Neil Gershenfeld: Self-Replicating Robots and the Future of Fabrication | Lex Fridman Podcast #380

- The ribosome, who I mentioned a little while back, can make an elephant one molecule at a time. Ribosomes are slow. They run at about one molecule a second, but ribosomes make ribosomes, so you have trillions of them. and that makes an elephant. In the same way these little assembly robots I'm describing. can make giant structures, at heart. because the robot can make the robot. So more recently two of my students, Amira and Miana, had a nature communication paper. showing how this robot can be made out of the parts.

it's making so the robots can make the robot, so you build up the capacity of robotic assembly. - The following is a conversation with Neil Gershenfeld, the director of MIT's Center for Bits and Atoms, an amazing laboratory that is breaking down boundaries. between the digital and physical worlds, fabricating objects and machines at all scales of reality, including robots and automata. that can build copies of themselves.

and self-assemble into complex structures. His work inspires millions across the world. as part of the maker movement to build cool stuff, to create, the very act that makes life so beautiful. and fun. This is a Lex Fridman podcast. To support it, please check out our sponsors in the description. And now, dear friends, here's Neil Gershenfeld. You have spent your life working at the boundary.

between bits and atoms, so the digital and the physical. What have you learned about engineering. and about nature of reality from working at this divide, trying to bridge this divide? - I learned why von Neumann and Turing. made fundamental mistakes. I learned the secret of life. I learned how to solve many of the world's.

most important problems, which all sound presumptuous, but all of those are things I learned at that boundary. - So Turing and von Neumann, let's start there. Some of the most impactful, important humans. who have ever lived in computing, why were they wrong? - So I worked with Andy Gleason, who was Turing's counterpart. So just for background, if anybody doesn't know, Turing is credited with the modern architecture. of computing, among many other things. Andy Gleason was his US counterpart, and you might not have heard of Andy Gleason,

but you might have heard of the Hilbert problems. And Andy Gleason solved the fifth one. So he was a really notable mathematician. During the war, he was Turing's his counterpart. Then von Neumann is credited with the modern architecture. of computing and one of his students was Marvin Minsky. So I could ask Marvin what Johnny was thinking. and I could ask Andy what Alan was thinking. And what came out from that, what I came to appreciate as background, I never understood the difference between computer science.

and physical science. But Turing's machine. that's the foundation of modern computing. has a simple physics mistake, which is the head is distinct from the tape. So in the Turing machine, there's a head that programmatically moves and reads. and writes a tape. The head is distinct from the tape, which means persistence of information is separate from. interaction with information. Then von Neumann wrote deeply and beautifully.

about many things, but not computing. He wrote a horrible memo called the First Draft. of a Report on the Ed Vac, which is how you program a very early computer. In it, he essentially roughly took Turing's architecture. and built it into a machine. So the legacy of that is the computer somebody's using. to watch this is spending much of its effort. moving information from storage transistors. to processing transistors,

even though they have the same computational complexity. So in computer science, when you learn about computing, there's a ridiculous taxonomy of about 100. different models of computation. But they're all fictions. In physics, a patch of space occupies space, it stores state, it takes time to transit, and you can interact. That is the only model of computation that's physical. Everything else is a fiction.

I really came to appreciate that a few years back. when I did a keynote for the annual meeting. of the super computer industry. and then went into the halls and spent time with. the super computer builders and came to appreciate-. if you're familiar with the movie The Metropolis, people would frolic upstairs in the gardens. and down in the basement people would move levers. And that's how computing exists today, that we pretend software is not physical,

it's separate from hardware. And the whole canon of computer science. is based on this fiction that bits. aren't constrained by atoms. But all sorts of scaling issues in computing. come from that boundary. But all sorts of opportunities come from that boundary. And so you can trace it all the way back to. Turing's machine making this mistake. between the head and the tape, von Neumann, he never called it von Neumann's architecture. He wrote about it in this dreadful memo.

and then he wrote beautifully about other things. we'll talk about. Now to end a long answer, Turing and von Neumann. both knew this. So all of the canon of computer scientists credits them. for what was never meant to be a computer architecture. Both Turing and von Neumann ended their life. studying exactly how software becomes hardware. So von Neumann studied self-reproducing automata, how a machine communicates its own construction. Turing studied morphogenesis,

how genes give rise to form. They ended their life studying the embodiment. of computation, something that's been forgotten by the canon of computing, but developed sort of off to the sides by. a really interesting lineage. - So there's no distinction between the head and the tape, between the computer and the computation editor, it's all computation? - Right. I never understood the difference between. computer science and physical science. And working at that boundary helped lead to things like. my lab was part of doing with a number of.

interesting collaborators. The first faster than classical quantum computations, we were part of a collaboration creating. the minimal synthetic organism where you design life. in a computer. Those both involve domains where you just can't separate. hardware from software, computation is embodied in these really profound ways. - So the first quantum computations, synthetic life, so in the space of biology,

so the space of physics at the lowest level. and the space of biology at the lowest level. So let's talk about CBA, Center of Bits and Atoms. What's the origin story of this legendary MIT center. that you were a part of creating? - In high school, I really wanted to go to vocational school. where you learned to weld and fix cars and build houses. and I was told, no, you're smart. You have to sit in a room. And nobody could explain to me why. I couldn't go to vocational school.

I then worked at Bell Labs, this wonderful place before deregulation, legendary place, and I would get union grievances. because I would go into the workshop. and try to make something and they would say, no, you're smart, you have to tell somebody what to do. And it wasn't until MIT, and I'll explain how CBA started, but I could create CBA that I came to understand. this is a mistake that dates back to the Renaissance. So in the Renaissance,

the liberal arts emerged and liberal doesn't mean. politically liberal. This was the path to liberation, birth of humanism. And so the liberal arts with the trivium, quadrivium, roughly language, natural science. And at that moment what emerged was this sort of. dreadful concept of the illiberal arts. So anything that wasn't the liberal arts. was for commercial gain and was just making stuff. and wasn't valid for serious study. And so that's why we're left with learning to weld.

wasn't a subject for serious study. But the means of expression have changed. since the Renaissance, so micromachining. or embedded coding is every bit as expressive as. painting a painting or writing a sonnet. So never understanding this difference. between computer science and physical science, the path that led me to create CBA with colleagues, I was what's called a junior fellow at Harvard.

I was visiting MIT through Marvin because. I was interested in the physics of musical instruments. This'll be another slight digression. In Cornell, I would study physics. and then I would cross the street and go to the music. department where I played the bassoon. and I would trim reeds and play the reeds. And they'd be beautiful but then they'd get soggy. And then I discovered in the basement of the music. department at Cornell was David Borden, who you might not have heard of. but is legendary in electronic music 'cause he was really.

the first electronic musician. So Bob Moog, who invented Moog synthesizers. was a physics student at Cornell, like me crossing the street. And eventually he was kicked out. and invented electronic music. David Borden was the first musician. who created electronic music. So he is legendary for people like Phil Glass. and Steve Reich. And so that got me thinking about, I would behave as a scientist in the physics department. but not in the music department. Got me thinking about what's the computational capacity.

of a musical instrument. And through Marvin, he introduced me to Todd Machover at the Media Lab. who was just about to start a project with Yo-Yo Ma. that led to a collaboration to instrument a cello. to extract Yo-Yo's data and bring it out into. computational environments. - What is the computational capacity of musical instrument, as we continue on this tangent. and when we shall return to CBA. - One part of that is to understand the computing.

And if you look at like the finest time scale. and length scale, you need to model the physics. It's not heroic. A good GPU can do teraflops today. That used to be a national class supercomputer, now it's just a GPU. If you take the time scales and length scales. relevant for the physics, that's about the scale of the physics computing. For Yo-Yo, what was really driving it. was he's completely unsentimental about the Strad. It's not that it makes some magical wiggles.

in the sound wave, it's performance as a controller, how he can manipulate it as an interface device. - Interface between what and what exactly? - Him and sound. And so what it led to was, I had started by thinking about ops per second, but Yo-Yo's question was really resolution and bandwidth. It's how fast can you measure what he does. and the bandwidth and the resolution.

of detecting his controls. and then mapping them into sounds. And what he found was if you instrument everything he does. and connect it to almost anything, it sounds like Yo-Yo, that the magic is in the control, not in ineffable details in how the wood wiggles. And so with Yo-Yo and Todd, that led to a piece and towards the end I asked Yo-Yo,

what it would take for him to get rid of his Strad. and use our stuff. And his answer was just logistics. It was, at that time, our stuff was like. a rack of electronics. and lots of cables and some grad students to make it work. Once the technology becomes as invisible as the Strad, then, sure, absolutely he he would take it. And by the way, as a footnote on the footnote, an accident in the sensing of Yo-Yo's cello. led to a hundred million dollar a year. auto safety business to control airbags in cars. - How did that work?

- I had to instrument the bow without interfering with it. So I set up local electromagnetic fields. where I would detect how those fields interact with. the bow he's playing. But we had a problem that his hand, whenever his hand got near these sensing fields, I would start sensing his hand rather than. the materials on the bow. And I didn't quite understand what was going on. with that interference.

So my very first grad student ever, Josh Smith, did a thesis on tomography with electric fields, had to see in 3D with electric fields. Then through Todd, and at that point a research scientist. in my lab, Joe Paradiso, it led to a collaboration with Penn and Teller. where we did a magic trick in Las Vegas to contact Houdini. and sort of these fields are sort of like. contacting spirits. So we did a magic trick in Las Vegas.

And then the crazy thing that happened after that. was Phil Rittmuller came running into my lab. He worked with, this became with Honda and NEC, airbags were killing infants in rear-facing child seats. Cars need to distinguish a front-facing adult, where you'd save the life, versus a bag of groceries where you don't need. to fire the airbag versus a rear facing infant. where you would kill it. And so the seat needed to, in effect, see in 3D to understand the occupants.

And so we took the Penn and Teller magic trick. derived from Josh's thesis from Yo-Yo's cello. to an auto show. And all the car companies said, great, when can we buy it? And so that became ELESYS and it was a hundred million. dollar a year business making sensors. There wasn't a lot of publicity because it was in the car. so the car didn't kill you. So they didn't sort of advertise, we have nice sensors so the car doesn't kill you. But it became a leading auto safety sensor. - And that started from the cello and the question of.

the computational capacity of the musical instrument. - So now to get back to MIT. I was spending a lot of outside time at IBM research. that had gods of the foundations of computing. There's just amazing people there. And I'd always expected to go to IBM to take over a lab, but at the last minute pivoted and came to MIT. to take a position in the Media Lab.

and start what became the predecessor to CBA. Media Lab is well known for Nicholas Negroponte. What's less well known is the role of Jerry Wiesner. So Jerry was MIT's president, before that, Kennedy science advisor, grand old man of science. At the end of his life, he was frustrated by how knowledge was segregated. And so he wanted to create a department of. none of the above.

A department for work that didn't fit in departments. And the Media Lab, in a sense, was a cover story. for him to hide a department. As MIT's president towards the end of his tenure, if he said, I'm gonna make a department. for things that don't fit in departments, the departments would've screamed. But everybody was sort of paying attention to Nicholas. creating the Media Lab. And Jerry kind of hid in it a department called. Media Arts and Sciences. It's really the department of none of the above. And Jerry explaining that and Nicholas then confirming it.

is really why I pivoted and went to MIT. because my students who help create quantum computing. or synthetic life get degrees from Media Arts and Sciences, this department of none of the above. So that led to coming to MIT. With Todd and Joe Paradiso and Mike Holly, we started a consortium called Things That Think, and this was around the birth of Internet of Things. and RFID. But then we started doing things like work we can discuss.

that became the beginnings of quantum computing. and cryptography and materials and logic and microfluidics. And those needed much more significant infrastructure. and were much longer research arcs. So with a bigger team of about 20 people, we wrote a proposal to the NSF to assemble. one of every tool to make anything of any size, was roughly the proposal. - One of any tool to make anything of any size?

- So there're usually nanometers, micrometers, millimeters, meters are segregated, input and output is segregated. We wanted to look just very literally at how digital. becomes physical and physical becomes digital. And fortunately we got NSF on a good day. and they funded this facility of. one of almost every tool to make anything. And so with a group of core colleagues.

that included Joe Jacobson, Ike Chuang, Scott Manalis, we launched CBA. - And so you're talking about nanoscale, microscale, nano structures, microstructures, macro structures, electron microscopes, and focused high beam probes. for nano structures, laser micromachining, and x-ray microtomography. for microstructures, multi-axis machining and 3D printing for macro structures, just some examples. What are we talking about in terms of scale?

How can we build tiny things and big things. all in one place? How's that possible? - A well-equipped research lab has the sort of tools. we're talking about, but they're segregated. in different places. They're typically also run by technicians. where you then have an account and a project and you charge. All of these tools are essentially, when you don't know what you're doing, not when you do know what you're doing, in that they're when you need to work across length scales. Once projects are running in this facility,

we don't charge for time. You don't make a formal proposal to schedule. and the users really run the tools. and it's for work that's kind of inchoate, that needs to span these disciplines and length scales. And so work in the project today, work in CBA today ranges from developing zeptojoule. electronics for the lowest power computing. to micromachining diamond to take 10 million RPM bearings.

for molecular spectroscopy studies. up to exploring robots to build 100 meter structures. in space. - The three things you just mentioned. Let's start with the biggest. What are some of the biggest stuff you attempted to explore. how to build in a lab? - So viewed from one direction, what we're talking about is a crazy random-seeming. of almost unrelated projects,

but if you rotate 90 degrees, it's really just a core thought over and over again. Just very literally how bits and atoms relate, how digital and just going from digital to physical, in many different domains. But it's really just the same idea over and over again. So to understand the biggest things, let me go back to bring in now Shannon. as well as von Neumann. - Claude Shannon?

- So what is digital? The casual, obvious answer is digital in one in zero, but that's wrong. There's a much deeper answer, which is Claude Shannon at MIT wrote. the best master's thesis ever. In his master's thesis, he invented our modern notion of digital logic. Where it came from was Vannevar Bush. was a grand old man at MIT.

He created the post-war research establishment. that led to the National Science Foundation. And he made an important mistake, which we can talk about. But he also made the differential analyzer, which was the last great analog computer. So it was a room full of gears and pulleys. and the longer it ran, the worse the answer was. And Shannon worked on it as a student. And he got so annoyed, in his master's thesis, he invented digital logic. But he then went on to Bell Labs. And what he did there was communication.

was beginning to expand. There was more demand for phone lines. And so there's a question about how many phone messages. you could send down a wire. and you could try to just make it better and better. He asked a question nobody had asked, which is rather than make it better and better, what's the limit to how good it can be? And he proved a couple things, but one of the main things he proved was. a threshold theorem for channel capacity.

And so what he showed was. my voice to you right now is coming as a wave through sound. and the further you get, the worse it sounds. But people watching this are getting it as. packets of data in a network. When the computer they're watching this gets the packet. of information, it can detect and correct an error. And what Shannon showed is if the noise in the cable.

to the people watching this is above a threshold, they're doomed. But if the noise is below a threshold, for a linear increase in the energy. representing our conversation, the error rate goes down exponentially. Exponentials are fast, there's very few of them in engineering. And the exponential reduction of error below a threshold. if you restore state is called a threshold theorem. That's what led to digital, that means unreliable things can work reliably.

So Shannon did that for communication. Then von Neumann was inspired by that. and applied it to computation and he showed how. an unreliable computer can operate reliably. by using the same threshold property of restoring state. It was then forgotten many years. We had to rediscover it, in effect, in the quantum computing era when things. are very unreliable again. But now to go back to how does this relate to. the biggest things I've made.

So in fabrication, MIT invented. computer-controlled manufacturing in 1952. Jet aircraft were just emerging. There was a limit to turning cranks on a machine, on a milling machine to make parts for jet aircraft. Now this is a messy story. MIT actually stole computer-controlled machining. from an inventor who brought it to MIT, wanted to do a joint project with the Air Force, and MIT effectively stole it from him.

So it's kind of a messy history, but that sounds like the birth of. computer-controlled machining, 1952. There are a number of inventors of 3D printing. One of the companies spun off from my lab. by Max Lobovsky's Formlabs, which is now a billion dollar 3D printing company. That's the modern version. But all of that's analog, meaning the information is in the control computer, there's no information in the materials. And so it goes back to Vannevar Bush's analog computer.

If you make a mistake in printing or machining, just the mistake accumulates. The real birth of computerized digital manufacturing. is 4 billion years ago. That's the evolutionary age of the ribosome. So the way you are manufactured is there's a code. that describes you, the genetic code. It goes to a micromachine, the ribosome,

which is this molecular factory that builds the molecules. that are you. The key thing to know about that is there're about. 20 amino acids that get assembled. and in that machinery, it does everything. Shannon and von Neumann taught us. You detect and correct errors. So if you mix chemicals, the error rate is about a part in a hundred. When you elongate protein in the ribosome, it's about a part and 10 to the 4. When you replicate DNA,

there's an extra level of error correction, it's a part in 10 to the 8. And so in the molecules that make you, you can detect and correct errors. and you don't need a ruler to make you, the geometry comes from your parts. So now compare a child playing with Lego. and a state-of-the-art 3D printer. or computerized milling machine. The tower made by a child is more accurate. than their motor control because the act of snapping.

the bricks together gives you a constraint on the joints. You can join bricks made out of dissimilar materials. You don't need a ruler for Lego. 'cause the geometry locally gives you the global parts. and there's no LEGO trash. The parts have enough information to disassemble them. Those are exactly the properties of a digital code. - The unreliable is made reliable. - Yes, absolutely. So what the ribosome figured out 4 billion years ago. is how to embody these digital properties,

but not for communication or computation, in effect, but for construction. So a number of projects in my lab have been studying. the idea of digital materials. and think of a digital material just as LEGO bricks. The precise meaning is a discreet set of parts. reversibly joined with global geometry. determined from local constraints.

And so it's digitizing the materials. And so I'm coming back to what are the biggest things. I've made. My lab was working with the aerospace industry. So Spirit Aero was Boeing's factories. They asked us for how to join composites. When you make a composite airplane, you make these giant wing and fuselage parts. And they asked us for a better way to stick them together. 'cause the joints were a place of failure. And what we discovered was instead of. making a few big parts, if you make little loops of carbon fiber.

and you reversibly link them in joints. and you do it in a special geometry. that balances being underconstrained. and overconstrained with just the right degrees of freedom, we set the world record for the highest modulus. ultralight material just by, in effect, making carbon fiber Lego. Lightweight materials are crucial for energy efficiency. This let us make the lightest weight high modulus material.

We then showed that with just a few part types, we can tune the material properties. and then you can create really wild robots. that instead of having a tool the size of a jumbo jet. to make a jumbo jet, you can make little robots that walk on these. cellular structures to build the structures. where they error-correct their position on the structure. and they navigate on the structure. And so using all of that, with NASA,

we made morphing airplanes. A former student, Kenny Chung and Ben Jeannette, made a morphing airplane the size of NASA Langley's. biggest wind tunnel. With Toyota, we've made super efficiency race cars. We're right now looking at projects with NASA. to build these for things like space telescopes. and space habitats where the ribosome, who I mentioned a little while back, can make an elephant one molecule at a time. Ribosomes are slow, they run at about one molecule a second.

But ribosomes make ribosomes. So you have thousands of them, trillions of them, and that makes an elephant. In the same way, these little assembly robots. I'm describing can make giant structures, at heart because the robot can make the robot. So more recently, two of my students, Amira and Miana, had a nature communication paper. showing how this robot can be made out of the parts. it's making so the robots can make the robot. So you build up the capacity of robotic assembly.

- It can self-replicate. Can you linger on what that robot looks like? What is a robot that can walk along and do error correction? And what is a robot that can self-replicate. from the materials it is given? What does that look like? What are we talking? This is fascinating. - The answer is different at different length scales. So to explain that, in biology, primary structure is the code in the messenger RNA. that says what the ribosome should build.

Secondary structure are geometrical motifs. They're things like helices or sheets. Tertiary structures are functional elements. like electron donors or acceptors. Quaternary structure is things like molecular motors. that are moving my mouth or making the synapses. work in my brain. So there's that hierarchy of primary, secondary, tertiary, quaternary. Now what's interesting is if you wanna buy electronics today. from a vendor,

there are hundreds of thousands of types of resistors. or capacitors or transistors, huge inventory. All of biology is just made from this inventory. of 20 parts, the amino acids. And by composing them, you can create all of life. And so as part of this digitization of materials, we're in effect trying to create something. like amino acids for engineering, creating all of technology from 20 parts.

As another discussion, I helped start an office for science in Hollywood. And there was a fun thing for the movie The Martian. where I did a program with Bill Nye and a few others. on how to actually build a civilization on Mars. that they described in a way that I like as, I was talking about how to go to Mars without luggage. and at heart, it's sort of how to create life. in non-living materials. If you think about this primary, secondary, tertiary, quaternary structure, in my lab,

we're doing that but on different length scales. for different purposes. So we're making microrobots out of like nano bricks. and to make the robots to build large scale structures. in space, the elements of the robots now. are centimeters rather than micrometers. And so the assembly robots for the bigger structures, there're the cells that make up the structure, but then we have functional cells. And so cells that can process and actuate,

each cell can like move one degree of freedom. or attach or detach or process. Now those elements I just described, we can make out of the still smaller parts. So eventually, there's a hierarchy of the little parts. make little robots that make bigger parts of bigger robots. up through that hierarchy. - In that way you can move up to landscape? - Early on I tried to go in a straight line. from the bottom to the top. and that ended up being a bad idea.

Instead, we're kind of doing all of these in parallel. and then they're growing together. And so to make the larger scale structures, there's a lot of hype right now about 3D printing houses. where you have a printer the size of the house. We're right now working on using swarms of these. table scale robots that walk on the structures. to place the parts much more efficiently. - That's amazing. But you're saying you can't for now go from. the very small to the very large. - That'll come, that'll come in stages.

- Can we just linger on this idea, starting from von Neumann's self-replicating. automata that you mentioned. It's just a beautiful idea. - So that's at the heart of all of this. In the stack I described, so one student, Will Langford, made these microrobots. out of little parts that then we're using. for Miana's bigger robots up through this hierarchy. And it's really realizing this idea of. the self-reproducing automata. So von Neumann, when I complained about. the von Neumann architecture,

it's not fair to von Neumann 'cause he never claimed it. as his architecture. He really wrote about it in this one fairly dreadful memo. that led to all sorts of lawsuits and fights. about the early days of computing. He did beautiful work on reliable computation. and unreliable devices. And towards the end of his life what he studied was how, and I have to say this precisely, how a computation communicates its own construction. - So beautiful. - So a computation can store a description.

of how to build itself. But now there's a really hard problem, which is, if you have that in your mind, how do you transfer it and wake up a thing. that then can contain it. So how do you give birth to a thing. that knows how to make itself? And so with Stan Ulam, he invented cellular automata as a way to simulate these,

but that was theoretical. Now the work I'm describing in my lab. is fundamentally how to realize it, how to realize self-reproducing automata. And so this is something von Neumann thought. very deeply and very beautifully about theoretically. And it's right at this intersection. It's not communication or computation or fabrication. It's right at this intersection where communication. and computation meets fabrication.

Now the reason self-reproducing automata intellectually. is so important 'cause this is the foundation of life. This is really just understanding the essence of. how to life. And in effect we're trying to create life. in non-living material. The reason it's so important technologically. is because that's how you scale capacity. That's how you can make an elephant from a ribosome, 'cause assemblers make assemblers. - So simple building blocks that inside themselves.

contain the information. how to build more building blocks. And between each other, construct arbitrarily complex objects. - Now let me give you the numbers. So let me relate this to, right now we're living in AI mania explosion time. Let me relate that to what we're talking about. A 100 petaFLOP computer, which is a current generation supercomputer, not quite the biggest ones, does 10 to the 17 ops per second.

Your brain does 10 to the 17 ops per second. It has about 10 to the 15 synapses. and they run at about 100 hertz. So as of a year or two ago, the performance of a big computer matched a brain. So you could view AI as a breakthrough. But the real story is within about a year or two ago, the supercomputer has about 10 to the 15 transistors.

in the processors, 10 to the 15 transistors in the memory, which is the synopses in your brain. So the real breakthrough was the computers match. the computational capacity of a brain. And so we'd be sort of derelict if they couldn't do. about the same thing. But now the reason I'm mentioning that is the chip fab. making the super computer is placing about. 10 to the 10 transistors a second. While you're digesting your lunch right now, you're placing about 10 to the 18 parts per second.

There's an eight order of magnitude difference. So in computational capacity, it's done, we've caught up. But there's eight orders of magnitude difference. in the rate at which biology can build. versus state-of-the-art manufacturing can build. And that distinction is what we're talking about, that distinction is not analog, but this deep sense of digital fabrication, of embodying codes in construction.

So a description doesn't describe a thing, but the description becomes the thing. - So you're saying, this is one of the cases you're making, that this is this third revolution. We've seen the Moore's Law in communication, we've seen the Moore's Law-like type of growth. in computation, and you're anticipating we're going to see that. in digital fabrication. Can you actually, first of all, describe what you mean by this term digital fabrication? - The casual meaning is the computer controls. the tool to make something.

And that was invented when MIT stole it in 1952. There's the deep meaning of what the ribosome does, of a digital description doesn't describe a thing, a digital description becomes the thing. That's the path to the Star Trek replicator. And that's the thing that doesn't exist yet. Now I think the best way to understand. what this roadmap looks like is to now bring in FabLabs.

and how they relate to all of this. - What are FabLabs? - So here's a sequence. With colleagues, I accidentally started a network. of what's now 2,500 digital fabrication community labs. called FabLabs, right now in 125 countries. And they double every year and a half. That's called Lass' Law after Sherri Lassiter, who I'll explain. So here's the sequence. We started Center for Bits and Atoms to do the kind of.

research we're talking about. We had all of these machines and then had a problem. It would take a lifetime of classes. to learn to use all the machines. So with colleagues who helped start CBA, we began a class modestly called. How to Make Almost Anything. And there's no big agenda. It was aimed at a few research students to use the machines. And we were completely unprepared for the first time. we taught it. We were swamped by, every year since,

hundreds of students try to take the class. It's one of the most oversubscribed classes at MIT. Students would say things like, can you teach this at MIT? It seems too useful. It's just how to work these machines. And the students in the class, I would teach them all the skills to use all these tools. and then they would do projects integrating them. and they're amazing. So Kelly was a sculptor, no engineering background. Her project was she made a device that saves up screams. when you're mad and placed them back later.

- Saves up screams when you're mad. and plays them back later? - You scream into this device and it deadens the sound, records it, and then when it's convenient, releases your scream. - Can we just pause on the brilliance. of that invention, creation, the art, I don't know, the brilliance. Who is this that created this? - Kelly Dobson. Gone on to do a number of interesting things. Mejin, who's gone on to do a number of interesting things, made a dress instrumented with sensors and spines. And when somebody creepy comes close,

it would defend your personal space. - Also very useful. - Another project early on was a web browser for parrots, which have the cognitive ability of a young child. and let's parrots surf the internet. Another was an alarm clock you wrestle with. and prove you're awake. And what connects all of these is, so MIT made the first realtime computer, the Whirlwind. That was transistorized as the TX. The TX was spun off from MIT as the PDP.

PDPs were the mini computers that created the internet. So outside MIT was Deck, Prime, Wang, Data General, the whole mini computer industry, the whole computing industry was there, and it all failed when computing became personal. Ken Olson, the head of Digital, famously said, you don't need a computer at home. There's a little background to that, but Deck completely missed computing became personal. So I mention all of that because I was asking.

how to do digital fabrication, but not really why. The students in this how to make class. were showing me that the killer app of digital fabrication. is personal fabrication. - How do you jump to the personal fabrication? - So Kelly didn't make the screen body. because it was for a thesis. She wasn't writing a research paper, it wasn't a business model, it was 'cause she wanted one. It was personal expression, going back to me in vocational school. Personal expression in these new means of expression.

So that's happened every year since. - The course is literally called. How To Make Almost Anything. A legendary course at MIT. Every year. - And it's grown to multiple labs at MIT. with as many people involved as teaching as taking it. And there's even a Harvard lab for the MIT class. - What have you learned about humans. colliding with the FabLab about what the capacity.

of humans to be creative and to build? - I mentioned Marvin, another mentor at MIT, sadly no longer living, is Seymour Papert. So Papert studied with Piaget. He came to MIT to get access to the early-. Piaget was a pioneer in how kids learn. Papert came to MIT to get access to the early computers. with the goal of letting kids play with them. Piaget helped show kids are like scientists. They learn as scientists and it gets kind of.

throttled out of them. Seymour wanted to let kids have a broader landscape to play. Seymour's work led with Mitch Resnick to Lego, Logo, MindStorms, all of that stuff. As FabLab spread and we started creating. educational programs for kids in them, Seymour said something really interesting, he made a gesture. He said it was a thorn in his side. that they invented what's called the turtle, an early robot kids could program. to connect it to a mainframe computer. Seymour said the goal was not for the kids.

to program the robot, it was for the kids to create the robot. And so in that sense, the FabLabs, which for me were just this accident, he described as sort of this fulfillment of the arc. of kids learn by experimenting. It was to give them the tools to create, not just assemble things and program things, but actually create. So coming to your question. What I've learned is MIT a few years back, somebody added up businesses from spun off from MIT.

and it's the world's 10th economy. It falls between India and Russia. And I view that in a way as a bad number. because it's only a few thousand people. and these aren't uniquely the 4,000 brightest people. It's just a productive environment for them. And what we found is in rural Indian villages. and African shanty towns and arctic hamlets, I find exactly, precisely that profile. So Ling Sai did a few hours above Tromso,

way above the arctic circles. It's so far north, the satellite dishes look at the ground, not the sky. Hans Christian in the lab was considered a problem. in the local school 'cause they couldn't teach him anything. I showed him a few projects. Next time I came back he was designing and building. little robot vehicles. And in South Africa, I mentioned Sochengovi, in this apartheid township, the local technical institute taught kids.

how to make bricks and fold sheets. It was punitive. But Chapiso in the FabLab was actually doing all the work. of my MIT classes. And so over and over, we found precisely. the same kind of bright, inventive creativity. And historically, the answer was you're smart, go away. It's sort of like me and vocational school. But in this lab network, what we could then do is in effect bring the world to them.

Now let's look at the scaling of all of this. So there's one earth, a thousand cities, a million towns, a billion people, a trillion things. There was one Whirlwind computer. and my team made the first realtime computer. There were thousands of PDPs. There were millions of hobbyist computers. that came from that. Billions of personal computers. Trillions of internet of things. So now if we look at this FabLab story,

1952 was the NC Mill. There are now thousands of FabLabs. And the FabLab costs exactly the same cost. and complexity of the mini computer. So on the mini computer, it didn't fit in your pocket, it filled a room. But video games, email, word processing, really anything you do with the internet, anything you do with a computer today happened at that era. because it got on the scale of a work group, not a corporation.

In the same way, FabLabs are like the mini computers. inventing how does the world work. if anybody can make anything. Then if you look at that scaling, FabLabs today are transitioning from. buying a machine to machines making machines. So we're transitioning to, you can go to a FabLab not to make a project, but to make a new machine. So we talked about the deep sense of self-replication. There's a very practical sense of FabLab machines.

making FabLab machines. And so that's the equivalent of the hobbyist computer era, whatever it's called, the Altera, historically. Then the work we spent a while talking about. about assemblers and self-assemblers, that's the equivalent of smartphones and internet of things. That's when the assemblers are like the smartphone. where a smartphone today has the capacity of. what used to be a supercomputer in your pocket. And then the smart thermostat on your wall.

has the power of the original PDP computer. Not metaphorically, but literally. And now there's trillions of those. In the same sense that when we finally merge materials. with the machines in the self-assembly, that's like the internet of things stage. But here's the important lesson. If you look at the computing analogy, computing expanded exponentially. but it really didn't fundamentally change.

The core things happened in that transition. in the mini computer era. So in the same sense, the research now we spent a while talking about. is how we get to the replicator. Today, you can do all of that. if you close your eyes and view the whole FabLab. as a machine. In that room, you can make almost anything, but you need a lot of inputs. Bit by bit, the inputs will go down. and the size of the room will go down. as we go through each of these stages. - So how difficult is it to create.

a self-replicating assembler, self-replicating machine that builds copies of itself. or builds more complicated version of itself, which is kind of the dream towards which you're pushing. in a generic arbitrary sense? - I had a student, Nadia Peak with Jonathan Ward, who for me started this idea of how do we use the tools. in my lab to make the tools in the lab? In a very clear sense, they are making self-reproducing machines.

So one of the really cool things that's happened. is there's a whole network of machine builders. around the world. So there's Danielle now in Germany and Yens in Norway. And each of these people has learned the skills. to go into a FabLab and make a machine. And so we've started creating a network of super Fab. So the FabLab can make a machine, but it can't make a number of the precision parts. of the machine. So in places like Bhutan or Carroll in the south of India,

we've started creating super FabLabs that have more. advanced tools to make the parts of the machines. so that the machines themselves become even cheaper. So that is self-reproducing machines, but you need to feed it things like bearings. or microcontrollers. They can't make those parts. But other than that, they're making their own things. And I should note as a footnote, the stack I described of computers controlling machines. to machine making machines to assemblers.

to self assemblers, view that as fab One, two, three, four. So we're transitioning from Fab one to Fab two. and the research in the lab is three and four. At this Fab two stage, a big component of this. is sustainability in the material feed stocks. So Alicia, colleague in Chile, is leading a great effort. looking at how you take forest products and coffee grounds. and seashells and a range of locally available materials. and produce the high tech materials that go into the lab.

So all of that is machine building today. Then back in the lab, what we can do today is we have robots that can build. structures and can assemble more robots. that build structures. We have finer resolution robots. that can build micromechanical systems. So robots that can build robots. that can walk and manipulate. And we're just now we have a project at the layer below that.

where there's endless attention today. to billion dollar chip fab investments. But a really interesting thing we passed through. is today the smallest transistors you can buy. as a single transistor just commercially. for electronics is actually the size of. an early transistor in an integrated circuit. So we're using these machines making machines, making assemblers to place those parts to not use. a billion dollar chip fab to make integrated circuits,

but actually assemble little electronic components. - So have a fine enough, precise enough actuators. and manipulators that allow you to place these transistors. - That's a research project in my lab. called DICE, on discrete assembly of integrated electronics. And we're just at the point to really start to take. seriously this notion of not having a chip fab. make integrated electronics, but having, not a 3D printer, but a thing that's a cross between a pick and place.

makes circuit boards in 2D, the 3D printer extrudes in 3D, we're making sort of a micromanipulator. that acts like a printer. but it's placing to build electronics in 3D. - But this micromanipulator is distributed. So there's a bunch of them or is this one centralized thing? - So that's why that's a great question. So I have a prize that's almost but not been claimed. for the students whose thesis can walk out of the printer. - Oh, nice. - So you have to print the thesis with the means. to exit the printer and it has to contain its description.

of the thesis that says how to do that. - It's a really good, it's a fun example of exactly the thing we're talking about. - And I've had a few students almost get to that. And so in what I'm describing, there's this stack where we're getting closer, but it's still quite a few years to really go from a-. so there's a layer below the transistors. where we assemble the base materials.

that become the transistor. We're now just at the edge of assembling the transistors. to make the circuits. We can assemble the microparts to make the microrobots, we can assemble the bigger robots, and in the coming years, we'll be patching together all of those scales. - So do you see a vision of just endless billions of robots. at the different scales, self-assembling, self-replicating, and building more complicated structures? - Yes, and the but to the yes but, is.

let me clarify two things. One is, that immediately raises King Charles fear of. gray goo of runaway mutant self-reproducing things. The reason why there are many things I can tell you. to worry about, but that's not one of them, is if you want things to autonomously self-reproduce. and take over the world, that means they need to compete with nature. on using the resources of nature, of water and sunlight.

And in light of everything I'm describing, biology knows everything I told you. Every single thing I explain, biology already knows how to do. What I'm describing isn't new for biology, it's new for non-biological systems. So in the digital era, the economic win ended up being centralized, the big platforms. In this world of machines that can make machines, I'm asked for example, what's the killer opportunity?

Who's gonna make all the money, who to invest in? But if the machine can make the machine, it's not a great business to invest in the machine. In the same way that if you can produce, if you can think globally but produce locally, then the way the technology goes out into society. isn't a function of central control. but is fundamentally distributed. Now that raises an obvious kind of concern,

which is, well, doesn't this mean you could make bombs. and guns and all of that? The reason that's much less of a problem. than you would think is making bombs and guns. and all of that is a very well met market need. Anywhere we go, there's a fine supply chain for weapons. Now hobbyists have been making guns for ages. and guns are available just about anywhere. So you could go into the lab and make a gun. Today, it's not a very good gun. and guns are easily available.

And so generally, we've run these labs in war zones. What we find is people don't go to them to make weapons, which you can already do anyway. It's an alternative to making weapons. Coming back to your question, I'd say the single most important thing I've learned. is the greatest natural resource of the planet. is this amazing density of bright, inventive people. whose brains are underused. And you could view the social engineering of this lab work.

is creating the capacity for them. And so in the end, the way this is going to impact society. isn't gonna be command and control. It's how the world uses it. And it's been really gratifying for me. to see just how it does. - But what are the different ways the evolution. of the exponential scaling of digital fabrication. can evolve? Self-replicating nanobots, this is the gray goo fear. It's a caricature of a fear,

but nevertheless there's interesting, just like you said, spam and all these kinds of things that came with. the scaling of communication and computation. What are the different ways that malevolent actors. will use this technology? - First let me start with a benevolent story. which is trash is an analog concept. There's no trash in a forest. All the parts get disassembled and reused. Trash means something doesn't have enough information.

to tell you how to reuse it. It's as simple as there's no trash in a Lego room. When you assemble Lego, the Lego bricks have enough information to disassemble them. So as you go through this Fab one, two, three, four story, one of the implications of this transition. from printing to assembling. So the real breakthrough technologically. isn't additive versus subtractive, which is a subject of a lot of attention and hype.

3D printers are useful. We spun off companies like Formlabs led by Max. for 3D printing, but in a FabLab, it's 1 of maybe 10 machines. It's used but it's only part of the machines. The real technological change is when we go from printing. and cutting to assembling and dissembling, but that reduces inventories of hundreds of thousands. of parts to just having a few parts to make almost anything. It reduces global supply chains to locally sourcing.

these building blocks. But one of the key implications is it gets rid of. technological trash because you can disassemble and reuse. the parts, not throw them away. And so initially, that's of interest for things. at the end of long supply chains like satellites on orbit. But one of the things coming is eliminating technical trash. through reuse of the building blocks. - So like when you think about 3D printers, you're thinking about addition and subtraction. When you think about the other options available to you.

in that parameter space as you call it, that's going to be assembly, disassembly, cutting, you said? - So the 1952 NC mill was subtractive. You remove material. And 3D printing, additive. And there's a couple claims to the invention of 3D printing. that's closer to what's called net shape, which is you don't have to cut away the material. you don't need, you just put material where you do need it. And so that's the 3D printing revolution.

But there are all sorts of limitations on 3D printing. to the kinds of materials you can print, the kind of functionality you can print. We're just not gonna get to making. everything in a cell phone on a single printer. But I do expect to make everything in a cell phone. with an assembler. And so instead of printing and cutting technologically, it's this transition to assembling and dissembling. Going back to Shannon and von Neumann,

going back to the ribosome 4 billion years ago. You come to malevolent. Let me tell you a story about. I was doing a briefing for the National Academy of Sciences. group that advises the intelligence communities. and I talked about the kind of research we do. and at the very end I showed a little video clip. of Valentina in Ghana, a local girl, making surface mount electronics in the FabLab.

And I showed that to this room full of people. One of the members of the intelligence community. got up, livid, and said, how dare you waste our time showing us a young girl. in an African village making surface mount electronics. We need to know about disruptive threats. to the future of the United States. And somebody else got up in the room and yelled at him, you idiot, I can't think of anything more important than this.

But for two reasons. One reason was because if we rely on. informational superiority in the battlefield, it means other people could get access to it. But this intelligence person's point, bless him, wasn't that, it was getting at the root causes. of conflict is if this young girl in an African village. could actually master surface mount electronics, it changes some of the most fundamental things. about recruitment for terrorism,

impact of economic migration, basic assumptions about an economy. It's just existential for the future of the planet. - But you know, we've just lived through a pandemic. I would love to linger on this cause the possibilities. that are positive are endless. But the possibilities that are negative. are still nevertheless extremely important. With both positive and negative, what do you do with a large number of general assemblers?

- With the FabLab, you could roughly make a bio lab. then learn biotechnology. Now that's terrifying because making self-reproducing. gray goo that outcompetes biology, I consider doom because biology knows everything. I'm describing and is really good at what it does. In how to grow almost anything, you learn skills in biotechnology that let you.

make serious biological threats. - And when you combine some of the innovations you see. with large language models, some of the innovations you see with alpha fold, so applications of AI for designing biological systems, for writing programs, which you can with. large language models increasingly, so there seems to be an interesting dance here. of automating the design stage of complex systems using AI. And then that's the bits.

And you can leap now, the innovations you're talking about, you can leap from the complex systems in the digital space. to the printing, to the creation, to the assembly at scale of complex systems. in the physical space. - So something to be scared about is a FabLab. can make a bio lab, a bio lab can make biotechnology, somebody could learn to make a virus. That's scary.

Unlike some of the things I said I don't worry about, that's something I really worry about that is scary. Now how do you deal with that? Prior threats we dealt with command and control. So like early color copiers had unique codes. and you could tell which copier made them. Eventually you couldn't keep up with that. There was a famous meeting at Asilomar. in the early days of recombinant DNA.

where that community recognized the dangers. of what it was doing and put in place a regime. to help manage it. And so that led to the kind of research management. MIT has an office that supervises research. and it works with the national office. That works if you can identify who's doing it. and where, it doesn't work in this world. we're describing. So anybody could do this anywhere. And so what we've found is you can't contain this.

It's already out. You can't forbid because there isn't command and control. The most useful thing you can do is provide incentives. for transparency. But really the heart of what we do is you could do this. by yourself in a basement for nefarious reasons. or you could come into a place in the light. where you get help and you get community. and you get resources. And there's an incentive to do it in the open,

not in the dark. And that might sound naive, but in the sort of places we're working, again, bad people do bad things in these places already, but providing openness and providing transparency. is a key part of managing these. It transitions from regulating risks as regulation. to soft power to manage them.

- So there's so much potential for good, so much capacity for good that FabLabs and. the ability and the tools of creation. really unlock that potential. - I don't say that as sort of dewy-eyed naive. I say that empirically from just years of seeing. how this plays out in communities. - I wonder if it's the early days of personal computers. though, before we get spam. - In the end, most fundamentally,

literally the mother of all problems. is who designed us? So assume success in that we're gonna transition. to the machines making machines. and all of these new sort of social systems we're describing. will help manage them and curate them and democratize them. If we close the gap I just led off with. of 10 to the 10 to 10 to the 18 between chip fab and you,

we're ultimately, in marrying communication, computation, and fabrication, gonna be able to create unimaginable complexity. And how do you design that? And so I'd say the deepest of all questions. that I've been working on. goes back to the oldest part of our genome.

So in our genome what are called HOX gene, and these are morphogenes, and nowhere in your genome is the number five. It doesn't store the fact that you have five fingers. What it stores is what's called a developmental program. It's a series of steps. And the steps have the character of like grow up a gradient. or break symmetry.

And at the end of that developmental program, you have five fingers. So you are stored not as a body plan, but as a growth plan. And there's two reasons for that. One reason is just compression. Billions of genes can place trillions of cells. But the much deeper one is evolution. doesn't randomly perturb.

Almost anything you did randomly in the genome. would be fatal or inconsequential, but not interesting. But when you modify things in these developmental programs, you go from like webs for swimming to fingers. or you go from walking to wings for flying. It's a space in which search is interesting. So this is the heart of the success of AI. In part, it was the scaling we talked about a while ago.

And in part, it was the representations for which. search is effective. AI has found good representations. It hasn't found new ways to search, but it's found good representations of search. - And you're saying that's what biology, that's what evolution has done, is created. representations, structures, biological structures through which search is effective. - And so the developmental programs. in the genome beautifully encapsulate the lessons of AI.

And it's embodied, it's molecular intelligence. It's AI embodied in our genome. It's every bit as profound as the cognition in our brain. But now this is sort of thinking in molecular thinking. in how you design. And so I'd say the most fundamental problem we're working on. is it's kind of tautological that when you design.

a phone, you design the phone, you represent the design of the phone. But that actually fails when you get to the sort of. complexity that we're talking about. And so there's this profound transition to come. Once I can have self-reducing assemblers. placing 10 to the 18 parts, you need to, not sort of metaphorically, but create life in that you need to learn how to evolve. But evolutionary design has a really misleading,

trivial meaning. It's not as simple as you randomly mutate things. It's as much more deep embodiment. of AI and morphogenesis. - Is there a way for us to continue the kind of. evolutionary design that led us to this place. from the early days of bacteria, single cell organism. to ribosomes and the 20 amino acids? - You mean for human augmentation? - For life-. what would you call assemblers that are self-replicating.

and placing parts? What is the dynamic complex things built with. digital fabrication? What is that? That's life. - So ultimately, absolutely, if you add everything I'm talking about, it's building up to creating life in non-living materials. I don't view this as copying life. I view it as driving life. I didn't start from how does biology work.

and then I'm gonna copy it. I start from how to solve problems and then. it leads me to, in a sense, rediscover biology. So if you go back to Valentina in Ghana. making her circuit board, she still needs a chip fab very far away. to make the processor in her circuit board. For her to make the processor locally, for all the reasons we described, you actually need the deep things. we were just talking about. And so it really does lead you.

There's a wonderful series of books by Gingery. Book one is how to make a charcoal furnace. And at the end of book seven, you have a machine shop. It's sort of how you do your own personal. industrial revolution. ISRU is what NASA calls in situ resource utilization. And that's how do you go to a planet. and create a civilization. ISRU has essentially assumed Gingery.

You go go through the industrial revolution. and you create the inventory. of 100,00 resistors. What we're finding is the minimum building blocks. for a civilization is roughly 20 parts. So what's interesting about the amino acids. is they're not interesting. They're hydrophobic or hydrophilic, basic or acidic. They have typical but not extremal properties. But they're good enough you can combine them. to make you. So what this is leading towards is technology.

doesn't need enormous global supply chains. It just needs about 20 properties you can compose. to create all technology as the minimum building blocks. for a technological civilization. - So there's going to be 20 basic building blocks. based on which the self-replicating assemblers can work? - Right. And I say that not philosophically, just empirically, that's where it's heading. And I like thinking about.

how you bootstrap a civilization on Mars, that problem. There's a fun video on bonus material for the movie. where with a neat group of people. we talk about it because it has really profound implications. back here on earth about how we live sustainably. - What does that civilization on Mars look like. that's using ISRU, that's using these 20 building blocks. and does self-assembly. - Go through primary, secondary, tertiary, quaternary. You extract properties like conducting, insulating,

semiconducting, magnetic, dielectric, flexural. These are the kind of roughly 20 properties. With those, those are enough for us to assemble logic. and they're enough for us to assemble actuation. With logic and actuation, we can make microrobots.

The microrobots can build bigger robots. The bigger robots can then take the building block materials. and make the structural elements that you then do. to make construction. And then you boot up through the stages. of a technological civilization. - By the way, where in the span of logic and actuation. did the sensing come in? - Oh, I skipped over that. But my favorite sensor is a step response. So if you just make a step and measure the response.

to the electric field, that ranges from user interfaces to positioning. to material properties. And if you do it at higher frequencies, you get chemistry. And you can get all of that just from a step. in an electric field. So for example, once you have time resolution in logic, something as simple as two electrodes. let you do amazingly capable sensing. So we've been talking about all the work I do,

there's a story about how it happens, where do ideas come from? - That's an interesting story. Where do ideas come from? - So I had mentioned Vannevar Bush. and he wrote a really influential thing. called the Endless Frontier. So science won World War II. The more known story is nuclear bombs. The less well known story is the RAD lab. So at MIT, an amazing group of people invented radar,

which is really credited as winning the war. So after the war, grand old man from MIT. was charged with science won the war, how do we maintain that edge? And the report he wrote led to the National Science. Foundation and the modern notion we take for granted. but didn't really exist before then of public funding. of research, of research agencies.

In it, he made what I consider an important mistake, which is he described basic research leads to. applied research leads to applications. leads to commercialization leads to impact. And so we need to invest in that pipeline. The reason I consider it a mistake. is almost all of the examples we've been talking about.

in my lab went backwards. That the basic research came from applications. And further, almost all of the examples. we've been talking about came fundamentally from mistakes. Essentially everything I've ever worked on has failed, but in failing, something better happened. So the way I like to describe it is. ready, aim, fire is you do your homework,

you aim carefully at something, a target you wanna accomplish, and if everything goes right, you then hit the target and succeed. What I do you can think of is ready, fire, aim. So you do a lot of work to get ready, then you close your eyes and you don't really think about. where you're aiming, but you look very carefully at where you did aim, you aim after you fire. And the reason that's so important is.

if you do ready, aim, fire, the best you can hope is hit what you aim at. So let me give you some examples, cause this is a source of great-. - You're full of good lines today. - Source of great frustration. I mentioned the early quantum computing. Quantum computing is this power of using. quantum mechanics to make computers. that for some problems are dramatically more powerful. than classical computers.

Before it started, there was a really interesting group of people. who knew a lot about physics and computing. that were inventing what became quantum computing. before it was clear there was an opportunity there. It was just studying how those relate. Here's how it fits to the ready, fire, aim. I was doing really short term work in my lab. on shoplifting tags on. This was really before there was modern RFID.

And so how you put tags in objects to sense them. Something we just take for granted commercially. And there was a problem of how you can sense multiple. objects at the same time. And so I was studying how you can remotely sense materials. to make low-cost tags that could let you distinguish. multiple objects simultaneously. To do that you need non-linearity. so that the signal is modulated. And so I was looking for material sources of non-linearity.

and that led me to look at how nuclear spins. interact, just for spin resonance. This the sort of things you use when you go in. an MRI machine. And so I was studying how to use that. and it turns out that it was a bad idea. You couldn't remotely use it for shoplifting tags, but I realized you could compute.

And so with a group of colleagues. thinking about early quantum computing. like David DiVincenzo and Charlie Bennett was articulating, what are the properties you need to compute? And then looking at how to make the tags. It turns out the tags were a terrible idea. for sensing objects in a supermarket checkout, but I realized they were computing. So with Ike Chuang and a few other people, we realized we could program nuclear spins to compute.

And so that's what we use to do Grover's search algorithm. And then it was used for Shor's factoring algorithm. and it worked out. The systems we did it in nuclear magnetic resonance. don't scale beyond a few qubits, but the techniques have lived on. And so all the current quantum computing techniques. grew out of the ways we would talk to these spins. But I'm telling this whole story because. it came from a bad way to make a shoplifting tag.

- Starting with an application, mistakes led to breakthroughs of fundamental science. Can you just linger on that, using nuclear spins to do computation, what gave you the guts to try to think through this? From a digital fabrication perspective, actually, how to leap from one to the other. - I wouldn't call it guts, I would call it collaboration. So at IBM there was this amazing group of,

like I mentioned Charlie Bennett and David DiVincenzo. and Ralph Landau and Nabil Amer. And these were all gods of thinking about physics. and computing. I yelled at the whole computer industry. being based on a fiction metropolis, programmers frolicking in the garden. while somebody moves levers in the basement. There's a complete parallel history of.

Maxwell to Boltzmann to Zollar to Landau to Bennett. Most people won't know most of these names. but this whole parallel history. thinking deeply about how computation and physics relate. So I was collaborating with that whole group of people. And then, at MIT I was in this high traffic environment. I wasn't deeply inspired to think about better ways. to detect shoplifting tags. but stumbled across companies that needed help with that.

and was thinking about it. And then I realized those two worlds intersected. and we could use the failed approach. for the shoplifting tags to make early. quantum computing algorithms. - This kind of stumbling is fundamental to the FabLab idea? - Right. Here's one more example. With the student, Manu, we talked about ribosomes. and I was trying to build a ribosome. that worked on fluids so that I could place. the little parts we're talking about.

It kept failing 'cause bubbles would come into our system. and the bubbles would make the whole thing stop working. And we spent about half a year. trying to get rid of the bubbles. Then Manu said, wait a minute, the bubbles are actually better than what we're doing. We should just use the bubbles. And so we invented how to do universal object. logic with little bubbles and fluid. - You have to explain this microfluidic bubble logic, please.

How does this work? That's super interesting. - I'll come back and explain it. But what it led to was we showed fluids could do, it'd been known fluid could do logic, like your old automobile transmissions do logic, but that's macroscopic. It didn't work at little scales. We showed with these bubbles. we could do it at little scales. Then I'm gonna come back and explain it. But what came out of that is Manu then showed. you could make a 50 cent microscope using little bubbles.

And then the techniques we developed. are what we use to transplant genomes. to make synthetic life all came out of the failure. of trying to make the ribosome. The way the bubble logic works is in a little channel, fluid at small scales is fairly viscous. It's sort of like pushing jello, think of it as.

If a bubble gets stuck, the fluid has to detour around it. So now imagine a channel that has two wells and one bubble. If the bubble is in one well, the fluid has to go in the other channel. If the fluid is in the other well, it has to go in the first channel. So the position of the bubble can switch. It's a switch, it can switch the fluid between two channels.

So now we have one element of switch. and it's also a memory because you can detect. whether or not a bubble is stored there. Then if two bubbles meet, if you have two channels crossing, a bubble can go through one way. or a bubble can go through the other way. But if two bubbles come together, then they push on each other and one goes one way. and one goes the other way. That's a logic operation. That's a logic gate. So we now have a switch,

we have a memory, and we have a logic gate. And that's everything you need to make a universal computer. - The fact that you did that with bubbles. and microfluid, it's just kind of brilliant. - To stay with that example, what we proposed to do was to make a fluidic ribosome. and the project crashed and burned. It was a disaster. This is what came out of it. And so it was precisely ready, fire, aim.

in that we had to do a lot of homework to be able to. make these microfluidic systems. The fire part was we didn't think too hard. about making the ribosome, we just tried to do it. The aim part was we realized the ribosome failed. but something better had happened. And if you look all across research funding, research management, it doesn't anticipate this. So fail fast is familiar, but fail fast tends to miss ready and aim.

You can't just fail. You have to do your homework before the fail part. and you have to do the aim part after the fail part. And so the whole language of research. is about like milestones and deliverables, that works when you're going down a straight line, but it doesn't work for this kind of discovery. And to leap to something you said that's really important. is I view part of what the FabLab network is doing. is giving more people the opportunity to fail. - You've said that geometry is really important in biology.

What does fabrication biology look like? Why is geometry important? - So molecular biology is dominated by geometry. That's why the protein folding is so important, that the geometry gives the function. And there's this hierarchical construction. of as you go through primary, second, tertiary, quaternary,

the shapes of the molecules make the shape. of the molecular machines. And they really are exquisite machines. If you look at how your muscles move, if you were to see a simulation of it, it would look like a improbable science fiction cyborg world. of these little walking robots that walk on. a discreet lattice. They're really exquisite machines. And then from there, there's this whole hierarchical stack. of once you get to the top of that,

you then start making organelles that make cells. that make organs through the stack of that hierarchy. - Just stepping back, does it amaze you that from small building blocks. where amino acids, you mentioned molecules, let's go to the very beginning of hydrogen and helium. at the start of this universe, that we're able to build up such complex. and beautiful things like our human brain?

- So studying thermodynamics, which is exactly the question of. batteries run out and need recharging, cars get old and fail, yet life doesn't. That's why there's a sense in which life. seems to violate thermodynamics, although of course it doesn't.

- It seems to resist the march towards entropy, somehow. - Right. And so Maxwell, who helped give rise to. the science of thermodynamics. posited a problem that was so infuriating. it led to a series of suicides. There was a series of advisors and advisees, three in a row, that all ended up committing suicide. that happened to work on this problem. And Maxwell's demon is this simple but infamous problem.

where right now, in this room, we're surrounded by. molecules and they run at different velocities. Imagine a container that has a wall. and it's got gas on both sides and a little door. And if the door is a molecular-sized creature. and it could watch the molecules coming, and when a fast molecule is coming, it opens the door.

When a slow molecule is coming, it closes the door. After it does that for a while, one side is hot, one is cold. Once something is hot and is cold, you can make an engine. And so you close that and you make an engine. and you make energy. So the demon is violating thermodynamics. because it's never touching the molecule, yet by just opening and closing the door, it can make arbitrary amounts of energy and power a machine.

And in thermodynamics you can't do that. So that's Maxwell's demon. That problem is connected to everything we just spoke about. for the last few hours. So Leo Szilard around early 1900s was a deep physicist. who then had a lot to do with also.

post-war anti-nuclear things. But he reduced Maxwell's demon to a single molecule. So there's only one molecule. And the question is, which side of the partition is it on? That led to the idea of one bit of information. So Shannon credited Szilard's analysis. of Maxwell's demon for the invention of the bit. For many years, people tried to explain Maxwell's demon.

by like the energy in the demon looking at the molecule. or the energy to open and close the door. and nothing ever made sense. Finally, Ralph Landau, one of the colleagues I mentioned at IBM, finally solved the problem. He showed that you can explain Maxwell's demon by. you need the mind of the demon.

When the demon open and closes the door, as long as it remembers what it did, you can run the whole thing backwards. But when the demon forgets, then you can't run it backwards. And that's where you get dissipation. and that's where you get the violation of thermodynamics. And so the explanation of Maxwell's demon is that. it's in the demon's brain. So then Ralph's call colleague Charlie at IBM.

then shocked Ralph by showing you can compute. with arbitrarily low energy. So one of the things that's not well covered. is the big computers used for big machine learning, the data centers, use tens of megawatts of power. They use as much power as a city. Charlie showed you can actually compute. with arbitrarily low amounts of energy.

by making computers that can go backwards. as well as forwards. And what limits the speed of the computer. is how fast you want an answer. and how certain you want the answer to be. But we're orders of magnitude away from that. So I have a student, Cameron, working with Lincoln Labs on making superconducting. computers that operate near this Landau limit. that are orders of magnitude more efficient.

So stepping back to all of that, that whole tour was driven by your question about life. Right at the heart of it is Maxwell's demon: life exists because it can locally violate thermodynamics. It can locally violate thermodynamics. because of intelligence and it's molecular intelligence. I would even go out on a limb to say we can already see. we're beginning to come to the end of this current AI phase.

So depending on how you count, this is, I'd say, the fifth AI boom-bust cycle. And you can already, it's exploding, but you can already see where it's heading, how it's going to saturate, what happens on the far side. The big thing that's not yet on horizons. is embodied AI molecular intelligence. So to step back to this AI story,

there was automation and that was gonna change everything. Then there were expert systems. There was then the first phase. of the neural network systems. There've been about five of these. In each case, on the slope up, it's gonna change everything. Each case, what happens is on the slope down, we sort of move the goalposts. and it becomes sort of irrelevant. So a good example is on going up, computer chess.

was gonna change everything. Once computers could play chess, that fundamentally changes the world. Now on the downside, computers play chess. Winning at chess is no longer seen as a unique human thing. but people still play chess. This new phase is gonna take a new chunk of things. that we thought computers couldn't do. Now computers will be able to do, they have roughly our brain capacity, but we'll keep thinking as well as computers.

And as I described, while we've been going through these five boom-busts, if you just look at the numbers of ops per second, bits storage, bits of IO, that's the more interesting one. That's been steady and that's what finally. caught up to people. As we've talked about a couple times, there's eight orders of magnitude to go, not in the intelligence in the transistors or in the brain, but in the embodied intelligence, in the intelligence in our body. - So the intelligent constructions of physical systems.

that would embody the intelligence. versus contain it within the computation. - Right. There's a brain centrism that assumes our intelligence. is centered in our brain. and in endless ways in this conversation, we've been talking about molecular intelligence. Our molecular systems do a deep kind of. artificial intelligence. All the things you think of as artificial intelligence. does in representing knowledge, storing knowledge,

searching over knowledge, adapting to knowledge, our molecular systems do. But the output isn't just a thought. It's us. It's the evolution of us. And the real horizon to come is now embodying AI. of not just a processor and a robot. but building systems that really can grow and evolve. - So we've been speaking about this boundary. between bits and atoms.

So let me ask you about one of the big mysteries. of consciousness. Do you think it comes from somewhere between that boundary? - I won't name names, but if you know who I'm talking about it, it's probably clear. I once did a drive, in fact, up to the Mussolini-era villa outside Torino. in the early days of what became quantum computing. with a famous person who thinks about quantum mechanics.

and consciousness. And we had the most infuriating conversation. that went roughly along the lines of. consciousness is weird, quantum mechanics is weird, therefore quantum mechanics explains consciousness. That was roughly the logical process. - And you're not satisfied with that process. - No, and I say that very precisely in the following sense. I was a program manager somewhat by accident.

in a DARPA program on quantum biology. And so biology trivially uses quantum mechanics. that were made out of atoms. But the distinction is in quantum computing, quantum information, you need quantum coherence. and there's a lot of muddled thinking. about like collapse of the wave function. and claims of quantum computing.

that garbles quantum coherence. You can think of it as a wave that has very special. properties, but these wave-like properties. And so there's a small set of places where biology. uses quantum mechanics in that deeper sense. One is how light is converted to energy in photo systems. It looks like one is olfaction, how your nose is able to tell different smells.

Probably one has to do with how birds navigate, how they sense magnetic fields. That involves a coupling between a very weak energy. with the magnetic field coupling into chemical reactions. And there's a beautiful system. Standard in chemistry is magnetic fields like this. can influence chemistry, but there are biological circuits that are carefully.

balanced with two pathways that become unbalanced. with magnetic fields. So each of these areas are expensive for biology. It has to consume resources to use quantum mechanics. in this way. So those are places where we know. there's quantum mechanics in biology. In cognition, there's just no evidence. There's no evidence of anything quantum mechanical going on. in how cognition works. - Consciousness.

- I'm saying cognition, I'm not saying consciousness. But to get from cognition to consciousness... So McCulloch and Pitts made a model of neurons. That led to perceptrons that then. through a couple boom-busts led to deep learning. One of the interesting things about that sequence. is it diverged off. So deep neural networks used in machine learning.

diverged from trying to understand how the brain works. What makes them work, what's emerged is. it's a really interesting story. This may be too much of a technical detail. but it has to do with function approximation. We had talked about exponentials, a deep network needs an exponentially larger. shallow network to do the same function. And that exponential is what gives the power. to deep networks.

But what's interesting is the sort of lessons about. building these deep architectures. and how to train them have really interesting echoes. to how brains work. And there's an interesting conversation. that's sort of coming back of neuroscientists. looking over the shoulder of people training. these deep networks, seeing interesting echoes for how the brain works, interesting parallels with it. And so I didn't say consciousness, I just said cognition.

But I don't know any experimental evidence. that points to anything in neurobiology. that says we need quantum mechanics. I view the question about whether a large language model. is conscious as silly, in that biology. is full of hacks and it works.

There's no evidence we have that there's anything deeper. going on than just this sort of stacking up of hacks. in the brain. - And somehow consciousness is one of the hacks. or an emergent property of the hacks. - Absolutely. And just numerically, I said big computations. now have the degrees of freedom of the brain. and they're showing a lot of the phenomenology. of what we think is properties of what a brain can do. And I don't see any reason to invoke anything else.

- That makes you wonder what kind of beautiful stuff. digital fabrication will create. if biology created a few hacks on top of which. consciousness and cognition, some of the things we love about human beings was created, it makes you wonder what kind of beauty. in the complexity can create from digital fabrication. - There's an early peak at that which is, there's a misleading term which is generative design. Generative design is where you don't tell a computer.

how to design something. You tell the computer what you want it to do. That doesn't work. That only works in limited subdomains. You can't do really complex functionality that way. The one place it's matured though is topology optimization. for structure. So let's say you wanted to make a bicycle or a table. You describe the loads on it. and it figures out how to design it. And what it makes are beautiful, organic looking things.

These are things that look like they grew in a forest. and they look like they grew in a forest. 'cause that's sort of exactly what they are. They're solving the ways of how you handle loads. in the same way biology does. And so you get things that look like trees and shells. and all of that. And so that's a peak at this transition. from we design to we teach the machines how to design. - What can you say about, 'cause you mentioned cellular automata earlier,

about from this example you just gave. and in general the observation you can make. by looking at cellular automata that. from simple rules and simple building blocks. can emerge arbitrary complexity. Do you understand what that is? How that can be leveraged? - So understand what it is is much easier than it sounds. I complained about Turing's machine making. a physics mistake, but Turing never intended it to be a computer architecture.

He used it just to prove results about computability. What Turing did on what is computation is exquisite, is gorgeous. He gave us our notion of computational universality. And something that sounds deep. and turns out to be trivial. is it's really easy to show. almost everything is computationally universal. So Norm Margolis wrote a beautiful paper with Tom Toffoli.

showing in a cellular automata world. is like the game of life where you just move tokens around. They showed that modeling billiard balls on a billiard table. with cellular automata is a universal computer. To be universal, you need a persistent state, you need a non-linear operation to interact them,

and you need connectivity. So that's what you need to show computational universality. So they showed that a CA modeling billiard balls. is a universal computer. Chris Moore went on to show that instead of chaos-. Turing showed there're problems in computation. that you can't solve that, that they're harder than you can't predict.

They're actually in a deep reason. They are unsolvable. Chris Moore showed it's very easy to make physical systems. that are uncomputable, that what the physics system does, just bouncing balls and surfaces, you can make systems that solve uncomputable problems. And so almost any non-trivial physical system. is computationally universal. So the first part of the answer to your question is, this comes back to my comment about how do you bootstrap.

a civilization? You just don't need much to be computationally universal. There isn't today a notion of like. fabricational universality or fabricational complexity. The sort of numbers I've been giving you. about you eating lunch versus the chip fab, that's in the same spirit of what Shannon did. But once you connect computational universality.

to fabrication universality, you then get the ability to grow and adapt and evolve. - Because that evolution happens in the physical space? - And so that's why, for me, the heart of this whole conversation is morphogenesis. So just to come back to that, what Turing ended his sadly cut short life studying.

was how genes give rise to form. How the relatively, in effect, small amount of information. in the genome can give rise to the complexity. of who you are. And that's where what resides is this. molecular intelligence, which is first how to describe you, but then how to describe you. such that you can exist and you can reproduce.

and you can grow and you can evolve. And so that's the seat of our molecular intelligence. - The maker revolution in biology. - It really is. It it really is. And that's where you can't separate communication, computation, and fabrication. You can't separate computer science and physical science. You can't separate hardware and software. They all intersect right at that place. - Do you think of our universe.

as just one giant computation? - I would even kind of say quantum computing. is overhyped in that there's a few things. quantum computing's gonna be good at. One is breaking cryptosystems, what we know how to make new cryptosystems. What it's really good at is modeling other quantum systems. So for studying nanotechnology, it's gonna be powerful, but quantum computing is not going to. disrupt and change everything. But the reason I say that is this interesting group.

of strange people who helped invent quantum computing. before it was clear anything was there, one of the main reasons they did it. wasn't to make a computer that can break a cryptosystem. It was, you could turn this backwards, you could be surprised quantum mechanics. can compute or you can go in the opposite direction. and say if quantum mechanics can compute,

that's a description of nature. So physics is written in terms of. partial differential equations. That is an information technology from two centuries ago. The equations of physics are not, this will sound very strange to say, but the equations of physics, Schrodinger's equations and Maxwell's equations. and all of them, are not fundamental. They're a representation of physics.

that was accessible to us in the era of. having a pencil and a piece of paper. They have a fundamental problem. which is if you make a dot on a piece of paper, in traditional physics theory, there's infinite information in that dot. A point has infinite information. That can't be true because information.

is a fundamental resource that's connected to energy. And in fact, one of my favorite questions you can ask a cosmologist. to trip them up is ask, is information a conserved quantity in the universe? Was all the information created in the Big Bang. or can the universe create information? I've yet to meet a cosmologist who doesn't stutter. and not clearly know how to handle that.

existential question. But sort of putting that to a side, in physics theory the way it's taught, information comes late. You're taught about x, a variable, which can contain. infinite information, but physically that's unrealistic. And so physics theories have to find ways to cut that off. So instead, there are a number of people. who start with a theory of the universe.

should start with information and computation. as the fundamental resources that explain nature. And then you build up from that to something. that looks like throwing baseballs down a slope. And so in that sense, the work on physics and computation has many applications. that we've been talking about. But more deeply, it's really getting at new ways. to think about how the universe works.

And there are a number of things that are hard to do. in traditional physics that make more sense. when you start with information and computation. as the root of physical theory. - Information and computation being. the real fundamental thing in the universe. - That information is a resource. You can't have infinite information in finite space. Information propagates and interacts, and from there you erect the scaffolding of physics. Now it happens,

the words I just said look a lot like. quantum field theories, but there's an interesting way where instead of. starting with differential equations. to get to quantum field theories. and quantum field theories, you get to quantization. If you start from computation information, you begin sort of quantized and you build up from there. And so that's the sense in which absolutely. I think about the universe as a computer.

The easy way to understand that is. almost anything is computationally universal. But the deep way is it's a real fundamental way. to understand how the universe works. - Let me go a little bit to the personal. and with the Center of Bits and Atoms. You have worked with, the students you've worked with, have gone on to do some incredible things in this world,

including build super computers that power Facebook. and Twitter and so on. What advice would you give to young people? What advice have you given them. how to have one heck of a great career? One heck of a great life? - One important one is if you look at junior faculty. trying to get tenure at a place like MIT, the ones who try to figure out how to get tenure. are miserable and don't get tenure.

And the ones who don't try to figure it out. are happy and do get it. You have to love what you're doing and believe in it. and nothing else could possibly be. what you wanna be doing with your life. And it gets you outta bed in the morning. And again, it sounds naive, but within the limited domain I'm describing now. of getting tenure at MIT, that's the key attribute to it. And in the same sense, if you take the sort of outliers students.

were talking about, 99 out of 100 come to me and say. your work is very fascinating. I'd be interesting to work for you. And 1 out of 100 come and say. you're wrong, here's your mistake. Here's what you should have been doing. And they just sort of say I'm here. and get to work. I don't know how far this resource goes.

I've said I consider the world's greatest resource. this engine of bright inventive people. of which we only see a tiny little iceberg of it. And everywhere we open these labs, they come out of the woodwork. We didn't create all these educational programs, all these other things I'm describing. We tried to partner everywhere with local schools. and local companies and kept tripping over dysfunction. and find we had to create the environment. where people like this can flourish. And so I don't know if this is everyone,

if it's 1% of society, what the fraction is, but it's so many orders of magnitude bigger than. we see today. We've been racing to keep up with it. to take advantage of that resource. - Something tells me it's a very large fraction. of the population. - The thing that gives me most hope for the future. is that population. Once a year, this whole lab network meets. and it's my favorite gathering. It's in Bhutan this year because. it's every body shape, it's every language, every geography, but it's the same person in all those packages.

It's the same sense of bright, inventive joy and discovery. - If there's people listening to this. and they're just overwhelmed with how exciting this is, which I think they would be, how can they participate, how can they help, how can they encourage young people or themselves. to build stuff, to create stuff? - That's a great question. This is part of a much bigger maker movement. that has a lot of embodiments.

The part I've been involved in, this FabLab network, you can think of as a curated part that works as a network. So you don't benefit in a gym if somebody exercises. in another gym. But in the Fab network, you do in a sense benefit. when somebody works in another network, another lab in the way it functions as a network. You can come to cba.mit.edu to see the research. we're talking about. There's a Fab Foundation run by Sherry Lassiter. at fabfoundation.org.

Fab Labs IO is a portal into this lab network. Fabacademy.org is this distributed hands-on. educational program. Fab.city is the platform of cities producing. what they consume. Those are all nodes in this network. - So you can learn with Fab Academy. and you can perhaps launch or help launch. or participate in launching a FabLab. - And in particular, from one to a thousand, we carefully counted labs.

Now we're going from a thousand to a million. where it ceases to become interesting to count them. And in a thousand to the million, what's interesting about that stage is technologically, you go to a lab not to get access to the machine, but you go to the lab to make the machine. But the other thing interesting in it. is we have an interesting collaboration. on a FabLab in a box. And this came out of a collaboration with SolidWorks.

on how you can put a FabLab in a box, which is not just the tools but the knowledge. So you open the box and the box contains the knowledge. of how to use it as well as the tools within it. so that the knowledge can propagate. And so we have an interesting group of people working on... The original FabLabs, we'd have a whole team. to get involved in the setting up and training. And the Fab Academy is a real in-depth, deep, technical program in the training.

But in this next phase, how sort of the lab itself. knows how to do the lab. We've talked deeply about the intelligence in fabrication, but in a much more accessible one about how. the AI in the lab in effect becomes a collaborator with you. in this nearer term to help get started. And for people wanting to connect, it can seem like a big step, a big threshold,

but we've gotten to thousands of these. and they're doubling exactly that way, just from people opting in. - And in so doing, driving towards this kind of idea of. personal digital fabrication. - And it's not utopia, it's not free, but come back to today, we separately have education, we have big business, we have startups, we have entertainment, each of these things are segregated. When you have global connection to one of these.

local facilities, in that, you can do play and art and education. and create infrastructure. You can make many of the things you consume. You could make it for yourself. It could be done on a community scale, it could be done on a regional scale. I'd say the research we spent the last few hours. talking about, I thought was hard. And in a sense, it's non-trivial,

but in a sense, it's just sort of playing out, we're turning the crank. What I didn't think was hard is. if anybody can make almost anything anywhere, how do you live, how do you learn, how do you work, how you play, these very basic assumptions about how society functions. There's a way in which it's kind of back to the future. in that this mode where work is money is consumption.

and consumption is shopping. by selecting is only a kind of a few decade-old stretch. In some ways, we're getting back to. a Sami village in north Norway is deeply sustainable. But rather than just reverting to living the way. we did a few thousand years ago, being connected globally, having the benefits of modern society,

but connecting it back to older notions of sustainability, I hadn't remotely anticipated just how fundamentally. that challenges how a society functions. and how interesting and how hard it is. to figure out how we can make that work. - And it's possible that this kind of process. will give a deeper sense of meaning to each person. - Let me violently agree in two ways. One way is this community-making.

crosses many sensitive sectarian boundaries. in many parts of the world where there's just. implicit or explicit conflict, but sort of this act of making seems to transcend. a lot of historical divisions. I don't say that philosophically. I just say that as an observation. And I think there's something really fundamental.

in what you said, which is. deep in our brain is shaping our environment. A lot of what's strange about our society. is the way that we can't do that. The act of shaping our environment touches something. really, really deep that gets to the essence of who we are. That's, again, why I say that in a way. the most important thing made in made in these labs.

is making itself. - What do you think, if the shaping of our environment gets to something deep, what do you think is the meaning of it all? What's the meaning of life, Neil? - I can tell you my insights into how life works. I can tell you my insights in how to make life meaningful. and fulfilling and sustainable. I have no idea what the meaning of life is,

but maybe that's the meaning of life. - The uncertainty, the confusion, because there's a magic to it all. Everything you've talked about, from starting from the basic elements with the Big Bang. that somehow created the sun. that somehow said F you to thermodynamics. and created life and all the ways. that you've talked about from ribosomes. that created the machinery that created the machine, and then now the biological machine creating. through digital fabrication,

more complex, artificial machines, all of that. There's a magic to that creative process. And we notice, we humans are smart enough. to notice the magic. - You haven't said the S word yet. - Which one is that? - Singularity. I'm not sure if Ray Kurzweil is listening, if he is, hi Ray. But I have a complex relationship with Ray. because a lot of the things he projects I find annoying, but then he does his homework.

And then, somewhat annoyingly, he points out how almost everything I'm doing. fits on his roadmaps. And so the question is, are we heading towards a singularity? I'd have to say I lean towards sigmoids. rather than exponentials. - But we've done pretty well with sigmoids.

- Sigmoids are things grow and they taper, and then there can be one after it and one after it. I'll pass on whether there's enough of them. that they diverge. The selfish gene answer to the meaning of life. is the meaning of life is the propagation of life. It was a step for atoms to assemble into a molecule,

for molecules to assemble into a proto cell, for the proto cell to form, to then form organelles for the organ cells to form organs, the organs to form an organism. Then it was a step for organisms to form family units, then family units to form villages. You can view each of those as a stack. in the level of organizations. You could view everything we've spoken about.

as the imperative of life, just the next step in the hierarchy of that. And the fulfillment of the inexorable drive. of the violation of thermodynamics. I'm an embodiment of the will of the violation. of thermodynamics speaking. - The two of us, having an old chat. And so it continues, and even then the singularity. is just a transition up the ladder.

- There's nothing deeper to consciousness than. it's a derived property of distributed problem solving. There's nothing deeper to life than embodied AI. in morphogenesis. So why so much of this conversation in my life. is involved in these FabLabs. and initially it just started as outreach.

Then it started as keeping up with it, then it turned to it was rewarding. Then it turned to we're learning as much from these labs. in as goes out to them. It began as outreach, but now more knowledge is coming back from the labs. than is going into them. And then finally it ends with. what I described as competing with myself at MIT. but a better way to say that is tapping the brain power.

of the planet. And so I guess for me personally, that's the meaning of my life. - And maybe that's the meaning for the universe too. It's using us humans and our creations to understand itself. In a way, it's whatever the creative process. that created earth, it's competing with a self. - So you could take morphogenesis as a summary. of this whole conversation or you could take recursion,

that in a sense, what we've been talking about. is recursion all the way down. - And in the end, I think this whole thing is pretty fun. It's short, life is, but it's pretty fun. And so is this conversation. I mentioned to you offline, I'm going through some difficult stuff personally. And your passion for what you do is just really inspiring. and it just lights up my mood and lights up my heart. And you're an inspiration for, I know, thousands of people that work with you at MIT. and millions of people across the world.

It's a big honor that you would sit with me today. This was really fun. - This was a pleasure. - Thanks for listening to this conversation. with Neil Gershenfeld. To support this podcast, please check out our sponsors in the description. And now let me leave you with some words from Pablo Picasso. Every child is an artist. A challenge is staying an artist when you grow up. Thank you for listening and hope to see you next time.