尼爾·格申菲爾德：自我複製機器人與製造業的未來

Lex Fridman Podcast #380 – Neil Gershenfeld: Self-Replicating Robots and the Future of Fabrication Machine transcription by Qwen3-ASR 0.6B (MLX, local, Apache-2.0). May contain recognition errors. 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. And now a quick second mention of each sponsor. Check them out in the description is the best way to support this podcast. We got Element for zero sugar hydration, NetSuite for business management software and BetterHelp for mental health. Choose wisely, my friends. 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And now dear friends, here is 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 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. Yeah, 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. Okay, 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 is Turing's counterpart. So just 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 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 is 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 edvac, 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 is 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 is a ridiculous taxonomy of about a hundred 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. So I really came to appreciate that a few years back when I did a keynote for the annual meeting of the supercomputer industry and then went into the halls and spent time with the supercomputer builders and came to appreciate. Oh, see if you are 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 in in um 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. A 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 is no distinction between the head and the tape between the computer and the computation it is all computation. Right, so 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. The embodiment of computation is embodied in these really profound ways. So the first quantum computation synthetic life so in the space of biology, so the space of physics at the lowest level in the space of biology at the lowest level. So let's talk about CBA Center for Bits and Atoms. What's the origin story of this MIT legendary MIT center that you are a part of creating? In high school, I really wanted to go to vocational school where you learn to weld and fix cars and build houses, and I was told no you are 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 are 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 were the trivium quadrivium roughly language natural science and at that moment what emerged was this 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 micro machining or 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 was 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. I this will be another slight digression I and Cornell I would study physics and 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 electronic music because he was really the first electronic musician. So Bob Mogue who invented Mogue 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's legendary for people like Phil Glass and Steve Reich. And so that got me thinking about I would behave as a scientist in the music department but not in 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 MacOver at the Media Lab who is just about to start a project with YoYo Ma that led to a collaboration to instrument a cello to to extract YoYo's data and bring it out into computational environments. What is the computational capacity of a musical instrument? As we continue on this tangent and we shall return to CBA. Yeah, so one part of that is to understand the computing and if you look at like the finest timescale and length scale you need to model the physics it it's not heroic you a good GPU can do terraflaps today that that used to be a national class supercomputer now it's just a GPU and that's about. If you take the timescales and length scales relevant for the physics that's about the scale of the physics computing for YoYo 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 its performance as a controller how he can manipulate it as an interface device. Interface between what and what exactly him and sound and so so what it led to was I had started by thinking about ops per second but YoYo's question was really. Um resolution and bandwidth it's how fast can you measure what he does and um the the the bandwidth and the resolution of detecting his controls and then mapping them into sounds and what what we found what he found was if you instrument everything he does and connected to almost anything. It sounds like YoYo that that that the magic is in the control not in ineffable details in how the wood wiggles and so with YoYo and Todd that led to a piece and towards the end I asked YoYo what 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 to make it work once the technology becomes as invisible as the Strad that then sure absolutely he would take it and by the way as a footnote on the footnote an accident in the sensing of YoYo's cello led to a hundred million dollar 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 those that that interference so my very first grad student ever Josh Smith. Did a thesis on tomography with electric fields how to see in three D with electric fields then through Todd and at that point research scientist my lab Joe Paradiso it led to a collaboration with Penn and Teller who. Um, where we did a magic trick in Las Vegas to contact Toudini and sort of these fields are sort of like you know contacting spirits. So we did a magic trick in Las Vegas and then the 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 the seat need to in effect see in three D to understand the occupants and so. We took the Penn and Teller magic trick derived from Josh's thesis from YoYo's cello to an auto show and all the car companies said great when can we buy it and so that became Elisa and it was a hundred million dollar 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 musical instrument. Right. 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. Um, there is amazing people there and I had 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 Weisner. 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. It as MIT's president towards the end of his tenure if he said I am going to make a department for things that don't fit in departments the departments would have screamed. But everybody was sort of paying attention to Nicholas creating the Media Lab and Jerry kind of hid in it. 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, um 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, yeah with um ah Todd and Joe Paradiso and Mike Colly 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 yeah so they're usually nanometers micrometers millimeters meters are segregated input and output is segregated we wanted to look just very literally 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 trying Scott Menalas we launched CBA and so you're talking about nanoscale microscale nanostructures microstructures macrostructures electron microscopes and focused ion beam probes for nanostructures laser micro machining and X-ray microtomography for microstructures multi-axis machining and 3D printing for macrostructures 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 yeah so 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 there when you need to work across length scales where we don't 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 incoate that needs to span these disciplines and length scales and so you work in the project today work in CBA today ranges from developing zipped dual electronics for the lowest power computing to micro machining diamond to take million ten million RPM bearings for molecular spectroscopy studies up to exploring robots to build hundred meter structures in space okay can we 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 sure 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 yeah so what is digital the casual obvious answer is digital in one and zero but that's wrong there's a much deeper answer which is Claude Shannon at MIT wrote the best master thesis ever in his master thesis he invented our modern notion of digital logic where it came from was Vannever 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 the differential analyzer which was the last grade 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 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 how many phone lines you could 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 as in from packets of data in a network when they get when the computer they're watching this gets the packet of information it it can detect and correct an error and what Shannon showed is if the noise in 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 exponents 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 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 is 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 Labovsky's Form Labs 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 Vannever Bush's analog computer if you mistake 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're manufactured is there's a code that describes you the genetic code it goes to a a micro machine the ribosome which is this molecular factory that builds the molecules that that are you the key thing to know about that is it there are 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 make elongated protein in the ribosome it's about a part in ten to the four when you replicate DNA there's an extra level of error correction it's a part in ten to the eight 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 because 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 problem 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 discrete 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 because 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 under constrained and over constrained 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 so the so lightweight materials are crucial for energy efficiency this let us make make that the lightest weight high modulus material we then showed that with just 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 Chang and Benjinet 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 to my students Samira and Mianna 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 robots 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 that is given what does that look like what are we talking so this is fascinating yeah 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 are 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 primary secondary tertiary quaternary now what's interesting is if you want to 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 I see as another discretion 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 the at heart it's sort of how to create life in nonliving materials so if 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 micro robots 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 are there are 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 disdetach or process now those elements I just describe 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 that up through that hierarchy in that way you can move up the length scale right 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 we like 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 you know 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 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 micro robots out of little parts that then we're using for Mianna'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 because he never claimed it as this architecture he really wrote about it in this one fairly dreadful memo that led to all sorts of lawsuits and fights and 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 how 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 is fundamentally how to realize it how to realize self reproducing automata and so you know this is something von Neumann thought very deeply and very beautiful of beautifully about theoretically and it's right at this intersection it 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 because 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 nonliving 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 because the assemblers make assemblers with simple building blocks that inside themselves contain the information how to build more building blocks and so between each other construct arbitrarily complex objects right 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 hundred 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 compute the performance of a big computer matched a brain so you could view AI as a breakthrough but the real story is um Within about a year or two ago, and let's see that the supercomputer has about ten to the fifteen transistors in the processors, ten to the fifteen transistors in the memory, which is the synapses 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 am mentioning that is the chip fab making the supercomputer is placing about ten to the ten transistors a second. While you are digesting your lunch right now, you are placing about ten to the eighteen parts per second. There is an eight order of magnitude difference. Not so in computational capacity, it's done. We've caught up, but there is 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 are saying, I mean, this is one of the cases you are making in that this is this third revolution. We've seen the Moore's law communication, we've seen the Moore's law like type of growth in computation. And you are 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? So the casual meaning is a computer controls the tool to make something, and that was invented when MIT stole it in nineteen fifty two. Yeah, there is the deep meaning of what the ribosome does of a compute of a digital description doesn't describe a thing, a digital description becomes the thing. That's where the that's that's the path to the Star Trek replicator. And that's the thing that doesn't exist yet. Now I think the the best way to understand what this roadmap looks like is to now bring in fab labs and how they relate to all of this. What are fab labs? So here here is a sequence. With colleagues, I accidentally started a network of what's now twenty five hundred digital fabrication community labs called fab labs right now in one hundred twenty five countries. And they double every year and a half. That's called Lass's law after Sherry Lasseter who I'll explain. So here is the sequence. We started Center for Bits and Adams 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 is no big agenda. It was just 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 over subscribed 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 were amazing. So Kelly was a sculptor, no engineering background. Her project was she made a device that saves up screams when you are mad and plays them back later. And saves up screams when you are mad and plays them back later. You scream into this device and it it it deadens the sound, records it, and then when it's convenient releases your scream. Can we just just like pause on the brilliance of that invention, creation, the art. I don't know the brilliance. Who is this that created? Kelly Dobson. Kelly Dobson. Gone on to do a number of interesting things. Meijin, 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. They're also very. Another project early on was a web browser for parrots, which have the cognitive ability of a young child. Let's parrots surf the internet. You know another another was an alarm clock. An alarm clock you wrestle with and prove you are awake. And what connects all of these is so MIT made the first real time computer, the whirlwind. That was transistorized as the TX. The TX was spun off from MIT as the PDPs. Where 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 Olsen, the head of Digital, famously said, "You don't need a computer at home." There is a little background to that, but but Deck you know completely missed computing became personal. So I mentioned 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. Yeah, 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. She wanted it was because she wanted one. It was personal expression going back to me in vocational school is personal expression in these new means of expression. So that's happened every year since. They literally is called 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 in teaching as taking it. There is even a Harvard lab for the MIT class. What have you learned about humans? Colliding