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 of the robot can make the robot so more recently to 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 is 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 Alex Friedman 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 reality from uh working at this divide trying to bridge this divide I learned why Von Neumann and Turing made fundamental mistakes um it's I learned the secret of life yeah um I 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 uh touring 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 touring's counterparts 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 U.S 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 throwing his counterpart then van 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 yeah then van Neumann wrote deeply and beautifully about many things but not Computing he wrote a horrible men memo called the first draft of a report in 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's using to watch this is spending much of its effort moving information from Storage Transit 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 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 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 see if you're familiar with the movie The Metropolis uh 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 and 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 create he never called it vinomen'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 van Neumann studied self-reproducing automata how a machine communicates its own construction a touring 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 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 computations synthetic life so in the space of biology so space of physics at the lowest level in the space of biology at the lowest level so uh let's talk about CBA Center of bits and atoms what's the origin story of this MIT legendary MIT Center that you're 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 uh I then worked at Bell labs this wonderful place uh 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 Dreadful concept of the ill liberal 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 um but the means of expression of changed since the Renaissance so micro Machining or embedded coding is every bit as expressive as painting a painting or writing a sonnet so uh never understanding this difference between computer science and physical science uh 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 uh this will be another slight aggression I uh 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 reads and play the reads right 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 uh who you might not have heard of but it's legendary electronic music because he was really the first electronic musician so Bob Moog who invented um 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'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 mackover at the media lab who was just about to start a project with Yo-Yo Ma um that led to a collaboration uh to instrumenticello 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 when we shall return to CBA yeah so 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 you know a a good GPU can do teraflops today that used to be a national class supercomputer now it's just a GPU and that's about if you take the time scales and length scales relevant for the physics that's about the scale of the physics Computing for yoyo it 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 one and one exactly human sound okay and so so what it led to was I had started by thinking about Ops per second but the yoyo's question was really um resolution and bandwidth it's um how fast can you measure what he does and um uh 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 connect it to almost anything it sounds like yo-yo that 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 what it would take for him to get rid of his Strat 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 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 a year Auto Safety business to control airbags and cars how did that work I had to instrument the bow without interfering with it so I um set up um local electromagnetic fields where I would um detect um 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 3d with electric fields then through Todd and at that point research scientists my lab Joe Paradiso it led to a collaboration with uh Penn and Teller who um where we did a magic trick in Las Vegas to contact Houdini 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 uh Phil ritmuller came running into my lab he worked with um this became with Honda and NEC airbags were killing infants and rear-facing child seats um 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 the rear-facing infant where you would kill it and so the the the seat need to in effect see in 3d to understand the occupants and so we took the pen and Teller magic trick derived from Josh's thesis from yo-yo's Cello to an auto show and all the card companies said great when can we buy it and so that became ellisis and it was 100 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 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 this is 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 it as mit's president towards the end of his tenure if he said I'm 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 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 helped 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 uh Todd and Joe Paradiso and my colleague we started a Consortium called things that think and this was around the birth of Internet of things and um 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 uh 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 uh with um a group of core colleagues um that included Joe Jacobson like trying Scott minnellis we launched CBA and so you're talking about nanoscale micro scale nanostructures microstructures macro structures electron microscopes and focused on beam probes for nanostructures laser micro Machining 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 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 they're they're 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 in Kuwait that needs to span these disciplines and length scales um and so you know uh work in the project today work in CBA today ranges from developing zeptidual electronics for the lowest power Computing to micro Machining Diamond to take million 10 million RPM bearings for molecular spectroscopy studies up to exploring robots to build 100 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 uh bring in now Shannon as well as Von Neumann 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's thesis ever in his master's thesis he invented our modern notion of digital logic where it came from was Van ever Bush uh was a grand old man at MIT uh 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 let 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 um but he then went on to Bell labs and what he did there was communication was beginning to expand there is more demand for phone lines and so there's a question about how much 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 um when they get when the computer they're watching this gets the packet of information um 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 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 that means unreliable things can work reliably so Shannon did that for communication then van 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 my lab by Max lebowsky'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 Van ever 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 four 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 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 vanyman 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 elongate a protein in the ribosome it's about a part in 10 to the four when you replicate DNA there's an extra level of error correction it's a part in 10 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 four billion years ago is how to embody these problems 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 degree discrete set of Parts reversibly joined um 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 era 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 if in effect making carbon fiber Lego so so lightweight materials are crucial for Energy Efficiency this let us 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 um NASA we made more airplanes a former student Kenny Chung and benjinette 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 uh at heart because of the robot can make the robot so more recently to 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 robots so you build up the capacity of robotic assembly you can self-replicate can you Linger on what that robot looks like what is a robot it can walk along and do error correction and what is a robot that can self-replicate uh from the materials that is given what does that look like what are we talking so um this is fascinating yeah the answer is different at different length scales so so to explain that in biology primary structure is the code in the messenger RNA that says what the ribosome should build yeah um secondary structure or 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 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 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 um I see as another discretion I helped start an office for science in Hollywood and um 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 non-living materials so if if you think about this primary secondary tertiary quaternary structure um 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 uh 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 disk detach or process now those elements I just described we can make out of the still smaller parts So eventually there's the 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 line 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 um like there's a lot of a 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 um that'll come in stages can we just Linger on this idea starting from vinelman's uh 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 miana's bigger robots up through this hierarchy and it's really realizing this idea of the self-reproducing automata so van Neumann when I complained about the weinerman architecture it's not fair Devon Neumann because 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 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 um so how do you give birth to a thing that knows how to make itself and so um with Stan ulam he invented cellular automata as a way to simulate these uh but that was theoretical now the work I'm describing in my lab is is fundamentally how to realize it how to re um realize self-reproducing uh automata and so you know this is something van 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 and 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 because the assemblers make assemblers so simple building blocks yeah that inside themselves contain the information how to build more building blocks and so uh 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 uh 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 that the super computer has about 10 to the 15 transistors in the processors 10 to the 15 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'm mentioning that is the chip Fab making the supercomputer is placing about 10 to the 10 transistors a second while you're digesting your lunch right now you're make you're placing about 10 to the 18 parts per second um there's an eight order of magnitude difference not 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 I mean this is one of the cases you're making and 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 uh 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 so the Casual meaning is the computer controls the tool to make something and that was invented when MIT stole it in 1952. yeah um there's the deep meaning of what the ribosome does of a computation of a dis a digital description doesn't describe a thing a digital description becomes the thing yeah 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's a sequence um with colleagues I accidentally started a network of what's now 2500 digital fabrication Community Labs called Fab Labs right now in 125 countries and they double every year and a half that's called lassa's law after Sherry Lasseter who I'll explain so here's the sequence uh 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 you know colleagues who helped start CBA we began a class modestly called how to make almost anything yeah and there's no big agenda it was just it was aimed at a few research students to use the machines and it 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 um 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 uh her project was she made a device that saves up screams when you're mad and placed them back later and saves up screams when you're 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 screen 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 going on to do a number of interesting things uh me Jin who's gone on to do a number of interesting things uh made a dress instrumented with sensors and spines and when somebody creepy comes close it would defend your personal space they're also very easy um another project early on was a web browser for parrots which have the cognitive ability of a young child and lets parrots surf the Internet an alarm clock you wrestle with and prove you're 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 PDP pdp's 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's 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 yeah it was personal expression going back to me and vocational schools personal expression in these new means of expression so that's happened every year since it literally is called the course is literally called how to make almost anything yep a legendary course at MIT yep yep every year um and it's grown to multiple Labs um at MIT with as many people involved in teaching is taking it and there's even a Harvard lab for the MIT class what what have you learned about humans colliding with the Fab Lab about what the capacity experience to be creative and to build I I mentioned Marvin another Mentor at MIT sadly no longer living is Seymour pepper so pepper studied with Piaget he came to MIT to get access to the early compute Piaget was a Pioneer in how kids learn um 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 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 Fab Lab 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 a robot kids could 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 Fab Labs 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 come into your question what I've learned is MIT a few years back somebody added 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 four thousand brightest people it's just a productive environment for them and what we found is in rural Indian villages in African Shanty towns and Arctic um Hamlet I find exactly precisely that profile so um link cited a few hours above Trump so way above the Arctic circles it's so far north the satellite dishes look at the ground not the sky um Hans Christian in the lab was considered a problem in the local school because 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 um South Africa in I mentioned social Govi in this apartheid Township the local Technical Institute taught kids how to make bricks and fold sheets it was it was punitive but to piso in the Fab Lab was actually doing all the work of my MIT classes and so over and over we found precisely the same kind of bright invent of um creativity uh and historically the answer was go 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 teammate uh the first real-time 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 Fab Lab story 1952 was the NC Mill there are now thousands of Fab labs and the Fab Lab costs exactly the same cost and complexity of the mini computer so on the mini computer it 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 Fab labs are like the mini computers inventing how does the world work if anybody can make anything then if you look at that scaling Fab Labs today are transitioning from buying a machine to make machines making machines so we're transitioning to you can go to a Fab Lab not to make a project to make but to make a new machine so we talked about the Deep sense of self-replication there's a very practical sense of Fab Lab machines making Fab Lab machines and so that's the equivalent of the uh hobbyist computer era what it's called the Altair 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 so the 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 the core things happened in in that transition in the mini computer era so in the same sense the research now I'm 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 Fab Lab 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 who for me started this idea of how do we use the tools in my lab to make the tools in the lab yes 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 and now in Germany and yens in Norway and um each of these people is has learned the skills to go into a Fab Lab and make a machine and so we've started creating a network of superfap so the Fab Lab can make a machine but it can't make a number of the Precision parts of the machine so in places like Bhutan or Carol in the south of India we started creating super Fab Labs that have more advanced tools to make the parts of the machines so that the machines themselves become even cheaper so that 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 fab1234 so we're transitioning from fab 1 to Fab two and the research in the lab is three and four at this Fab two stage a big component of this is uh sustainability in the material feedstocks 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 micro mechanical 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 uh 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 I have a fine enough precise enough actuators and manipulators that allow you to place these transistors right that's a research project in my lab on 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 micro manipulator that acts like a printer but it's placing to build Electronics in 3D but this micro manipulator 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 um 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 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 uh I mean it's a it's a it's a fun example of exactly the thing we're talking about and I've had a few students almost get to that um and so um 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 us 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 micro parts to make the micro robots we can assemble the bigger robots and in the coming years we'll be patching together all of those uh scales so do you see a vision of just endless billions of robots at the different scales self-assembling uh self-replicating and building the complicated structures yes yes and the butt 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 um uh 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 I'm asked for example um you know what what's the killer opportunity you know who's going to make all the money um who to invest in but if the machine can make the machine it's not a great business to invest in the machine um 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 run these lab 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 it 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 Brighton event of people whose brains are underused and um you could view the the social engineering of this lab work as creating the capacity for them and so it you know in the end the way this is going to impact Society isn't going to 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 yeah but what are the different ways uh the evolution of the exponential scaling of digital fabrication can evolve so you said uh yeah self-replicating Nanobots right this is the the gray goo fear it's the 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 yeah well first let me start with a benevolent story which is uh 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 yeah 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 one of the so as you go through this Fab one two three four story one of the implications of this transition to from printing to assembling so the real breakthrough technologically isn't additive versus subtractive which is the subject of a lot of attention and hype um yep 3D printers are useful um you know we spun off companies like form Labs led by Max for 3D printing but in a Fab Lab it's one of maybe 10 machines it's 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 uh and disassembling 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 solution 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 going to get to making a um 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 disassembling it going back to Shannon and Von Neumann going back to the ribosome for a billion years ago now you come to malevolent um 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 and Ghana um making a local girl making surface mount Electronics in the Fab Lab and I showed that to this room full of people uh 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 service non-electronics we're looking at 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 and you idiot I can't think of anything more important than this yeah but for two reasons one reason was um because if we rely on like 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 um uh 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 because the possibilities that are positive are endless yeah but the possibility is a negative are still nevertheless extremely important was both positive and negative what do you do with a large number of General assemblers yeah with the Fab Lab you could roughly make a bio lab then learn biotechnology now that's terrifying because making self-reproducing gray goo that out competes biology I consider Doom because biology knows everything I'm describing and is really good at what it does um in how to grow almost anything you learn skills in biotechnology that would let that let you make serious biological threats and when you combine uh 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 uh writing programs which you can 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 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 uh complex systems in the physical space yeah so something to be scared about is a Fab Lab can make a bio lab a bio lab can make biotechnology somebody could learn to make a virus that's scary that that's 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 uh prior threats we dealt with command and control so like uh early color copiers had unique codes and you could tell which copier made them eventually you couldn't keep up with that uh there there was a famous meeting at asilamar 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 so you know 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 found is you can't contain this it's already L you can't forbid because there isn't command and control the most useful thing you can do is provide incentives for transparency yes so 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 oh you know um again bad people do bad things in these places already but providing openness and providing transparency is a key part of managing these and so it it transitions from regulating risks as regulation to to soft power to manage them so there's so much potential for good so much capacity for good that Fab labs and the uh the the ability um and the tools of creation really unlock that potential yeah and I don't say that as sort of dewey-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 right in the end most fundamentally literally the mother of all problems is who designed us so so assume success and that we're going to 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 let off with of 10 to the 10 to 10 to the 18 between chip Fab and you um we're ultimately in marrying communication computation and Fabrication going to be able to create unimaginable complexity um and how do you design that and so I'd say the deepest of all questions that I've been working on is goes back to the oldest part of our genome so in our genome what are called Hox genes and these are morphogenes and nowhere in your genome is the number five it doesn't store the fact that you have five fingers um what it stores is What's called the 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 that's you're saying that's what biology that's what evolution has done is creative representation structures biological structures through which search effective and so the the developmental programs in the genome beautifully encapsulate the lessons of AI and this is It's embody it's it's molecular intelligence it's AI embodied in our genome it it it 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 um 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-readressing assemblers placing 10 to the 18 parts um 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 this much more deep embodiment of of AI and morphogenesis is there a way for us to continue the kind of evolution of design that led us to this place from the early days of bacteria single cell organisms to ribosomes and the 20 amino acids you mean for human augmentation or no for Life augment I mean what would you call assemblers that are self-replicating and placing Parts what is that the the dynamic complex things built with digital fabrication what is that that's the light so yeah so ultimately absolutely if you add everything I'm talking about it's building up to creating life in non-living materials yes and I I don't view this as copying life I view it as driving life I I didn't start from how does biology work and then I'm going to copy it I start from how to solve problems and then it it leads me to in a sense ReDiscover biology so if we go back to Valentina in Ghana making her circuit board um she still needs a chip Fab very far away to make the processor under 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 so let's see 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 so it is it it's sort of how how you do your own personal Industrial Revolution uh isru is what NASA calls in-situ resource utilization and that's how do you go to a planet and create a civilization uh isru has essentially assumed gingery you go through the Industrial Revolution and you create the inventory of a hundred thousand resistors what we're finding is the way you 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 sort of that's that that's where it's heading and yeah that 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 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 is that Civilization on Mars looks like that's using a isru that's using these 20 building blocks and does self-assembly yeah go go through primary secondary tertiary quaternary um you know you you extract properties like uh conducting insulating semiconducting uh magnetic uh dielectric flexural these are the kind of you know roughly 20 properties um with those those are enough for us to assemble logic and they're enough for us to assemble actuation um with logic and actuation we can make micro robots the micro robots can build bigger robots um 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 to 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 elect roads 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 you know where do ideas come from and that's an interesting story where do I just come from so I had mentioned veniver Bush and uh he wrote a really influential thing called the endless Frontier so uh science won World War II the the the 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 uh grand old man from MIT and it's a um uh 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 or research agencies in it he made again what I consider an important mistake which is he described basic research leads to applied research search leads to Applications leads to commercialization leads to impact and so we need to invest in that pipeline the reason I considered 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 so yeah 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 um you aim carefully at something a Target you want to accomplish and if everything goes right you then hit the target and succeed um what I do you can think of is ready fire Aim so you 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 didn't you aim after you fire and the the reason that's so important is it if you do Ready Aim Fire there's the best you can hope is hit what you aim at so let me give you some examples uh because this is a source of great full of good lines today source of great frustration so I mentioned the early Quantum Computing so 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 um physics and computing that were inventing what became Quantum Computing before it was clear anything there was an opportunity there it was just studying how those relate here's how it fits to the ready fire aim in 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 materials sources of non-linearity and that led me to look at how nuclear spins interact just just for for um spin resonance this is the sort of things you use when you let 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 um shoplifting tags but I realized you could compute and so um 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 um sensing objects in a supermarket checkout but I realized they were Computing so with Ike Trang 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 a shortest 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 you know 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 it came from a bad way to make a shoplifting tag starting with an application mistakes led to the fundamental science fundamental science yeah I mean can you can you just link on that I mean just just in using nuclear expensive do computation that like what gave you the guts to try to think through this the from a fabric from a digital fabrication perspective actually how to LEAP from one to the other yeah I wouldn't call it guts I would call it collaboration so I so at IBM there was this amazing group of like I mentioned Charlie Bennett and David divincenzo and Ralph Landau and Nabil Amir and these were all gods of thinking about physics and Computing so I I I I I yelled at the whole computer industry being based on uh a fiction Metropolis you know programmers frolicking the garden while somebody moves levers in the basement there's a complete parallel history of um uh Maxwell the boltzman to zillard to um landower to Bennett and most people won't know most of these names but this whole parallel history thinking deeply about how computation and physics relate so um I was collaborating with that whole group of people and then you know at MIT I was in this high traffic environment I wasn't deeply inspired to think about better ways to detect shoplifting tags but you know 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 um early Quantum Computing algorithms and this kind of stumbling is fundamental to the Fab Lab idea right right here's one more example with a student Manu we talked about ribosomes and I was trying to build a ribosome um that worked on fluids so that I could place the little Parts we're talking about and we it kept failing because 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 with little logic with little Bubbles and fluid okay you have to you have to explain this microfluidic bubble logic please how does this work so yeah that's super interesting yeah and so over so I'll come back and explain it but what it led to was um we showed fluids could do um it had been known fluid could do logic like your old automobile transition 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 that then I'm going to 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 um 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 a the genome the the the ribosome now so the way the bubble logic works is um in a little child Channel uh fluid at small scales is fairly viscous it's sort of like pushing Jello think of it as um 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 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 um 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 eight and that's everything you need to make a universal computer I mean the fact that you did that with bubbles and microfluids just kind of brilliant well so I mean to stay with that example uh it it what we propose to do was to make a fluidic ribosome and the project crashed and burned it was a disaster um 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 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 Fab Lab network is doing is giving more people the opportunity to fail you've said that geometry is really important in biology um what is 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 that that the geometry gives the function and uh 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 um 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 discrete lattice they're really Exquisite machines and and then from there this 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 um amino acids you mentioned molecules let's go to the very beginning of hydrogen and helium at the start of this universe they were able to build up such um complex and beautiful things like our human brain so studying thermodynamics which is exactly the question of you know that batteries run out and need recharging you know equipment you know cars get old and fail yet life doesn't and it 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 uh posited a a problem that was so infuriating it led to a series of suicides there was a series of advisors and advisees um three in a row that all ended up committing suicide that happened to work on this problem and uh 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 um 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 when something is hot and is cold you can make an engine and so you close that you make an engine and you make energy so the demon is violating thermodynamics because it's it's not 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 uh that problem is connected to everything we just spoke about for the last few hours so uh Leo zillard uh 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 the molecule one 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 zillard's analysis of Maxwell's Neiman for the invention of the bit um 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 landauer one of the colleagues I mentioned at IBM finally solve the problem he showed that you can explain Maxwell's demon by you need the mind of the demon when the demon opened 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 it's in the Demon's brain so then Ross Khalid colleague Charlie at IBM uh then shocked Ralph by showing you can compute with arbitrarily low energy so one of the things that's not well covered is the 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 um 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 where orders of magnitude away from that so I have a student Cameron working with Lincoln Labs on making superconducting computers that operate near this land hour limit that are orders of magnitude more efficient um so stepping back to all of that that whole tour was driven by your question about life and you know right at the heart of it is Maxwell's demon life exists because it can locally violate thermodynamics they can locally violate thermodynamics because of intelligence and it's its molecular intelligence that you know 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 you know it it's exploding but you can already see where it's heading you know how it's going to saturate what happens on the far side um the big thing that's not yet on Horizons is is embodied AI molecular intelligence so to step back to this AI story um there was Automation and that was going to change everything then there were expert systems um uh there was then the you know the first phase of the neural network systems there's been about five of these um in each case on the slope up it's going to change everything um in each case what happens is on the slope down um we sort of move the goal posts and it becomes sort of irrelevant so a good example is going up computer chess was going to 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 um uh people still play chess this new phase is going to 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 um but you know we'll keep thinking as well as computers um and as I described wow we've been going through these five boom busts if you just look at the numbers of Ops per second bits storage bits of i o That's the more interesting one that's been steady and that's what finally caught up to people but you know as we've talked about a couple times there's eight orders of magnitude to go not in the intelligence and 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 container within the computation right and 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 it's us it's the evolution of us and that's you know the real Horizon to come is now embodying ai if not not just a processor and a robot but but you know 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 one of the 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's probably clear I once did a drive in fact up up to the mussoline era Villa outside Torino um in the early days of what became Quantum computing with a a 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 rough ly the The Logical process then you're not as satisfied with that process no and I say that very precisely in the following sense uh I was a program manager somewhat by accident in a DARPA program on Quantum biology and so biology trivially uses quantum mechanics and 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 just Quantum coherence that um that 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 photosystems um it looks like one is olfaction how your nose is able to tell different smells um probably one has to do with how birds navigate how they sense magnetic fields that involves the coupling between a very weak energy with a magnetic field coupling into chemical reactions and there's a beautiful system it 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 again those are places where we know there's quantum mechanics in biology in cognition there's just no evidence there there's uh there's no evidence of anything quantum mechanical going on in how cognition Works Consciousness well I'm saying I'm saying cognition I'm not saying Consciousness but to get from cognition to consciousness so McCullough and Pitts made a model of neurons um that led to perceptrons that then threw 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 um what what makes them work what's emerged is they it's a really interesting story this may be too much of a technical detail but it has to do with function approximation that that uh we talked about exponentials a deep Network needs an exponentially larger shallow Network to do the same function and that 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 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 and um I view the question about whether a large language model is conscious as silly in 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 hex absolutely and um 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 as properties of what a brain can do um 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 yeah it's a digital family there's there's an early peek at that which is um 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 that'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 because that's sort of exactly what they are that they're they're 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 to um from we we design to to we teach the machines how to design what can you say about because 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 there's a from simple rules and simple building blocks can emerge arbitrary complexity do we understand like 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 uh results about uncomputability um what what Turing did on what his computation is exquisite it's 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 margulis wrote a beautiful paper um with Tom tofully showing in a cellular 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 to be Universal you need a persistent state you need a non-linear operation to interact them um 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 um Chris Moore went on to show that instead of chaos let's see um Turing showed there are computable their problems in computation that you can't solve that they're harder than you can't predict they're actually in a deep reason they are unsolvable um Chris Moore showed it's very easy to make physical systems that are uncomputable that what 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 how you know my comment about how do you bootstrap a civilization you just don't need much to be computationally Universal so then that 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 sort of that that that's in the same Spirit of what Shannon did but once you connect computational universality to kind of fabricational universality you then get the ability to grow and adapt and evolve because that Evolution happens in the physical space yeah and so that's why you know for me the heart of this whole conversation is morphogenesis so just to come back to that um what touring ended his sadly cut short life studying was how genes give rise to form so so how the the the small amount of it relatively in effect small amount of information in the genome can give rise to the complexity of Who You Are and and that that 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 you know that that's the seat of our molecular intelligence the make a revolution in biology yeah it really is um it really is and and that 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 I would even kind of say Quantum Computing is overhyped in that there's a few things Quantum Computing is going to be good at one is breaking crypto systems but we know how to make new crypto systems what it's really good at is modeling other Quantum systems so for studying nanotechnology it's going to 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 crypto system it was you could turn this backwards you could be surprised quantum mechanics can compute or you can go in the opposite opposite direction and say if quantum mechanics can compute um that's a description of nature so physics is written in terms of partial differential equations that is an information technology from uh two centuries ago the the equations of physics are not this would 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 information infinite information in that dot a point has infinite information that can't be true because in information is is um a fundamental resource that's connected to energy and in fact it um one of my favorite questions you can ask a cosmologist to trip them up is ask is information a conserved quantity in the universe with all the information created in the Big Bang or can the universe create information and 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 in information comes late you know 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 so information and competition being the the the real fundamental thing in the universe right that information is a resource you can't have 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 different differential equations to get to Quantum field theories and Quantum field theories you get to quantization [Music] um if you if 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 uh uh absolutely I think about the universe as a computer the easy way to understand that is uh just 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 in the center bits and atoms you have uh uh 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 uh 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 what one important one is uh it 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 or miserable and don't get tenure and the ones who don't try to figure it out are happy and do get it yeah I mean you know you have to love what you're doing and believe in it and nothing else could possibly be what you want to be doing with your life and it gets you out of bed in the morning and again it sounds naive but um it within like The Limited domain I'm describing now of getting tenure at MIT that that's the key attribute to it and then same sense um if you take the sort of outliers students were talking about you know 99 out of 100 come to me and say your work is very fascinating I'd be interesting to work um for you and one out of a hundred come and say um here you're wrong here here here's your mistake here's here's what you should have been doing yeah um and uh that they just sort of say I'm here and and get to work and again that's I I don't know how far this resource goes so you know I've said I consider the world's greatest resource this engine of Brighton event of people of which we only see a tiny little Iceberg of it and everywhere we open these labs they come out of the woodwork they come 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 one percent of society what the fraction is but it's so many orders of magnitude bigger than we see today you know we've been racing to keep up with it to take advantage of that resource if something tells me it's a very large fraction of the population I mean 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 it's every body shape it's every language every geography but it's the same person in all those packages it's it's the same sense of bright inventive joy and discovery if there's people listening to this in there just uh 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 uh to build stuff to create stuff yeah that's a great question so um the the this is part of a much bigger maker movement that has a lot a lot of embodiments the part I've been involved in this Fab Lab 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 and if you do in a sense benefit when somebody works in another Network another lab in the way it functions as a network so um you can come to cba.mit.edu to see the research we're talking about um there's a Fab Foundation run by Sherry Lasseter at fabfoundation.org Fab Labs IO is a portal into the slab Network um uh Fab academy.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 Fab Lab well an in particular um 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 the Thousand to the million uh what's interesting about that stage is uh 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 a Fab Lab in a box and um this came out of a collaboration with SolidWorks on how you can put a Fab Lab 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 you know the original Fab Labs which 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 that that it's you know it we've talked deeply about the intelligence in fabrication but in a much more accessible one about how the the the the AI in the lab in effect becomes a collaborator with you in this nearer term to help get started and for 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 uh in so doing driving towards this kind of idea of uh personal digital fabrication yeah 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 sort of 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 um you can make many of the things you consume you could make it for yourself it could be done on a community skull it could be done on a regional scale um it really I'd say the research we spent the last few hours talking about I thought was hard and in a sense I mean it's it 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 um in some ways we're getting back to you know a 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 um I 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 in two ways one way is uh this community making crosses many sensitive sectarian boundaries in many parts of the world where there's just you know 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 you know deep in our brain is shaping our environment um 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 you know 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 something deep what do you think is the meaning of it all what's the meaning of life now I can tell you my insights into how life works I can tell you in my insights and how to make life meaningful and fulfilling and sustainable um I have no idea what the meaning of life is but maybe that's the meaning of life now the uncertainty the confusion of um 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 uh said Fu to uh 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 so it's you haven't said the s word yet um which one is that singularity [Laughter] yeah I'm not sure if Ray Kurzweil is listening if he is high Ray but I have a complex relationship with Rey 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 road maps yeah um and so you know the the the question is are we heading towards the singularity I so I'd have to say I lean towards sigmoids rather than exponentials um we've done pretty well with the sigmoids yeah so sigmoids are things grow and they taper and then there can be one after it and one after it so um you know I'll pass on whether there's enough of them that that they diverge but you know to the selfish Gene answer to the meaning of life is the meaning of life is the propagation of life and so um you know it it it was a step for S atoms to assemble into a molecule for molecules to assemble into a protocell for the protocell 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 you know each of those as a stack in the level of organizations so 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 so you know you could view you know I'm an embodiment of the will of the violation of thermodynamics speaking the two of us having having an old chat yes yeah um and so continues and even then the singularity is just a transition up the ladder there's nothing deeper to Consciousness than it it it's a derived property of distributed problem solving um 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 Fab labs and initially it just started as Outreach then it started as keeping up with it then it turned to uh 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 that is going into them um and then finally it ends with um you know 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 from I guess for me personally that's the meaning of my life and maybe that's the meaning for the universe too it's uh it's using us humans and our Creations to understand itself in a way it's uh whatever the creative process that created Earth is competing with itself yeah so you could take morphogenesis as a summary of this whole conversation or you could take recursion that that in a sense what we've been talking about is recursion all the way down and in the end I think uh this whole thing is pretty fun it's short life is but it's pretty fun and so is this conversation you know I mentioned you offline them going through some difficult stuff personally and your passion for what you do is just really inspiring and it just uh lights up my mood and lights up my heart and your inspiration for I know thousands of people that work with unmit and millions people across the world it's a big honor to use it with me today this is 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