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Annotated captions of Ray Kurzweil on how technology will transform us in English

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Well, it's great to be here.

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We've heard a lot about the promise of technology, and the peril.

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I've been quite interested in both.

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If we could convert 0.03 percent

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of the sunlight that falls on the earth into energy,

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we could meet all of our projected needs for 2030.

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We can't do that today because solar panels are heavy,

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expensive and very inefficient.

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There are nano-engineered designs,

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which at least have been analyzed theoretically,

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that show the potential to be very lightweight,

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very inexpensive, very efficient,

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and we'd be able to actually provide all of our energy needs in this renewable way.

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Nano-engineered fuel cells

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could provide the energy where it's needed.

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That's a key trend, which is decentralization,

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moving from centralized nuclear power plants and

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liquid natural gas tankers

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to decentralized resources that are environmentally more friendly,

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a lot more efficient

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and capable and safe from disruption.

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Bono spoke very eloquently,

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that we have the tools, for the first time,

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to address age-old problems of disease and poverty.

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Most regions of the world are moving in that direction.

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In 1990, in East Asia and the Pacific region,

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there were 500 million people living in poverty --

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that number now is under 200 million.

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The World Bank projects by 2011, it will be under 20 million,

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which is a reduction of 95 percent.

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I did enjoy Bono's comment

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linking Haight-Ashbury to Silicon Valley.

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Being from the Massachusetts high-tech community myself,

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I'd point out that we were hippies also in the 1960s,

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although we hung around Harvard Square.

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But we do have the potential to overcome disease and poverty,

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and I'm going to talk about those issues, if we have the will.

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Kevin Kelly talked about the acceleration of technology.

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That's been a strong interest of mine,

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and a theme that I've developed for some 30 years.

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I realized that my technologies had to make sense when I finished a project.

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That invariably, the world was a different place

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when I would introduce a technology.

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And, I noticed that most inventions fail,

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not because the R&D department can't get it to work --

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if you look at most business plans, they will actually succeed

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if given the opportunity to build what they say they're going to build --

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and 90 percent of those projects or more will fail, because the timing is wrong --

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not all the enabling factors will be in place when they're needed.

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So I began to be an ardent student of technology trends,

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and track where technology would be at different points in time,

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and began to build the mathematical models of that.

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It's kind of taken on a life of its own.

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I've got a group of 10 people that work with me to gather data

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on key measures of technology in many different areas, and we build models.

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And you'll hear people say, well, we can't predict the future.

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And if you ask me,

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will the price of Google be higher or lower than it is today three years from now,

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that's very hard to say.

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Will WiMax CDMA G3

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be the wireless standard three years from now? That's hard to say.

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But if you ask me, what will it cost

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for one MIPS of computing in 2010,

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or the cost to sequence a base pair of DNA in 2012,

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or the cost of sending a megabyte of data wirelessly in 2014,

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it turns out that those are very predictable.

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There are remarkably smooth exponential curves

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that govern price performance, capacity, bandwidth.

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And I'm going to show you a small sample of this,

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but there's really a theoretical reason

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why technology develops in an exponential fashion.

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And a lot of people, when they think about the future, think about it linearly.

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They think they're going to continue

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to develop a problem

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or address a problem using today's tools,

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at today's pace of progress,

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and fail to take into consideration this exponential growth.

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The Genome Project was a controversial project in 1990.

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We had our best Ph.D. students,

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our most advanced equipment around the world,

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we got 1/10,000th of the project done,

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so how're we going to get this done in 15 years?

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And 10 years into the project,

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the skeptics were still going strong -- says, "You're two-thirds through this project,

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and you've managed to only sequence

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a very tiny percentage of the whole genome."

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But it's the nature of exponential growth

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that once it reaches the knee of the curve, it explodes.

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Most of the project was done in the last

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few years of the project.

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It took us 15 years to sequence HIV --

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we sequenced SARS in 31 days.

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So we are gaining the potential to overcome these problems.

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I'm going to show you just a few examples

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of how pervasive this phenomena is.

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The actual paradigm-shift rate, the rate of adopting new ideas,

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is doubling every decade, according to our models.

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These are all logarithmic graphs,

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so as you go up the levels it represents, generally multiplying by factor of 10 or 100.

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It took us half a century to adopt the telephone,

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the first virtual-reality technology.

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Cell phones were adopted in about eight years.

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If you put different communication technologies

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on this logarithmic graph,

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television, radio, telephone

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were adopted in decades.

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Recent technologies -- like the PC, the web, cell phones --

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were under a decade.

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Now this is an interesting chart,

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and this really gets at the fundamental reason why

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an evolutionary process -- and both biology and technology are evolutionary processes --

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accelerate.

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They work through interaction -- they create a capability,

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and then it uses that capability to bring on the next stage.

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So the first step in biological evolution,

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the evolution of DNA -- actually it was RNA came first --

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took billions of years,

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but then evolution used that information-processing backbone

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to bring on the next stage.

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So the Cambrian Explosion, when all the body plans of the animals were evolved,

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took only 10 million years. It was 200 times faster.

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And then evolution used those body plans

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to evolve higher cognitive functions,

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and biological evolution kept accelerating.

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It's an inherent nature of an evolutionary process.

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So Homo sapiens, the first technology-creating species,

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the species that combined a cognitive function

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with an opposable appendage --

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and by the way, chimpanzees don't really have a very good opposable thumb --

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so we could actually manipulate our environment with a power grip

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and fine motor coordination,

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and use our mental models to actually change the world

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and bring on technology.

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But anyway, the evolution of our species took hundreds of thousands of years,

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and then working through interaction,

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evolution used, essentially,

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the technology-creating species to bring on the next stage,

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which were the first steps in technological evolution.

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And the first step took tens of thousands of years --

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stone tools, fire, the wheel -- kept accelerating.

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We always used then the latest generation of technology

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to create the next generation.

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Printing press took a century to be adopted;

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the first computers were designed pen-on-paper -- now we use computers.

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And we've had a continual acceleration of this process.

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Now by the way, if you look at this on a linear graph, it looks like everything has just happened,

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but some observer says, "Well, Kurzweil just put points on this graph

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that fall on that straight line."

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So, I took 15 different lists from key thinkers,

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like the Encyclopedia Britannica, the Museum of Natural History, Carl Sagan's Cosmic Calendar

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on the same -- and these people were not trying to make my point;

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these were just lists in reference works,

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and I think that's what they thought the key events were

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in biological evolution and technological evolution.

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And again, it forms the same straight line. You have a little bit of thickening in the line

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because people do have disagreements, what the key points are,

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there's differences of opinion when agriculture started,

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or how long the Cambrian Explosion took.

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But you see a very clear trend.

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There's a basic, profound acceleration of this evolutionary process.

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Information technologies double their capacity, price performance, bandwidth,

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every year.

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And that's a very profound explosion of exponential growth.

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A personal experience, when I was at MIT --

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computer taking up about the size of this room,

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less powerful than the computer in your cell phone.

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But Moore's Law, which is very often identified with this exponential growth,

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is just one example of many, because it's basically

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a property of the evolutionary process of technology.

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I put 49 famous computers on this logarithmic graph --

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by the way, a straight line on a logarithmic graph is exponential growth --

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that's another exponential.

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It took us three years to double our price performance of computing in 1900,

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two years in the middle; we're now doubling it every one year.

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And that's exponential growth through five different paradigms.

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Moore's Law was just the last part of that,

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where we were shrinking transistors on an integrated circuit,

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but we had electro-mechanical calculators,

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relay-based computers that cracked the German Enigma Code,

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vacuum tubes in the 1950s predicted the election of Eisenhower,

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discreet transistors used in the first space flights

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and then Moore's Law.

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Every time one paradigm ran out of steam,

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another paradigm came out of left field to continue the exponential growth.

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They were shrinking vacuum tubes, making them smaller and smaller.

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That hit a wall. They couldn't shrink them and keep the vacuum.

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Whole different paradigm -- transistors came out of the woodwork.

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In fact, when we see the end of the line for a particular paradigm,

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it creates research pressure to create the next paradigm.

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And because we've been predicting the end of Moore's Law

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for quite a long time -- the first prediction said 2002, until now it says 2022.

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But by the teen years,

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the features of transistors will be a few atoms in width,

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and we won't be able to shrink them any more.

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That'll be the end of Moore's Law, but it won't be the end of

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the exponential growth of computing, because chips are flat.

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We live in a three-dimensional world; we might as well use the third dimension.

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We will go into the third dimension

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and there's been tremendous progress, just in the last few years,

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of getting three-dimensional, self-organizing molecular circuits to work.

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We'll have those ready well before Moore's Law runs out of steam.

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Supercomputers -- same thing.

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Processor performance on Intel chips,

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the average price of a transistor --

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1968, you could buy one transistor for a dollar.

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You could buy 10 million in 2002.

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It's pretty remarkable how smooth

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an exponential process that is.

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I mean, you'd think this is the result of some tabletop experiment,

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but this is the result of worldwide chaotic behavior --

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countries accusing each other of dumping products,

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IPOs, bankruptcies, marketing programs.

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You would think it would be a very erratic process,

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and you have a very smooth

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outcome of this chaotic process.

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Just as we can't predict

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what one molecule in a gas will do --

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it's hopeless to predict a single molecule --

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yet we can predict the properties of the whole gas,

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using thermodynamics, very accurately.

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It's the same thing here. We can't predict any particular project,

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but the result of this whole worldwide,

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chaotic, unpredictable activity of competition

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and the evolutionary process of technology is very predictable.

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And we can predict these trends far into the future.

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Unlike Gertrude Stein's roses,

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it's not the case that a transistor is a transistor.

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As we make them smaller and less expensive,

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the electrons have less distance to travel.

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They're faster, so you've got exponential growth in the speed of transistors,

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so the cost of a cycle of one transistor

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has been coming down with a halving rate of 1.1 years.

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You add other forms of innovation and processor design,

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you get a doubling of price performance of computing every one year.

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And that's basically deflation --

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50 percent deflation.

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And it's not just computers. I mean, it's true of DNA sequencing;

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it's true of brain scanning;

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it's true of the World Wide Web. I mean, anything that we can quantify,

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we have hundreds of different measurements

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of different, information-related measurements --

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capacity, adoption rates --

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and they basically double every 12, 13, 15 months,

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depending on what you're looking at.

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In terms of price performance, that's a 40 to 50 percent deflation rate.

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And economists have actually started worrying about that.

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We had deflation during the Depression,

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but that was collapse of the money supply,

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collapse of consumer confidence, a completely different phenomena.

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This is due to greater productivity,

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but the economist says, "But there's no way you're going to be able to keep up with that.

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If you have 50 percent deflation, people may increase their volume

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30, 40 percent, but they won't keep up with it."

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But what we're actually seeing is that

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we actually more than keep up with it.

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We've had 28 percent per year compounded growth in dollars

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in information technology over the last 50 years.

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I mean, people didn't build iPods for 10,000 dollars 10 years ago.

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As the price performance makes new applications feasible,

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new applications come to the market.

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And this is a very widespread phenomena.

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Magnetic data storage --

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that's not Moore's Law, it's shrinking magnetic spots,

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different engineers, different companies, same exponential process.

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A key revolution is that we're understanding our own biology

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in these information terms.

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We're understanding the software programs

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that make our body run.

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These were evolved in very different times --

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we'd like to actually change those programs.

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One little software program, called the fat insulin receptor gene,

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basically says, "Hold onto every calorie,

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because the next hunting season may not work out so well."

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That was in the interests of the species tens of thousands of years ago.

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We'd like to actually turn that program off.

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They tried that in animals, and these mice ate ravenously

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and remained slim and got the health benefits of being slim.

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They didn't get diabetes; they didn't get heart disease;

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they lived 20 percent longer; they got the health benefits of caloric restriction

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without the restriction.

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Four or five pharmaceutical companies have noticed this,

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felt that would be

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interesting drug for the human market,

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and that's just one of the 30,000 genes

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that affect our biochemistry.

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We were evolved in an era where it wasn't in the interests of people

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at the age of most people at this conference, like myself,

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to live much longer, because we were using up the precious resources

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which were better deployed towards the children

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and those caring for them.

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So, life -- long lifespans --

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like, that is to say, much more than 30 --

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weren't selected for,

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but we are learning to actually manipulate

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and change these software programs

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through the biotechnology revolution.

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For example, we can inhibit genes now with RNA interference.

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There are exciting new forms of gene therapy

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that overcome the problem of placing the genetic material

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in the right place on the chromosome.

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There's actually a -- for the first time now,

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something going to human trials, that actually cures pulmonary hypertension --

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a fatal disease -- using gene therapy.

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So we'll have not just designer babies, but designer baby boomers.

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And this technology is also accelerating.

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It cost 10 dollars per base pair in 1990,

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then a penny in 2000.

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It's now under a 10th of a cent.

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The amount of genetic data --

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basically this shows that smooth exponential growth

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doubled every year,

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enabling the genome project to be completed.

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Another major revolution: the communications revolution.

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The price performance, bandwidth, capacity of communications measured many different ways;

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wired, wireless is growing exponentially.

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The Internet has been doubling in power and continues to,

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measured many different ways.

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This is based on the number of hosts.

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Miniaturization -- we're shrinking the size of technology

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at an exponential rate,

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both wired and wireless.

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These are some designs from Eric Drexler's book --

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15:06

which we're now showing are feasible

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15:08

with super-computing simulations,

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15:10

where actually there are scientists building

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15:12

molecule-scale robots.

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15:14

One has one that actually walks with a surprisingly human-like gait,

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15:17

that's built out of molecules.

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15:21

There are little machines doing things in experimental bases.

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15:24

The most exciting opportunity

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15:26

is actually to go inside the human body

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15:29

and perform therapeutic and diagnostic functions.

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15:31

And this is less futuristic than it may sound.

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15:33

These things have already been done in animals.

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15:37

There's one nano-engineered device that cures type 1 diabetes. It's blood cell-sized.

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15:39

They put tens of thousands of these

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15:41

in the blood cell -- they tried this in rats --

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15:43

it lets insulin out in a controlled fashion,

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15:45

and actually cures type 1 diabetes.

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15:48

What you're watching is a design

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15:50

of a robotic red blood cell,

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15:52

and it does bring up the issue that our biology

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15:54

is actually very sub-optimal,

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15:57

even though it's remarkable in its intricacy.

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16:00

Once we understand its principles of operation,

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16:03

and the pace with which we are reverse-engineering biology is accelerating,

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16:06

we can actually design these things to be

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16:08

thousands of times more capable.

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16:12

An analysis of this respirocyte, designed by Rob Freitas,

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16:15

indicates if you replace 10 percent of your red blood cells with these robotic versions,

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16:19

you could do an Olympic sprint for 15 minutes without taking a breath.

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16:22

You could sit at the bottom of your pool for four hours --

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16:26

so, "Honey, I'm in the pool," will take on a whole new meaning.

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16:28

It will be interesting to see what we do in our Olympic trials.

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16:30

Presumably we'll ban them,

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16:32

but then we'll have the specter of teenagers in their high schools gyms

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16:35

routinely out-performing the Olympic athletes.

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16:40

Freitas has a design for a robotic white blood cell.

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16:44

These are 2020-circa scenarios,

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16:46

but they're not as futuristic as it may sound.

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16:50

There are four major conferences on building blood cell-sized devices;

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16:52

there are many experiments in animals.

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16:54

There's actually one going into human trial,

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16:57

so this is feasible technology.

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17:00

If we come back to our exponential growth of computing,

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17:03

1,000 dollars of computing is now somewhere between an insect and a mouse brain.

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17:06

It will intersect human intelligence

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17:09

in terms of capacity in the 2020s,

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17:11

but that'll be the hardware side of the equation.

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17:13

Where will we get the software?

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17:15

Well, it turns out we can see inside the human brain,

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17:17

and in fact not surprisingly,

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17:21

the spatial and temporal resolution of brain scanning is doubling every year.

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17:23

And with the new generation of scanning tools,

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17:25

for the first time we can actually see

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17:27

individual inter-neural fibers

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17:30

and see them processing and signaling in real time --

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17:32

but then the question is, OK, we can get this data now,

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17:34

but can we understand it?

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17:37

Doug Hofstadter wonders, well, maybe our intelligence

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17:40

just isn't great enough to understand our intelligence,

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17:43

and if we were smarter, well, then our brains would be that much more complicated,

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17:45

and we'd never catch up to it.

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17:49

It turns out that we can understand it.

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17:52

This is a block diagram of

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17:56

a model and simulation of the human auditory cortex

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17:58

that actually works quite well --

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18:00

in applying psychoacoustic tests, gets very similar results to human auditory perception.

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18:05

There's another simulation of the cerebellum --

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18:07

that's more than half the neurons in the brain --

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18:10

again, works very similarly to human skill formation.

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18:14

This is at an early stage, but you can show

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18:17

with the exponential growth of the amount of information about the brain

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18:19

and the exponential improvement

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18:21

in the resolution of brain scanning,

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18:24

we will succeed in reverse-engineering the human brain

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18:26

by the 2020s.

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18:29

We've already had very good models and simulation of about 15 regions

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18:32

out of the several hundred.

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18:34

All of this is driving

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18:36

exponentially growing economic progress.

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18:39

We've had productivity go from 30 dollars to 150 dollars per hour

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18:43

of labor in the last 50 years.

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18:46

E-commerce has been growing exponentially. It's now a trillion dollars.

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18:48

You might wonder, well, wasn't there a boom and a bust?

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18:50

That was strictly a capital-markets phenomena.

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18:54

Wall Street noticed that this was a revolutionary technology, which it was,

tedtalks 18:54
18:57

but then six months later, when it hadn't revolutionized all business models,

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18:59

they figured, well, that was wrong,

tedtalks 18:59
19:01

and then we had this bust.

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19:04

All right, this is a technology

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19:07

that we put together using some of the technologies we're involved in.

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19:11

This will be a routine feature in a cell phone.

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19:13

It would be able to translate from one language to another.

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19:25

So let me just end with a couple of scenarios.

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19:28

By 2010 computers will disappear.

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19:32

They'll be so small, they'll be embedded in our clothing, in our environment.

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19:34

Images will be written directly to our retina,

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19:36

providing full-immersion virtual reality,

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19:39

augmented real reality. We'll be interacting with virtual personalities.

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19:44

But if we go to 2029, we really have the full maturity of these trends,

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19:47

and you have to appreciate how many turns of the screw

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19:51

in terms of generations of technology, which are getting faster and faster, we'll have at that point.

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19:53

I mean, we will have two-to-the-25th-power

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19:56

greater price performance, capacity and bandwidth

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19:58

of these technologies, which is pretty phenomenal.

tedtalks 19:58
20:00

It'll be millions of times more powerful than it is today.

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20:02

We'll have completed the reverse-engineering of the human brain,

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20:06

1,000 dollars of computing will be far more powerful

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20:10

than the human brain in terms of basic raw capacity.

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20:12

Computers will combine

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20:14

the subtle pan-recognition powers

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20:17

of human intelligence with ways in which machines are already superior,

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20:19

in terms of doing analytic thinking,

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20:21

remembering billions of facts accurately.

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20:23

Machines can share their knowledge very quickly.

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20:28

But it's not just an alien invasion of intelligent machines.

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20:30

We are going to merge with our technology.

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20:32

These nano-bots I mentioned

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20:36

will first be used for medical and health applications:

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20:39

cleaning up the environment, providing powerful fuel cells

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20:44

and widely distributed decentralized solar panels and so on in the environment.

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20:46

But they'll also go inside our brain,

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20:48

interact with our biological neurons.

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20:51

We've demonstrated the key principles of being able to do this.

tedtalks 20:51
20:53

So, for example,

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20:55

full-immersion virtual reality from within the nervous system,

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20:58

the nano-bots shut down the signals coming from your real senses,

tedtalks 20:58
21:01

replace them with the signals that your brain would be receiving

tedtalks 21:01
21:03

if you were in the virtual environment,

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21:05

and then it'll feel like you're in that virtual environment.

tedtalks 21:05
21:07

You can go there with other people, have any kind of experience

tedtalks 21:07
21:09

with anyone involving all of the senses.

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21:13

"Experience beamers," I call them, will put their whole flow of sensory experiences

tedtalks 21:13
21:16

in the neurological correlates of their emotions out on the Internet.

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21:19

You can plug in and experience what it's like to be someone else.

tedtalks 21:19
21:21

But most importantly,

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21:23

it'll be a tremendous expansion

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21:27

of human intelligence through this direct merger with our technology,

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21:29

which in some sense we're doing already.

tedtalks 21:29
21:31

We routinely do intellectual feats

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21:33

that would be impossible without our technology.

tedtalks 21:33
21:36

Human life expectancy is expanding. It was 37 in 1800,

tedtalks 21:36
21:41

and with this sort of biotechnology, nano-technology revolutions,

tedtalks 21:41
21:43

this will move up very rapidly

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21:45

in the years ahead.

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21:49

My main message is that progress in technology

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21:52

is exponential, not linear.

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21:56

Many -- even scientists -- assume a linear model,

tedtalks 21:56
21:58

so they'll say, "Oh, it'll be hundreds of years

tedtalks 21:58
22:01

before we have self-replicating nano-technology assembly

tedtalks 22:01
22:03

or artificial intelligence."

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22:06

If you really look at the power of exponential growth,

tedtalks 22:06
22:09

you'll see that these things are pretty soon at hand.

tedtalks 22:09
22:12

And information technology is increasingly encompassing

tedtalks 22:12
22:16

all of our lives, from our music to our manufacturing

tedtalks 22:16
22:20

to our biology to our energy to materials.

tedtalks 22:20
22:23

We'll be able to manufacture almost anything we need in the 2020s,

tedtalks 22:23
22:25

from information, in very inexpensive raw materials,

tedtalks 22:25
22:28

using nano-technology.

tedtalks 22:28
22:30

These are very powerful technologies.

tedtalks 22:30
22:34

They both empower our promise and our peril.

tedtalks 22:34
22:37

So we have to have the will to apply them to the right problems.

tedtalks 22:37
22:38

Thank you very much.

tedtalks 22:38
22:39

(Applause)