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  • 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 mine at MIT.

  • 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 bind at all.

  • 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?"