman has a chronic disease, and goes to her doctor, she is in touch with millions of people around the world who have the same condition, and absolutely knows everything going on, and the latest research, and may very well know more than her doctor does. In fact, we have not a decision making, but actually problem solving, when groups of, for example, patients are getting together, hundreds of thousands of patients, and actually saying, hey, we have the skills to solve this problem, and we have the motivation. And they are doing collaborative decision making. So this communication is having a positive effect, sometimes it's called the wisdom of crowds. You can take a thousand people, or a million people, and if you harness their collective wisdom in the right way, you can get much wiser decisions than even if you pick the wisest person in that group. So I think we actually are making better decisions and solving problems more effectively because of this social communication.

Transcription and translation of Raymond Kurzweil's presentation was done by Evgeny Homichuk, "Russia 2045" member.

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