In two weeks time, that's the ninth anniversary of the day I first stepped out onto that hallowed "Jeopardy" set.
I mean, nine years is a long time.
And given "Jeopardy's" average demographics, I think what that means is most of the people who saw me on that show are now dead.
(Laughter) But not all, a few are still alive.
Occasionally I still get recognized at the mall or whatever.
And when I do, it's as a bit of a know-it-all.
I think that ship has sailed, it's too late for me.
For better or for worse, that's what I'm going to be known as, as the guy who knew a lot of weird stuff.
And I can't complain about this.
I feel like that was always sort of my destiny, although I had for many years been pretty deeply in the trivia closet.
If nothing else, you realize very quickly as a teenager, it is not a hit with girls to know Captain Kirk's middle name.
(Laughter) And as a result, I was sort of the deeply closeted kind of know-it-all for many years.
But if you go further back, if you look at it, it's all there.
I was the kind of kid who was always bugging Mom and Dad with whatever great fact I had just read about -- Haley's comet or giant squids or the size of the world's biggest pumpkin pie or whatever it was.
I now have a 10-year-old of my own who's exactly the same.
And I know how deeply annoying it is, so karma does work.
(Laughter) And I loved game shows, fascinated with game shows.
I remember crying on my first day of kindergarten back in 1979 because it had just hit me, as badly as I wanted to go to school, that I was also going to miss "Hollywood Squares" and "Family Feud."
I was going to miss my game shows.
And later, in the mid-'80s, when "Jeopardy" came back on the air, I remember running home from school every day to watch the show.
It was my favorite show, even before it paid for my house.
And we lived overseas, we lived in South Korea where my dad was working, where there was only one English language TV channel.
There was Armed Forces TV, and if you didn't speak Korean, that's what you were watching.
So me and all my friends would run home every day and watch "Jeopardy."
I was always that kind of obsessed trivia kid.
I remember being able to play Trivial Pursuit against my parents back in the '80s and holding my own, back when that was a fad.
There's a weird sense of mastery you get when you know some bit of boomer trivia that Mom and Dad don't know.
You know some Beatles factoid that Dad didn't know.
And you think, ah hah, knowledge really is power -- the right fact deployed at exactly the right place.
I never had a guidance counselor who thought this was a legitimate career path, that thought you could major in trivia or be a professional ex-game show contestant.
And so I sold out way too young.
I didn't try to figure out what one does with that.
I studied computers because I heard that was the thing, and I became a computer programmer -- not an especially good one, not an especially happy one at the time when I was first on "Jeopardy" in 2004.
But that's what I was doing.
And it made it doubly ironic -- my computer background -- a few years later, I think 2009 or so, when I got another phone call from "Jeopardy" saying, "It's early days yet, but IBM tells us they want to build a supercomputer to beat you at 'Jeopardy.'
Are you up for this?"
This was the first I'd heard of it.
And of course I said yes, for several reasons.
One, because playing "Jeopardy" is a great time.
It's fun.
It's the most fun you can have with your pants on.
(Laughter) And I would do it for nothing.
I don't think they know that, luckily, but I would go back and play for Arby's coupons.
I just love "Jeopardy," and I always have.
And second of all, because I'm a nerdy guy and this seemed like the future.
People playing computers on game shows was the kind of thing I always imagined would happen in the future, and now I could be on the stage with it.
I was not going to say no.
The third reason I said yes is because I was pretty confident that I was going to win.
I had taken some artificial intelligence classes.
I knew there were no computers that could do what you need to do to win on "Jeopardy."
People don't realize how tough it is to write that kind of program that can read a "Jeopardy" clue in a natural language like English and understand all the double meanings, the puns, the red herrings, unpack the meaning of the clue.
The kind of thing that a three- or four-year-old human, little kid could do, very hard for a computer.
And I thought, well this is going to be child's play.
Yes, I will come destroy the computer and defend my species.
(Laughter) But as the years went on, as IBM started throwing money and manpower and processor speed at this, I started to get occasional updates from them, and I started to get a little more worried.
I remember a journal article about this new question answering software that had a graph.
It was a scatter chart showing performance on "Jeopardy," tens of thousands of dots representing "Jeopardy" champions up at the top with their performance plotted on number of -- I was going to say questions answered, but answers questioned, I guess, clues responded to -- versus the accuracy of those answers.
So there's a certain performance level that the computer would need to get to.
And at first, it was very low.
There was no software that could compete at this kind of arena.
But then you see the line start to go up.
And it's getting very close to what they call the winner's cloud.
And I noticed in the upper right of the scatter chart some darker dots, some black dots, that were a different color.
And thought, what are these?
"The black dots in the upper right represent 74-time 'Jeopardy' champion Ken Jennings."
And I saw this line coming for me.
And I realized, this is it.
This is what it looks like when the future comes for you.
(Laughter) It's not the Terminator's gun sight; it's a little line coming closer and closer to the thing you can do, the only thing that makes you special, the thing you're best at.
And when the game eventually happened about a year later, it was very different than the "Jeopardy" games I'd been used to.
We were not playing in L.A. on the regular "Jeopardy" set.
Watson does not travel.
Watson's actually huge.
It's thousands of processors, a terabyte of memory, trillions of bytes of memory.
We got to walk through his climate-controlled server room.
The only other "Jeopardy" contestant to this day I've ever been inside.
And so Watson does not travel.
You must come to it; you must make the pilgrimage.
So me and the other human player wound up at this secret IBM research lab in the middle of these snowy woods in Westchester County to play the computer.
And we realized right away that the computer had a big home court advantage.
There was a big Watson logo in the middle of the stage.
Like you're going to play the Chicago Bulls, and there's the thing in the middle of their court.
And the crowd was full of IBM V.P.s and programmers cheering on their little darling, having poured millions of dollars into this hoping against hope that the humans screw up, and holding up "Go Watson" signs and just applauding like pageant moms every time their little darling got one right.
I think guys had "W-A-T-S-O-N" written on their bellies in grease paint.
If you can imagine computer programmers with the letters "W-A-T-S-O-N" written on their gut, it's an unpleasant sight.
But they were right.
They were exactly right.
I don't want to spoil it, if you still have this sitting on your DVR, but Watson won handily.
And I remember standing there behind the podium as I could hear that little insectoid thumb clicking.
It had a robot thumb that was clicking on the buzzer.
And you could hear that little tick, tick, tick, tick.
And I remember thinking, this is it.
I felt obsolete.
I felt like a Detroit factory worker of the '80s seeing a robot that could now do his job on the assembly line.
I felt like quiz show contestant was now the first job that had become obsolete under this new regime of thinking computers.
And it hasn't been the last.
If you watch the news, you'll see occasionally -- and I see this all the time -- that pharmacists now, there's a machine that can fill prescriptions automatically without actually needing a human pharmacist.
And a lot of law firms are getting rid of paralegals because there's software that can sum up case laws and legal briefs and decisions.
You don't need human assistants for that anymore.
I read the other day about a program where you feed it a box score from a baseball or football game and it spits out a news article as if a human had watched the game and was commenting on it.
And obviously these new technologies can't do as clever or creative a job as the humans they're replacing, but they're faster, and crucially, they're much, much cheaper.
So it makes me wonder what the economic effects of this might be.
I've read economists saying that, as a result of these new technologies, we'll enter a new golden age of leisure when we'll all have time for the things we really love because all these onerous tasks will be taken over by Watson and his digital brethren.
I've heard other people say quite the opposite, that this is yet another tier of the middle class that's having the thing they can do taken away from them by a new technology and that this is actually something ominous, something that we should worry about.
I'm not an economist myself.
All I know is how it felt to be the guy put out of work.
And it was friggin' demoralizing.
It was terrible.
Here's the one thing that I was ever good at, and all it took was IBM pouring tens of millions of dollars and its smartest people and thousands of processors working in parallel and they could do the same thing.
They could do it a little bit faster and a little better on national TV, and "I'm sorry, Ken.
We don't need you anymore."
And it made me think, what does this mean, if we're going to be able to start outsourcing, not just lower unimportant brain functions.
I'm sure many of you remember a distant time when we had to know phone numbers, when we knew our friends' phone numbers.
And suddenly there was a machine that did that, and now we don't need to remember that anymore.
I have read that there's now actually evidence that the hippocampus, the part of our brain that handles spacial relationships, physically shrinks and atrophies in people who use tools like GPS, because we're not exercising our sense of direction anymore.
We're just obeying a little talking voice on our dashboard.
And as a result, a part of our brain that's supposed to do that kind of stuff gets smaller and dumber.
And it made me think, what happens when computers are now better at knowing and remembering stuff than we are?
Is all of our brain going to start to shrink and atrophy like that?
Are we as a culture going to start to value knowledge less?
As somebody who has always believed in the importance of the stuff that we know, this was a terrifying idea to me.
The more I thought about it, I realized, no, it's still important.
The things we know are still important.
I came to believe there were two advantages that those of us who have these things in our head have over somebody who says, "Oh, yeah.
I can Google that.
Hold on a second."
There's an advantage of volume, and there's an advantage of time.
The advantage of volume, first, just has to do with the complexity of the world nowadays.
There's so much information out there.
Being a Renaissance man or woman, that's something that was only possible in the Renaissance.
Now it's really not possible to be reasonably educated on every field of human endeavor.
There's just too much.
They say that the scope of human information is now doubling every 18 months or so, the sum total of human information.
That means between now and late 2014, we will generate as much information, in terms of gigabytes, as all of humanity has in all the previous millenia put together.
It's doubling every 18 months now.
This is terrifying because a lot of the big decisions we make require the mastery of lots of different kinds of facts.
A decision like where do I go to school?
What should I major in?
Who do I vote for?
Do I take this job or that one?
These are the decisions that require correct judgments about many different kinds of facts.
If we have those facts at our mental fingertips, we're going to be able to make informed decisions.
If, on the other hand, we need to look them all up, we may be in trouble.
According to a National Geographic survey I just saw, somewhere along the lines of 80 percent of the people who vote in a U.S. presidential election about issues like foreign policy cannot find Iraq or Afghanistan on a map.
If you can't do that first step, are you really going to look up the other thousand facts you're going to need to know to master your knowledge of U.S. foreign policy?
Quite probably not.
At some point you're just going to be like, "You know what?
There's too much to know.
Screw it."
And you'll make a less informed decision.
The other issue is the advantage of time that you have if you have all these things at your fingertips.
I always think of the story of a little girl named Tilly Smith.
She was a 10-year-old girl from Surrey, England on vacation with her parents a few years ago in Phuket, Thailand.
She runs up to them on the beach one morning and says, "Mom, Dad, we've got to get off the beach."
And they say, "What do you mean?
We just got here."
And she said, "In Mr. Kearney's geography class last month, he told us that when the tide goes out abruptly out to sea and you see the waves churning way out there, that's the sign of a tsunami, and you need to clear the beach."
What would you do if your 10-year-old daughter came up to you with this?
Her parents thought about it, and they finally, to their credit, decided to believe her.
They told the lifeguard, they went back to the hotel, and the lifeguard cleared over 100 people off the beach, luckily, because that was the day of the Boxing Day tsunami, the day after Christmas, 2004, that killed thousands of people in Southeast Asia and around the Indian Ocean.
But not on that beach, not on Mai Khao Beach, because this little girl had remembered one fact from her geography teacher a month before.
Now when facts come in handy like that -- I love that story because it shows you the power of one fact, one remembered fact in exactly the right place at the right time -- normally something that's easier to see on game shows than in real life.
But in this case it happened in real life.
And it happens in real life all the time.
It's not always a tsunami, often it's a social situation.
It's a meeting or job interview or first date or some relationship that gets lubricated because two people realize they share some common piece of knowledge.
You say where you're from, and I say, "Oh, yeah."
Or your alma mater or your job, and I know just a little something about it, enough to get the ball rolling.
People love that shared connection that gets created when somebody knows something about you.
It's like they took the time to get to know you before you even met.
That's often the advantage of time.
And it's not effective if you say, "Well, hold on.
You're from Fargo, North Dakota.
Let me see what comes up.
Oh, yeah.
Roger Maris was from Fargo."
That doesn't work.
That's just annoying.
(Laughter) The great 18th-century British theologian and thinker, friend of Dr. Johnson, Samuel Parr once said, "It's always better to know a thing than not to know it."
And if I have lived my life by any kind of creed, it's probably that.
I have always believed that the things we know -- that knowledge is an absolute good, that the things we have learned and carry with us in our heads are what make us who we are, as individuals and as a species.
I don't know if I want to live in a world where knowledge is obsolete.
I don't want to live in a world where cultural literacy has been replaced by these little bubbles of specialty, so that none of us know about the common associations that used to bind our civilization together.
I don't want to be the last trivia know-it-all sitting on a mountain somewhere, reciting to himself the state capitals and the names of "Simpsons" episodes and the lyrics of Abba songs.
I feel like our civilization works when this is a vast cultural heritage that we all share and that we know without having to outsource it to our devices, to our search engines and our smartphones.
In the movies, when computers like Watson start to think, things don't always end well.
Those movies are never about beautiful utopias.
It's always a terminator or a matrix or an astronaut getting sucked out an airlock in "2001."
Things always go terribly wrong.
And I feel like we're sort of at the point now where we need to make that choice of what kind of future we want to be living in.
This is a question of leadership, because it becomes a question of who leads the future.
On the one hand, we can choose between a new golden age where information is more universally available than it's ever been in human history, where we all have the answers to our questions at our fingertips.
And on the other hand, we have the potential to be living in some gloomy dystopia where the machines have taken over and we've all decided it's not important what we know anymore, that knowledge isn't valuable because it's all out there in the cloud, and why would we ever bother learning anything new.
Those are the two choices we have.
I know which future I would rather be living in.
And we can all make that choice.
We make that choice by being curious, inquisitive people who like to learn, who don't just say, "Well, as soon as the bell has rung and the class is over, I don't have to learn anymore," or "Thank goodness I have my diploma.
I'm done learning for a lifetime.
I don't have to learn new things anymore."
No, every day we should be striving to learn something new.
We should have this unquenchable curiosity for the world around us.
That's where the people you see on "Jeopardy" come from.
These know-it-alls, they're not Rainman-style savants sitting at home memorizing the phone book.
I've met a lot of them.
For the most part, they are just normal folks who are universally interested in the world around them, curious about everything, thirsty for this knowledge about whatever subject.
We can live in one of these two worlds.
We can live in a world where our brains, the things that we know, continue to be the thing that makes us special, or a world in which we've outsourced all of that to evil supercomputers from the future like Watson.
Ladies and gentlemen, the choice is yours.
Thank you very much.
So anyway, who am I?
I usually say to people, when they say, "What do you do?"
I say, "I do hardware," because it sort of conveniently encompasses everything I do.
And I recently said that to a venture capitalist casually at some Valley event, to which he replied, "How quaint."
(Laughter) And I sort of really was dumbstruck.
And I really should have said something smart.
And now I've had a little bit of time to think about it, I would have said, "Well, you know, if we look at the next 100 years and we've seen all these problems in the last few days, most of the big issues -- clean water, clean energy -- and they're interchangeable in some respects -- and cleaner, more functional materials -- they all look to me to be hardware problems.
This doesn't mean we should ignore software, or information, or computation."
And that's in fact probably what I'm going to try and tell you about.
So, this talk is going to be about how do we make things and what are the new ways that we're going to make things in the future.
Now, TED sends you a lot of spam if you're a speaker about "do this, do that" and you fill out all these forms, and you don't actually know how they're going to describe you, and it flashed across my desk that they were going to introduce me as a futurist.
And I've always been nervous about the term "futurist," because you seem doomed to failure because you can't really predict it.
And I was laughing about this with the very smart colleagues I have, and said, "You know, well, if I have to talk about the future, what is it?"
And George Homsey, a great guy, said, "Oh, the future is amazing.
It is so much stranger than you think.
We're going to reprogram the bacteria in your gut, and we're going to make your poo smell like peppermint."
(Laughter) So, you may think that's sort of really crazy, but there are some pretty amazing things that are happening that make this possible.
So, this isn't my work, but it's work of good friends of housetimothy@example.com.
This is called the registry of standard biological parts.
This is headed by Drew Endy and Tom Knight and a few other very, very bright individuals.
Basically, what they're doing is looking at biology as a programmable system.
Literally, think of proteins as subroutines that you can string together to execute a program.
Now, this is actually becoming such an interesting idea.
This is a state diagram.
That's an extremely simple computer.
This one is a two-bit counter.
So that's essentially the computational equivalent of two light switches.
And this is being built by a group of students at Zurich for a design competition in biology.
And from the results of the same competition last year, a University of Texas team of students programmed bacteria so that they can detect light and switch on and off.
So this is interesting in the sense that you can now do "if-then-for" statements in materials, in structure.
This is a pretty interesting trend, because we used to live in a world where everyone's said glibly, "Form follows function," but I think I've sort of grown up in a world -- you listened to Neil Gershenfeld yesterday; I was in a lab associated with his -- where it's really a world where information defines form and function.
I spent six years thinking about that, but to show you the power of art over science -- this is actually one of the cartoons I write.
These are called "HowToons."
I work with a fabulous illustrator called Nick Dragotta.
Took me six years at MIT, and about that many pages to describe what I was doing, and it took him one page.
And so this is our muse Tucker.
He's an interesting little kid -- and his sister, Celine -- and what he's doing here is observing the self-assembly of his Cheerios in his cereal bowl.
And in fact you can program the self-assembly of things, so he starts chocolate-dipping edges, changing the hydrophobicity and the hydrophylicity.
In theory, if you program those sufficiently, you should be able to do something pretty interesting and make a very complex structure.
In this case, he's done self-replication of a complex 3D structure.
And that's what I thought about for a long time, because this is how we currently make things.
This is a silicon wafer, and essentially that's just a whole bunch of layers of two-dimensional stuff, sort of layered up.
The feature side is -- you know, people will say, [unclear] down around about 65 nanometers now.
On the right, that's a radiolara.
That's a unicellular organism ubiquitous in the oceans.
And that has feature sizes down to about 20 nanometers, and it's a complex 3D structure.
We could do a lot more with computers and things generally if we knew how to build things this way.
The secret to biology is, it builds computation into the way it makes things.
So this little thing here, polymerase, is essentially a supercomputer designed for replicating DNA.
And the ribosome here is another little computer that helps in the translation of the proteins.
I thought about this in the sense that it's great to build in biological materials, but can we do similar things?
Can we get self-replicating-type behavior?
Can we get complex 3D structure automatically assembling in inorganic systems?
Because there are some advantages to inorganic systems, like higher speed semiconductors, etc.
So, this is some of my work on how do you do an autonomously self-replicating system.
And this is sort of Babbage's revenge.
These are little mechanical computers.
These are five-state state machines.
So, that's about three light switches lined up.
In a neutral state, they won't housetimothy@example.com.
Now, if I make a string of these, a bit string, they will be able to replicate.
So we start with white, blue, blue, white.
That encodes; that will now copy.
From one comes two, and then from two comes three.
And so you've got this sort of replicating system.
It was work actually by Lionel Penrose, father of Roger Penrose, the tiles guy.
He did a lot of this work in the '60s, and so a lot of this logic theory lay fallow as we went down the digital computer revolution, but it's now coming back.
So now I'm going to show you the hands-free, autonomous self-replication.
So we've tracked in the video the input string, which was green, green, yellow, yellow, green.
We set them off on this air hockey table.
You know, high science uses air hockey tables -- (Laughter) -- and if you watch this thing long enough you get dizzy, but what you're actually seeing is copies of that original string emerging from the parts bin that you have here.
So we've got autonomous replication of bit strings.
So, why would you want to replicate bit strings?
Well, it turns out biology has this other very interesting meme, that you can take a linear string, which is a convenient thing to copy, and you can fold that into an arbitrarily complex 3D structure.
So I was trying to, you know, take the engineer's version: Can we build a mechanical system in inorganic materials that will do the same thing?
So what I'm showing you here is that we can make a 2D shape -- the B -- assemble from a string of components that follow extremely simple rules.
And the whole point of going with the extremely simple rules here, and the incredibly simple state machines in the previous design, was that you don't need digital logic to do computation.
And that way you can scale things much smaller than microchips.
So you can literally use these as the tiny components in the assembly process.
So, Neil Gershenfeld showed you this video on Wednesday, I believe, but I'll show you again.
This is literally the colored sequence of those tiles.
Each different color has a different magnetic polarity, and the sequence is uniquely specifying the structure that is coming out.
Now, hopefully, those of you who know anything about graph theory can look at that, and that will satisfy you that that can also do arbitrary 3D structure, and in fact, you know, I can now take a dog, carve it up and then reassemble it so it's a linear string that will fold from a sequence.
And now I can actually define that three-dimensional object as a sequence of bits.
So, you know, it's a pretty interesting world when you start looking at the world a little bit differently.
And the universe is now a compiler.
And so I'm thinking about, you know, what are the programs for programming the physical universe?
And how do we think about materials and structure, sort of as an information and computation problem?
Not just where you attach a micro-controller to the end point, but that the structure and the mechanisms are the logic, are the computers.
Having totally absorbed this philosophy, I started looking at a lot of problems a little differently.
With the universe as a computer, you can look at this droplet of water as having performed the computations.
You set a couple of boundary conditions, like gravity, the surface tension, density, etc., and then you press "execute," and magically, the universe produces you a perfect ball lens.
So, this actually applied to the problem of -- so there's a half a billion to a billion people in the world don't have access to cheap eyeglasses.
So can you make a machine that could make any prescription lens very quickly on site?
This is a machine where you literally define a boundary condition.
If it's circular, you make a spherical lens.
If it's elliptical, you can make an astigmatic lens.
You then put a membrane on that and you apply pressure -- so that's part of the extra program.
And literally with only those two inputs -- so, the shape of your boundary condition and the pressure -- you can define an infinite number of lenses that cover the range of human refractive error, from minus 12 to plus eight diopters, up to four diopters of cylinder.
And then literally, you now pour on a monomer.
You know, I'll do a Julia Childs here.
This is three minutes of UV light.
And you reverse the pressure on your membrane once you've cooked it.
Pop it out.
I've seen this video, but I still don't know if it's going to end right.
(Laughter) So you reverse this.
This is a very old movie, so with the new prototypes, actually both surfaces are flexible, but this will show you the point.
Now you've finished the lens, you literally pop it out.
That's next year's Yves Klein, you know, eyeglasses shape.
And you can see that that has a mild prescription of about minus two diopters.
And as I rotate it against this side shot, you'll see that that has cylinder, and that was programmed in -- literally into the physics of the system.
So, this sort of thinking about structure as computation and structure as information leads to other things, like this.
This is something that my people at SQUID Labs are working on at the moment, called "electronic rope."
So literally, you think about a rope.
It has very complex structure in the weave.
And under no load, it's one structure.
Under a different load, it's a different structure.
And you can actually exploit that by putting in a very small number of conducting fibers to actually make it a sensor.
So this is now a rope that knows the load on the rope at any particular point in the rope.
Just by thinking about the physics of the world, materials as the computer, you can start to do things like this.
I'm going to segue a little here.
I guess I'm just going to casually tell you the types of things that I think about with this.
One thing I'm really interested about this right now is, how, if you're really taking this view of the universe as a computer, how do we make things in a very general sense, and how might we share the way we make things in a general sense the same way you share open source hardware?
And a lot of talks here have espoused the benefits of having lots of people look at problems, share the information and work on those things together.
So, a convenient thing about being a human is you move in linear time, and unless Lisa Randall changes that, we'll continue to move in linear time.
So that means anything you do, or anything you make, you produce a sequence of steps -- and I think Lego in the '70s nailed this, and they did it most elegantly.
But they can show you how to build things in sequence.
So, I'm thinking about, how can we generalize the way we make all sorts of things, so you end up with this sort of guy, right?
And I think this applies across a very broad -- sort of, a lot of concepts.
You know, Cameron Sinclair yesterday said, "How do I get everyone to collaborate on design globally to do housing for humanity?"
And if you've seen Amy Smith, she talks about how you get students at MIT to work with communities in Haiti.
And I think we have to sort of redefine and rethink how we define structure and materials and assembly things, so that we can really share the information on how you do those things in a more profound way and build on each other's source code for structure.
I don't know exactly how to do this yet, but, you know, it's something being actively thought about.
So, you know, that leads to questions like, is this a compiler?
Is this a sub-routine?
Interesting things like that.
Maybe I'm getting a little too abstract, but you know, this is the sort of -- returning to our comic characters -- this is sort of the universe, or a different universe view, that I think is going to be very prevalent in the future -- from biotech to materials assembly.
It was great to hear Bill Joy.
They're starting to invest in materials science, but these are the new things in materials science.
How do we put real information and real structure into new ideas, and see the world in a different way?
And it's not going to be binary code that defines the computers of the universe -- it's sort of an analog computer.
But it's definitely an interesting new worldview.
I've gone too far.
So that sounds like it's it.
I've probably got a couple of minutes of questions, or I can show -- I think they also said that I do extreme stuff in the introduction, so I may have to explain that.
So maybe I'll do that with this short video.
So this is actually a 3,000-square-foot kite, which also happens to be a minimal energy surface.
So returning to the droplet, again, thinking about the universe in a new way.
This is a kite designed by a guy called Dave Kulp.
And why do you want a 3,000-square-foot kite?
So that's a kite the size of your house.
And so you want that to tow boats very fast.
So I've been working on this a little, also, with a couple of other guys.
But, you know, this is another way to look at the -- if you abstract again, this is a structure that is defined by the physics of the universe.
You could just hang it as a bed sheet, but again, the computation of all the physics gives you the aerodynamic shape.
And so you can actually sort of almost double your boat speed with systems like that.
So that's sort of another interesting aspect of the future.
(Applause)