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

the ribosome who I mentioned a little the ribosome who I mentioned a little while back can make an elephant one while back can make an elephant one while back can make an elephant one molecule at a time ribosomes are slow molecule at a time ribosomes are slow molecule at a time ribosomes are slow they run at about one molecule a second they run at about one molecule a second they run at about one molecule a second but ribosomes make ribosomes so you have but ribosomes make ribosomes so you have but ribosomes make ribosomes so you have trillions of them and that makes an trillions of them and that makes an trillions of them and that makes an elephant in the same way these little elephant in the same way these little elephant in the same way these little assembly robots I'm describing can make assembly robots I'm describing can make assembly robots I'm describing can make giant structures giant structures giant structures at heart because of the robot can make at heart because of the robot can make at heart because of the robot can make the robot so more recently to my the robot so more recently to my the robot so more recently to my students Amira and Miana had a nature students Amira and Miana had a nature students Amira and Miana had a nature communication paper showing how this communication paper showing how this communication paper showing how this robot can be made out of the parts it's robot can be made out of the parts it's robot can be made out of the parts it's making so the robots can make the robot making so the robots can make the robot making so the robots can make the robot so you build up the capacity of robotic so you build up the capacity of robotic so you build up the capacity of robotic assembly the following is a conversation with the following is a conversation with Neil gershenfeld the director of MIT is Neil gershenfeld the director of MIT is Neil gershenfeld the director of MIT is Center for bits and atoms an amazing Center for bits and atoms an amazing Center for bits and atoms an amazing laboratory that is breaking down laboratory that is breaking down laboratory that is breaking down boundaries between the digital and boundaries between the digital and boundaries between the digital and physical worlds fabricating objects and physical worlds fabricating objects and physical worlds fabricating objects and machines at all scales of reality machines at all scales of reality machines at all scales of reality including robots and automata that can including robots and automata that can including robots and automata that can build copies of themselves and build copies of themselves and build copies of themselves and self-assemble into complex structures self-assemble into complex structures self-assemble into complex structures his work inspires Millions across the his work inspires Millions across the his work inspires Millions across the world as part of the maker movement to world as part of the maker movement to world as part of the maker movement to build cool stuff build cool stuff build cool stuff to create the very act that makes life to create the very act that makes life to create the very act that makes life so beautiful and fun so beautiful and fun so beautiful and fun this is Alex Friedman podcast to support this is Alex Friedman podcast to support this is Alex Friedman podcast to support it please check out our sponsors in the it please check out our sponsors in the it please check out our sponsors in the description and now dear friends here's description and now dear friends here's description and now dear friends here's Neil gershenfeld Neil gershenfeld Neil gershenfeld you have spent your life working at the you have spent your life working at the you have spent your life working at the boundary between bits and atoms so the

boundary between bits and atoms so the boundary between bits and atoms so the digital and the physical what have you digital and the physical what have you digital and the physical what have you learned about engineering and about learned about engineering and about learned about engineering and about nature reality from uh working at this nature reality from uh working at this nature reality from uh working at this divide trying to bridge this divide I divide trying to bridge this divide I divide trying to bridge this divide I learned why Von Neumann and Turing made learned why Von Neumann and Turing made learned why Von Neumann and Turing made fundamental mistakes fundamental mistakes fundamental mistakes um it's I learned the secret of life um it's I learned the secret of life um it's I learned the secret of life yeah yeah yeah um I I learned how to solve many of the um I I learned how to solve many of the um I I learned how to solve many of the world's most important problems which world's most important problems which world's most important problems which all sound presumptuous but all of those all sound presumptuous but all of those all sound presumptuous but all of those are things I learned at that boundary are things I learned at that boundary are things I learned at that boundary okay so uh touring and Von Neumann let's okay so uh touring and Von Neumann let's okay so uh touring and Von Neumann let's start there some of the most impactful start there some of the most impactful start there some of the most impactful important humans who have ever lived in important humans who have ever lived in important humans who have ever lived in Computing why were they wrong so I Computing why were they wrong so I Computing why were they wrong so I worked with Andy Gleason who is worked with Andy Gleason who is worked with Andy Gleason who is touring's counterparts so just just for touring's counterparts so just just for touring's counterparts so just just for background if anybody doesn't know background if anybody doesn't know background if anybody doesn't know Turing is credited with the modern Turing is credited with the modern Turing is credited with the modern architecture of computing architecture of computing architecture of computing among many other things Andy Gleason was among many other things Andy Gleason was among many other things Andy Gleason was his U.S counterpart and you might not his U.S counterpart and you might not his U.S counterpart and you might not have heard of Andy Gleason but you might have heard of Andy Gleason but you might have heard of Andy Gleason but you might have heard of the Hilbert problems and have heard of the Hilbert problems and have heard of the Hilbert problems and Andy Gleason solved the fifth one Andy Gleason solved the fifth one Andy Gleason solved the fifth one so he was a really notable mathematician so he was a really notable mathematician so he was a really notable mathematician during the war he was throwing his during the war he was throwing his during the war he was throwing his counterpart then van Neumann is credited counterpart then van Neumann is credited counterpart then van Neumann is credited with the modern architecture of with the modern architecture of with the modern architecture of computing and one of his students was computing and one of his students was computing and one of his students was Marvin Minsky so I could ask Marvin what Marvin Minsky so I could ask Marvin what Marvin Minsky so I could ask Marvin what Johnny was thinking and I could ask Andy Johnny was thinking and I could ask Andy Johnny was thinking and I could ask Andy what Alan was thinking what Alan was thinking what Alan was thinking and what came out from that what I came and what came out from that what I came and what came out from that what I came to appreciate to appreciate to appreciate as background I never understood the as background I never understood the as background I never understood the difference between computer science and difference between computer science and difference between computer science and physical science but physical science but physical science but turing's machine that's the foundation

turing's machine that's the foundation turing's machine that's the foundation of modern Computing has a simple physics of modern Computing has a simple physics of modern Computing has a simple physics mistake mistake mistake which is the head is distinct from the which is the head is distinct from the which is the head is distinct from the tape so in the turing machine there's a tape so in the turing machine there's a tape so in the turing machine there's a head that programmatically moves and head that programmatically moves and head that programmatically moves and reads and writes a tape the head is reads and writes a tape the head is reads and writes a tape the head is distinct from the tape which means distinct from the tape which means distinct from the tape which means Persistence of information is separate Persistence of information is separate Persistence of information is separate from interaction with information yeah from interaction with information yeah from interaction with information yeah then van Neumann wrote deeply and then van Neumann wrote deeply and then van Neumann wrote deeply and beautifully about many things but not beautifully about many things but not beautifully about many things but not Computing he wrote a horrible men memo Computing he wrote a horrible men memo Computing he wrote a horrible men memo called the first draft of a report in called the first draft of a report in called the first draft of a report in the edvac which is how you program a the edvac which is how you program a the edvac which is how you program a very early computer in it he essentially very early computer in it he essentially very early computer in it he essentially roughly took turing's architecture and roughly took turing's architecture and roughly took turing's architecture and built it into a machine built it into a machine built it into a machine so the legacy of that is the computer so the legacy of that is the computer so the legacy of that is the computer somebody's using to watch this is somebody's using to watch this is somebody's using to watch this is spending much of its effort moving spending much of its effort moving spending much of its effort moving information from Storage Transit information from Storage Transit information from Storage Transit transistors to processing transistors transistors to processing transistors transistors to processing transistors even though they have the same even though they have the same even though they have the same computational complexity so in computer computational complexity so in computer computational complexity so in computer science when you learn about Computing science when you learn about Computing science when you learn about Computing there's a ridiculous taxonomy of about a there's a ridiculous taxonomy of about a there's a ridiculous taxonomy of about a hundred different models of computation hundred different models of computation hundred different models of computation but they're all fictions in physics a but they're all fictions in physics a but they're all fictions in physics a patch of space occupies space patch of space occupies space patch of space occupies space it stores state it takes time to Transit it stores state it takes time to Transit it stores state it takes time to Transit and you can interact that is the only and you can interact that is the only and you can interact that is the only model of computation that's physical model of computation that's physical model of computation that's physical everything else is a fiction everything else is a fiction everything else is a fiction so I I really came to appreciate that a so I I really came to appreciate that a so I I really came to appreciate that a few years back when I did a keynote for

few years back when I did a keynote for few years back when I did a keynote for the annual meeting of the supercomputer the annual meeting of the supercomputer the annual meeting of the supercomputer industry and then went into the halls industry and then went into the halls industry and then went into the halls and spent time with the supercomputer and spent time with the supercomputer and spent time with the supercomputer Builders and came to appreciate Builders and came to appreciate Builders and came to appreciate see if you're familiar with the movie see if you're familiar with the movie see if you're familiar with the movie The Metropolis uh people would Frolic The Metropolis uh people would Frolic The Metropolis uh people would Frolic upstairs in the gardens and down in the upstairs in the gardens and down in the upstairs in the gardens and down in the basement people would move levers and basement people would move levers and basement people would move levers and that's how Computing exists today that that's how Computing exists today that that's how Computing exists today that we pretend software is not physical it's we pretend software is not physical it's we pretend software is not physical it's separate from hardware and the whole separate from hardware and the whole separate from hardware and the whole Canon of Computer Science is based on Canon of Computer Science is based on Canon of Computer Science is based on this fiction that bits aren't this fiction that bits aren't this fiction that bits aren't constrained by atoms but all sorts of constrained by atoms but all sorts of constrained by atoms but all sorts of scaling issues and Computing come from scaling issues and Computing come from scaling issues and Computing come from that boundary but all sorts of that boundary but all sorts of that boundary but all sorts of opportunities come from that boundary opportunities come from that boundary opportunities come from that boundary and so you can trace it all the way back and so you can trace it all the way back and so you can trace it all the way back to turing's machine making this mistake to turing's machine making this mistake to turing's machine making this mistake between the head and the tape Von between the head and the tape Von between the head and the tape Von Neumann in in Neumann in in Neumann in in um create he never called it vinomen's um create he never called it vinomen's um create he never called it vinomen's architecture he wrote about it in this architecture he wrote about it in this architecture he wrote about it in this Dreadful memo and then he wrote Dreadful memo and then he wrote Dreadful memo and then he wrote beautifully about other things we'll beautifully about other things we'll beautifully about other things we'll talk about now to end a long answer talk about now to end a long answer talk about now to end a long answer Turing and Von Neumann both knew this so Turing and Von Neumann both knew this so Turing and Von Neumann both knew this so all of the Canon of computer scientists all of the Canon of computer scientists all of the Canon of computer scientists credits them for what was never meant to credits them for what was never meant to credits them for what was never meant to be a computer architecture both Turing be a computer architecture both Turing be a computer architecture both Turing and Von Neumann ended their life and Von Neumann ended their life and Von Neumann ended their life studying exactly how software becomes studying exactly how software becomes studying exactly how software becomes Hardware so van Neumann studied Hardware so van Neumann studied Hardware so van Neumann studied self-reproducing automata how a machine self-reproducing automata how a machine self-reproducing automata how a machine communicates its own construction a communicates its own construction a communicates its own construction a touring studied morphogenesis how genes touring studied morphogenesis how genes touring studied morphogenesis how genes give rise to form they ended their life give rise to form they ended their life give rise to form they ended their life studying the embodiment of computation studying the embodiment of computation studying the embodiment of computation something that's been forgotten by the

something that's been forgotten by the something that's been forgotten by the Canon of computing but developed sort of Canon of computing but developed sort of Canon of computing but developed sort of off to the sides by a really interesting off to the sides by a really interesting off to the sides by a really interesting lineage lineage lineage so there's no distinction between the so there's no distinction between the so there's no distinction between the head and the tape between the computer head and the tape between the computer head and the tape between the computer and the computation it is all and the computation it is all and the computation it is all computation right so I never understood computation right so I never understood computation right so I never understood the difference between computer science the difference between computer science the difference between computer science and physical science and working at that and physical science and working at that and physical science and working at that boundary helped lead to things like my boundary helped lead to things like my boundary helped lead to things like my lab was part of doing with a number of lab was part of doing with a number of lab was part of doing with a number of interesting collaborators the first interesting collaborators the first interesting collaborators the first faster than classical Quantum faster than classical Quantum faster than classical Quantum computations we were part of a computations we were part of a computations we were part of a collaboration creating the minimal collaboration creating the minimal collaboration creating the minimal synthetic organism where you design life synthetic organism where you design life synthetic organism where you design life in a computer those both involve in a computer those both involve in a computer those both involve domains where you just can't separate domains where you just can't separate domains where you just can't separate Hardware from software the embodiment of Hardware from software the embodiment of Hardware from software the embodiment of computation is embodied in these really computation is embodied in these really computation is embodied in these really profound ways profound ways profound ways so the first quantum computations so the first quantum computations so the first quantum computations synthetic life so in the space of synthetic life so in the space of synthetic life so in the space of biology biology biology so space of physics at the lowest level so space of physics at the lowest level so space of physics at the lowest level in the space of biology at the lowest in the space of biology at the lowest in the space of biology at the lowest level level level so uh let's talk about CBA Center of so uh let's talk about CBA Center of so uh let's talk about CBA Center of bits and atoms what's the origin story bits and atoms what's the origin story bits and atoms what's the origin story of this MIT legendary MIT Center that of this MIT legendary MIT Center that of this MIT legendary MIT Center that you're a part of creating you're a part of creating you're a part of creating in high school I really wanted to go to in high school I really wanted to go to in high school I really wanted to go to vocational school where you learned to vocational school where you learned to vocational school where you learned to weld and fix cars and build houses weld and fix cars and build houses weld and fix cars and build houses and I was told no you're smart you have and I was told no you're smart you have and I was told no you're smart you have to sit in a room and nobody could to sit in a room and nobody could to sit in a room and nobody could explain to me why I couldn't explain to me why I couldn't explain to me why I couldn't go to Vocational School go to Vocational School go to Vocational School uh I then worked at Bell labs this uh I then worked at Bell labs this uh I then worked at Bell labs this wonderful place uh before deregulation

wonderful place uh before deregulation wonderful place uh before deregulation legendary place and I would get Union legendary place and I would get Union legendary place and I would get Union grievances because I would go into the grievances because I would go into the grievances because I would go into the workshop and try to make something and workshop and try to make something and workshop and try to make something and they would say no you're smart you have they would say no you're smart you have they would say no you're smart you have to tell somebody what to do to tell somebody what to do to tell somebody what to do and it wasn't until MIT and I'll explain and it wasn't until MIT and I'll explain and it wasn't until MIT and I'll explain how CBA started but I could create CBA how CBA started but I could create CBA how CBA started but I could create CBA that I came to understand this is a that I came to understand this is a that I came to understand this is a mistake that dates back to the mistake that dates back to the mistake that dates back to the Renaissance so in the Renaissance the Renaissance so in the Renaissance the Renaissance so in the Renaissance the liberal arts emerged and liberal doesn't liberal arts emerged and liberal doesn't liberal arts emerged and liberal doesn't mean politically liberal this was the mean politically liberal this was the mean politically liberal this was the path to Liberation birth of humanism and path to Liberation birth of humanism and path to Liberation birth of humanism and so the liberal arts with the Trivium so the liberal arts with the Trivium so the liberal arts with the Trivium quadrivium roughly language Natural quadrivium roughly language Natural quadrivium roughly language Natural Science and Science and Science and at that moment what emerged was this at that moment what emerged was this at that moment what emerged was this Dreadful concept of the ill liberal arts Dreadful concept of the ill liberal arts Dreadful concept of the ill liberal arts so anything that wasn't the liberal arts so anything that wasn't the liberal arts so anything that wasn't the liberal arts was for commercial gain and was just was for commercial gain and was just was for commercial gain and was just making stuff and wasn't valid for making stuff and wasn't valid for making stuff and wasn't valid for serious study and so that's why we're serious study and so that's why we're serious study and so that's why we're left with learning to weld wasn't a left with learning to weld wasn't a left with learning to weld wasn't a subject for serious study subject for serious study subject for serious study um but the means of expression of um but the means of expression of um but the means of expression of changed since the Renaissance so micro changed since the Renaissance so micro changed since the Renaissance so micro Machining or embedded coding is every Machining or embedded coding is every Machining or embedded coding is every bit as expressive as painting a painting bit as expressive as painting a painting bit as expressive as painting a painting or writing a sonnet so uh never or writing a sonnet so uh never or writing a sonnet so uh never understanding this difference between understanding this difference between understanding this difference between computer science and physical science computer science and physical science computer science and physical science uh the path that led me to create CBA uh the path that led me to create CBA uh the path that led me to create CBA with colleagues was with colleagues was with colleagues was I was what's called a junior fellow at I was what's called a junior fellow at I was what's called a junior fellow at Harvard I was visiting MIT through Harvard I was visiting MIT through Harvard I was visiting MIT through Marvin because I was interested in the Marvin because I was interested in the Marvin because I was interested in the physics of musical instruments I

physics of musical instruments I physics of musical instruments I uh this will be another slight uh this will be another slight uh this will be another slight aggression I uh and Cornell I would aggression I uh and Cornell I would aggression I uh and Cornell I would study Physics and and then I would cross study Physics and and then I would cross study Physics and and then I would cross the street and go to the music the street and go to the music the street and go to the music department where I played the bassoon department where I played the bassoon department where I played the bassoon and I would trim reads and play the and I would trim reads and play the and I would trim reads and play the reads right and they'd be beautiful but reads right and they'd be beautiful but reads right and they'd be beautiful but then they'd get soggy and then I then they'd get soggy and then I then they'd get soggy and then I discovered in the basement of the music discovered in the basement of the music discovered in the basement of the music department at Cornell was David Borden department at Cornell was David Borden department at Cornell was David Borden uh who you might not have heard of but uh who you might not have heard of but uh who you might not have heard of but it's legendary electronic music because it's legendary electronic music because it's legendary electronic music because he was really the first electronic he was really the first electronic he was really the first electronic musician so Bob Moog who invented um musician so Bob Moog who invented um musician so Bob Moog who invented um Moog synthesizers was a physics student Moog synthesizers was a physics student Moog synthesizers was a physics student at Cornell like me crossing the street at Cornell like me crossing the street at Cornell like me crossing the street and eventually he was kicked out and and eventually he was kicked out and and eventually he was kicked out and invented electronic music David Borden invented electronic music David Borden invented electronic music David Borden was the first musician who created was the first musician who created was the first musician who created electronic music so he's legendary for electronic music so he's legendary for electronic music so he's legendary for people like Phil glass and Steve Reich people like Phil glass and Steve Reich people like Phil glass and Steve Reich and so that got me thinking about I and so that got me thinking about I and so that got me thinking about I would behave as a scientist in the music would behave as a scientist in the music would behave as a scientist in the music department but not in in the physics department but not in in the physics department but not in in the physics department but not in the music department but not in the music department but not in the music department got me thinking about what's department got me thinking about what's department got me thinking about what's the computational capacity of a musical the computational capacity of a musical the computational capacity of a musical instrument instrument instrument and through Marvin he introduced me to and through Marvin he introduced me to and through Marvin he introduced me to Todd mackover at the media lab who was Todd mackover at the media lab who was Todd mackover at the media lab who was just about to start a project with Yo-Yo just about to start a project with Yo-Yo just about to start a project with Yo-Yo Ma Ma Ma um that led to a collaboration uh to um that led to a collaboration uh to um that led to a collaboration uh to instrumenticello to to extract yoyo's instrumenticello to to extract yoyo's instrumenticello to to extract yoyo's data and bring it out into computational data and bring it out into computational data and bring it out into computational environments what is the computational environments what is the computational environments what is the computational capacity of a musical instrument as we capacity of a musical instrument as we capacity of a musical instrument as we continue on this tangent and when we continue on this tangent and when we continue on this tangent and when we shall return to CBA yeah so shall return to CBA yeah so shall return to CBA yeah so one part of that is to understand the one part of that is to understand the one part of that is to understand the Computing and if you look at like the Computing and if you look at like the Computing and if you look at like the finest time scale and length scale you finest time scale and length scale you finest time scale and length scale you need to model the physics it's not

need to model the physics it's not need to model the physics it's not heroic you know a a good GPU can do heroic you know a a good GPU can do heroic you know a a good GPU can do teraflops today that used to be a teraflops today that used to be a teraflops today that used to be a national class supercomputer now it's national class supercomputer now it's national class supercomputer now it's just a GPU and that's about if you take just a GPU and that's about if you take just a GPU and that's about if you take the time scales and length scales the time scales and length scales the time scales and length scales relevant for the physics that's about relevant for the physics that's about relevant for the physics that's about the scale of the physics Computing for the scale of the physics Computing for the scale of the physics Computing for yoyo it was really driving it was he's yoyo it was really driving it was he's yoyo it was really driving it was he's completely unsentimental about the strad completely unsentimental about the strad completely unsentimental about the strad it's not that it makes some magical it's not that it makes some magical it's not that it makes some magical Wiggles in the sound wave it's its Wiggles in the sound wave it's its Wiggles in the sound wave it's its performance as a controller how he can performance as a controller how he can performance as a controller how he can manipulate it as an interface device manipulate it as an interface device manipulate it as an interface device interface between one and one exactly interface between one and one exactly interface between one and one exactly human sound okay and so so what it led human sound okay and so so what it led human sound okay and so so what it led to was I had started by thinking about to was I had started by thinking about to was I had started by thinking about Ops per second but the yoyo's question Ops per second but the yoyo's question Ops per second but the yoyo's question was really was really was really um resolution and bandwidth it's um resolution and bandwidth it's um resolution and bandwidth it's um how fast can you measure what he does um how fast can you measure what he does um how fast can you measure what he does and and and um uh the the the bandwidth and the um uh the the the bandwidth and the um uh the the the bandwidth and the resolution of detecting his controls and resolution of detecting his controls and resolution of detecting his controls and then mapping them into sounds and what then mapping them into sounds and what then mapping them into sounds and what what we found what he found was if you what we found what he found was if you what we found what he found was if you instrument everything he does and instrument everything he does and instrument everything he does and connect it to almost anything it sounds connect it to almost anything it sounds connect it to almost anything it sounds like yo-yo that that the magic is in the like yo-yo that that the magic is in the like yo-yo that that the magic is in the control not in ineffable details in how control not in ineffable details in how control not in ineffable details in how the wood Wiggles and so with yo-yo and the wood Wiggles and so with yo-yo and the wood Wiggles and so with yo-yo and Todd that led to a piece and towards the Todd that led to a piece and towards the Todd that led to a piece and towards the end I asked yo-yo what what it would end I asked yo-yo what what it would end I asked yo-yo what what it would take for him to get rid of his Strat and take for him to get rid of his Strat and take for him to get rid of his Strat and use our stuff and his answer was just use our stuff and his answer was just use our stuff and his answer was just Logistics it was at that time our stuff Logistics it was at that time our stuff Logistics it was at that time our stuff was like a rack of electronics and lots

was like a rack of electronics and lots was like a rack of electronics and lots of cables and some grad students to to of cables and some grad students to to of cables and some grad students to to make it work once the technology becomes make it work once the technology becomes make it work once the technology becomes as invisible as the strad then sure as invisible as the strad then sure as invisible as the strad then sure absolutely he would take it and by the absolutely he would take it and by the absolutely he would take it and by the way as a footnote on the footnote an way as a footnote on the footnote an way as a footnote on the footnote an accident in the sensing of yoyo's cello accident in the sensing of yoyo's cello accident in the sensing of yoyo's cello led to a hundred million dollar a year led to a hundred million dollar a year led to a hundred million dollar a year Auto Safety business to control airbags Auto Safety business to control airbags Auto Safety business to control airbags and cars how did that work I had to and cars how did that work I had to and cars how did that work I had to instrument the bow without interfering instrument the bow without interfering instrument the bow without interfering with it so I um set up with it so I um set up with it so I um set up um local electromagnetic fields where I um local electromagnetic fields where I um local electromagnetic fields where I would um detect would um detect would um detect um how those fields interact with the um how those fields interact with the um how those fields interact with the bow he's playing but we had a problem bow he's playing but we had a problem bow he's playing but we had a problem that his hand whenever his hand got near that his hand whenever his hand got near that his hand whenever his hand got near these sensing Fields I would start these sensing Fields I would start these sensing Fields I would start sensing his hand rather than the sensing his hand rather than the sensing his hand rather than the materials on the bow materials on the bow materials on the bow and I didn't quite understand what was and I didn't quite understand what was and I didn't quite understand what was going on with those that that going on with those that that going on with those that that interference so my very first grad interference so my very first grad interference so my very first grad student ever Josh Smith student ever Josh Smith student ever Josh Smith did a thesis on tomography with electric did a thesis on tomography with electric did a thesis on tomography with electric Fields how to see in 3d with electric Fields how to see in 3d with electric Fields how to see in 3d with electric fields fields fields then through Todd and at that point then through Todd and at that point then through Todd and at that point research scientists my lab Joe Paradiso research scientists my lab Joe Paradiso research scientists my lab Joe Paradiso it led to a collaboration with uh Penn it led to a collaboration with uh Penn it led to a collaboration with uh Penn and Teller who and Teller who and Teller who um where we did a magic trick in Las um where we did a magic trick in Las um where we did a magic trick in Las Vegas to contact Houdini and sort of Vegas to contact Houdini and sort of Vegas to contact Houdini and sort of these fields are sort of like you know these fields are sort of like you know these fields are sort of like you know contacting spirits contacting spirits contacting spirits so we did a magic trick in Las Vegas and so we did a magic trick in Las Vegas and so we did a magic trick in Las Vegas and then the the crazy thing that happened then the the crazy thing that happened then the the crazy thing that happened after that was uh Phil ritmuller came after that was uh Phil ritmuller came after that was uh Phil ritmuller came running into my lab he worked with um running into my lab he worked with um running into my lab he worked with um this became with Honda and NEC airbags

this became with Honda and NEC airbags this became with Honda and NEC airbags were killing infants and rear-facing were killing infants and rear-facing were killing infants and rear-facing child seats child seats child seats um cars need to distinguish um cars need to distinguish um cars need to distinguish a front-facing adult where you'd save a front-facing adult where you'd save a front-facing adult where you'd save the life versus a bag of groceries where the life versus a bag of groceries where the life versus a bag of groceries where you don't need to fire the airbag versus you don't need to fire the airbag versus you don't need to fire the airbag versus the rear-facing infant where you would the rear-facing infant where you would the rear-facing infant where you would kill it and so the the the seat need to kill it and so the the the seat need to kill it and so the the the seat need to in effect see in 3d to understand the in effect see in 3d to understand the in effect see in 3d to understand the occupants and so we took the pen and occupants and so we took the pen and occupants and so we took the pen and Teller magic trick derived from Josh's Teller magic trick derived from Josh's Teller magic trick derived from Josh's thesis from yo-yo's Cello to an auto thesis from yo-yo's Cello to an auto thesis from yo-yo's Cello to an auto show and all the card companies said show and all the card companies said show and all the card companies said great when can we buy it and so that great when can we buy it and so that great when can we buy it and so that became ellisis and it was 100 million became ellisis and it was 100 million became ellisis and it was 100 million dollar a year business making sensors dollar a year business making sensors dollar a year business making sensors there wasn't a lot of publicity because there wasn't a lot of publicity because there wasn't a lot of publicity because it was in the car so the car didn't kill it was in the car so the car didn't kill it was in the car so the car didn't kill you you you so they didn't sort of advertise we have so they didn't sort of advertise we have so they didn't sort of advertise we have nice sensors so the car doesn't kill you nice sensors so the car doesn't kill you nice sensors so the car doesn't kill you but it became a leading Auto Safety but it became a leading Auto Safety but it became a leading Auto Safety sensor and that started from the cello sensor and that started from the cello sensor and that started from the cello and the question of the computational and the question of the computational and the question of the computational capacity musical instrument right so now capacity musical instrument right so now capacity musical instrument right so now to get back to to get back to to get back to MIT I was spending a lot of outside time MIT I was spending a lot of outside time MIT I was spending a lot of outside time at IBM research that had gods of the at IBM research that had gods of the at IBM research that had gods of the foundations of computing foundations of computing foundations of computing um this is amazing people there and I'd um this is amazing people there and I'd um this is amazing people there and I'd always expected to go to IBM to take always expected to go to IBM to take always expected to go to IBM to take over a lab but at the last minute over a lab but at the last minute over a lab but at the last minute pivoted and came to MIT to take a pivoted and came to MIT to take a pivoted and came to MIT to take a position position position in the media lab and start what became in the media lab and start what became in the media lab and start what became the predecessor to CBA media lab is well the predecessor to CBA media lab is well the predecessor to CBA media lab is well known for Nicholas negroponte what's known for Nicholas negroponte what's known for Nicholas negroponte what's less well known is the role of Jerry

less well known is the role of Jerry less well known is the role of Jerry Wiesner so Jerry was mit's president Wiesner so Jerry was mit's president Wiesner so Jerry was mit's president before that Kennedy science advisor before that Kennedy science advisor before that Kennedy science advisor grand old man of science at the end of grand old man of science at the end of grand old man of science at the end of his life he was frustrated by how his life he was frustrated by how his life he was frustrated by how knowledge was segregated knowledge was segregated knowledge was segregated and so he wanted to create a department and so he wanted to create a department and so he wanted to create a department of none of the above a department for of none of the above a department for of none of the above a department for work that didn't fit in departments work that didn't fit in departments work that didn't fit in departments and the media lab in a sense was a cover and the media lab in a sense was a cover and the media lab in a sense was a cover story for him to hide a department it as story for him to hide a department it as story for him to hide a department it as mit's president towards the end of his mit's president towards the end of his mit's president towards the end of his tenure if he said I'm going to make a tenure if he said I'm going to make a tenure if he said I'm going to make a department for things that don't fit in department for things that don't fit in department for things that don't fit in departments the Departments would have departments the Departments would have departments the Departments would have screamed but everybody was sort of screamed but everybody was sort of screamed but everybody was sort of paying attention to Nicholas creating paying attention to Nicholas creating paying attention to Nicholas creating the media lab and Jerry kind of hid in the media lab and Jerry kind of hid in the media lab and Jerry kind of hid in in it a department called Media Arts and in it a department called Media Arts and in it a department called Media Arts and Sciences it's really the department of Sciences it's really the department of Sciences it's really the department of none of the above none of the above none of the above and Jerry explaining that and Nicholas and Jerry explaining that and Nicholas and Jerry explaining that and Nicholas then confirming it is really why I then confirming it is really why I then confirming it is really why I pivoted and went to MIT pivoted and went to MIT pivoted and went to MIT um because my students who helped create um because my students who helped create um because my students who helped create Quantum Computing or synthetic life get Quantum Computing or synthetic life get Quantum Computing or synthetic life get degrees from Media Arts and Sciences degrees from Media Arts and Sciences degrees from Media Arts and Sciences this department of none of the above this department of none of the above this department of none of the above so that led to coming to MIT yeah with so that led to coming to MIT yeah with so that led to coming to MIT yeah with um uh Todd and Joe Paradiso and my um uh Todd and Joe Paradiso and my um uh Todd and Joe Paradiso and my colleague we started a Consortium called colleague we started a Consortium called colleague we started a Consortium called things that think and this was around things that think and this was around things that think and this was around the birth of Internet of things and the birth of Internet of things and the birth of Internet of things and um RFID but then we started doing things um RFID but then we started doing things um RFID but then we started doing things like work we can discuss that became the like work we can discuss that became the like work we can discuss that became the beginnings of quantum Computing and beginnings of quantum Computing and beginnings of quantum Computing and cryptography and materials and logic and cryptography and materials and logic and cryptography and materials and logic and microfluidics and those needed microfluidics and those needed microfluidics and those needed uh much more significant infrastructure

uh much more significant infrastructure uh much more significant infrastructure and were much longer research arcs so and were much longer research arcs so and were much longer research arcs so with a bigger team of about 20 people we with a bigger team of about 20 people we with a bigger team of about 20 people we wrote a proposal to the NSF to assemble wrote a proposal to the NSF to assemble wrote a proposal to the NSF to assemble one of every tool to make anything of one of every tool to make anything of one of every tool to make anything of any size was roughly the proposal one of any size was roughly the proposal one of any size was roughly the proposal one of any tool to make anything of any size any tool to make anything of any size any tool to make anything of any size yeah so they're usually nanometers yeah so they're usually nanometers yeah so they're usually nanometers micrometers millimeters meters are micrometers millimeters meters are micrometers millimeters meters are segregated input and output is segregated input and output is segregated input and output is segregated we wanted to look just very segregated we wanted to look just very segregated we wanted to look just very literally how digital becomes physical literally how digital becomes physical literally how digital becomes physical and physical becomes digital and and physical becomes digital and and physical becomes digital and fortunately we got NSF on a good day and fortunately we got NSF on a good day and fortunately we got NSF on a good day and they funded this facility of one of they funded this facility of one of they funded this facility of one of almost every tool to make anything and almost every tool to make anything and almost every tool to make anything and so uh with so uh with so uh with um a group of core colleagues um a group of core colleagues um a group of core colleagues um that included Joe Jacobson like um that included Joe Jacobson like um that included Joe Jacobson like trying Scott minnellis we launched CBA trying Scott minnellis we launched CBA trying Scott minnellis we launched CBA and so you're talking about nanoscale and so you're talking about nanoscale and so you're talking about nanoscale micro scale nanostructures micro scale nanostructures micro scale nanostructures microstructures macro structures microstructures macro structures microstructures macro structures electron microscopes and focused on beam electron microscopes and focused on beam electron microscopes and focused on beam probes for nanostructures laser micro probes for nanostructures laser micro probes for nanostructures laser micro Machining and x-ray microtomography for Machining and x-ray microtomography for Machining and x-ray microtomography for microstructures multi-axis Machining and microstructures multi-axis Machining and microstructures multi-axis Machining and 3D printing for macro structures just 3D printing for macro structures just 3D printing for macro structures just some examples what are we talking about some examples what are we talking about some examples what are we talking about in terms of scale how can we build tiny in terms of scale how can we build tiny in terms of scale how can we build tiny things and big things all in one place things and big things all in one place things and big things all in one place yeah so a well-equipped research lab has yeah so a well-equipped research lab has yeah so a well-equipped research lab has the sort of tools we're talking about the sort of tools we're talking about the sort of tools we're talking about but they're segregated in different but they're segregated in different but they're segregated in different places they're typically also run by

places they're typically also run by places they're typically also run by technicians where you then have an technicians where you then have an technicians where you then have an account and a project and you charge all account and a project and you charge all account and a project and you charge all of these tools are essentially of these tools are essentially of these tools are essentially when you don't know what you're doing when you don't know what you're doing when you don't know what you're doing not when you do know what you're doing not when you do know what you're doing not when you do know what you're doing in that they're they're when you need to in that they're they're when you need to in that they're they're when you need to work across length scales where we don't work across length scales where we don't work across length scales where we don't once projects are running in this once projects are running in this once projects are running in this facility we don't charge for time you facility we don't charge for time you facility we don't charge for time you don't make a formal proposal to schedule don't make a formal proposal to schedule don't make a formal proposal to schedule and the users really run the tools and and the users really run the tools and and the users really run the tools and it's for work that's kind of in Kuwait it's for work that's kind of in Kuwait it's for work that's kind of in Kuwait that needs to span these disciplines and that needs to span these disciplines and that needs to span these disciplines and length scales length scales length scales um and so you know uh um and so you know uh um and so you know uh work in the project today work in CBA work in the project today work in CBA work in the project today work in CBA today ranges from today ranges from today ranges from developing zeptidual electronics for the developing zeptidual electronics for the developing zeptidual electronics for the lowest power Computing to micro lowest power Computing to micro lowest power Computing to micro Machining Diamond to take million 10 Machining Diamond to take million 10 Machining Diamond to take million 10 million RPM bearings for molecular million RPM bearings for molecular million RPM bearings for molecular spectroscopy studies up to exploring spectroscopy studies up to exploring spectroscopy studies up to exploring robots to build 100 meter structures in robots to build 100 meter structures in robots to build 100 meter structures in space space space okay can we the three things you just okay can we the three things you just okay can we the three things you just mentioned let's start with the biggest mentioned let's start with the biggest mentioned let's start with the biggest what are some of the biggest stuff you what are some of the biggest stuff you what are some of the biggest stuff you attempted to explore how to build in a attempted to explore how to build in a attempted to explore how to build in a lab sure so viewed from One Direction lab sure so viewed from One Direction lab sure so viewed from One Direction what we're talking about is a crazy what we're talking about is a crazy what we're talking about is a crazy random seeming of almost unrelated random seeming of almost unrelated random seeming of almost unrelated projects but if you rotate 90 degrees projects but if you rotate 90 degrees projects but if you rotate 90 degrees it's really just a core thought over and it's really just a core thought over and it's really just a core thought over and over again just very literally how bits over again just very literally how bits over again just very literally how bits and atoms relate how digital and just and atoms relate how digital and just and atoms relate how digital and just going from digital to physical in many going from digital to physical in many going from digital to physical in many different domains but it's really just

different domains but it's really just different domains but it's really just the same idea over and over again the same idea over and over again the same idea over and over again so to understand the biggest things so to understand the biggest things so to understand the biggest things let me go back to uh bring in now let me go back to uh bring in now let me go back to uh bring in now Shannon as well as Von Neumann yeah so Shannon as well as Von Neumann yeah so Shannon as well as Von Neumann yeah so what is digital what is digital what is digital the Casual obvious answer is digital in the Casual obvious answer is digital in the Casual obvious answer is digital in one and zero but that's wrong there's a one and zero but that's wrong there's a one and zero but that's wrong there's a much deeper answer which is much deeper answer which is much deeper answer which is Claude Shannon at MIT wrote the best Claude Shannon at MIT wrote the best Claude Shannon at MIT wrote the best Master's thesis ever in his master's Master's thesis ever in his master's Master's thesis ever in his master's thesis he invented our modern notion of thesis he invented our modern notion of thesis he invented our modern notion of digital logic digital logic digital logic where it came from was Van ever Bush uh where it came from was Van ever Bush uh where it came from was Van ever Bush uh was a grand old man at MIT uh he created was a grand old man at MIT uh he created was a grand old man at MIT uh he created the post-war research establishment that the post-war research establishment that the post-war research establishment that led to the National Science Foundation led to the National Science Foundation led to the National Science Foundation and he made an important mistake which and he made an important mistake which and he made an important mistake which we can talk about we can talk about we can talk about but he also made the let the but he also made the let the but he also made the let the differential analyzer which was the last differential analyzer which was the last differential analyzer which was the last great analog computer so it was a room great analog computer so it was a room great analog computer so it was a room full of gears and pulleys and the longer full of gears and pulleys and the longer full of gears and pulleys and the longer it ran the worse the answer was it ran the worse the answer was it ran the worse the answer was and Shannon worked on it as a student and Shannon worked on it as a student and Shannon worked on it as a student and he got so annoyed in his master's and he got so annoyed in his master's and he got so annoyed in his master's thesis he invented digital logic thesis he invented digital logic thesis he invented digital logic um but he then went on to Bell labs and um but he then went on to Bell labs and um but he then went on to Bell labs and what he did there was what he did there was what he did there was communication was beginning to expand communication was beginning to expand communication was beginning to expand there is more demand for phone lines and there is more demand for phone lines and there is more demand for phone lines and so there's a question about how much how so there's a question about how much how so there's a question about how much how many phone lines you could phone many phone lines you could phone many phone lines you could phone messages you could send down a wire messages you could send down a wire messages you could send down a wire and you could try to just make it better

and you could try to just make it better and you could try to just make it better and better he asked a question nobody and better he asked a question nobody and better he asked a question nobody had asked which is rather than make it had asked which is rather than make it had asked which is rather than make it better and better what's the limit to better and better what's the limit to better and better what's the limit to how good it can be and he proved a how good it can be and he proved a how good it can be and he proved a couple things but one of the main things couple things but one of the main things couple things but one of the main things he proved was a threshold theorem for he proved was a threshold theorem for he proved was a threshold theorem for channel capacity and so what he showed channel capacity and so what he showed channel capacity and so what he showed was my voice to you right now is coming was my voice to you right now is coming was my voice to you right now is coming as a wave through sound and the further as a wave through sound and the further as a wave through sound and the further you get the worse it sounds but people you get the worse it sounds but people you get the worse it sounds but people watching this are getting it as as in watching this are getting it as as in watching this are getting it as as in from packets of data in a network from packets of data in a network from packets of data in a network um when they get when the computer um when they get when the computer um when they get when the computer they're watching this gets the packet of they're watching this gets the packet of they're watching this gets the packet of information information information um it it can detect and correct an error um it it can detect and correct an error um it it can detect and correct an error and what Shannon showed is if the noise and what Shannon showed is if the noise and what Shannon showed is if the noise in in the cable to the people watching in in the cable to the people watching in in the cable to the people watching this is above a threshold they're doomed this is above a threshold they're doomed this is above a threshold they're doomed but if the noise is below a threshold but if the noise is below a threshold but if the noise is below a threshold for a linear increase in the energy for a linear increase in the energy for a linear increase in the energy representing our conversation the error representing our conversation the error representing our conversation the error rate goes down exponentially rate goes down exponentially rate goes down exponentially exponentials are fast there's very few exponentials are fast there's very few exponentials are fast there's very few of them in engineering and the of them in engineering and the of them in engineering and the exponential reduction of error below a exponential reduction of error below a exponential reduction of error below a threshold if you restore state is called threshold if you restore state is called threshold if you restore state is called a threshold theorem a threshold theorem a threshold theorem that's what led to digital that that that's what led to digital that that that's what led to digital that that means unreliable things can work means unreliable things can work means unreliable things can work reliably so Shannon did that for reliably so Shannon did that for reliably so Shannon did that for communication then van Neumann was communication then van Neumann was communication then van Neumann was inspired by that and applied it to inspired by that and applied it to inspired by that and applied it to computation and he showed how an computation and he showed how an computation and he showed how an unreliable computer can operate reliably unreliable computer can operate reliably unreliable computer can operate reliably by using the same threshold property of by using the same threshold property of by using the same threshold property of restoring state it was then forgotten

restoring state it was then forgotten restoring state it was then forgotten many years we had to ReDiscover it in many years we had to ReDiscover it in many years we had to ReDiscover it in effect in the quantum Computing era when effect in the quantum Computing era when effect in the quantum Computing era when things are very unreliable again things are very unreliable again things are very unreliable again but now to go back to how does this but now to go back to how does this but now to go back to how does this relate to the biggest things I've made relate to the biggest things I've made relate to the biggest things I've made so so so in fabrication MIT in fabrication MIT in fabrication MIT invented computer-controlled invented computer-controlled invented computer-controlled Manufacturing in 1952 jet aircraft were Manufacturing in 1952 jet aircraft were Manufacturing in 1952 jet aircraft were just emerging there is a limit to just emerging there is a limit to just emerging there is a limit to Turning cranks on a machine on a milling Turning cranks on a machine on a milling Turning cranks on a machine on a milling machine to make parts for jet aircraft machine to make parts for jet aircraft machine to make parts for jet aircraft now this is a messy story MIT actually now this is a messy story MIT actually now this is a messy story MIT actually stole computer controlled Machining from stole computer controlled Machining from stole computer controlled Machining from an inventor who brought it to MIT wanted an inventor who brought it to MIT wanted an inventor who brought it to MIT wanted to do a joint project with the Air Force to do a joint project with the Air Force to do a joint project with the Air Force and MIT effectively stole it from him so and MIT effectively stole it from him so and MIT effectively stole it from him so it's kind of a messy history but it's kind of a messy history but it's kind of a messy history but that sounds like the birth of that sounds like the birth of that sounds like the birth of computer-controlled Machining 1952. computer-controlled Machining 1952. computer-controlled Machining 1952. there are a number of inventors of 3D there are a number of inventors of 3D there are a number of inventors of 3D printing one of the companies spun off printing one of the companies spun off printing one of the companies spun off my lab by Max lebowsky's form Labs which my lab by Max lebowsky's form Labs which my lab by Max lebowsky's form Labs which is now a billion dollar 3D printing is now a billion dollar 3D printing is now a billion dollar 3D printing company that's the modern version company that's the modern version company that's the modern version but all of that's analog meaning the but all of that's analog meaning the but all of that's analog meaning the information is in the control computer information is in the control computer information is in the control computer there's no information in the materials there's no information in the materials there's no information in the materials and so it goes back to Van ever Bush's and so it goes back to Van ever Bush's and so it goes back to Van ever Bush's analog computer if you mistake make a analog computer if you mistake make a analog computer if you mistake make a mistake in printing or Machining just mistake in printing or Machining just mistake in printing or Machining just the mistake accumulates the mistake accumulates the mistake accumulates the real birth of computerized digital the real birth of computerized digital the real birth of computerized digital manufacturing is four billion years ago manufacturing is four billion years ago manufacturing is four billion years ago that's the evolutionary age of the that's the evolutionary age of the that's the evolutionary age of the ribosome ribosome ribosome so the way you're manufactured is

so the way you're manufactured is so the way you're manufactured is there's a code that describes you there's a code that describes you there's a code that describes you the genetic code it goes to a micro the genetic code it goes to a micro the genetic code it goes to a micro machine the ribosome which is this machine the ribosome which is this machine the ribosome which is this molecular Factory that builds the molecular Factory that builds the molecular Factory that builds the molecules that that are you molecules that that are you molecules that that are you the key thing to know about that is it the key thing to know about that is it the key thing to know about that is it there are about 20 amino acids that get there are about 20 amino acids that get there are about 20 amino acids that get assembled and in that Machinery it does assembled and in that Machinery it does assembled and in that Machinery it does everything Shannon and vanyman taught us everything Shannon and vanyman taught us everything Shannon and vanyman taught us you detect and correct errors so if you you detect and correct errors so if you you detect and correct errors so if you mix chemicals the error rate is about a mix chemicals the error rate is about a mix chemicals the error rate is about a part in a hundred part in a hundred part in a hundred when you make elongate a protein in the when you make elongate a protein in the when you make elongate a protein in the ribosome it's about a part in 10 to the ribosome it's about a part in 10 to the ribosome it's about a part in 10 to the four when you replicate DNA there's an four when you replicate DNA there's an four when you replicate DNA there's an extra level of error correction it's a extra level of error correction it's a extra level of error correction it's a part in 10 to the eight and so in the part in 10 to the eight and so in the part in 10 to the eight and so in the molecules that make you molecules that make you molecules that make you you can detect and correct errors and you can detect and correct errors and you can detect and correct errors and you don't need a ruler to make you the you don't need a ruler to make you the you don't need a ruler to make you the geometry comes from your parts geometry comes from your parts geometry comes from your parts so now so now so now compare a child playing with Lego and a compare a child playing with Lego and a compare a child playing with Lego and a state-of-the-art 3D printer or state-of-the-art 3D printer or state-of-the-art 3D printer or computerized milling machine computerized milling machine computerized milling machine the Tower made by a child is more the Tower made by a child is more the Tower made by a child is more accurate than their motor control accurate than their motor control accurate than their motor control because the act of snapping the bricks because the act of snapping the bricks because the act of snapping the bricks together gives you a constraint on the together gives you a constraint on the together gives you a constraint on the joints joints joints you can join bricks made out of you can join bricks made out of you can join bricks made out of dissimilar materials you don't need a dissimilar materials you don't need a dissimilar materials you don't need a ruler for Lego because the geometry ruler for Lego because the geometry ruler for Lego because the geometry locally gives you the global parts and locally gives you the global parts and locally gives you the global parts and there's no Lego trash the parts have there's no Lego trash the parts have there's no Lego trash the parts have enough information to disassemble them enough information to disassemble them enough information to disassemble them those are exactly the properties of a those are exactly the properties of a those are exactly the properties of a digital code the unreliable is made

digital code the unreliable is made digital code the unreliable is made reliable yes absolutely so what the reliable yes absolutely so what the reliable yes absolutely so what the ribosome figured out four billion years ribosome figured out four billion years ribosome figured out four billion years ago is how to embody these problems ago is how to embody these problems ago is how to embody these problems these digital properties but not for these digital properties but not for these digital properties but not for communication or computation in effect communication or computation in effect communication or computation in effect but for construction but for construction but for construction so a number of projects in my lab have so a number of projects in my lab have so a number of projects in my lab have been studying the idea of digital been studying the idea of digital been studying the idea of digital materials and think of a digital materials and think of a digital materials and think of a digital material just as Lego bricks the precise material just as Lego bricks the precise material just as Lego bricks the precise meaning is a degree discrete set of meaning is a degree discrete set of meaning is a degree discrete set of Parts reversibly joined Parts reversibly joined Parts reversibly joined um with global geometry determined from um with global geometry determined from um with global geometry determined from local constraints and so it's digitizing local constraints and so it's digitizing local constraints and so it's digitizing the materials and so I'm coming back to the materials and so I'm coming back to the materials and so I'm coming back to what are the biggest things I've made my what are the biggest things I've made my what are the biggest things I've made my lab was working with the Aerospace lab was working with the Aerospace lab was working with the Aerospace industry so Spirit era was Boeing's industry so Spirit era was Boeing's industry so Spirit era was Boeing's factories factories factories they asked us for how to join Composites they asked us for how to join Composites they asked us for how to join Composites when you make a composite airplane you when you make a composite airplane you when you make a composite airplane you make these giant wing and fuselage parts make these giant wing and fuselage parts make these giant wing and fuselage parts and they asked us for a better way to and they asked us for a better way to and they asked us for a better way to stick them together because the joints stick them together because the joints stick them together because the joints were a place of failure and what we were a place of failure and what we were a place of failure and what we discovered was instead of making a few discovered was instead of making a few discovered was instead of making a few big Parts if you make little Loops of big Parts if you make little Loops of big Parts if you make little Loops of carbon fiber carbon fiber carbon fiber and you reversibly link them in joints and you reversibly link them in joints and you reversibly link them in joints and you do it in a special geometry that and you do it in a special geometry that and you do it in a special geometry that balances being under constrained and balances being under constrained and balances being under constrained and over constrained with just the right over constrained with just the right over constrained with just the right degrees of freedom we set the world degrees of freedom we set the world degrees of freedom we set the world record for the highest modulus record for the highest modulus record for the highest modulus ultralight material just by if in effect ultralight material just by if in effect ultralight material just by if in effect making carbon fiber Lego

making carbon fiber Lego making carbon fiber Lego so so lightweight materials are crucial so so lightweight materials are crucial so so lightweight materials are crucial for Energy Efficiency this let us make for Energy Efficiency this let us make for Energy Efficiency this let us make that the lightest weight High modulus that the lightest weight High modulus that the lightest weight High modulus material we then showed that with just material we then showed that with just material we then showed that with just just a few part types we can tune the just a few part types we can tune the just a few part types we can tune the material properties and then you can material properties and then you can material properties and then you can create really wild robots that instead create really wild robots that instead create really wild robots that instead of having a tool the size of a jumbo jet of having a tool the size of a jumbo jet of having a tool the size of a jumbo jet to make a jumbo jet you can make little to make a jumbo jet you can make little to make a jumbo jet you can make little robots that walk on these cellular robots that walk on these cellular robots that walk on these cellular structures to build the structures where structures to build the structures where structures to build the structures where they error correct their position on the they error correct their position on the they error correct their position on the structure and they navigate on the structure and they navigate on the structure and they navigate on the structure and so using all of that with structure and so using all of that with structure and so using all of that with um NASA we made more airplanes a former um NASA we made more airplanes a former um NASA we made more airplanes a former student Kenny student Kenny student Kenny Chung and benjinette made a morphing Chung and benjinette made a morphing Chung and benjinette made a morphing airplane the size of NASA Langley's airplane the size of NASA Langley's airplane the size of NASA Langley's biggest wind tunnel with Toyota we've biggest wind tunnel with Toyota we've biggest wind tunnel with Toyota we've made super efficiency race cars we're made super efficiency race cars we're made super efficiency race cars we're right now looking at projects with NASA right now looking at projects with NASA right now looking at projects with NASA to build these for things like space to build these for things like space to build these for things like space telescopes and space habitats where the telescopes and space habitats where the telescopes and space habitats where the ribosome who I mentioned a little while ribosome who I mentioned a little while ribosome who I mentioned a little while back can make an elephant one molecule back can make an elephant one molecule back can make an elephant one molecule at a time ribosomes are slow they run at at a time ribosomes are slow they run at at a time ribosomes are slow they run at about one molecule a second but about one molecule a second but about one molecule a second but ribosomes make ribosomes so you have ribosomes make ribosomes so you have ribosomes make ribosomes so you have thousands of them trillions of them and thousands of them trillions of them and thousands of them trillions of them and that makes an elephant in the same way that makes an elephant in the same way that makes an elephant in the same way these little assembly robots I'm these little assembly robots I'm these little assembly robots I'm describing can make giant structures describing can make giant structures describing can make giant structures uh at heart because of the robot can uh at heart because of the robot can uh at heart because of the robot can make the robot so more recently to my make the robot so more recently to my make the robot so more recently to my students Amira and Miana had a nature students Amira and Miana had a nature students Amira and Miana had a nature communication paper showing how this

communication paper showing how this communication paper showing how this robot can be made out of the parts it's robot can be made out of the parts it's robot can be made out of the parts it's making so the robots can make the robots making so the robots can make the robots making so the robots can make the robots so you build up the capacity of robotic so you build up the capacity of robotic so you build up the capacity of robotic assembly you can self-replicate can you assembly you can self-replicate can you assembly you can self-replicate can you Linger on what that robot looks like Linger on what that robot looks like Linger on what that robot looks like what is a robot it can walk along and do what is a robot it can walk along and do what is a robot it can walk along and do error correction and what is a robot error correction and what is a robot error correction and what is a robot that can self-replicate uh from the that can self-replicate uh from the that can self-replicate uh from the materials that is given what does that materials that is given what does that materials that is given what does that look like what are we talking so um this look like what are we talking so um this look like what are we talking so um this is fascinating yeah the answer is is fascinating yeah the answer is is fascinating yeah the answer is different at different length scales so different at different length scales so different at different length scales so so to explain that in biology primary so to explain that in biology primary so to explain that in biology primary structure is the code in the messenger structure is the code in the messenger structure is the code in the messenger RNA that says what the ribosome should RNA that says what the ribosome should RNA that says what the ribosome should build yeah build yeah build yeah um secondary structure or geometrical um secondary structure or geometrical um secondary structure or geometrical motifs they're things like helices or motifs they're things like helices or motifs they're things like helices or sheets tertiary structures are sheets tertiary structures are sheets tertiary structures are functional elements like electron donors functional elements like electron donors functional elements like electron donors or acceptors quaternary structure is or acceptors quaternary structure is or acceptors quaternary structure is things like molecular Motors that are things like molecular Motors that are things like molecular Motors that are moving my mouth or making the synapses moving my mouth or making the synapses moving my mouth or making the synapses work in my brain so there's that work in my brain so there's that work in my brain so there's that hierarchy of primary secondary tertiary hierarchy of primary secondary tertiary hierarchy of primary secondary tertiary quaternary quaternary quaternary now what's interesting is now what's interesting is now what's interesting is if you want to buy Electronics today if you want to buy Electronics today if you want to buy Electronics today from a vendor there are hundreds of from a vendor there are hundreds of from a vendor there are hundreds of thousands of types of resistors or thousands of types of resistors or thousands of types of resistors or capacitors or transistors huge inventory capacitors or transistors huge inventory capacitors or transistors huge inventory all of biology is just made from this all of biology is just made from this all of biology is just made from this inventory of 20 Parts amino acids and by inventory of 20 Parts amino acids and by inventory of 20 Parts amino acids and by composing them you can create all of composing them you can create all of composing them you can create all of life life life and so and so and so as part of this digitization of as part of this digitization of as part of this digitization of materials materials materials we're in effect trying to create we're in effect trying to create we're in effect trying to create something like amino acids for something like amino acids for something like amino acids for engineering creating all of Technology

engineering creating all of Technology engineering creating all of Technology from 20 Parts I from 20 Parts I from 20 Parts I um I see as another discretion I helped um I see as another discretion I helped um I see as another discretion I helped start an office for science in Hollywood start an office for science in Hollywood start an office for science in Hollywood and and and um there was a fun thing for the movie um there was a fun thing for the movie um there was a fun thing for the movie The Martian where I did a program with The Martian where I did a program with The Martian where I did a program with Bill Nye and a few others on how to Bill Nye and a few others on how to Bill Nye and a few others on how to actually build a civilization on Mars actually build a civilization on Mars actually build a civilization on Mars that they described in a way that I like that they described in a way that I like that they described in a way that I like as I was talking about how to go to Mars as I was talking about how to go to Mars as I was talking about how to go to Mars without luggage and the at heart it's without luggage and the at heart it's without luggage and the at heart it's sort of how to create life in non-living sort of how to create life in non-living sort of how to create life in non-living materials so if if you think about this materials so if if you think about this materials so if if you think about this primary secondary tertiary quaternary primary secondary tertiary quaternary primary secondary tertiary quaternary structure structure structure um in my lab we're doing that but on um in my lab we're doing that but on um in my lab we're doing that but on different length scales for different different length scales for different different length scales for different purposes so we're making micro robots purposes so we're making micro robots purposes so we're making micro robots out of like Nano bricks and to make the out of like Nano bricks and to make the out of like Nano bricks and to make the robots to build large-scale structures robots to build large-scale structures robots to build large-scale structures in Space the elements of the robots now in Space the elements of the robots now in Space the elements of the robots now are centimeters rather than micrometers are centimeters rather than micrometers are centimeters rather than micrometers and so the assembly robots for the and so the assembly robots for the and so the assembly robots for the bigger structures are bigger structures are bigger structures are uh there are the cells that make up the uh there are the cells that make up the uh there are the cells that make up the structure but then we have functional structure but then we have functional structure but then we have functional cells and so cells that can process and cells and so cells that can process and cells and so cells that can process and actuate each cell can like move one actuate each cell can like move one actuate each cell can like move one degree of Freedom or attach or disk degree of Freedom or attach or disk degree of Freedom or attach or disk detach or process now those elements I detach or process now those elements I detach or process now those elements I just described we can make out of the just described we can make out of the just described we can make out of the still smaller parts So eventually still smaller parts So eventually still smaller parts So eventually there's the hierarchy of the little there's the hierarchy of the little there's the hierarchy of the little Parts make little robots that make Parts make little robots that make Parts make little robots that make bigger parts of bigger robots that up bigger parts of bigger robots that up bigger parts of bigger robots that up through that hierarchy in that way you through that hierarchy in that way you through that hierarchy in that way you can move up the line scale right early can move up the line scale right early can move up the line scale right early on I tried to go in a straight line from

on I tried to go in a straight line from on I tried to go in a straight line from the bottom to the top and that ended up the bottom to the top and that ended up the bottom to the top and that ended up being a bad idea instead we're kind of being a bad idea instead we're kind of being a bad idea instead we're kind of doing all of these in parallel and then doing all of these in parallel and then doing all of these in parallel and then they're growing together and so to make they're growing together and so to make they're growing together and so to make the larger scale structures we um like the larger scale structures we um like the larger scale structures we um like there's a lot of a hype right now about there's a lot of a hype right now about there's a lot of a hype right now about 3D printing houses where you have a 3D printing houses where you have a 3D printing houses where you have a printer the size of the house we're printer the size of the house we're printer the size of the house we're right now working on using swarms of right now working on using swarms of right now working on using swarms of these you know table scale robots that these you know table scale robots that these you know table scale robots that walk on the structures to place the walk on the structures to place the walk on the structures to place the parts much more efficiently that's parts much more efficiently that's parts much more efficiently that's amazing but you're saying you can't for amazing but you're saying you can't for amazing but you're saying you can't for now go from the very small to the very now go from the very small to the very now go from the very small to the very large that'll come large that'll come large that'll come um that'll come in stages can we just um that'll come in stages can we just um that'll come in stages can we just Linger on this idea starting from Linger on this idea starting from Linger on this idea starting from vinelman's uh self-replicating automata vinelman's uh self-replicating automata vinelman's uh self-replicating automata that you mentioned that you mentioned that you mentioned it's just a beautiful idea so that's at it's just a beautiful idea so that's at it's just a beautiful idea so that's at the heart of all of this in the stack I the heart of all of this in the stack I the heart of all of this in the stack I described so one student will Langford described so one student will Langford described so one student will Langford made these micro robots out of little made these micro robots out of little made these micro robots out of little parts that then we're using for miana's parts that then we're using for miana's parts that then we're using for miana's bigger robots up through this hierarchy bigger robots up through this hierarchy bigger robots up through this hierarchy and it's really realizing this idea of and it's really realizing this idea of and it's really realizing this idea of the self-reproducing automata so van the self-reproducing automata so van the self-reproducing automata so van Neumann when I complained about the Neumann when I complained about the Neumann when I complained about the weinerman architecture it's not fair weinerman architecture it's not fair weinerman architecture it's not fair Devon Neumann because he never claimed Devon Neumann because he never claimed Devon Neumann because he never claimed it as his architecture he really wrote it as his architecture he really wrote it as his architecture he really wrote about it in this one fairly Dreadful about it in this one fairly Dreadful about it in this one fairly Dreadful memo that led to all sorts of lawsuits memo that led to all sorts of lawsuits memo that led to all sorts of lawsuits and fights and about the early days of and fights and about the early days of and fights and about the early days of computing he did beautiful work on computing he did beautiful work on computing he did beautiful work on reliable computation and unreliable reliable computation and unreliable reliable computation and unreliable devices and towards the end of his life devices and towards the end of his life devices and towards the end of his life what he studied was how and I have to what he studied was how and I have to what he studied was how and I have to say this precisely how a computation say this precisely how a computation say this precisely how a computation communicates its own construction

communicates its own construction communicates its own construction so beautiful so a computation can store so beautiful so a computation can store so beautiful so a computation can store a description of how to build itself but a description of how to build itself but a description of how to build itself but now there's a really hard problem which now there's a really hard problem which now there's a really hard problem which is is is how if you have that in your mind how do how if you have that in your mind how do how if you have that in your mind how do you transfer it and wake up a thing that you transfer it and wake up a thing that you transfer it and wake up a thing that then can contain it then can contain it then can contain it um so how do you give birth to a thing um so how do you give birth to a thing um so how do you give birth to a thing that knows how to make itself and so um that knows how to make itself and so um that knows how to make itself and so um with Stan ulam he invented cellular with Stan ulam he invented cellular with Stan ulam he invented cellular automata as a way to simulate these uh automata as a way to simulate these uh automata as a way to simulate these uh but that was theoretical now the work but that was theoretical now the work but that was theoretical now the work I'm describing in my lab is is I'm describing in my lab is is I'm describing in my lab is is fundamentally how to realize it how to fundamentally how to realize it how to fundamentally how to realize it how to re um realize self-reproducing uh re um realize self-reproducing uh re um realize self-reproducing uh automata and so you know this is automata and so you know this is automata and so you know this is something van Neumann thought very something van Neumann thought very something van Neumann thought very deeply and very beautiful of beautifully deeply and very beautiful of beautifully deeply and very beautiful of beautifully about theoretically and it's right at about theoretically and it's right at about theoretically and it's right at this intersection it it's not this intersection it it's not this intersection it it's not communication or computation or communication or computation or communication or computation or fabrication fabrication fabrication it's right at this intersection where it's right at this intersection where it's right at this intersection where communication and computation meets communication and computation meets communication and computation meets fabrication fabrication fabrication now the reason self-reproducing automata now the reason self-reproducing automata now the reason self-reproducing automata intellectually is so important because intellectually is so important because intellectually is so important because this is the foundation of life this is this is the foundation of life this is this is the foundation of life this is really just understanding the essence of really just understanding the essence of really just understanding the essence of how to life and in effect we're trying how to life and in effect we're trying how to life and in effect we're trying to create life and non-living material to create life and non-living material to create life and non-living material the reason it's so important the reason it's so important the reason it's so important technologically is because that's how technologically is because that's how technologically is because that's how you scale capacity that's how you can you scale capacity that's how you can you scale capacity that's how you can make an elephant from a ribosome because make an elephant from a ribosome because make an elephant from a ribosome because the assemblers make assemblers so simple the assemblers make assemblers so simple the assemblers make assemblers so simple building blocks yeah that inside

building blocks yeah that inside building blocks yeah that inside themselves contain the information how themselves contain the information how themselves contain the information how to build more building blocks and so uh to build more building blocks and so uh to build more building blocks and so uh between each other construct arbitrarily between each other construct arbitrarily between each other construct arbitrarily complex objects right now let me give complex objects right now let me give complex objects right now let me give you the numbers so let me relate this to you the numbers so let me relate this to you the numbers so let me relate this to right now we're living in AI Mania right now we're living in AI Mania right now we're living in AI Mania explosion time explosion time explosion time let me relate that to what we're talking let me relate that to what we're talking let me relate that to what we're talking about about about a hundred petaflop computer a hundred petaflop computer a hundred petaflop computer which is a current generation uh which is a current generation uh which is a current generation uh supercomputer not quite the biggest ones supercomputer not quite the biggest ones supercomputer not quite the biggest ones does 10 to the 17 Ops per second does 10 to the 17 Ops per second does 10 to the 17 Ops per second your brain does 10 to the 17 Ops per your brain does 10 to the 17 Ops per your brain does 10 to the 17 Ops per second it has about 10 to the 15 second it has about 10 to the 15 second it has about 10 to the 15 synapses and they run at about 100 Hertz synapses and they run at about 100 Hertz synapses and they run at about 100 Hertz so as of a year or two ago so as of a year or two ago so as of a year or two ago the compute the performance of a big the compute the performance of a big the compute the performance of a big computer matched a brain so you could computer matched a brain so you could computer matched a brain so you could view AI as a breakthrough but the real view AI as a breakthrough but the real view AI as a breakthrough but the real story is story is story is um within about a year or two ago and um within about a year or two ago and um within about a year or two ago and let's see that that the super computer let's see that that the super computer let's see that that the super computer has about 10 to the 15 transistors in has about 10 to the 15 transistors in has about 10 to the 15 transistors in the processors 10 to the 15 transistors the processors 10 to the 15 transistors the processors 10 to the 15 transistors in the memory which is the synapses in in the memory which is the synapses in in the memory which is the synapses in your brain so the real breakthrough was your brain so the real breakthrough was your brain so the real breakthrough was the computers match the computational the computers match the computational the computers match the computational capacity of a brain and so we'd be sort capacity of a brain and so we'd be sort capacity of a brain and so we'd be sort of derelict if they couldn't do about of derelict if they couldn't do about of derelict if they couldn't do about the same thing but now the reason I'm the same thing but now the reason I'm the same thing but now the reason I'm mentioning that is mentioning that is mentioning that is the the the chip Fab making the supercomputer is chip Fab making the supercomputer is chip Fab making the supercomputer is placing about 10 to the 10 transistors a placing about 10 to the 10 transistors a placing about 10 to the 10 transistors a second second second while you're digesting your lunch right while you're digesting your lunch right while you're digesting your lunch right now you're make you're placing about 10

now you're make you're placing about 10 now you're make you're placing about 10 to the 18 parts per second to the 18 parts per second to the 18 parts per second um there's an eight order of magnitude um there's an eight order of magnitude um there's an eight order of magnitude difference not so in computational difference not so in computational difference not so in computational capacity it's done we've caught up capacity it's done we've caught up capacity it's done we've caught up but there's eight orders of magnitude but there's eight orders of magnitude but there's eight orders of magnitude difference in the rate at which biology difference in the rate at which biology difference in the rate at which biology can build versus state-of-the-art can build versus state-of-the-art can build versus state-of-the-art manufacturing can build manufacturing can build manufacturing can build and that distinction is what we're and that distinction is what we're and that distinction is what we're talking about that distinction is not talking about that distinction is not talking about that distinction is not analog but this deep sense of digital analog but this deep sense of digital analog but this deep sense of digital fabrication of embodying codes in fabrication of embodying codes in fabrication of embodying codes in construction so a description doesn't construction so a description doesn't construction so a description doesn't describe a thing but the description describe a thing but the description describe a thing but the description becomes the thing so you're saying I becomes the thing so you're saying I becomes the thing so you're saying I mean this is one of the cases you're mean this is one of the cases you're mean this is one of the cases you're making and that this is this third making and that this is this third making and that this is this third Revolution we've seen the Moore's law in Revolution we've seen the Moore's law in Revolution we've seen the Moore's law in communication we've seen the Moore's Law communication we've seen the Moore's Law communication we've seen the Moore's Law like type of growth in uh computation like type of growth in uh computation like type of growth in uh computation and you're anticipating we're going to and you're anticipating we're going to and you're anticipating we're going to see that in digital fabrication can you see that in digital fabrication can you see that in digital fabrication can you actually first of all describe what you actually first of all describe what you actually first of all describe what you mean by this term digital fabrication so mean by this term digital fabrication so mean by this term digital fabrication so the Casual meaning is the computer the Casual meaning is the computer the Casual meaning is the computer controls the tool to make something and controls the tool to make something and controls the tool to make something and that was invented when MIT stole it in that was invented when MIT stole it in that was invented when MIT stole it in 1952. yeah um there's the deep meaning 1952. yeah um there's the deep meaning 1952. yeah um there's the deep meaning of what the ribosome does of a of what the ribosome does of a of what the ribosome does of a computation of a dis a digital computation of a dis a digital computation of a dis a digital description doesn't describe a thing a description doesn't describe a thing a description doesn't describe a thing a digital description becomes the thing digital description becomes the thing digital description becomes the thing yeah that's where the that's that's the yeah that's where the that's that's the yeah that's where the that's that's the path to the Star Trek replicator path to the Star Trek replicator path to the Star Trek replicator and that's the thing that doesn't exist and that's the thing that doesn't exist and that's the thing that doesn't exist yet yet yet now I think the the best way to now I think the the best way to now I think the the best way to understand what this roadmap looks like understand what this roadmap looks like understand what this roadmap looks like is to now bring in Fab labs and how they

is to now bring in Fab labs and how they is to now bring in Fab labs and how they relate to all of this what are Fab Labs relate to all of this what are Fab Labs relate to all of this what are Fab Labs so here here's a sequence so here here's a sequence so here here's a sequence um with colleagues I accidentally um with colleagues I accidentally um with colleagues I accidentally started a network of what's now 2500 started a network of what's now 2500 started a network of what's now 2500 digital fabrication Community Labs digital fabrication Community Labs digital fabrication Community Labs called Fab Labs right now in 125 called Fab Labs right now in 125 called Fab Labs right now in 125 countries and they double every year and countries and they double every year and countries and they double every year and a half that's called lassa's law after a half that's called lassa's law after a half that's called lassa's law after Sherry Lasseter who I'll explain so Sherry Lasseter who I'll explain so Sherry Lasseter who I'll explain so here's the sequence here's the sequence here's the sequence uh we started Center for bits and atoms uh we started Center for bits and atoms uh we started Center for bits and atoms to do the kind of research we're talking to do the kind of research we're talking to do the kind of research we're talking about we had all of these machines and about we had all of these machines and about we had all of these machines and then had a problem it would take a then had a problem it would take a then had a problem it would take a lifetime of classes to learn to use all lifetime of classes to learn to use all lifetime of classes to learn to use all the machines the machines the machines so with so with so with you know colleagues who helped start CBA you know colleagues who helped start CBA you know colleagues who helped start CBA we began a class modestly called how to we began a class modestly called how to we began a class modestly called how to make almost anything yeah and there's no make almost anything yeah and there's no make almost anything yeah and there's no big agenda it was just it was aimed at a big agenda it was just it was aimed at a big agenda it was just it was aimed at a few research students to use the few research students to use the few research students to use the machines and it were completely machines and it were completely machines and it were completely unprepared for the first time we taught unprepared for the first time we taught unprepared for the first time we taught it we were swamped by every year since it we were swamped by every year since it we were swamped by every year since hundreds of students try to take the hundreds of students try to take the hundreds of students try to take the class it's one of the most over class it's one of the most over class it's one of the most over subscribed classes at MIT subscribed classes at MIT subscribed classes at MIT um students would say things like can um students would say things like can um students would say things like can you teach this at MIT it seems too you teach this at MIT it seems too you teach this at MIT it seems too useful it's just how to work these useful it's just how to work these useful it's just how to work these machines and the students in the class I machines and the students in the class I machines and the students in the class I would teach them all the skills to use would teach them all the skills to use would teach them all the skills to use all these tools and then they would do all these tools and then they would do all these tools and then they would do projects integrating them and they were projects integrating them and they were projects integrating them and they were amazing so Kelly was a sculptor no amazing so Kelly was a sculptor no amazing so Kelly was a sculptor no engineering background uh her project engineering background uh her project engineering background uh her project was she made a device that saves up was she made a device that saves up was she made a device that saves up screams when you're mad and placed them screams when you're mad and placed them screams when you're mad and placed them back later back later back later and saves up screams when you're mad and and saves up screams when you're mad and and saves up screams when you're mad and plays them back later you scream into

plays them back later you scream into plays them back later you scream into this device and it it it deadens The this device and it it it deadens The this device and it it it deadens The Sound records it and then when it's Sound records it and then when it's Sound records it and then when it's convenient releases your screen can we convenient releases your screen can we convenient releases your screen can we just just like pause on the Brilliance just just like pause on the Brilliance just just like pause on the Brilliance of that invention creation the art of that invention creation the art of that invention creation the art I don't know the Brilliance who is this I don't know the Brilliance who is this I don't know the Brilliance who is this that created Kelly Dobson going on to do that created Kelly Dobson going on to do that created Kelly Dobson going on to do a number of interesting things uh me Jin a number of interesting things uh me Jin a number of interesting things uh me Jin who's gone on to do a number of who's gone on to do a number of who's gone on to do a number of interesting things uh made a dress interesting things uh made a dress interesting things uh made a dress instrumented with sensors and spines and instrumented with sensors and spines and instrumented with sensors and spines and when somebody creepy comes close it when somebody creepy comes close it when somebody creepy comes close it would defend your personal space they're would defend your personal space they're would defend your personal space they're also very easy um another project early also very easy um another project early also very easy um another project early on was a web browser for parrots which on was a web browser for parrots which on was a web browser for parrots which have the cognitive ability of a young have the cognitive ability of a young have the cognitive ability of a young child and lets parrots surf the Internet an alarm clock you wrestle with and an alarm clock you wrestle with and prove you're awake and what connects all prove you're awake and what connects all prove you're awake and what connects all of these is of these is of these is so MIT made the first real-time computer so MIT made the first real-time computer so MIT made the first real-time computer the Whirlwind that was transistorized as the Whirlwind that was transistorized as the Whirlwind that was transistorized as the TX the TX was spun off from MIT as the TX the TX was spun off from MIT as the TX the TX was spun off from MIT as the PDP pdp's the PDP pdp's the PDP pdp's where the mini computers that created where the mini computers that created where the mini computers that created the internet the internet the internet so outside MIT was deck Prime Wang data so outside MIT was deck Prime Wang data so outside MIT was deck Prime Wang data General the whole mini computer industry General the whole mini computer industry General the whole mini computer industry the whole Computing industry was there the whole Computing industry was there the whole Computing industry was there and it all failed when Computing became and it all failed when Computing became and it all failed when Computing became personal personal personal Ken Olsen the head of digital famously Ken Olsen the head of digital famously Ken Olsen the head of digital famously said you don't need a computer at home said you don't need a computer at home said you don't need a computer at home there's a little background to that but there's a little background to that but there's a little background to that but but deck you know completely missed but deck you know completely missed but deck you know completely missed Computing became personal so I mentioned Computing became personal so I mentioned Computing became personal so I mentioned all of that because all of that because all of that because I was asking how to do digital I was asking how to do digital I was asking how to do digital fabrication but not really why the fabrication but not really why the fabrication but not really why the students in this how to make class were

students in this how to make class were students in this how to make class were showing me that the killer app of showing me that the killer app of showing me that the killer app of digital fabrication is personal digital fabrication is personal digital fabrication is personal fabrication yeah how do you jump to the fabrication yeah how do you jump to the fabrication yeah how do you jump to the personal fabrication so Kelly didn't personal fabrication so Kelly didn't personal fabrication so Kelly didn't make the screen body because it was for make the screen body because it was for make the screen body because it was for a thesis she wasn't writing a research a thesis she wasn't writing a research a thesis she wasn't writing a research paper it wasn't a business model she paper it wasn't a business model she paper it wasn't a business model she wanted it was because she wanted one wanted it was because she wanted one wanted it was because she wanted one yeah it was personal expression going yeah it was personal expression going yeah it was personal expression going back to me and vocational schools back to me and vocational schools back to me and vocational schools personal expression in these new means personal expression in these new means personal expression in these new means of expression so that's happened every of expression so that's happened every of expression so that's happened every year since it literally is called the year since it literally is called the year since it literally is called the course is literally called how to make course is literally called how to make course is literally called how to make almost anything yep a legendary course almost anything yep a legendary course almost anything yep a legendary course at MIT yep yep every year at MIT yep yep every year at MIT yep yep every year um and it's grown to multiple Labs um and it's grown to multiple Labs um and it's grown to multiple Labs um at MIT with as many people involved um at MIT with as many people involved um at MIT with as many people involved in teaching is taking it and there's in teaching is taking it and there's in teaching is taking it and there's even a Harvard lab for the MIT class even a Harvard lab for the MIT class even a Harvard lab for the MIT class what what have you learned about humans what what have you learned about humans what what have you learned about humans colliding with the Fab Lab about what colliding with the Fab Lab about what colliding with the Fab Lab about what the capacity experience to be creative the capacity experience to be creative the capacity experience to be creative and to build I I mentioned Marvin and to build I I mentioned Marvin and to build I I mentioned Marvin another Mentor at MIT sadly no longer another Mentor at MIT sadly no longer another Mentor at MIT sadly no longer living is Seymour pepper so pepper living is Seymour pepper so pepper living is Seymour pepper so pepper studied with Piaget he came to MIT to studied with Piaget he came to MIT to studied with Piaget he came to MIT to get access to the early compute Piaget get access to the early compute Piaget get access to the early compute Piaget was a Pioneer in how kids learn was a Pioneer in how kids learn was a Pioneer in how kids learn um papert came to MIT to get access to um papert came to MIT to get access to um papert came to MIT to get access to the early computers with the goal of the early computers with the goal of the early computers with the goal of letting kids play with them Piaget letting kids play with them Piaget letting kids play with them Piaget helped show kids are like scientists helped show kids are like scientists helped show kids are like scientists they they learn as scientists and it they they learn as scientists and it they they learn as scientists and it gets kind of throttled out of them gets kind of throttled out of them gets kind of throttled out of them Seymour wanted to let kids have a Seymour wanted to let kids have a Seymour wanted to let kids have a broader landscape to play Seymour's work broader landscape to play Seymour's work broader landscape to play Seymour's work LED with Mitch Resnick to Lego logo

LED with Mitch Resnick to Lego logo LED with Mitch Resnick to Lego logo Mindstorms all of that stuff as Fab Lab Mindstorms all of that stuff as Fab Lab Mindstorms all of that stuff as Fab Lab spread and we started creating spread and we started creating spread and we started creating educational programs for kids in them educational programs for kids in them educational programs for kids in them Seymour said something really Seymour said something really Seymour said something really interesting he made a gesture he said it interesting he made a gesture he said it interesting he made a gesture he said it was a thorn in his side was a thorn in his side was a thorn in his side that they invented What's called the that they invented What's called the that they invented What's called the turtle a robot kids could early robot turtle a robot kids could early robot turtle a robot kids could early robot kids could program to connect it to a kids could program to connect it to a kids could program to connect it to a Mainframe computer Seymour said Mainframe computer Seymour said Mainframe computer Seymour said the goal was not for the kids to program the goal was not for the kids to program the goal was not for the kids to program the robot it was for the kids to create the robot it was for the kids to create the robot it was for the kids to create the robot the robot the robot and so in that sense the Fab Labs which and so in that sense the Fab Labs which and so in that sense the Fab Labs which for me were just this accident he for me were just this accident he for me were just this accident he described as sort of this fulfillment of described as sort of this fulfillment of described as sort of this fulfillment of the Arc of kids learn by experimenting the Arc of kids learn by experimenting the Arc of kids learn by experimenting it was to give them the tools to create it was to give them the tools to create it was to give them the tools to create not just assemble things and program not just assemble things and program not just assemble things and program things but actually create so come into things but actually create so come into things but actually create so come into your question your question your question what I've learned what I've learned what I've learned is is is MIT a few years back somebody added MIT a few years back somebody added MIT a few years back somebody added added up businesses from spun off from added up businesses from spun off from added up businesses from spun off from MIT and it's the world's 10th economy it MIT and it's the world's 10th economy it MIT and it's the world's 10th economy it falls between India and Russia and I falls between India and Russia and I falls between India and Russia and I view that in a way as a bad number view that in a way as a bad number view that in a way as a bad number because it's only a few thousand people because it's only a few thousand people because it's only a few thousand people and these aren't uniquely the four and these aren't uniquely the four and these aren't uniquely the four thousand brightest people it's just a thousand brightest people it's just a thousand brightest people it's just a productive environment for them and what productive environment for them and what productive environment for them and what we found is in rural Indian villages in we found is in rural Indian villages in we found is in rural Indian villages in African Shanty towns and Arctic African Shanty towns and Arctic African Shanty towns and Arctic um Hamlet I find exactly precisely that um Hamlet I find exactly precisely that um Hamlet I find exactly precisely that profile so profile so profile so um link cited a few hours above Trump so um link cited a few hours above Trump so um link cited a few hours above Trump so way above the Arctic circles it's so far way above the Arctic circles it's so far way above the Arctic circles it's so far north the satellite dishes look at the

north the satellite dishes look at the north the satellite dishes look at the ground not the sky ground not the sky ground not the sky um Hans Christian in the lab was um Hans Christian in the lab was um Hans Christian in the lab was considered a problem in the local school considered a problem in the local school considered a problem in the local school because they couldn't teach him anything because they couldn't teach him anything because they couldn't teach him anything I showed him a few projects next time I I showed him a few projects next time I I showed him a few projects next time I came back he was designing and building came back he was designing and building came back he was designing and building Little Robot vehicles and in Little Robot vehicles and in Little Robot vehicles and in um South Africa in I mentioned social um South Africa in I mentioned social um South Africa in I mentioned social Govi in this apartheid Township the Govi in this apartheid Township the Govi in this apartheid Township the local Technical Institute taught kids local Technical Institute taught kids local Technical Institute taught kids how to make bricks and fold sheets it how to make bricks and fold sheets it how to make bricks and fold sheets it was it was punitive but to piso in the was it was punitive but to piso in the was it was punitive but to piso in the Fab Lab was actually doing all the work Fab Lab was actually doing all the work Fab Lab was actually doing all the work of my MIT classes and so over and over of my MIT classes and so over and over of my MIT classes and so over and over we found precisely the same kind of we found precisely the same kind of we found precisely the same kind of bright invent of bright invent of bright invent of um creativity um creativity um creativity uh and historically the answer was uh and historically the answer was uh and historically the answer was go you're smart go away it's sort of go you're smart go away it's sort of go you're smart go away it's sort of like me and vocational school but in like me and vocational school but in like me and vocational school but in this lab Network what we could then do this lab Network what we could then do this lab Network what we could then do is in effect bring the world to them now is in effect bring the world to them now is in effect bring the world to them now let's look at the scaling of all of this let's look at the scaling of all of this let's look at the scaling of all of this so there's one Earth a thousand cities a so there's one Earth a thousand cities a so there's one Earth a thousand cities a million towns a billion people a million towns a billion people a million towns a billion people a trillion things trillion things trillion things there was one Whirlwind computer and my there was one Whirlwind computer and my there was one Whirlwind computer and my teammate uh the first real-time computer teammate uh the first real-time computer teammate uh the first real-time computer there were thousands of pdps there were there were thousands of pdps there were there were thousands of pdps there were millions of hobbyist computers that came millions of hobbyist computers that came millions of hobbyist computers that came from that billions of personal computers from that billions of personal computers from that billions of personal computers trillions of Internet of things so now trillions of Internet of things so now trillions of Internet of things so now if we look at this Fab Lab story 1952 if we look at this Fab Lab story 1952 if we look at this Fab Lab story 1952 was the NC Mill was the NC Mill was the NC Mill there are now thousands of Fab labs and there are now thousands of Fab labs and there are now thousands of Fab labs and the Fab Lab costs exactly the same cost

the Fab Lab costs exactly the same cost the Fab Lab costs exactly the same cost and complexity of the mini computer so and complexity of the mini computer so and complexity of the mini computer so on the mini computer it it didn't fit in on the mini computer it it didn't fit in on the mini computer it it didn't fit in your pocket it filled a room but video your pocket it filled a room but video your pocket it filled a room but video games email word processing really games email word processing really games email word processing really anything you do with the internet anything you do with the internet anything you do with the internet anything you do with a computer today anything you do with a computer today anything you do with a computer today happened at that era because it got on happened at that era because it got on happened at that era because it got on the scale of a work group not a the scale of a work group not a the scale of a work group not a corporation corporation corporation in the same way Fab labs are like the in the same way Fab labs are like the in the same way Fab labs are like the mini computers inventing how does the mini computers inventing how does the mini computers inventing how does the world work if anybody can make anything world work if anybody can make anything world work if anybody can make anything then if you look at that scaling then if you look at that scaling then if you look at that scaling Fab Labs today are transitioning from Fab Labs today are transitioning from Fab Labs today are transitioning from buying a machine to make machines making buying a machine to make machines making buying a machine to make machines making machines so we're transitioning to you machines so we're transitioning to you machines so we're transitioning to you can go to a Fab Lab not to make a can go to a Fab Lab not to make a can go to a Fab Lab not to make a project to make but to make a new project to make but to make a new project to make but to make a new machine machine machine so we talked about the Deep sense of so we talked about the Deep sense of so we talked about the Deep sense of self-replication there's a very self-replication there's a very self-replication there's a very practical sense of Fab Lab machines practical sense of Fab Lab machines practical sense of Fab Lab machines making Fab Lab machines making Fab Lab machines making Fab Lab machines and so that's the equivalent of the uh and so that's the equivalent of the uh and so that's the equivalent of the uh hobbyist computer era what it's called hobbyist computer era what it's called hobbyist computer era what it's called the Altair historically then the work we the Altair historically then the work we the Altair historically then the work we spent a while talking about about spent a while talking about about spent a while talking about about assemblers and self-assemblers that's assemblers and self-assemblers that's assemblers and self-assemblers that's the equivalent of smartphones and the equivalent of smartphones and the equivalent of smartphones and internet of things that's when so the internet of things that's when so the internet of things that's when so the the assemblers are like the smartphone the assemblers are like the smartphone the assemblers are like the smartphone where a smartphone today has the where a smartphone today has the where a smartphone today has the capacity of what used to be a capacity of what used to be a capacity of what used to be a supercomputer in your pocket and then supercomputer in your pocket and then supercomputer in your pocket and then the smart thermostat on your wall has the smart thermostat on your wall has the smart thermostat on your wall has the power of the original PDP computer the power of the original PDP computer the power of the original PDP computer not metaphorically but literally and now not metaphorically but literally and now not metaphorically but literally and now there's trillions of those in the same

there's trillions of those in the same there's trillions of those in the same sense that when we finally merge sense that when we finally merge sense that when we finally merge materials with the machines in the materials with the machines in the materials with the machines in the self-assembly that's like the Internet self-assembly that's like the Internet self-assembly that's like the Internet of Things stage but here's the important of Things stage but here's the important of Things stage but here's the important lesson lesson lesson if you look at the Computing analogy if you look at the Computing analogy if you look at the Computing analogy Computing expanded exponentially but it Computing expanded exponentially but it Computing expanded exponentially but it really didn't fundamentally change the really didn't fundamentally change the really didn't fundamentally change the the core things happened in in that the core things happened in in that the core things happened in in that transition in the mini computer era so transition in the mini computer era so transition in the mini computer era so in the same sense the research now I'm in the same sense the research now I'm in the same sense the research now I'm we spent a while talking about is how we we spent a while talking about is how we we spent a while talking about is how we get to the replicator get to the replicator get to the replicator today you can do all of that if you today you can do all of that if you today you can do all of that if you close your eyes and view the whole Fab close your eyes and view the whole Fab close your eyes and view the whole Fab Lab as a machine in that room you can Lab as a machine in that room you can Lab as a machine in that room you can make almost anything but you need a lot make almost anything but you need a lot make almost anything but you need a lot of inputs bit by bit the inputs will go of inputs bit by bit the inputs will go of inputs bit by bit the inputs will go down and the size of the room will go down and the size of the room will go down and the size of the room will go down as we go through each of these down as we go through each of these down as we go through each of these stages stages stages so how difficult is it to create a so how difficult is it to create a so how difficult is it to create a self-replicating assembler self-replicating assembler self-replicating assembler self-replicating machine that builds self-replicating machine that builds self-replicating machine that builds copies of itself or builds more copies of itself or builds more copies of itself or builds more complicated version of itself which is complicated version of itself which is complicated version of itself which is kind of the dream towards which you're kind of the dream towards which you're kind of the dream towards which you're pushing in a generic arbitrary sense I pushing in a generic arbitrary sense I pushing in a generic arbitrary sense I had a student Nadia Peak with Jonathan had a student Nadia Peak with Jonathan had a student Nadia Peak with Jonathan Ward who who for me started this idea of Ward who who for me started this idea of Ward who who for me started this idea of how do we use the tools in my lab to how do we use the tools in my lab to how do we use the tools in my lab to make the tools in the lab yes in a very make the tools in the lab yes in a very make the tools in the lab yes in a very clear sense they are making clear sense they are making clear sense they are making self-reproducing machines so one of the self-reproducing machines so one of the self-reproducing machines so one of the really cool things that's happened is really cool things that's happened is really cool things that's happened is there's a whole network of machine there's a whole network of machine there's a whole network of machine Builders around the world so there's Builders around the world so there's Builders around the world so there's Danielle and now in Germany and yens in

Danielle and now in Germany and yens in Danielle and now in Germany and yens in Norway and Norway and Norway and um each of these people is has learned um each of these people is has learned um each of these people is has learned the skills to go into a Fab Lab and make the skills to go into a Fab Lab and make the skills to go into a Fab Lab and make a machine and so we've started creating a machine and so we've started creating a machine and so we've started creating a network of superfap so the Fab Lab can a network of superfap so the Fab Lab can a network of superfap so the Fab Lab can make a machine but it can't make a make a machine but it can't make a make a machine but it can't make a number of the Precision parts of the number of the Precision parts of the number of the Precision parts of the machine so in places like Bhutan or machine so in places like Bhutan or machine so in places like Bhutan or Carol in the south of India we started Carol in the south of India we started Carol in the south of India we started creating super Fab Labs that have more creating super Fab Labs that have more creating super Fab Labs that have more advanced tools to make the parts of the advanced tools to make the parts of the advanced tools to make the parts of the machines so that the machines themselves machines so that the machines themselves machines so that the machines themselves become even cheaper become even cheaper become even cheaper so so so that that is self-reproducing machines that that is self-reproducing machines that that is self-reproducing machines but you need to feed it things like but you need to feed it things like but you need to feed it things like bearings or microcontrollers they can't bearings or microcontrollers they can't bearings or microcontrollers they can't make those parts but other than that make those parts but other than that make those parts but other than that they're making their own things and I they're making their own things and I they're making their own things and I should note as a footnote the stack I should note as a footnote the stack I should note as a footnote the stack I described of computers controlling described of computers controlling described of computers controlling machines to machine making machines to machines to machine making machines to machines to machine making machines to assemblers to self-assemblers view that assemblers to self-assemblers view that assemblers to self-assemblers view that as fab1234 as fab1234 as fab1234 so we're transitioning from fab 1 to Fab so we're transitioning from fab 1 to Fab so we're transitioning from fab 1 to Fab two and the research in the lab is three two and the research in the lab is three two and the research in the lab is three and four at this Fab two stage a big and four at this Fab two stage a big and four at this Fab two stage a big component of this is uh sustainability component of this is uh sustainability component of this is uh sustainability in the material feedstocks so Alicia in the material feedstocks so Alicia in the material feedstocks so Alicia colleague in Chile is leading a great colleague in Chile is leading a great colleague in Chile is leading a great effort looking at how you take Forest effort looking at how you take Forest effort looking at how you take Forest Products and coffee grounds and Products and coffee grounds and Products and coffee grounds and seashells and a range of locally seashells and a range of locally seashells and a range of locally available materials and produce the available materials and produce the available materials and produce the high-tech materials that go into the lab high-tech materials that go into the lab high-tech materials that go into the lab so all of that is machine building today so all of that is machine building today so all of that is machine building today then then then back in the lab what we can do today is back in the lab what we can do today is back in the lab what we can do today is we have robots that can build structures

we have robots that can build structures we have robots that can build structures and can assemble more robots that build and can assemble more robots that build and can assemble more robots that build structures structures structures we have finer resolution robots that can we have finer resolution robots that can we have finer resolution robots that can build micro mechanical systems so robots build micro mechanical systems so robots build micro mechanical systems so robots that can build robots that can walk and that can build robots that can walk and that can build robots that can walk and manipulate and we're just now we have a manipulate and we're just now we have a manipulate and we're just now we have a project project project at the layer below that where there's at the layer below that where there's at the layer below that where there's endless attention today to billion endless attention today to billion endless attention today to billion dollar chip Fab Investments uh but a dollar chip Fab Investments uh but a dollar chip Fab Investments uh but a really interesting thing we passed really interesting thing we passed really interesting thing we passed through is today the smallest through is today the smallest through is today the smallest transistors you can buy as a single transistors you can buy as a single transistors you can buy as a single transistor just commercially for transistor just commercially for transistor just commercially for electronics is actually the size of an electronics is actually the size of an electronics is actually the size of an early transistor in an integrated early transistor in an integrated early transistor in an integrated circuit circuit circuit so we're using these machines making so we're using these machines making so we're using these machines making machines making assemblers to place machines making assemblers to place machines making assemblers to place those parts to not use a billion dollar those parts to not use a billion dollar those parts to not use a billion dollar chip Fab to make integrated circuits but chip Fab to make integrated circuits but chip Fab to make integrated circuits but actually assemble little electronic actually assemble little electronic actually assemble little electronic components so I have a fine enough components so I have a fine enough components so I have a fine enough precise enough actuators and precise enough actuators and precise enough actuators and manipulators that allow you to place manipulators that allow you to place manipulators that allow you to place these transistors right that's a these transistors right that's a these transistors right that's a research project in my lab research project in my lab research project in my lab on called dice on discrete assembly of on called dice on discrete assembly of on called dice on discrete assembly of integrated electronics and we're just at integrated electronics and we're just at integrated electronics and we're just at the point to really start to take the point to really start to take the point to really start to take seriously this notion of not having a seriously this notion of not having a seriously this notion of not having a chip Fab make integrated Electronics but chip Fab make integrated Electronics but chip Fab make integrated Electronics but having not a 3D printer but a thing having not a 3D printer but a thing having not a 3D printer but a thing that's a cross between a pick and place that's a cross between a pick and place that's a cross between a pick and place makes circuit boards in 2D the 3D makes circuit boards in 2D the 3D makes circuit boards in 2D the 3D printer extrudes in 3D we're making sort printer extrudes in 3D we're making sort printer extrudes in 3D we're making sort of a micro manipulator that acts like a of a micro manipulator that acts like a of a micro manipulator that acts like a printer but it's placing to build printer but it's placing to build printer but it's placing to build Electronics in 3D but this micro Electronics in 3D but this micro Electronics in 3D but this micro manipulator is distributed so there's a manipulator is distributed so there's a manipulator is distributed so there's a bunch of them or is this one centralized

bunch of them or is this one centralized bunch of them or is this one centralized thing so that's why that's a great thing so that's why that's a great thing so that's why that's a great question so um I have a prize that's question so um I have a prize that's question so um I have a prize that's almost but not been claimed for the almost but not been claimed for the almost but not been claimed for the students whose thesis can walk out of students whose thesis can walk out of students whose thesis can walk out of the printer oh nice so you have to print the printer oh nice so you have to print the printer oh nice so you have to print the thesis the thesis the thesis with the means to to exit the printer with the means to to exit the printer with the means to to exit the printer and it has to contain its description of and it has to contain its description of and it has to contain its description of the thesis that says how to do that the thesis that says how to do that the thesis that says how to do that it's a really good uh I mean it's a it's it's a really good uh I mean it's a it's it's a really good uh I mean it's a it's a it's a fun example of exactly the a it's a fun example of exactly the a it's a fun example of exactly the thing we're talking about and I've had a thing we're talking about and I've had a thing we're talking about and I've had a few students almost few students almost few students almost get to that get to that get to that um and so um and so um and so um in what I'm describing there's this um in what I'm describing there's this um in what I'm describing there's this stack where we're getting closer but stack where we're getting closer but stack where we're getting closer but it's still quite a few years to really it's still quite a few years to really it's still quite a few years to really go from us so there's a layer below the go from us so there's a layer below the go from us so there's a layer below the transistors where we assemble the base transistors where we assemble the base transistors where we assemble the base materials that become the transistor materials that become the transistor materials that become the transistor we're now just at the edge of assembling we're now just at the edge of assembling we're now just at the edge of assembling the transistors to make the circuits the transistors to make the circuits the transistors to make the circuits we can assemble the micro parts to make we can assemble the micro parts to make we can assemble the micro parts to make the micro robots we can assemble the the micro robots we can assemble the the micro robots we can assemble the bigger robots and in the coming years bigger robots and in the coming years bigger robots and in the coming years we'll be patching together all of those we'll be patching together all of those we'll be patching together all of those uh scales so do you see a vision of just uh scales so do you see a vision of just uh scales so do you see a vision of just endless billions of robots at the endless billions of robots at the endless billions of robots at the different scales self-assembling uh different scales self-assembling uh different scales self-assembling uh self-replicating and building the self-replicating and building the self-replicating and building the complicated structures yes complicated structures yes complicated structures yes yes and the butt to the yes but is let yes and the butt to the yes but is let yes and the butt to the yes but is let me clarify two things one is that me clarify two things one is that me clarify two things one is that immediately immediately immediately raises King Charles fear of gray goo of raises King Charles fear of gray goo of raises King Charles fear of gray goo of runaway mutant self-reproducing things runaway mutant self-reproducing things runaway mutant self-reproducing things the reason why there are many things I

the reason why there are many things I the reason why there are many things I can tell you to worry about but that's can tell you to worry about but that's can tell you to worry about but that's not one of them not one of them not one of them is if you want things to autonomously is if you want things to autonomously is if you want things to autonomously self-reproduce and take over the world self-reproduce and take over the world self-reproduce and take over the world that means they need to compete with that means they need to compete with that means they need to compete with nature on using the resources of nature nature on using the resources of nature nature on using the resources of nature of water and sunlight and in light of of water and sunlight and in light of of water and sunlight and in light of everything I'm describing biology knows everything I'm describing biology knows everything I'm describing biology knows everything I told you every single thing everything I told you every single thing everything I told you every single thing I explain biology already knows how to I explain biology already knows how to I explain biology already knows how to do do do um uh what I'm describing isn't new for um uh what I'm describing isn't new for um uh what I'm describing isn't new for biology it's new for non-biological biology it's new for non-biological biology it's new for non-biological systems so in the digital era the systems so in the digital era the systems so in the digital era the economic win ended up being centralized economic win ended up being centralized economic win ended up being centralized the big platforms the big platforms the big platforms in this world of machines that can make in this world of machines that can make in this world of machines that can make machines I'm I'm asked for example machines I'm I'm asked for example machines I'm I'm asked for example um you know what what's the killer um you know what what's the killer um you know what what's the killer opportunity you know who's going to make opportunity you know who's going to make opportunity you know who's going to make all the money all the money all the money um who to invest in but if the machine um who to invest in but if the machine um who to invest in but if the machine can make the machine it's not a great can make the machine it's not a great can make the machine it's not a great business to invest in the machine business to invest in the machine business to invest in the machine um in the same way that if you can um in the same way that if you can um in the same way that if you can produce if you can think globally but produce if you can think globally but produce if you can think globally but produce locally then the way the produce locally then the way the produce locally then the way the technology goes out into society isn't a technology goes out into society isn't a technology goes out into society isn't a function of central control but is function of central control but is function of central control but is fundamentally distributed now that fundamentally distributed now that fundamentally distributed now that raises an obvious kind of concern which raises an obvious kind of concern which raises an obvious kind of concern which is well doesn't this mean you could make is well doesn't this mean you could make is well doesn't this mean you could make bombs and guns and all of that bombs and guns and all of that bombs and guns and all of that the reason that's much less of a problem the reason that's much less of a problem the reason that's much less of a problem than you would think is making bombs and than you would think is making bombs and than you would think is making bombs and guns and all of that is a very well met guns and all of that is a very well met guns and all of that is a very well met Market need anywhere we go there's a

Market need anywhere we go there's a Market need anywhere we go there's a fine supply chain for weapons now fine supply chain for weapons now fine supply chain for weapons now hobbyists have been making guns for ages hobbyists have been making guns for ages hobbyists have been making guns for ages and guns are available just about and guns are available just about and guns are available just about anywhere so you could go into the lab anywhere so you could go into the lab anywhere so you could go into the lab and make a gun today it's not a very and make a gun today it's not a very and make a gun today it's not a very good gun and guns are easily available good gun and guns are easily available good gun and guns are easily available and so generally we run these lab in war and so generally we run these lab in war and so generally we run these lab in war zones what we find is zones what we find is zones what we find is people don't go to them to make weapons people don't go to them to make weapons people don't go to them to make weapons which you can already do anyway it's an which you can already do anyway it's an which you can already do anyway it's an alternative to making weapons it coming alternative to making weapons it coming alternative to making weapons it coming back to your question I'd say the single back to your question I'd say the single back to your question I'd say the single most important thing I've learned is most important thing I've learned is most important thing I've learned is the greatest natural resource of the the greatest natural resource of the the greatest natural resource of the planet is this amazing density of planet is this amazing density of planet is this amazing density of Brighton event of people whose brains Brighton event of people whose brains Brighton event of people whose brains are underused and are underused and are underused and um you could view the the social um you could view the the social um you could view the the social engineering of this lab work as creating engineering of this lab work as creating engineering of this lab work as creating the capacity for them and so it you know the capacity for them and so it you know the capacity for them and so it you know in the end the way this is going to in the end the way this is going to in the end the way this is going to impact Society isn't going to be command impact Society isn't going to be command impact Society isn't going to be command and control it's how the world uses it and control it's how the world uses it and control it's how the world uses it and it's been really gratifying for me and it's been really gratifying for me and it's been really gratifying for me to see just how it does yeah but what to see just how it does yeah but what to see just how it does yeah but what are the different ways uh the evolution are the different ways uh the evolution are the different ways uh the evolution of the exponential scaling of digital of the exponential scaling of digital of the exponential scaling of digital fabrication can evolve so you said uh fabrication can evolve so you said uh fabrication can evolve so you said uh yeah self-replicating Nanobots right yeah self-replicating Nanobots right yeah self-replicating Nanobots right this is the the gray goo this is the the gray goo this is the the gray goo fear it's the caricature of a fear but fear it's the caricature of a fear but fear it's the caricature of a fear but nevertheless there's interesting just nevertheless there's interesting just nevertheless there's interesting just like you said spam and all these kinds like you said spam and all these kinds like you said spam and all these kinds of things that came with the scaling of of things that came with the scaling of of things that came with the scaling of communication and computation what are communication and computation what are communication and computation what are the different ways that malevolent the different ways that malevolent the different ways that malevolent actors will use this technology yeah actors will use this technology yeah actors will use this technology yeah well first let me start with a well first let me start with a well first let me start with a benevolent story which is

benevolent story which is benevolent story which is uh trash is an analog concept there's no uh trash is an analog concept there's no uh trash is an analog concept there's no trash in a forest all the parts get trash in a forest all the parts get trash in a forest all the parts get disassembled and reused trash means disassembled and reused trash means disassembled and reused trash means something doesn't have enough something doesn't have enough something doesn't have enough information to tell you how to reuse it information to tell you how to reuse it information to tell you how to reuse it yeah it's as simple as there's no trash yeah it's as simple as there's no trash yeah it's as simple as there's no trash in a Lego room in a Lego room in a Lego room when you assemble Lego the Lego bricks when you assemble Lego the Lego bricks when you assemble Lego the Lego bricks have enough information to disassemble have enough information to disassemble have enough information to disassemble them so one of the so as you go through them so one of the so as you go through them so one of the so as you go through this Fab one two three four story one of this Fab one two three four story one of this Fab one two three four story one of the implications of this transition to the implications of this transition to the implications of this transition to from printing to assembling so the real from printing to assembling so the real from printing to assembling so the real breakthrough technologically isn't breakthrough technologically isn't breakthrough technologically isn't additive versus subtractive which is the additive versus subtractive which is the additive versus subtractive which is the subject of a lot of attention and hype subject of a lot of attention and hype subject of a lot of attention and hype um yep 3D printers are useful um yep 3D printers are useful um yep 3D printers are useful um you know we spun off companies like um you know we spun off companies like um you know we spun off companies like form Labs led by Max for 3D printing but form Labs led by Max for 3D printing but form Labs led by Max for 3D printing but in a Fab Lab it's one of maybe 10 in a Fab Lab it's one of maybe 10 in a Fab Lab it's one of maybe 10 machines it's it's used but it's only machines it's it's used but it's only machines it's it's used but it's only part of the machines the real part of the machines the real part of the machines the real technological change is when we go from technological change is when we go from technological change is when we go from Printing and cutting to assembling uh Printing and cutting to assembling uh Printing and cutting to assembling uh and disassembling but that reduces and disassembling but that reduces and disassembling but that reduces inventories of hundreds of thousands of inventories of hundreds of thousands of inventories of hundreds of thousands of parts to just having a few parts to make parts to just having a few parts to make parts to just having a few parts to make almost anything it reduces Global Supply almost anything it reduces Global Supply almost anything it reduces Global Supply chains to locally sourcing these chains to locally sourcing these chains to locally sourcing these building blocks but one of the key building blocks but one of the key building blocks but one of the key implications is it gets rid of implications is it gets rid of implications is it gets rid of technological trash technological trash technological trash because you can disassemble and reuse because you can disassemble and reuse because you can disassemble and reuse the parts not throw them away and so the parts not throw them away and so the parts not throw them away and so initially that's of interest for things initially that's of interest for things initially that's of interest for things at the end of long Supply chains Like at the end of long Supply chains Like at the end of long Supply chains Like Satellites on orbit but one of the

Satellites on orbit but one of the Satellites on orbit but one of the things coming is eliminating technical things coming is eliminating technical things coming is eliminating technical trash through reuse of the building trash through reuse of the building trash through reuse of the building blocks so like when you think about 3D blocks so like when you think about 3D blocks so like when you think about 3D printers you're thinking about solution printers you're thinking about solution printers you're thinking about solution and subtraction and subtraction and subtraction when you think about the other options when you think about the other options when you think about the other options available to you in that parameter space available to you in that parameter space available to you in that parameter space as you call it that's going to be as you call it that's going to be as you call it that's going to be assembly disassembly cutting you said so assembly disassembly cutting you said so assembly disassembly cutting you said so the 1952 NC Mill was subtractive you the 1952 NC Mill was subtractive you the 1952 NC Mill was subtractive you remove material and 3D printing additive remove material and 3D printing additive remove material and 3D printing additive and there's a couple claims to the and there's a couple claims to the and there's a couple claims to the invention of 3D printing that's closer invention of 3D printing that's closer invention of 3D printing that's closer to what's called net shape which is you to what's called net shape which is you to what's called net shape which is you don't have to cut away the material you don't have to cut away the material you don't have to cut away the material you don't need you just put material where don't need you just put material where don't need you just put material where you do need it and so that's the 3D you do need it and so that's the 3D you do need it and so that's the 3D printing Revolution but printing Revolution but printing Revolution but there are all sorts of limitations on 3D there are all sorts of limitations on 3D there are all sorts of limitations on 3D printing to the kinds of materials you printing to the kinds of materials you printing to the kinds of materials you can print the kind of functionality you can print the kind of functionality you can print the kind of functionality you can print we're just not going to get to can print we're just not going to get to can print we're just not going to get to making a making a making a um everything in a cell phone on a um everything in a cell phone on a um everything in a cell phone on a single printer but I do expect to make single printer but I do expect to make single printer but I do expect to make everything in a cell phone with an everything in a cell phone with an everything in a cell phone with an assembler and so instead of printing and assembler and so instead of printing and assembler and so instead of printing and cutting technologically it's this cutting technologically it's this cutting technologically it's this transition to assembling and transition to assembling and transition to assembling and disassembling it going back to Shannon disassembling it going back to Shannon disassembling it going back to Shannon and Von Neumann going back to the and Von Neumann going back to the and Von Neumann going back to the ribosome for a billion years ago ribosome for a billion years ago ribosome for a billion years ago now you come to malevolent now you come to malevolent now you come to malevolent um let me tell you a story about um let me tell you a story about um let me tell you a story about I was I was I was doing a briefing for the National doing a briefing for the National doing a briefing for the National Academy of Sciences group that advises Academy of Sciences group that advises Academy of Sciences group that advises the intelligence communities the intelligence communities the intelligence communities and I talked about the kind of research

and I talked about the kind of research and I talked about the kind of research we do we do we do and at the very end I showed a little and at the very end I showed a little and at the very end I showed a little video clip of Valentina and Ghana video clip of Valentina and Ghana video clip of Valentina and Ghana um making a local girl making surface um making a local girl making surface um making a local girl making surface mount Electronics in the Fab Lab and I mount Electronics in the Fab Lab and I mount Electronics in the Fab Lab and I showed that to this room full of people showed that to this room full of people showed that to this room full of people uh one of the members of the uh one of the members of the uh one of the members of the intelligence Community got up livid and intelligence Community got up livid and intelligence Community got up livid and said how dare you waste our time showing said how dare you waste our time showing said how dare you waste our time showing us a young girl in an African village us a young girl in an African village us a young girl in an African village making service non-electronics we're making service non-electronics we're making service non-electronics we're looking at we need to know about looking at we need to know about looking at we need to know about disruptive threats to the future of the disruptive threats to the future of the disruptive threats to the future of the United States United States United States and somebody else got up in the room and and somebody else got up in the room and and somebody else got up in the room and yelled at him and you idiot I can't yelled at him and you idiot I can't yelled at him and you idiot I can't think of anything more important than think of anything more important than think of anything more important than this yeah but for two reasons one reason this yeah but for two reasons one reason this yeah but for two reasons one reason was was was um because if we rely on like um because if we rely on like um because if we rely on like informational superiority in the informational superiority in the informational superiority in the battlefield it means other people could battlefield it means other people could battlefield it means other people could get access to it but this intelligence get access to it but this intelligence get access to it but this intelligence person's point bless him wasn't that it person's point bless him wasn't that it person's point bless him wasn't that it was was was getting at the root causes of conflict getting at the root causes of conflict getting at the root causes of conflict is if this young girl in an African is if this young girl in an African is if this young girl in an African village could actually Master surface village could actually Master surface village could actually Master surface mount Electronics it changes some of the mount Electronics it changes some of the mount Electronics it changes some of the most fundamental things about most fundamental things about most fundamental things about recruitment for terrorism recruitment for terrorism recruitment for terrorism um uh impact of economic migration basic um uh impact of economic migration basic um uh impact of economic migration basic assumptions about an economy it's just assumptions about an economy it's just assumptions about an economy it's just existential for the future of the planet existential for the future of the planet existential for the future of the planet but you know we've just lived through a but you know we've just lived through a but you know we've just lived through a pandemic pandemic pandemic I would love to linger on this because I would love to linger on this because I would love to linger on this because the possibilities that are positive are the possibilities that are positive are the possibilities that are positive are endless yeah but the possibility is a

endless yeah but the possibility is a endless yeah but the possibility is a negative are still nevertheless negative are still nevertheless negative are still nevertheless extremely important was both positive extremely important was both positive extremely important was both positive and negative what do you do and negative what do you do and negative what do you do with a large number of General with a large number of General with a large number of General assemblers yeah with the Fab Lab you assemblers yeah with the Fab Lab you assemblers yeah with the Fab Lab you could roughly make a bio lab then learn could roughly make a bio lab then learn could roughly make a bio lab then learn biotechnology now that's terrifying biotechnology now that's terrifying biotechnology now that's terrifying because making self-reproducing gray goo because making self-reproducing gray goo because making self-reproducing gray goo that out competes biology I consider that out competes biology I consider that out competes biology I consider Doom because biology knows everything Doom because biology knows everything Doom because biology knows everything I'm describing and is really good at I'm describing and is really good at I'm describing and is really good at what it does what it does what it does um um um in in in how to grow almost anything you learn how to grow almost anything you learn how to grow almost anything you learn skills in biotechnology that would let skills in biotechnology that would let skills in biotechnology that would let that let you make serious biological that let you make serious biological that let you make serious biological threats and when you combine threats and when you combine threats and when you combine uh some of the Innovations you see with uh some of the Innovations you see with uh some of the Innovations you see with large language models some of the large language models some of the large language models some of the Innovations you see with Alpha fold so Innovations you see with Alpha fold so Innovations you see with Alpha fold so applications of AI for Designing applications of AI for Designing applications of AI for Designing biological systems for uh writing biological systems for uh writing biological systems for uh writing programs which you can large language programs which you can large language programs which you can large language models increasingly so there seems to be models increasingly so there seems to be models increasingly so there seems to be an interesting dance here of automating an interesting dance here of automating an interesting dance here of automating the design stage of complex systems the design stage of complex systems the design stage of complex systems using Ai and then that's the that's the using Ai and then that's the that's the using Ai and then that's the that's the bits and you can leap now the bits and you can leap now the bits and you can leap now the Innovations you're talking about you can Innovations you're talking about you can Innovations you're talking about you can leap from the complex systems in the leap from the complex systems in the leap from the complex systems in the digital space to the printing to the digital space to the printing to the digital space to the printing to the creation to the assembly creation to the assembly creation to the assembly at scale at scale at scale of uh complex systems in the physical of uh complex systems in the physical of uh complex systems in the physical space yeah so something to be scared space yeah so something to be scared space yeah so something to be scared about is

about is about is a Fab Lab can make a bio lab a bio lab a Fab Lab can make a bio lab a bio lab a Fab Lab can make a bio lab a bio lab can make biotechnology somebody could can make biotechnology somebody could can make biotechnology somebody could learn to make a virus that's scary that learn to make a virus that's scary that learn to make a virus that's scary that that's unlike some of the things I said that's unlike some of the things I said that's unlike some of the things I said I don't worry about that's something I I don't worry about that's something I I don't worry about that's something I really worry about that is scary now how really worry about that is scary now how really worry about that is scary now how do you deal with that uh do you deal with that uh do you deal with that uh prior threats we dealt with prior threats we dealt with prior threats we dealt with command and control command and control command and control so like uh so like uh so like uh early color copiers had unique codes and early color copiers had unique codes and early color copiers had unique codes and you could tell which copier made them you could tell which copier made them you could tell which copier made them eventually you couldn't keep up with eventually you couldn't keep up with eventually you couldn't keep up with that uh there there was a famous meeting that uh there there was a famous meeting that uh there there was a famous meeting at asilamar in the early days of at asilamar in the early days of at asilamar in the early days of recombinant DNA where that Community recombinant DNA where that Community recombinant DNA where that Community recognized the dangers of what it was recognized the dangers of what it was recognized the dangers of what it was doing and put in place a regime to help doing and put in place a regime to help doing and put in place a regime to help manage it and so that led to the kind of manage it and so that led to the kind of manage it and so that led to the kind of research management so you know MIT has research management so you know MIT has research management so you know MIT has an office that supervises research and an office that supervises research and an office that supervises research and it works with the national office that it works with the national office that it works with the national office that works if you can identify who's doing it works if you can identify who's doing it works if you can identify who's doing it and where it doesn't work in this world and where it doesn't work in this world and where it doesn't work in this world we're describing we're describing we're describing so anybody could do this anywhere and so so anybody could do this anywhere and so so anybody could do this anywhere and so what we found is you can't what we found is you can't what we found is you can't contain this it's already L you can't contain this it's already L you can't contain this it's already L you can't forbid because there isn't command and forbid because there isn't command and forbid because there isn't command and control the most useful thing you can do control the most useful thing you can do control the most useful thing you can do is provide incentives for transparency is provide incentives for transparency is provide incentives for transparency yes so but really the heart of what we yes so but really the heart of what we yes so but really the heart of what we do is you could do this by yourself in a do is you could do this by yourself in a do is you could do this by yourself in a basement for nefarious reasons or you

basement for nefarious reasons or you basement for nefarious reasons or you could come into a place in the light could come into a place in the light could come into a place in the light where you get help and you get community where you get help and you get community where you get help and you get community and you get resources and there's an and you get resources and there's an and you get resources and there's an incentive to do it in the open not in incentive to do it in the open not in incentive to do it in the open not in the dark and that might sound naive but the dark and that might sound naive but the dark and that might sound naive but in the sort of places we're working oh in the sort of places we're working oh in the sort of places we're working oh you know you know you know um again bad people do bad things in um again bad people do bad things in um again bad people do bad things in these places already but providing these places already but providing these places already but providing openness and providing transparency is a openness and providing transparency is a openness and providing transparency is a key part of managing these and so it it key part of managing these and so it it key part of managing these and so it it transitions from regulating risks as transitions from regulating risks as transitions from regulating risks as regulation to to soft power to manage regulation to to soft power to manage regulation to to soft power to manage them so there's so much potential for them so there's so much potential for them so there's so much potential for good so much capacity for good that Fab good so much capacity for good that Fab good so much capacity for good that Fab labs and the uh the the ability labs and the uh the the ability labs and the uh the the ability um and the tools of creation really um and the tools of creation really um and the tools of creation really unlock that potential unlock that potential unlock that potential yeah and I don't say that as sort of yeah and I don't say that as sort of yeah and I don't say that as sort of dewey-eyed naive I say that empirically dewey-eyed naive I say that empirically dewey-eyed naive I say that empirically from just years of seeing how this plays from just years of seeing how this plays from just years of seeing how this plays out in communities I wonder if it's the out in communities I wonder if it's the out in communities I wonder if it's the early days of personal computers though early days of personal computers though early days of personal computers though before we get spam right in the end most before we get spam right in the end most before we get spam right in the end most fundamentally fundamentally fundamentally literally the mother of all problems literally the mother of all problems literally the mother of all problems is is is who designed us so so assume who designed us so so assume who designed us so so assume success and that we're going to success and that we're going to success and that we're going to transition to the machines making transition to the machines making transition to the machines making machines and all of these new sort of machines and all of these new sort of machines and all of these new sort of social systems we're describing will social systems we're describing will social systems we're describing will help manage them and curate them and help manage them and curate them and help manage them and curate them and democratize them

democratize them democratize them if we close the gap I just let off with if we close the gap I just let off with if we close the gap I just let off with of 10 to the 10 to 10 to the 18 between of 10 to the 10 to 10 to the 18 between of 10 to the 10 to 10 to the 18 between chip Fab and you chip Fab and you chip Fab and you um we're ultimately in marrying um we're ultimately in marrying um we're ultimately in marrying communication computation and communication computation and communication computation and Fabrication going to be able to create Fabrication going to be able to create Fabrication going to be able to create unimaginable complexity um and how do you design that um and how do you design that and so I'd say and so I'd say and so I'd say the deepest of all questions that I've the deepest of all questions that I've the deepest of all questions that I've been working on been working on been working on is is is goes back to the oldest part of our goes back to the oldest part of our goes back to the oldest part of our genome so genome so genome so in our genome what are called Hox genes in our genome what are called Hox genes in our genome what are called Hox genes and these are morphogenes and these are morphogenes and these are morphogenes and and and nowhere in your genome is the number nowhere in your genome is the number nowhere in your genome is the number five it doesn't store the fact that you five it doesn't store the fact that you five it doesn't store the fact that you have five fingers have five fingers have five fingers um what it stores is What's called the um what it stores is What's called the um what it stores is What's called the developmental program it's a series of developmental program it's a series of developmental program it's a series of steps and the steps have the character steps and the steps have the character steps and the steps have the character of like grow up a gradient or break of like grow up a gradient or break of like grow up a gradient or break symmetry symmetry symmetry and at the end of that developmental and at the end of that developmental and at the end of that developmental program you have five fingers program you have five fingers program you have five fingers so so so you are stored not as a body plan you are stored not as a body plan you are stored not as a body plan but as a growth Plan and there's two but as a growth Plan and there's two but as a growth Plan and there's two reasons for that one reason is just reasons for that one reason is just reasons for that one reason is just compression billions of genes can place compression billions of genes can place compression billions of genes can place trillions of cells but the much deeper

trillions of cells but the much deeper trillions of cells but the much deeper one is evolution doesn't randomly one is evolution doesn't randomly one is evolution doesn't randomly perturb almost anything you did randomly perturb almost anything you did randomly perturb almost anything you did randomly in the genome would be fatal or in the genome would be fatal or in the genome would be fatal or inconsequential but not interesting but inconsequential but not interesting but inconsequential but not interesting but when you modify things in these when you modify things in these when you modify things in these developmental programs you go from like developmental programs you go from like developmental programs you go from like webs for swimming to fingers or you go webs for swimming to fingers or you go webs for swimming to fingers or you go from walking to wings for flying it's a from walking to wings for flying it's a from walking to wings for flying it's a space in which search is interesting so space in which search is interesting so space in which search is interesting so this is the heart of the success of AI this is the heart of the success of AI this is the heart of the success of AI in part it was the scaling we talked in part it was the scaling we talked in part it was the scaling we talked about a while ago about a while ago about a while ago and in part it was the representations and in part it was the representations and in part it was the representations for which search is effective for which search is effective for which search is effective AI has found good representations it AI has found good representations it AI has found good representations it hasn't found new ways to search but it's hasn't found new ways to search but it's hasn't found new ways to search but it's found good representations of search and found good representations of search and found good representations of search and that's you're saying that's what biology that's you're saying that's what biology that's you're saying that's what biology that's what evolution has done is that's what evolution has done is that's what evolution has done is creative representation structures creative representation structures creative representation structures biological structures through which biological structures through which biological structures through which search effective and so the the search effective and so the the search effective and so the the developmental programs in the genome developmental programs in the genome developmental programs in the genome beautifully encapsulate the lessons of beautifully encapsulate the lessons of beautifully encapsulate the lessons of AI and this is It's embody it's it's AI and this is It's embody it's it's AI and this is It's embody it's it's molecular intelligence it's AI embodied molecular intelligence it's AI embodied molecular intelligence it's AI embodied in our genome it it it it's every bit as in our genome it it it it's every bit as in our genome it it it it's every bit as profound as the cognition in our brain profound as the cognition in our brain profound as the cognition in our brain but now this is sort of thinking in but now this is sort of thinking in but now this is sort of thinking in molecular thinking in how you design molecular thinking in how you design molecular thinking in how you design and so and so and so um I'd say the most fundamental problem

um I'd say the most fundamental problem um I'd say the most fundamental problem we're working on is it's kind of we're working on is it's kind of we're working on is it's kind of tautological that when you design a tautological that when you design a tautological that when you design a phone phone phone you design the phone you represent the you design the phone you represent the you design the phone you represent the design of the phone but that actually design of the phone but that actually design of the phone but that actually fails when you get to the sort of fails when you get to the sort of fails when you get to the sort of complexity that we're talking about and complexity that we're talking about and complexity that we're talking about and so there's this profound transition to so there's this profound transition to so there's this profound transition to come once I can have self-readressing come once I can have self-readressing come once I can have self-readressing assemblers placing 10 to the 18 parts assemblers placing 10 to the 18 parts assemblers placing 10 to the 18 parts um you need to not sort of um you need to not sort of um you need to not sort of metaphorically but create life metaphorically but create life metaphorically but create life in that you need to learn how to evolve in that you need to learn how to evolve in that you need to learn how to evolve but evolutionary design has a really but evolutionary design has a really but evolutionary design has a really misleading trivial meaning it's not as misleading trivial meaning it's not as misleading trivial meaning it's not as simple as you randomly mutate things simple as you randomly mutate things simple as you randomly mutate things it's this much more deep embodiment of it's this much more deep embodiment of it's this much more deep embodiment of of AI and morphogenesis is there a way of AI and morphogenesis is there a way of AI and morphogenesis is there a way for us to continue the kind of evolution for us to continue the kind of evolution for us to continue the kind of evolution of design that led us to this place from of design that led us to this place from of design that led us to this place from the early days of bacteria single cell the early days of bacteria single cell the early days of bacteria single cell organisms to ribosomes and the 20 amino organisms to ribosomes and the 20 amino organisms to ribosomes and the 20 amino acids you mean for human augmentation or acids you mean for human augmentation or acids you mean for human augmentation or no for Life augment I mean what would no for Life augment I mean what would no for Life augment I mean what would you call assemblers that are you call assemblers that are you call assemblers that are self-replicating and placing Parts what self-replicating and placing Parts what self-replicating and placing Parts what is that the the dynamic complex things is that the the dynamic complex things is that the the dynamic complex things built with digital fabrication what is built with digital fabrication what is built with digital fabrication what is that that's the light so yeah so that that's the light so yeah so that that's the light so yeah so ultimately absolutely ultimately absolutely ultimately absolutely if you add everything I'm talking about if you add everything I'm talking about if you add everything I'm talking about it's building up to creating life in it's building up to creating life in it's building up to creating life in non-living materials yes and I I don't non-living materials yes and I I don't non-living materials yes and I I don't view this as copying life I view it as

view this as copying life I view it as view this as copying life I view it as driving life I I didn't start from how driving life I I didn't start from how driving life I I didn't start from how does biology work and then I'm going to does biology work and then I'm going to does biology work and then I'm going to copy it I start from how to solve copy it I start from how to solve copy it I start from how to solve problems and then it it leads me to in a problems and then it it leads me to in a problems and then it it leads me to in a sense ReDiscover biology so if we go sense ReDiscover biology so if we go sense ReDiscover biology so if we go back to Valentina in Ghana making her back to Valentina in Ghana making her back to Valentina in Ghana making her circuit board circuit board circuit board um she still needs a chip Fab very far um she still needs a chip Fab very far um she still needs a chip Fab very far away to make the processor under circuit away to make the processor under circuit away to make the processor under circuit board for her to make the processor board for her to make the processor board for her to make the processor locally for all the reasons we described locally for all the reasons we described locally for all the reasons we described you actually need the Deep things we you actually need the Deep things we you actually need the Deep things we were just talking about and so it really were just talking about and so it really were just talking about and so it really does lead you so let's see there's a does lead you so let's see there's a does lead you so let's see there's a wonderful series of books by gingery wonderful series of books by gingery wonderful series of books by gingery book one is how to make a charcoal book one is how to make a charcoal book one is how to make a charcoal furnace and at the end of book Seven you furnace and at the end of book Seven you furnace and at the end of book Seven you have a machine shop have a machine shop have a machine shop so it is it it's sort of how how you do so it is it it's sort of how how you do so it is it it's sort of how how you do your own personal Industrial Revolution your own personal Industrial Revolution your own personal Industrial Revolution uh isru is what NASA calls in-situ uh isru is what NASA calls in-situ uh isru is what NASA calls in-situ resource utilization and that's how do resource utilization and that's how do resource utilization and that's how do you go to a planet and create a you go to a planet and create a you go to a planet and create a civilization uh isru has essentially civilization uh isru has essentially civilization uh isru has essentially assumed gingery you go through the assumed gingery you go through the assumed gingery you go through the Industrial Revolution and you create the Industrial Revolution and you create the Industrial Revolution and you create the inventory of a hundred thousand inventory of a hundred thousand inventory of a hundred thousand resistors what we're finding is the way resistors what we're finding is the way resistors what we're finding is the way you the minimum building blocks for a you the minimum building blocks for a you the minimum building blocks for a civilization is civilization is civilization is roughly 20 parts so what's interesting roughly 20 parts so what's interesting roughly 20 parts so what's interesting about the amino acids is they're not about the amino acids is they're not about the amino acids is they're not interesting they're hydrophobic or interesting they're hydrophobic or interesting they're hydrophobic or hydrophilic basic or acidic they have hydrophilic basic or acidic they have hydrophilic basic or acidic they have typical but not extremal properties but typical but not extremal properties but typical but not extremal properties but they're good enough you can combine them

they're good enough you can combine them they're good enough you can combine them to make you to make you to make you so what this is leading towards is so what this is leading towards is so what this is leading towards is technology doesn't need enormous Global technology doesn't need enormous Global technology doesn't need enormous Global Supply chains it just needs about 20 Supply chains it just needs about 20 Supply chains it just needs about 20 properties you can compose to create all properties you can compose to create all properties you can compose to create all technology as the minimum building technology as the minimum building technology as the minimum building blocks for a technological civilization blocks for a technological civilization blocks for a technological civilization so there's going to be 20 basic building so there's going to be 20 basic building so there's going to be 20 basic building blocks based on which the blocks based on which the blocks based on which the self-replicating assemblers can work self-replicating assemblers can work self-replicating assemblers can work right and I say that not philosophically right and I say that not philosophically right and I say that not philosophically just empirically sort of that's that just empirically sort of that's that just empirically sort of that's that that's where it's heading and that's where it's heading and that's where it's heading and yeah that I like thinking about how you yeah that I like thinking about how you yeah that I like thinking about how you bootstrap a civilization on Mars that bootstrap a civilization on Mars that bootstrap a civilization on Mars that problem there's a fun video on bonus problem there's a fun video on bonus problem there's a fun video on bonus material for the movie where where with material for the movie where where with material for the movie where where with a neat group of people we talk about it a neat group of people we talk about it a neat group of people we talk about it because it has really profound because it has really profound because it has really profound implications back here on Earth about implications back here on Earth about implications back here on Earth about how we live sustainably how we live sustainably how we live sustainably what is that Civilization on Mars looks what is that Civilization on Mars looks what is that Civilization on Mars looks like that's using a isru that's using like that's using a isru that's using like that's using a isru that's using these 20 building blocks and does these 20 building blocks and does these 20 building blocks and does self-assembly yeah go go through primary self-assembly yeah go go through primary self-assembly yeah go go through primary secondary tertiary quaternary secondary tertiary quaternary secondary tertiary quaternary um you know you you extract properties um you know you you extract properties um you know you you extract properties like uh conducting insulating like uh conducting insulating like uh conducting insulating semiconducting uh magnetic uh dielectric semiconducting uh magnetic uh dielectric semiconducting uh magnetic uh dielectric flexural these are the kind of you know flexural these are the kind of you know flexural these are the kind of you know roughly 20 properties roughly 20 properties roughly 20 properties um with those um with those um with those those are enough for us to assemble those are enough for us to assemble those are enough for us to assemble logic logic logic and they're enough for us to assemble and they're enough for us to assemble and they're enough for us to assemble actuation actuation actuation um with logic and actuation we can make um with logic and actuation we can make um with logic and actuation we can make micro robots

micro robots micro robots the micro robots can build bigger robots the micro robots can build bigger robots the micro robots can build bigger robots um the bigger robots can then take the um the bigger robots can then take the um the bigger robots can then take the building block materials and make the building block materials and make the building block materials and make the structural elements that you then do to structural elements that you then do to structural elements that you then do to make construction and then you boot up make construction and then you boot up make construction and then you boot up through the stages of a technological through the stages of a technological through the stages of a technological Civilization by the way where in the Civilization by the way where in the Civilization by the way where in the span of logic and actuation to the span of logic and actuation to the span of logic and actuation to the sensing come in oh I skipped over that sensing come in oh I skipped over that sensing come in oh I skipped over that but my favorite sensor is a step but my favorite sensor is a step but my favorite sensor is a step response so if you just make a step and response so if you just make a step and response so if you just make a step and measure the response to the electric measure the response to the electric measure the response to the electric field field field that ranges from user interfaces to that ranges from user interfaces to that ranges from user interfaces to positioning to material properties and positioning to material properties and positioning to material properties and if you do it at higher frequencies you if you do it at higher frequencies you if you do it at higher frequencies you get chemistry and you can get all of get chemistry and you can get all of get chemistry and you can get all of that just from a step in an electric that just from a step in an electric that just from a step in an electric field so for example once you have time field so for example once you have time field so for example once you have time resolution in logic something as simple resolution in logic something as simple resolution in logic something as simple as two elect roads let you do amazingly as two elect roads let you do amazingly as two elect roads let you do amazingly capable sensing so we've been talking capable sensing so we've been talking capable sensing so we've been talking about all the work I do there's a story about all the work I do there's a story about all the work I do there's a story about about about how it happens you know where do ideas how it happens you know where do ideas how it happens you know where do ideas come from and that's an interesting come from and that's an interesting come from and that's an interesting story where do I just come from so I had story where do I just come from so I had story where do I just come from so I had mentioned veniver Bush and mentioned veniver Bush and mentioned veniver Bush and uh he wrote a really influential thing uh he wrote a really influential thing uh he wrote a really influential thing called the endless Frontier so uh called the endless Frontier so uh called the endless Frontier so uh science won World War II the the the the science won World War II the the the the science won World War II the the the the more known story is nuclear bombs the more known story is nuclear bombs the more known story is nuclear bombs the less well-known story is the rad lab so

less well-known story is the rad lab so less well-known story is the rad lab so at MIT an amazing group of people at MIT an amazing group of people at MIT an amazing group of people invented radar which is really credited invented radar which is really credited invented radar which is really credited as winning the war so after the war uh as winning the war so after the war uh as winning the war so after the war uh grand old man from MIT and it's a grand old man from MIT and it's a grand old man from MIT and it's a um uh was charged with science won the um uh was charged with science won the um uh was charged with science won the war how do we maintain that edge and the war how do we maintain that edge and the war how do we maintain that edge and the report he wrote led to the National report he wrote led to the National report he wrote led to the National Science Foundation and the modern notion Science Foundation and the modern notion Science Foundation and the modern notion we take for granted but didn't really we take for granted but didn't really we take for granted but didn't really exist before then of Public Funding of exist before then of Public Funding of exist before then of Public Funding of research or research agencies research or research agencies research or research agencies in it he made again what I consider an in it he made again what I consider an in it he made again what I consider an important mistake which is he described important mistake which is he described important mistake which is he described basic research leads to applied research basic research leads to applied research basic research leads to applied research search leads to Applications leads to search leads to Applications leads to search leads to Applications leads to commercialization leads to impact and so commercialization leads to impact and so commercialization leads to impact and so we need to invest in that pipeline we need to invest in that pipeline we need to invest in that pipeline the reason I considered a mistake the reason I considered a mistake the reason I considered a mistake is almost all of the examples we've been is almost all of the examples we've been is almost all of the examples we've been talking about talking about talking about in my lab went backwards that the basic in my lab went backwards that the basic in my lab went backwards that the basic research came from applications research came from applications research came from applications and further almost all of the examples and further almost all of the examples and further almost all of the examples we've been talking about came we've been talking about came we've been talking about came fundamentally from mistakes so yeah fundamentally from mistakes so yeah fundamentally from mistakes so yeah essentially everything I've ever worked essentially everything I've ever worked essentially everything I've ever worked on has failed on has failed on has failed but in failing something better happened but in failing something better happened but in failing something better happened so the way I like to describe it is so the way I like to describe it is so the way I like to describe it is ready aim fire is you do your homework

ready aim fire is you do your homework ready aim fire is you do your homework um you aim carefully at something a um you aim carefully at something a um you aim carefully at something a Target you want to accomplish and if Target you want to accomplish and if Target you want to accomplish and if everything goes right you then hit the everything goes right you then hit the everything goes right you then hit the target and succeed target and succeed target and succeed um what I do you can think of is ready um what I do you can think of is ready um what I do you can think of is ready fire Aim so you you do a lot of work to fire Aim so you you do a lot of work to fire Aim so you you do a lot of work to get ready get ready get ready then you close your eyes and you don't then you close your eyes and you don't then you close your eyes and you don't really think about where you're aiming really think about where you're aiming really think about where you're aiming but you look very carefully at where you but you look very carefully at where you but you look very carefully at where you didn't didn't didn't you aim after you fire you aim after you fire you aim after you fire and the the reason that's so important and the the reason that's so important and the the reason that's so important is it if you do Ready Aim Fire there's is it if you do Ready Aim Fire there's is it if you do Ready Aim Fire there's the best you can hope is hit what you the best you can hope is hit what you the best you can hope is hit what you aim at so let me give you some examples aim at so let me give you some examples aim at so let me give you some examples uh because this is a source of great uh because this is a source of great uh because this is a source of great full of good lines today full of good lines today full of good lines today source of great frustration so I source of great frustration so I source of great frustration so I mentioned the early Quantum Computing so mentioned the early Quantum Computing so mentioned the early Quantum Computing so Quantum Computing is this power of using Quantum Computing is this power of using Quantum Computing is this power of using quantum mechanics to make computers that quantum mechanics to make computers that quantum mechanics to make computers that for some problems are dramatically more for some problems are dramatically more for some problems are dramatically more powerful than classical computers powerful than classical computers powerful than classical computers before it started there was a really before it started there was a really before it started there was a really interesting group of people who knew a interesting group of people who knew a interesting group of people who knew a lot about lot about lot about um physics and computing um physics and computing um physics and computing that were inventing what became Quantum that were inventing what became Quantum that were inventing what became Quantum Computing before it was clear anything Computing before it was clear anything Computing before it was clear anything there was an opportunity there it was there was an opportunity there it was there was an opportunity there it was just studying how those relate here's just studying how those relate here's just studying how those relate here's how it fits to the ready fire aim in I how it fits to the ready fire aim in I how it fits to the ready fire aim in I was doing really short-term work in my was doing really short-term work in my was doing really short-term work in my lab on shoplifting tags lab on shoplifting tags lab on shoplifting tags on this was really before there was on this was really before there was on this was really before there was Modern RFID and so how you put tags in

Modern RFID and so how you put tags in Modern RFID and so how you put tags in objects to sense them objects to sense them objects to sense them something we just take for granted something we just take for granted something we just take for granted commercially and there was a problem of commercially and there was a problem of commercially and there was a problem of how you can sense multiple objects at how you can sense multiple objects at how you can sense multiple objects at the same time the same time the same time and so I was studying how you can and so I was studying how you can and so I was studying how you can remotely sense materials to make remotely sense materials to make remotely sense materials to make low-cost tags that could let you low-cost tags that could let you low-cost tags that could let you distinguish multiple objects distinguish multiple objects distinguish multiple objects simultaneously to do that you need simultaneously to do that you need simultaneously to do that you need non-linearity so that the signal is non-linearity so that the signal is non-linearity so that the signal is modulated modulated modulated and so I was looking for materials and so I was looking for materials and so I was looking for materials sources of non-linearity and that led me sources of non-linearity and that led me sources of non-linearity and that led me to look at how nuclear spins interact to look at how nuclear spins interact to look at how nuclear spins interact just just for for just just for for just just for for um spin resonance this is the sort of um spin resonance this is the sort of um spin resonance this is the sort of things you use when you let go in an MRI things you use when you let go in an MRI things you use when you let go in an MRI machine machine machine and so I was studying how to use that and so I was studying how to use that and so I was studying how to use that and it turns out that it was a bad idea and it turns out that it was a bad idea and it turns out that it was a bad idea you couldn't remotely use it for you couldn't remotely use it for you couldn't remotely use it for um shoplifting tags um shoplifting tags um shoplifting tags but I realized you could compute and so but I realized you could compute and so but I realized you could compute and so um with a group of colleagues thinking um with a group of colleagues thinking um with a group of colleagues thinking about early Quantum Computing like David about early Quantum Computing like David about early Quantum Computing like David divincenzo and Charlie Bennett was divincenzo and Charlie Bennett was divincenzo and Charlie Bennett was articulating what are the properties you articulating what are the properties you articulating what are the properties you need to compute and then looking at how need to compute and then looking at how need to compute and then looking at how to make the tags it turns out the tags to make the tags it turns out the tags to make the tags it turns out the tags were a terrible idea were a terrible idea were a terrible idea for for for um sensing um sensing um sensing objects in a supermarket checkout but I objects in a supermarket checkout but I objects in a supermarket checkout but I realized they were Computing so with Ike realized they were Computing so with Ike realized they were Computing so with Ike Trang and a few other people we realized Trang and a few other people we realized Trang and a few other people we realized we could program nuclear spins to we could program nuclear spins to we could program nuclear spins to compute and so that's what we use to do

compute and so that's what we use to do compute and so that's what we use to do Grover's search algorithm and then it Grover's search algorithm and then it Grover's search algorithm and then it was used for a shortest factoring was used for a shortest factoring was used for a shortest factoring algorithm and it worked out the systems algorithm and it worked out the systems algorithm and it worked out the systems we did it in nuclear magnetic resonance we did it in nuclear magnetic resonance we did it in nuclear magnetic resonance don't scale Beyond a few qubits but the don't scale Beyond a few qubits but the don't scale Beyond a few qubits but the techniques have lived on and so you know techniques have lived on and so you know techniques have lived on and so you know all the current Quantum Computing all the current Quantum Computing all the current Quantum Computing techniques grew out of the ways we would techniques grew out of the ways we would techniques grew out of the ways we would talk to these spins but I'm telling this talk to these spins but I'm telling this talk to these spins but I'm telling this whole story because it it came from a whole story because it it came from a whole story because it it came from a bad way to make a shoplifting tag bad way to make a shoplifting tag bad way to make a shoplifting tag starting with an application mistakes starting with an application mistakes starting with an application mistakes led to the fundamental science led to the fundamental science led to the fundamental science fundamental science yeah I mean can you fundamental science yeah I mean can you fundamental science yeah I mean can you can you just link on that I mean just can you just link on that I mean just can you just link on that I mean just just in using nuclear expensive do just in using nuclear expensive do just in using nuclear expensive do computation that like computation that like computation that like what gave you the guts to try to think what gave you the guts to try to think what gave you the guts to try to think through this the from a fabric from a through this the from a fabric from a through this the from a fabric from a digital fabrication perspective actually digital fabrication perspective actually digital fabrication perspective actually how to LEAP from one to the other yeah I how to LEAP from one to the other yeah I how to LEAP from one to the other yeah I wouldn't call it guts I would call it wouldn't call it guts I would call it wouldn't call it guts I would call it collaboration so I so at IBM there was collaboration so I so at IBM there was collaboration so I so at IBM there was this amazing group of like I mentioned this amazing group of like I mentioned this amazing group of like I mentioned Charlie Bennett and David divincenzo and Charlie Bennett and David divincenzo and Charlie Bennett and David divincenzo and Ralph Landau and Nabil Amir and these Ralph Landau and Nabil Amir and these Ralph Landau and Nabil Amir and these were all gods of thinking about physics were all gods of thinking about physics were all gods of thinking about physics and Computing so I I I I I yelled at the and Computing so I I I I I yelled at the and Computing so I I I I I yelled at the whole computer industry being based on whole computer industry being based on whole computer industry being based on uh a fiction Metropolis you know uh a fiction Metropolis you know uh a fiction Metropolis you know programmers frolicking the garden while programmers frolicking the garden while programmers frolicking the garden while somebody moves levers in the basement somebody moves levers in the basement somebody moves levers in the basement there's a complete parallel history of there's a complete parallel history of there's a complete parallel history of um uh Maxwell the boltzman to zillard to

um uh Maxwell the boltzman to zillard to um uh Maxwell the boltzman to zillard to um landower to Bennett and most people um landower to Bennett and most people um landower to Bennett and most people won't know most of these names but this won't know most of these names but this won't know most of these names but this whole parallel history thinking deeply whole parallel history thinking deeply whole parallel history thinking deeply about how computation and physics relate about how computation and physics relate about how computation and physics relate so so so um I was collaborating with that whole um I was collaborating with that whole um I was collaborating with that whole group of people group of people group of people and then and then and then you know at MIT I was in this high you know at MIT I was in this high you know at MIT I was in this high traffic environment I wasn't deeply traffic environment I wasn't deeply traffic environment I wasn't deeply inspired to think about better ways to inspired to think about better ways to inspired to think about better ways to detect shoplifting tags but you know detect shoplifting tags but you know detect shoplifting tags but you know stumbled across companies that needed stumbled across companies that needed stumbled across companies that needed help with that and was thinking about it help with that and was thinking about it help with that and was thinking about it and then I realized those two worlds and then I realized those two worlds and then I realized those two worlds intersected and we could use the failed intersected and we could use the failed intersected and we could use the failed approach for the shoplifting tags to approach for the shoplifting tags to approach for the shoplifting tags to make make make um early Quantum Computing algorithms um early Quantum Computing algorithms um early Quantum Computing algorithms and this kind of stumbling is and this kind of stumbling is and this kind of stumbling is fundamental to the Fab Lab idea right fundamental to the Fab Lab idea right fundamental to the Fab Lab idea right right here's one more example with a right here's one more example with a right here's one more example with a student Manu we talked about ribosomes student Manu we talked about ribosomes student Manu we talked about ribosomes and I was trying to build a ribosome and I was trying to build a ribosome and I was trying to build a ribosome um that worked on fluids so that I could um that worked on fluids so that I could um that worked on fluids so that I could place the little Parts we're talking place the little Parts we're talking place the little Parts we're talking about and we it kept failing because about and we it kept failing because about and we it kept failing because bubbles would come into our system and bubbles would come into our system and bubbles would come into our system and the bubbles would make the whole thing the bubbles would make the whole thing the bubbles would make the whole thing stop working and we spent about half a stop working and we spent about half a stop working and we spent about half a year trying to get rid of the bubbles year trying to get rid of the bubbles year trying to get rid of the bubbles then Manu said wait a minute the bubbles then Manu said wait a minute the bubbles then Manu said wait a minute the bubbles are actually are actually are actually better than what we're doing we should better than what we're doing we should better than what we're doing we should just use the bubbles and so we invented just use the bubbles and so we invented just use the bubbles and so we invented how to do Universal object with little how to do Universal object with little how to do Universal object with little logic with little Bubbles and fluid okay logic with little Bubbles and fluid okay logic with little Bubbles and fluid okay you have to you have to explain this you have to you have to explain this you have to you have to explain this microfluidic bubble logic please how microfluidic bubble logic please how microfluidic bubble logic please how does this work so yeah that's super does this work so yeah that's super does this work so yeah that's super interesting yeah and so over so I'll

interesting yeah and so over so I'll interesting yeah and so over so I'll come back and explain it but what it led come back and explain it but what it led come back and explain it but what it led to was to was to was um we showed fluids could do um we showed fluids could do um we showed fluids could do um it had been known fluid could do um it had been known fluid could do um it had been known fluid could do logic like your old automobile logic like your old automobile logic like your old automobile transition Transmissions do logic but transition Transmissions do logic but transition Transmissions do logic but that's macroscopic it didn't work at that's macroscopic it didn't work at that's macroscopic it didn't work at little scales we showed with these little scales we showed with these little scales we showed with these bubbles we could do it at little scales bubbles we could do it at little scales bubbles we could do it at little scales that then I'm going to come back and that then I'm going to come back and that then I'm going to come back and explain it but what came out of that is explain it but what came out of that is explain it but what came out of that is Manu then showed you could make a 50 Manu then showed you could make a 50 Manu then showed you could make a 50 Cent microscope using little Bubbles and Cent microscope using little Bubbles and Cent microscope using little Bubbles and then then then um the techniques we developed are what um the techniques we developed are what um the techniques we developed are what we use to transplant genomes to make we use to transplant genomes to make we use to transplant genomes to make synthetic life all came out of the synthetic life all came out of the synthetic life all came out of the failure of trying to make a the genome failure of trying to make a the genome failure of trying to make a the genome the the the ribosome now so the way the the the the ribosome now so the way the the the the ribosome now so the way the bubble logic works is bubble logic works is bubble logic works is um in a little child Channel um in a little child Channel um in a little child Channel uh fluid at small scales is fairly uh fluid at small scales is fairly uh fluid at small scales is fairly viscous it's sort of like pushing Jello viscous it's sort of like pushing Jello viscous it's sort of like pushing Jello think of it as think of it as think of it as um um um if a bubble gets stuck the fluid has to if a bubble gets stuck the fluid has to if a bubble gets stuck the fluid has to detour around it detour around it detour around it so now imagine so now imagine so now imagine a channel that has two Wells and one a channel that has two Wells and one a channel that has two Wells and one bubble bubble bubble if the bubble is in one well the fluid if the bubble is in one well the fluid if the bubble is in one well the fluid has to go in the other channel has to go in the other channel has to go in the other channel if the fluid is in the other well it has if the fluid is in the other well it has if the fluid is in the other well it has to go in the first channel to go in the first channel to go in the first channel so the the position of the bubble so the the position of the bubble so the the position of the bubble can switch can switch can switch it's a switch it can switch the fluid it's a switch it can switch the fluid it's a switch it can switch the fluid between two channels so now we have one between two channels so now we have one between two channels so now we have one element of switch and it's also a memory element of switch and it's also a memory element of switch and it's also a memory because you can detect whether or not a

because you can detect whether or not a because you can detect whether or not a bubble is stored there bubble is stored there bubble is stored there then if two bubbles meet then if two bubbles meet then if two bubbles meet um if you have two channels crossing a um if you have two channels crossing a um if you have two channels crossing a bubble can go through one way or a bubble can go through one way or a bubble can go through one way or a bubble can go through the other way but bubble can go through the other way but bubble can go through the other way but if two bubbles come together then they if two bubbles come together then they if two bubbles come together then they push on each other and one goes one way push on each other and one goes one way push on each other and one goes one way and one goes the other way that's a and one goes the other way that's a and one goes the other way that's a logic operation that's a logic gate so logic operation that's a logic gate so logic operation that's a logic gate so we now have a switch we have a memory we now have a switch we have a memory we now have a switch we have a memory and we have a logic eight and that's and we have a logic eight and that's and we have a logic eight and that's everything you need to make a universal everything you need to make a universal everything you need to make a universal computer computer computer I mean the fact that you did that with I mean the fact that you did that with I mean the fact that you did that with bubbles and microfluids just bubbles and microfluids just bubbles and microfluids just kind of brilliant well so I mean to stay kind of brilliant well so I mean to stay kind of brilliant well so I mean to stay with that example uh it it what we with that example uh it it what we with that example uh it it what we propose to do was to make a fluidic propose to do was to make a fluidic propose to do was to make a fluidic ribosome and the project crashed and ribosome and the project crashed and ribosome and the project crashed and burned it was a disaster burned it was a disaster burned it was a disaster um this is what came out of it and so it um this is what came out of it and so it um this is what came out of it and so it was was was precisely ready fire aim in that we had precisely ready fire aim in that we had precisely ready fire aim in that we had to do a lot of homework to be able to to do a lot of homework to be able to to do a lot of homework to be able to make these microfluidic systems make these microfluidic systems make these microfluidic systems the the Fire part was we didn't think the the Fire part was we didn't think the the Fire part was we didn't think too hard about making the ribosome we too hard about making the ribosome we too hard about making the ribosome we just tried to do it the aim part was we just tried to do it the aim part was we just tried to do it the aim part was we realized the ribosome failed but realized the ribosome failed but realized the ribosome failed but something better had happened and if you something better had happened and if you something better had happened and if you look all across research funding look all across research funding look all across research funding research management research management research management it doesn't it doesn't it doesn't anticipate this so fail fast is familiar anticipate this so fail fast is familiar anticipate this so fail fast is familiar but fail fast tends to miss ready and but fail fast tends to miss ready and but fail fast tends to miss ready and aim you can't just fail you have to do aim you can't just fail you have to do aim you can't just fail you have to do your homework before the fail part and your homework before the fail part and your homework before the fail part and you have to do the aim part after the

you have to do the aim part after the you have to do the aim part after the fail part and so the whole language of fail part and so the whole language of fail part and so the whole language of research is about like milestones and research is about like milestones and research is about like milestones and deliverables that works when you're deliverables that works when you're deliverables that works when you're going down a straight line but it going down a straight line but it going down a straight line but it doesn't work for this kind of Discovery doesn't work for this kind of Discovery doesn't work for this kind of Discovery and to LEAP to something you said that's and to LEAP to something you said that's and to LEAP to something you said that's really important is I view part of what really important is I view part of what really important is I view part of what the Fab Lab network is doing is giving the Fab Lab network is doing is giving the Fab Lab network is doing is giving more people the opportunity to fail more people the opportunity to fail more people the opportunity to fail you've said that geometry is really you've said that geometry is really you've said that geometry is really important in biology important in biology important in biology um um um what is fabrication biology look like what is fabrication biology look like what is fabrication biology look like why is geometry important so molecular why is geometry important so molecular why is geometry important so molecular biology is dominated by geometry that's biology is dominated by geometry that's biology is dominated by geometry that's why the protein folding is so important why the protein folding is so important why the protein folding is so important that that that the geometry gives the that that that the geometry gives the that that that the geometry gives the function function function and and and uh there's this hierarchical uh there's this hierarchical uh there's this hierarchical construction of as you go through construction of as you go through construction of as you go through primary second tertiary quaternary the primary second tertiary quaternary the primary second tertiary quaternary the shapes of the molecules make the shape shapes of the molecules make the shape shapes of the molecules make the shape of the molecular machines and they of the molecular machines and they of the molecular machines and they really are Exquisite machines if you really are Exquisite machines if you really are Exquisite machines if you look at how look at how look at how um if you look at how your muscles move um if you look at how your muscles move um if you look at how your muscles move if you were to see a simulation of it it if you were to see a simulation of it it if you were to see a simulation of it it would look like a improbable science would look like a improbable science would look like a improbable science fiction cyborg world of these little fiction cyborg world of these little fiction cyborg world of these little walking robots that walk on a discrete walking robots that walk on a discrete walking robots that walk on a discrete lattice they're really Exquisite lattice they're really Exquisite lattice they're really Exquisite machines and and then from there this machines and and then from there this machines and and then from there this this whole hierarchical stack of once this whole hierarchical stack of once this whole hierarchical stack of once you get to the top of that you then you get to the top of that you then you get to the top of that you then start making organelles that make cells start making organelles that make cells start making organelles that make cells that make organs through the stack of

that make organs through the stack of that make organs through the stack of that hierarchy that hierarchy that hierarchy just stepping back does it Amaze you just stepping back does it Amaze you just stepping back does it Amaze you that from small building blocks where um that from small building blocks where um that from small building blocks where um amino acids you mentioned molecules amino acids you mentioned molecules amino acids you mentioned molecules let's go to the very beginning of let's go to the very beginning of let's go to the very beginning of hydrogen and helium at the start of this hydrogen and helium at the start of this hydrogen and helium at the start of this universe they were able to build up such universe they were able to build up such universe they were able to build up such um um um complex and beautiful things like our complex and beautiful things like our complex and beautiful things like our human brain so studying thermodynamics which is exactly the question of which is exactly the question of you know that batteries run out and need you know that batteries run out and need you know that batteries run out and need recharging recharging recharging you know equipment you know equipment you know equipment you know cars get old and fail yet life you know cars get old and fail yet life you know cars get old and fail yet life doesn't and it that's why there's a doesn't and it that's why there's a doesn't and it that's why there's a sense in which life seems to violate sense in which life seems to violate sense in which life seems to violate thermodynamics although of course it thermodynamics although of course it thermodynamics although of course it doesn't it seems to resist the March doesn't it seems to resist the March doesn't it seems to resist the March towards entropy somehow right and so towards entropy somehow right and so towards entropy somehow right and so Maxwell who helped give rise to the Maxwell who helped give rise to the Maxwell who helped give rise to the science of thermodynamics uh posited a a science of thermodynamics uh posited a a science of thermodynamics uh posited a a problem that was so infuriating it led problem that was so infuriating it led problem that was so infuriating it led to a series of suicides there was a to a series of suicides there was a to a series of suicides there was a series of series of series of advisors and advisees advisors and advisees advisors and advisees um three in a row that all ended up um three in a row that all ended up um three in a row that all ended up committing suicide that happened to work committing suicide that happened to work committing suicide that happened to work on this problem on this problem on this problem and uh Maxwell's demon and uh Maxwell's demon and uh Maxwell's demon is this simple but Infamous problem is this simple but Infamous problem is this simple but Infamous problem where

where where right now in this room we're surrounded right now in this room we're surrounded right now in this room we're surrounded by molecules and they run at different by molecules and they run at different by molecules and they run at different velocities velocities velocities um imagine a container that has a wall um imagine a container that has a wall um imagine a container that has a wall and it's got gas on both sides and a and it's got gas on both sides and a and it's got gas on both sides and a little door and if the door is a little door and if the door is a little door and if the door is a molecular sized creature molecular sized creature molecular sized creature and it could watch the molecules coming and it could watch the molecules coming and it could watch the molecules coming and when a fast molecule is coming it and when a fast molecule is coming it and when a fast molecule is coming it opens the door when a slow molecule is opens the door when a slow molecule is opens the door when a slow molecule is coming it closes the door coming it closes the door coming it closes the door after it does that for a while one side after it does that for a while one side after it does that for a while one side is hot one is cold when something is hot is hot one is cold when something is hot is hot one is cold when something is hot and is cold you can make an engine and and is cold you can make an engine and and is cold you can make an engine and so you close that you make an engine and so you close that you make an engine and so you close that you make an engine and you make energy you make energy you make energy so the demon is violating thermodynamics so the demon is violating thermodynamics so the demon is violating thermodynamics because it's it's not it's never because it's it's not it's never because it's it's not it's never touching the molecule touching the molecule touching the molecule yet by just opening and closing the door yet by just opening and closing the door yet by just opening and closing the door it can make arbitrary amounts of energy it can make arbitrary amounts of energy it can make arbitrary amounts of energy and power a machine and in and power a machine and in and power a machine and in thermodynamics you can't do that so thermodynamics you can't do that so thermodynamics you can't do that so that's Maxwell's demon that's Maxwell's demon that's Maxwell's demon uh uh uh that problem is connected to everything that problem is connected to everything that problem is connected to everything we just spoke about for the last few we just spoke about for the last few we just spoke about for the last few hours so uh Leo zillard hours so uh Leo zillard hours so uh Leo zillard uh around uh around uh around early 1900s was a deep physicist who early 1900s was a deep physicist who early 1900s was a deep physicist who then had a lot to do with also then had a lot to do with also then had a lot to do with also post-war anti-nuclear things but he post-war anti-nuclear things but he post-war anti-nuclear things but he reduced Maxwell's demon to a single reduced Maxwell's demon to a single reduced Maxwell's demon to a single molecule so the molecule one there's

molecule so the molecule one there's molecule so the molecule one there's only one molecule and the question is only one molecule and the question is only one molecule and the question is which side of the partition is it on which side of the partition is it on which side of the partition is it on that led to the idea of one bit of that led to the idea of one bit of that led to the idea of one bit of information so Shannon credited information so Shannon credited information so Shannon credited zillard's analysis of Maxwell's Neiman zillard's analysis of Maxwell's Neiman zillard's analysis of Maxwell's Neiman for the invention of the bit for the invention of the bit for the invention of the bit um for many years people tried to um for many years people tried to um for many years people tried to explain Maxwell's demon by like the explain Maxwell's demon by like the explain Maxwell's demon by like the energy in the demon looking at the energy in the demon looking at the energy in the demon looking at the molecule molecule molecule or the energy to open and close the door or the energy to open and close the door or the energy to open and close the door and nothing ever made sense and nothing ever made sense and nothing ever made sense finally Ralph landauer one of the finally Ralph landauer one of the finally Ralph landauer one of the colleagues I mentioned at IBM colleagues I mentioned at IBM colleagues I mentioned at IBM finally solve the problem finally solve the problem finally solve the problem he showed that you can explain Maxwell's he showed that you can explain Maxwell's he showed that you can explain Maxwell's demon demon demon by you need the mind of the demon when the demon opened and closes the when the demon opened and closes the door as long as it remembers what it did door as long as it remembers what it did door as long as it remembers what it did you can run the whole thing backwards you can run the whole thing backwards you can run the whole thing backwards but when the demon forgets but when the demon forgets but when the demon forgets then you can't run it backwards then you can't run it backwards then you can't run it backwards and that's where you get dissipation and and that's where you get dissipation and and that's where you get dissipation and that's where you get the violation of that's where you get the violation of that's where you get the violation of thermodynamics and so the explanation of thermodynamics and so the explanation of thermodynamics and so the explanation of Maxwell's demon is that it's it's in the Maxwell's demon is that it's it's in the Maxwell's demon is that it's it's in the Demon's brain so then Demon's brain so then Demon's brain so then Ross Khalid colleague Charlie at IBM Ross Khalid colleague Charlie at IBM Ross Khalid colleague Charlie at IBM uh then shocked Ralph by showing you can uh then shocked Ralph by showing you can uh then shocked Ralph by showing you can compute with arbitrarily low energy compute with arbitrarily low energy compute with arbitrarily low energy so one of the things that's not well

so one of the things that's not well so one of the things that's not well covered is the the big computers used covered is the the big computers used covered is the the big computers used for big machine learning the data for big machine learning the data for big machine learning the data centers use tens of megawatts of power centers use tens of megawatts of power centers use tens of megawatts of power they use as much power as a city they use as much power as a city they use as much power as a city um Charlie showed you can actually um Charlie showed you can actually um Charlie showed you can actually compute with arbitrarily low amounts of compute with arbitrarily low amounts of compute with arbitrarily low amounts of energy energy energy by making computers that can go by making computers that can go by making computers that can go backwards as well as forwards backwards as well as forwards backwards as well as forwards and what limits the speed of the and what limits the speed of the and what limits the speed of the computer is computer is computer is how fast you want an answer and how how fast you want an answer and how how fast you want an answer and how certain you want the answer to be certain you want the answer to be certain you want the answer to be but where orders of magnitude away from but where orders of magnitude away from but where orders of magnitude away from that so I have a student Cameron working that so I have a student Cameron working that so I have a student Cameron working with Lincoln Labs on making with Lincoln Labs on making with Lincoln Labs on making superconducting computers that operate superconducting computers that operate superconducting computers that operate near this land hour limit that are near this land hour limit that are near this land hour limit that are orders of magnitude more efficient orders of magnitude more efficient orders of magnitude more efficient um so stepping back to all of that that um so stepping back to all of that that um so stepping back to all of that that whole tour was driven by your question whole tour was driven by your question whole tour was driven by your question about life about life about life and you know right at the heart of it is and you know right at the heart of it is and you know right at the heart of it is Maxwell's demon life exists because it Maxwell's demon life exists because it Maxwell's demon life exists because it can locally violate thermodynamics can locally violate thermodynamics can locally violate thermodynamics they can locally violate thermodynamics they can locally violate thermodynamics they can locally violate thermodynamics because of intelligence because of intelligence because of intelligence and it's its molecular intelligence that and it's its molecular intelligence that and it's its molecular intelligence that you know I would even go out on a limb you know I would even go out on a limb you know I would even go out on a limb to say we can already see we're to say we can already see we're to say we can already see we're beginning to come to the end of this beginning to come to the end of this beginning to come to the end of this current AI phase so depending on how you current AI phase so depending on how you current AI phase so depending on how you count this is I'd say the fifth AI boom count this is I'd say the fifth AI boom count this is I'd say the fifth AI boom bust cycle bust cycle bust cycle and you can already you know it it's and you can already you know it it's and you can already you know it it's exploding but you can already see where exploding but you can already see where exploding but you can already see where it's heading you know how it's going to

it's heading you know how it's going to it's heading you know how it's going to saturate what happens on the far side saturate what happens on the far side saturate what happens on the far side um the big thing that's not yet on um the big thing that's not yet on um the big thing that's not yet on Horizons is is Horizons is is Horizons is is embodied AI molecular intelligence so to embodied AI molecular intelligence so to embodied AI molecular intelligence so to step back to this AI story step back to this AI story step back to this AI story um there was um there was um there was Automation and that was going to change Automation and that was going to change Automation and that was going to change everything then there were expert everything then there were expert everything then there were expert systems systems systems um uh there was then the you know the um uh there was then the you know the um uh there was then the you know the first phase of the neural network first phase of the neural network first phase of the neural network systems there's been about five of these systems there's been about five of these systems there's been about five of these um in each case on the slope up it's um in each case on the slope up it's um in each case on the slope up it's going to change everything going to change everything going to change everything um in each case what happens is on the um in each case what happens is on the um in each case what happens is on the slope down slope down slope down um we sort of move the goal posts and it um we sort of move the goal posts and it um we sort of move the goal posts and it becomes sort of irrelevant so a good becomes sort of irrelevant so a good becomes sort of irrelevant so a good example is going up computer chess was example is going up computer chess was example is going up computer chess was going to change everything once going to change everything once going to change everything once computers could play chess that computers could play chess that computers could play chess that fundamentally changes the world now on fundamentally changes the world now on fundamentally changes the world now on the downside computers play chess the downside computers play chess the downside computers play chess winning at chess is no longer seen as a winning at chess is no longer seen as a winning at chess is no longer seen as a unique human thing but unique human thing but unique human thing but um uh people still play chess this new um uh people still play chess this new um uh people still play chess this new phase is going to take a new chunk of phase is going to take a new chunk of phase is going to take a new chunk of things that we thought computers things that we thought computers things that we thought computers couldn't do now computers will be able couldn't do now computers will be able couldn't do now computers will be able to do they have roughly our brain to do they have roughly our brain to do they have roughly our brain capacity capacity capacity um but you know we'll keep thinking as um but you know we'll keep thinking as um but you know we'll keep thinking as well as computers well as computers well as computers um and as I described wow we've been um and as I described wow we've been um and as I described wow we've been going through these five boom busts if going through these five boom busts if going through these five boom busts if you just look at the numbers of Ops per you just look at the numbers of Ops per you just look at the numbers of Ops per second bits storage bits of i o That's second bits storage bits of i o That's second bits storage bits of i o That's the more interesting one that's been the more interesting one that's been the more interesting one that's been steady and that's what finally caught up

steady and that's what finally caught up steady and that's what finally caught up to people but to people but to people but you know as we've talked about a couple you know as we've talked about a couple you know as we've talked about a couple times there's eight orders of magnitude times there's eight orders of magnitude times there's eight orders of magnitude to go not in the intelligence and the to go not in the intelligence and the to go not in the intelligence and the transistors or in the brain but in the transistors or in the brain but in the transistors or in the brain but in the embodied intelligence in the embodied intelligence in the embodied intelligence in the intelligence in our body so the intelligence in our body so the intelligence in our body so the intelligent constructions of physical intelligent constructions of physical intelligent constructions of physical systems that would embody the systems that would embody the systems that would embody the intelligence versus container within the intelligence versus container within the intelligence versus container within the computation right and there's a brain computation right and there's a brain computation right and there's a brain centrism that assumes our intelligence centrism that assumes our intelligence centrism that assumes our intelligence is centered in our brain is centered in our brain is centered in our brain and in Endless ways in this conversation and in Endless ways in this conversation and in Endless ways in this conversation we've been talking about molecular we've been talking about molecular we've been talking about molecular intelligence our molecular systems do a intelligence our molecular systems do a intelligence our molecular systems do a deep kind of artificial intelligence all deep kind of artificial intelligence all deep kind of artificial intelligence all the things you think of as artificial the things you think of as artificial the things you think of as artificial intelligence does in intelligence does in intelligence does in representing knowledge storing knowledge representing knowledge storing knowledge representing knowledge storing knowledge searching over knowledge adapting to searching over knowledge adapting to searching over knowledge adapting to knowledge our molecular systems do knowledge our molecular systems do knowledge our molecular systems do but the output isn't just a thought it's but the output isn't just a thought it's but the output isn't just a thought it's it's us it's the evolution of us and it's us it's the evolution of us and it's us it's the evolution of us and that's you know the real Horizon to come that's you know the real Horizon to come that's you know the real Horizon to come is now embodying ai if not not just a is now embodying ai if not not just a is now embodying ai if not not just a processor and a robot but but you know processor and a robot but but you know processor and a robot but but you know Building Systems that really can Building Systems that really can Building Systems that really can grow and evolve grow and evolve grow and evolve so we've been speaking about this so we've been speaking about this so we've been speaking about this boundary between bits and atoms so let boundary between bits and atoms so let boundary between bits and atoms so let me ask you one of the about one of the me ask you one of the about one of the me ask you one of the about one of the big mysteries of consciousness big mysteries of consciousness big mysteries of consciousness do you think do you think do you think it comes from somewhere between that it comes from somewhere between that it comes from somewhere between that boundary I won't name names but if you

boundary I won't name names but if you boundary I won't name names but if you know who I'm talking about it's probably know who I'm talking about it's probably know who I'm talking about it's probably clear I once did a drive in fact up up clear I once did a drive in fact up up clear I once did a drive in fact up up to the mussoline era Villa outside to the mussoline era Villa outside to the mussoline era Villa outside Torino Torino Torino um in the early days of what became um in the early days of what became um in the early days of what became Quantum computing Quantum computing Quantum computing with with with a a a famous person who thinks about a a a famous person who thinks about a a a famous person who thinks about quantum mechanics and Consciousness and quantum mechanics and Consciousness and quantum mechanics and Consciousness and we had the most infuriating conversation we had the most infuriating conversation we had the most infuriating conversation that went roughly along the lines of that went roughly along the lines of that went roughly along the lines of Consciousness is weird Consciousness is weird Consciousness is weird quantum mechanics is weird therefore quantum mechanics is weird therefore quantum mechanics is weird therefore quantum mechanics explains Consciousness quantum mechanics explains Consciousness quantum mechanics explains Consciousness that was rough ly the The Logical that was rough ly the The Logical that was rough ly the The Logical process process process then you're not as satisfied with that then you're not as satisfied with that then you're not as satisfied with that process no and I say that very precisely process no and I say that very precisely process no and I say that very precisely in the following sense uh I was a in the following sense uh I was a in the following sense uh I was a program manager somewhat by accident in program manager somewhat by accident in program manager somewhat by accident in a DARPA program a DARPA program a DARPA program on Quantum biology and so biology trivially uses quantum mechanics and trivially uses quantum mechanics and that were made out of atoms but the that were made out of atoms but the that were made out of atoms but the distinction is distinction is distinction is in Quantum Computing Quantum information in Quantum Computing Quantum information in Quantum Computing Quantum information you need Quantum coherence you need Quantum coherence you need Quantum coherence and there's a lot of muddled thinking and there's a lot of muddled thinking and there's a lot of muddled thinking about like about like about like collapse of the wave function and claims collapse of the wave function and claims collapse of the wave function and claims of quantum Computing that garbles just of quantum Computing that garbles just of quantum Computing that garbles just Quantum coherence that Quantum coherence that Quantum coherence that um that you can think of it as a wave um that you can think of it as a wave um that you can think of it as a wave that has very special properties but that has very special properties but that has very special properties but these wave like properties and so these wave like properties and so these wave like properties and so there's a small set of places where

there's a small set of places where there's a small set of places where biology uses quantum mechanics in that biology uses quantum mechanics in that biology uses quantum mechanics in that deeper sense one is how light is deeper sense one is how light is deeper sense one is how light is converted to energy in photosystems converted to energy in photosystems converted to energy in photosystems um it looks like one is olfaction how um it looks like one is olfaction how um it looks like one is olfaction how your nose is able to tell different your nose is able to tell different your nose is able to tell different smells smells smells um probably one has to do with how birds um probably one has to do with how birds um probably one has to do with how birds navigate navigate navigate how they sense magnetic fields how they sense magnetic fields how they sense magnetic fields that involves the coupling between a that involves the coupling between a that involves the coupling between a very weak energy with a magnetic field very weak energy with a magnetic field very weak energy with a magnetic field coupling into chemical reactions and coupling into chemical reactions and coupling into chemical reactions and there's a beautiful system it there's a beautiful system it there's a beautiful system it standard in chemistry is magnetic fields standard in chemistry is magnetic fields standard in chemistry is magnetic fields like this can influence chemistry but like this can influence chemistry but like this can influence chemistry but there are biological circuits that are there are biological circuits that are there are biological circuits that are carefully balanced with two Pathways carefully balanced with two Pathways carefully balanced with two Pathways that become unbalanced with magnetic that become unbalanced with magnetic that become unbalanced with magnetic fields so each of these areas are fields so each of these areas are fields so each of these areas are expensive for biology it has to consume expensive for biology it has to consume expensive for biology it has to consume resources to use quantum mechanics in resources to use quantum mechanics in resources to use quantum mechanics in this way this way this way so again those are places where we know so again those are places where we know so again those are places where we know there's quantum mechanics in biology in there's quantum mechanics in biology in there's quantum mechanics in biology in cognition there's just no evidence there cognition there's just no evidence there cognition there's just no evidence there there's uh there's no evidence of there's uh there's no evidence of there's uh there's no evidence of anything quantum mechanical going on in anything quantum mechanical going on in anything quantum mechanical going on in how cognition Works Consciousness well how cognition Works Consciousness well how cognition Works Consciousness well I'm saying I'm saying cognition I'm not I'm saying I'm saying cognition I'm not I'm saying I'm saying cognition I'm not saying Consciousness but to get from saying Consciousness but to get from saying Consciousness but to get from cognition to consciousness cognition to consciousness cognition to consciousness so McCullough and Pitts made a model of so McCullough and Pitts made a model of so McCullough and Pitts made a model of neurons neurons neurons um that led to perceptrons um that led to perceptrons um that led to perceptrons that then threw a couple boom busts led

that then threw a couple boom busts led that then threw a couple boom busts led to deep learning one of the interesting to deep learning one of the interesting to deep learning one of the interesting things about that sequence is it things about that sequence is it things about that sequence is it diverged off so deep neural networks diverged off so deep neural networks diverged off so deep neural networks used in machine learning diverged from used in machine learning diverged from used in machine learning diverged from trying to understand how the brain works trying to understand how the brain works trying to understand how the brain works um what what makes them work what's um what what makes them work what's um what what makes them work what's emerged is they it's a really emerged is they it's a really emerged is they it's a really interesting story this may be too much interesting story this may be too much interesting story this may be too much of a technical detail but it has to do of a technical detail but it has to do of a technical detail but it has to do with function approximation that that uh with function approximation that that uh with function approximation that that uh we talked about exponentials a deep we talked about exponentials a deep we talked about exponentials a deep Network Network Network needs an exponentially larger shallow needs an exponentially larger shallow needs an exponentially larger shallow Network to do the same function Network to do the same function Network to do the same function and that that exponential is what gives and that that exponential is what gives and that that exponential is what gives the power to deep networks but what's the power to deep networks but what's the power to deep networks but what's interesting is the sort of lessons about interesting is the sort of lessons about interesting is the sort of lessons about building these deep architectures and building these deep architectures and building these deep architectures and how to train them how to train them how to train them have really interesting Echoes to how have really interesting Echoes to how have really interesting Echoes to how brains work brains work brains work and there's an interesting conversation and there's an interesting conversation and there's an interesting conversation that's sort of coming back of that's sort of coming back of that's sort of coming back of neuroscientists looking over the neuroscientists looking over the neuroscientists looking over the shoulder of people training these deep shoulder of people training these deep shoulder of people training these deep networks seeing interesting Echoes for networks seeing interesting Echoes for networks seeing interesting Echoes for how the brain works how the brain works how the brain works interesting parallels with it and so I I interesting parallels with it and so I I interesting parallels with it and so I I didn't say Consciousness I just said didn't say Consciousness I just said didn't say Consciousness I just said cognition but cognition but cognition but I don't know any experimental evidence I don't know any experimental evidence I don't know any experimental evidence that points to anything in neurobiology that points to anything in neurobiology that points to anything in neurobiology that says we need quantum mechanics that says we need quantum mechanics that says we need quantum mechanics and and and um I view the question about whether a um I view the question about whether a um I view the question about whether a large language model is conscious as

large language model is conscious as large language model is conscious as silly in in that silly in in that silly in in that biology is full of hacks biology is full of hacks biology is full of hacks and it works and it works and it works there's no evidence we have that there's there's no evidence we have that there's there's no evidence we have that there's anything deeper going on than just this anything deeper going on than just this anything deeper going on than just this sort of stacking up of hacks in the sort of stacking up of hacks in the sort of stacking up of hacks in the brain and somehow Consciousness is one brain and somehow Consciousness is one brain and somehow Consciousness is one of the hacks or an emergent property of of the hacks or an emergent property of of the hacks or an emergent property of the hex absolutely and um just the hex absolutely and um just the hex absolutely and um just numerically I said big computations now numerically I said big computations now numerically I said big computations now have the degrees of freedom of the brain have the degrees of freedom of the brain have the degrees of freedom of the brain and they're showing a lot of the and they're showing a lot of the and they're showing a lot of the phenomenology of what we think as phenomenology of what we think as phenomenology of what we think as properties of what a brain can do properties of what a brain can do properties of what a brain can do um um um and I don't see any reason to invoke and I don't see any reason to invoke and I don't see any reason to invoke anything else that makes you wonder what anything else that makes you wonder what anything else that makes you wonder what kind of beautiful stuff digital kind of beautiful stuff digital kind of beautiful stuff digital fabrication will create if biology fabrication will create if biology fabrication will create if biology created a few hacks on top of which created a few hacks on top of which created a few hacks on top of which Consciousness and cognition some of the Consciousness and cognition some of the Consciousness and cognition some of the things we love about human beings was things we love about human beings was things we love about human beings was created it makes you wonder what kind of created it makes you wonder what kind of created it makes you wonder what kind of Beauty in the complexity yeah it's a Beauty in the complexity yeah it's a Beauty in the complexity yeah it's a digital family there's there's an early digital family there's there's an early digital family there's there's an early peek at that which is peek at that which is peek at that which is um there's a misleading term which is um there's a misleading term which is um there's a misleading term which is generative design generative design generative design generative design is where you don't generative design is where you don't generative design is where you don't tell a computer how to design something tell a computer how to design something tell a computer how to design something you tell the computer what you want it you tell the computer what you want it you tell the computer what you want it to do that doesn't work that only works to do that doesn't work that only works to do that doesn't work that only works in limited subdomains you can't do in limited subdomains you can't do in limited subdomains you can't do really complex functionality that way really complex functionality that way really complex functionality that way the one place that's matured though is the one place that's matured though is the one place that's matured though is topology optimization for structure so topology optimization for structure so topology optimization for structure so let's say you wanted to make a bicycle

let's say you wanted to make a bicycle let's say you wanted to make a bicycle or a table or a table or a table you describe the loads on it and it you describe the loads on it and it you describe the loads on it and it figures out how to design it and what it figures out how to design it and what it figures out how to design it and what it makes are beautiful organic looking makes are beautiful organic looking makes are beautiful organic looking things these are things that look like things these are things that look like things these are things that look like they grew in a forest and they grew in a forest and they grew in a forest and they look like they grew in a forest they look like they grew in a forest they look like they grew in a forest because that's sort of exactly what they because that's sort of exactly what they because that's sort of exactly what they are that they're they're they're solving are that they're they're they're solving are that they're they're they're solving the ways of how you handle loads in the the ways of how you handle loads in the the ways of how you handle loads in the same way biology does and so you get same way biology does and so you get same way biology does and so you get things that look like trees and shells things that look like trees and shells things that look like trees and shells and all of that and so that's a Peak at and all of that and so that's a Peak at and all of that and so that's a Peak at this transition to this transition to this transition to um from we we design to to we teach the um from we we design to to we teach the um from we we design to to we teach the machines how to design what can you say machines how to design what can you say machines how to design what can you say about because you mentioned cellular about because you mentioned cellular about because you mentioned cellular automata earlier about from this example automata earlier about from this example automata earlier about from this example you just gave and in general the you just gave and in general the you just gave and in general the observation you can make by looking at observation you can make by looking at observation you can make by looking at cellular automata that there's a cellular automata that there's a cellular automata that there's a from simple rules and simple building from simple rules and simple building from simple rules and simple building blocks can emerge arbitrary complexity blocks can emerge arbitrary complexity blocks can emerge arbitrary complexity do we understand like do you understand do we understand like do you understand do we understand like do you understand what that is how that can be leveraged what that is how that can be leveraged what that is how that can be leveraged so understand what it is is much easier so understand what it is is much easier so understand what it is is much easier than it sounds I complained about than it sounds I complained about than it sounds I complained about turing's machine making a physics turing's machine making a physics turing's machine making a physics mistake but Turing never intended it to mistake but Turing never intended it to mistake but Turing never intended it to be a computer architecture he used it be a computer architecture he used it be a computer architecture he used it just to prove uh results about just to prove uh results about just to prove uh results about uncomputability uncomputability uncomputability um what what Turing did on what his um what what Turing did on what his um what what Turing did on what his computation is exquisite it's gorgeous computation is exquisite it's gorgeous computation is exquisite it's gorgeous he gave us our notion of computational he gave us our notion of computational he gave us our notion of computational universality and something that sounds universality and something that sounds universality and something that sounds deep and turns out to be trivial is

deep and turns out to be trivial is deep and turns out to be trivial is it's really easy to show almost it's really easy to show almost it's really easy to show almost everything is computationally universal everything is computationally universal everything is computationally universal so Norm margulis wrote a beautiful paper so Norm margulis wrote a beautiful paper so Norm margulis wrote a beautiful paper um with Tom tofully showing in a um with Tom tofully showing in a um with Tom tofully showing in a cellular a cellular automata world is cellular a cellular automata world is cellular a cellular automata world is like The Game of Life where you just like The Game of Life where you just like The Game of Life where you just move tokens around move tokens around move tokens around they showed that modeling billiard balls they showed that modeling billiard balls they showed that modeling billiard balls on a billiard table with cellular on a billiard table with cellular on a billiard table with cellular automata is a universal computer automata is a universal computer automata is a universal computer to to be Universal you need a persistent to to be Universal you need a persistent to to be Universal you need a persistent state state state you need a non-linear operation to you need a non-linear operation to you need a non-linear operation to interact them interact them interact them um and you need connectivity um and you need connectivity um and you need connectivity so that's what you need to show so that's what you need to show so that's what you need to show computational universality so they computational universality so they computational universality so they showed that a CA modeling billiard balls showed that a CA modeling billiard balls showed that a CA modeling billiard balls is a universal computer is a universal computer is a universal computer um Chris Moore went on to show that um Chris Moore went on to show that um Chris Moore went on to show that instead of chaos let's see instead of chaos let's see instead of chaos let's see um Turing showed there are computable um Turing showed there are computable um Turing showed there are computable their problems in computation that you their problems in computation that you their problems in computation that you can't solve can't solve can't solve that they're harder than you can't that they're harder than you can't that they're harder than you can't predict they're actually in a deep predict they're actually in a deep predict they're actually in a deep reason they are unsolvable reason they are unsolvable reason they are unsolvable um Chris Moore showed it's very easy to um Chris Moore showed it's very easy to um Chris Moore showed it's very easy to make physical systems that are make physical systems that are make physical systems that are uncomputable that what what the physics uncomputable that what what the physics uncomputable that what what the physics system does system does system does just bouncing balls and surfaces you can just bouncing balls and surfaces you can just bouncing balls and surfaces you can make systems that solve uncomputable make systems that solve uncomputable make systems that solve uncomputable problems and so almost any non-trivial problems and so almost any non-trivial problems and so almost any non-trivial physical system is computationally

physical system is computationally physical system is computationally universal universal universal so the first part of the answer to your so the first part of the answer to your so the first part of the answer to your question is this comes back to how you question is this comes back to how you question is this comes back to how you know my comment about how do you know my comment about how do you know my comment about how do you bootstrap a civilization you just don't bootstrap a civilization you just don't bootstrap a civilization you just don't need much to be computationally need much to be computationally need much to be computationally Universal so then Universal so then Universal so then that there isn't today a notion of like that there isn't today a notion of like that there isn't today a notion of like fabricational universality or fabricational universality or fabricational universality or fabricational complexity the sort of fabricational complexity the sort of fabricational complexity the sort of numbers I've been giving you about you numbers I've been giving you about you numbers I've been giving you about you eating lunch versus the chip Fab sort of eating lunch versus the chip Fab sort of eating lunch versus the chip Fab sort of that that that's in the same Spirit of that that that's in the same Spirit of that that that's in the same Spirit of what Shannon did but once you connect what Shannon did but once you connect what Shannon did but once you connect computational computational computational universality to kind of fabricational universality to kind of fabricational universality to kind of fabricational universality you then get the ability to universality you then get the ability to universality you then get the ability to grow and adapt and evolve grow and adapt and evolve grow and adapt and evolve because that Evolution happens in the because that Evolution happens in the because that Evolution happens in the physical space yeah and so that's why physical space yeah and so that's why physical space yeah and so that's why you know for me the heart of this whole you know for me the heart of this whole you know for me the heart of this whole conversation is morphogenesis so just to conversation is morphogenesis so just to conversation is morphogenesis so just to come back to that come back to that come back to that um um um what touring what touring what touring ended his sadly cut short life ended his sadly cut short life ended his sadly cut short life studying studying studying was how genes give rise to form so so was how genes give rise to form so so was how genes give rise to form so so how the the the small amount of it how the the the small amount of it how the the the small amount of it relatively in effect small amount of relatively in effect small amount of relatively in effect small amount of information in the genome can give rise information in the genome can give rise information in the genome can give rise to the complexity of Who You Are to the complexity of Who You Are to the complexity of Who You Are and and that that that's where and and that that that's where and and that that that's where what resides is this molecular what resides is this molecular what resides is this molecular intelligence intelligence intelligence which is first how to describe you but

which is first how to describe you but which is first how to describe you but then how to describe you such that you then how to describe you such that you then how to describe you such that you can exist and you can reproduce and you can exist and you can reproduce and you can exist and you can reproduce and you can grow and you can evolve can grow and you can evolve can grow and you can evolve and so you know that that's the seat of and so you know that that's the seat of and so you know that that's the seat of our molecular intelligence our molecular intelligence our molecular intelligence the make a revolution in biology yeah it the make a revolution in biology yeah it the make a revolution in biology yeah it really is really is really is um it really is and and that that's um it really is and and that that's um it really is and and that that's where you can't separate communication where you can't separate communication where you can't separate communication computation and Fabrication you can't computation and Fabrication you can't computation and Fabrication you can't separate computer science and physical separate computer science and physical separate computer science and physical science you can't separate hardware and science you can't separate hardware and science you can't separate hardware and software they all intersect right at software they all intersect right at software they all intersect right at that place that place that place do you think of our universe as just one do you think of our universe as just one do you think of our universe as just one giant computation giant computation giant computation I I would even kind of say Quantum I I would even kind of say Quantum I I would even kind of say Quantum Computing is overhyped in that there's a Computing is overhyped in that there's a Computing is overhyped in that there's a few things Quantum Computing is going to few things Quantum Computing is going to few things Quantum Computing is going to be good at one is breaking crypto be good at one is breaking crypto be good at one is breaking crypto systems but we know how to make new systems but we know how to make new systems but we know how to make new crypto systems what it's really good at crypto systems what it's really good at crypto systems what it's really good at is modeling other Quantum systems so for is modeling other Quantum systems so for is modeling other Quantum systems so for studying studying studying nanotechnology it's going to be powerful nanotechnology it's going to be powerful nanotechnology it's going to be powerful but Quantum Computing is not going to but Quantum Computing is not going to but Quantum Computing is not going to disrupt and change everything disrupt and change everything disrupt and change everything but the reason I say that is this but the reason I say that is this but the reason I say that is this interesting group of strange people who interesting group of strange people who interesting group of strange people who helped invent Quantum Computing before helped invent Quantum Computing before helped invent Quantum Computing before it was clear anything was there it was clear anything was there it was clear anything was there one of the main reasons they did it one of the main reasons they did it one of the main reasons they did it wasn't to make a computer that can break wasn't to make a computer that can break wasn't to make a computer that can break a crypto system a crypto system a crypto system it was you could turn this backwards you it was you could turn this backwards you it was you could turn this backwards you could be surprised quantum mechanics can could be surprised quantum mechanics can could be surprised quantum mechanics can compute compute compute or you can go in the opposite opposite or you can go in the opposite opposite or you can go in the opposite opposite direction and say if quantum mechanics

direction and say if quantum mechanics direction and say if quantum mechanics can compute can compute can compute um that's a description of nature so um that's a description of nature so um that's a description of nature so physics physics physics is written in terms of partial is written in terms of partial is written in terms of partial differential equations differential equations differential equations that is an information technology that is an information technology that is an information technology from uh two centuries ago the the equations of physics are not the the equations of physics are not this would sound very strange to say but this would sound very strange to say but this would sound very strange to say but the equations of physics Schrodinger's the equations of physics Schrodinger's the equations of physics Schrodinger's equations and Maxwell's equations and equations and Maxwell's equations and equations and Maxwell's equations and all of them are not fundamental they're all of them are not fundamental they're all of them are not fundamental they're a representation of physics that was a representation of physics that was a representation of physics that was accessible to us accessible to us accessible to us in the era of having a pencil and a in the era of having a pencil and a in the era of having a pencil and a piece of paper they have a fundamental problem which is they have a fundamental problem which is if you make a DOT on a piece of paper in if you make a DOT on a piece of paper in if you make a DOT on a piece of paper in traditional physics theory there's traditional physics theory there's traditional physics theory there's information infinite information in that information infinite information in that information infinite information in that dot a point dot a point dot a point has infinite information has infinite information has infinite information that can't be true because in that can't be true because in that can't be true because in information is is information is is information is is um a fundamental resource that's um a fundamental resource that's um a fundamental resource that's connected to energy and in fact it connected to energy and in fact it connected to energy and in fact it um one of my favorite questions you can um one of my favorite questions you can um one of my favorite questions you can ask a cosmologist to trip them up is ask ask a cosmologist to trip them up is ask ask a cosmologist to trip them up is ask is information a conserved quantity in is information a conserved quantity in is information a conserved quantity in the universe the universe the universe with all the information created in the with all the information created in the with all the information created in the Big Bang or can the universe create Big Bang or can the universe create Big Bang or can the universe create information and I've yet to meet a information and I've yet to meet a information and I've yet to meet a cosmologist who doesn't stutter and

cosmologist who doesn't stutter and cosmologist who doesn't stutter and not clearly know how to handle that not clearly know how to handle that not clearly know how to handle that existential question but sort of putting existential question but sort of putting existential question but sort of putting that to a side that to a side that to a side in physics theory the way it's taught in physics theory the way it's taught in physics theory the way it's taught in information in information in information comes late you know you're taught about comes late you know you're taught about comes late you know you're taught about x a variable which can contain infinite x a variable which can contain infinite x a variable which can contain infinite information but physically that's information but physically that's information but physically that's unrealistic and so physics theories have unrealistic and so physics theories have unrealistic and so physics theories have to find ways to cut that off to find ways to cut that off to find ways to cut that off so instead so instead so instead there are a number of people there are a number of people there are a number of people Who start with Who start with Who start with a theory of the universe should start a theory of the universe should start a theory of the universe should start with information and computation as the with information and computation as the with information and computation as the fundamental resources that explain fundamental resources that explain fundamental resources that explain nature and then you build up from that nature and then you build up from that nature and then you build up from that to something that looks like throwing to something that looks like throwing to something that looks like throwing baseballs down a slope and so in that baseballs down a slope and so in that baseballs down a slope and so in that sense sense sense the work on physics and computation the work on physics and computation the work on physics and computation has many applications that we've been has many applications that we've been has many applications that we've been talking about but more deeply it's talking about but more deeply it's talking about but more deeply it's really getting at new ways to think really getting at new ways to think really getting at new ways to think about how the universe works and there about how the universe works and there about how the universe works and there are a number of things that are hard to are a number of things that are hard to are a number of things that are hard to do in traditional physics that make more do in traditional physics that make more do in traditional physics that make more sense when you start with information sense when you start with information sense when you start with information and computation as the root of physical and computation as the root of physical and computation as the root of physical Theory so information and competition Theory so information and competition Theory so information and competition being the the the real fundamental thing being the the the real fundamental thing being the the the real fundamental thing in the universe right that information in the universe right that information in the universe right that information is a resource you can't have you can't is a resource you can't have you can't is a resource you can't have you can't have infinite information in finite have infinite information in finite have infinite information in finite space space space information propagates and interacts and information propagates and interacts and information propagates and interacts and from there you erect the scaffolding of

from there you erect the scaffolding of from there you erect the scaffolding of physics now it happens physics now it happens physics now it happens the words I just said look a lot like the words I just said look a lot like the words I just said look a lot like Quantum field theories Quantum field theories Quantum field theories but there's an interesting way where but there's an interesting way where but there's an interesting way where instead of starting with different instead of starting with different instead of starting with different differential equations to get to Quantum differential equations to get to Quantum differential equations to get to Quantum field theories and Quantum field field theories and Quantum field field theories and Quantum field theories you get to quantization theories you get to quantization theories you get to quantization [Music] [Music] [Music] um if you if if you start from um if you if if you start from um if you if if you start from computation information you begin sort computation information you begin sort computation information you begin sort of quantized and you build up from there of quantized and you build up from there of quantized and you build up from there and so that's the sense in which uh and so that's the sense in which uh and so that's the sense in which uh uh absolutely I think about the universe uh absolutely I think about the universe uh absolutely I think about the universe as a computer the easy way to understand as a computer the easy way to understand as a computer the easy way to understand that is that is that is uh just almost anything is uh just almost anything is uh just almost anything is computationally universal but the Deep computationally universal but the Deep computationally universal but the Deep Way is it's a real fundamental way to Way is it's a real fundamental way to Way is it's a real fundamental way to understand how the universe works let me go a little bit to the personal let me go a little bit to the personal in the center bits and atoms in the center bits and atoms in the center bits and atoms you have uh you have uh you have uh uh worked with the students you've uh worked with the students you've uh worked with the students you've worked with have gone on to do some worked with have gone on to do some worked with have gone on to do some incredible things in this world incredible things in this world incredible things in this world including build super computers that including build super computers that including build super computers that power uh Facebook and Twitter and so on power uh Facebook and Twitter and so on power uh Facebook and Twitter and so on what advice would you give to young what advice would you give to young what advice would you give to young people what advice have you given them people what advice have you given them people what advice have you given them how to have one heck of a great career how to have one heck of a great career how to have one heck of a great career one heck of a great life what one one heck of a great life what one one heck of a great life what one important one is important one is important one is uh it if you look at Junior faculty uh it if you look at Junior faculty uh it if you look at Junior faculty trying to get tenure at a place like MIT trying to get tenure at a place like MIT trying to get tenure at a place like MIT the ones who try to figure out how to the ones who try to figure out how to the ones who try to figure out how to get tenure or miserable and don't get

get tenure or miserable and don't get get tenure or miserable and don't get tenure and the ones who don't try to tenure and the ones who don't try to tenure and the ones who don't try to figure it out are happy and do get it figure it out are happy and do get it figure it out are happy and do get it yeah I mean you know you have to love yeah I mean you know you have to love yeah I mean you know you have to love what you're doing and believe in it and what you're doing and believe in it and what you're doing and believe in it and nothing else could possibly be what you nothing else could possibly be what you nothing else could possibly be what you want to be doing with your life and it want to be doing with your life and it want to be doing with your life and it gets you out of bed in the morning and gets you out of bed in the morning and gets you out of bed in the morning and again it sounds naive but again it sounds naive but again it sounds naive but um it within like The Limited domain I'm um it within like The Limited domain I'm um it within like The Limited domain I'm describing now of getting tenure at MIT describing now of getting tenure at MIT describing now of getting tenure at MIT that that's the key attribute to it and that that's the key attribute to it and that that's the key attribute to it and then same sense then same sense then same sense um if you take the sort of outliers um if you take the sort of outliers um if you take the sort of outliers students were talking about you know 99 students were talking about you know 99 students were talking about you know 99 out of 100 come to me and say your work out of 100 come to me and say your work out of 100 come to me and say your work is very fascinating I'd be interesting is very fascinating I'd be interesting is very fascinating I'd be interesting to work to work to work um for you and one out of a hundred come um for you and one out of a hundred come um for you and one out of a hundred come and say and say and say um here you're wrong here here here's um here you're wrong here here here's um here you're wrong here here here's your mistake here's here's what you your mistake here's here's what you your mistake here's here's what you should have been doing yeah should have been doing yeah should have been doing yeah um and uh that they just sort of say I'm um and uh that they just sort of say I'm um and uh that they just sort of say I'm here and and get to work and again here and and get to work and again here and and get to work and again that's I I don't know how far this that's I I don't know how far this that's I I don't know how far this resource goes so you know I've said I resource goes so you know I've said I resource goes so you know I've said I consider the world's greatest resource consider the world's greatest resource consider the world's greatest resource this engine of Brighton event of people this engine of Brighton event of people this engine of Brighton event of people of which we only see a tiny little of which we only see a tiny little of which we only see a tiny little Iceberg of it and everywhere we open Iceberg of it and everywhere we open Iceberg of it and everywhere we open these labs they come out of the woodwork these labs they come out of the woodwork these labs they come out of the woodwork they come we didn't create all these they come we didn't create all these they come we didn't create all these educational programs all these other educational programs all these other educational programs all these other things I'm describing we tried to things I'm describing we tried to things I'm describing we tried to partner everywhere with local schools partner everywhere with local schools partner everywhere with local schools and local companies and kept tripping and local companies and kept tripping and local companies and kept tripping over dysfunction and find we had to over dysfunction and find we had to over dysfunction and find we had to create the environment where people like create the environment where people like create the environment where people like this can flourish and so I don't know if this can flourish and so I don't know if this can flourish and so I don't know if this is everyone if it's one percent of

this is everyone if it's one percent of this is everyone if it's one percent of society what the fraction is but it's so society what the fraction is but it's so society what the fraction is but it's so many orders of magnitude bigger than we many orders of magnitude bigger than we many orders of magnitude bigger than we see today you know we've been racing to see today you know we've been racing to see today you know we've been racing to keep up with it to take advantage of keep up with it to take advantage of keep up with it to take advantage of that resource if something tells me it's that resource if something tells me it's that resource if something tells me it's a very large fraction of the population a very large fraction of the population a very large fraction of the population I mean the thing that gives me most hope I mean the thing that gives me most hope I mean the thing that gives me most hope for the future is that population once a for the future is that population once a for the future is that population once a year this whole Lab Network meets and year this whole Lab Network meets and year this whole Lab Network meets and it's my favorite Gathering it's in it's my favorite Gathering it's in it's my favorite Gathering it's in Bhutan this year because it's it's every Bhutan this year because it's it's every Bhutan this year because it's it's every body shape it's every language every body shape it's every language every body shape it's every language every geography but it's the same person in geography but it's the same person in geography but it's the same person in all those packages it's it's the same all those packages it's it's the same all those packages it's it's the same sense of bright inventive joy and sense of bright inventive joy and sense of bright inventive joy and discovery discovery discovery if there's people listening to this in if there's people listening to this in if there's people listening to this in there just uh overwhelmed with how there just uh overwhelmed with how there just uh overwhelmed with how exciting this is which I think they exciting this is which I think they exciting this is which I think they would be how can they participate how would be how can they participate how would be how can they participate how can they help how can they encourage can they help how can they encourage can they help how can they encourage young people or themselves to uh to young people or themselves to uh to young people or themselves to uh to build stuff to create stuff yeah that's build stuff to create stuff yeah that's build stuff to create stuff yeah that's a great question so a great question so a great question so um the um the um the the this is part of a much bigger maker the this is part of a much bigger maker the this is part of a much bigger maker movement that has a lot a lot of movement that has a lot a lot of movement that has a lot a lot of embodiments the part I've been involved embodiments the part I've been involved embodiments the part I've been involved in this Fab Lab Network you can think of in this Fab Lab Network you can think of in this Fab Lab Network you can think of as a curated part that works as a as a curated part that works as a as a curated part that works as a network so you don't benefit in a gym if network so you don't benefit in a gym if network so you don't benefit in a gym if somebody exercises in another gym but in somebody exercises in another gym but in somebody exercises in another gym but in the Fab Network and if you do in a sense the Fab Network and if you do in a sense the Fab Network and if you do in a sense benefit when somebody works in another benefit when somebody works in another benefit when somebody works in another Network another lab in the way it Network another lab in the way it Network another lab in the way it functions as a network so functions as a network so functions as a network so um you can come to um you can come to um you can come to cba.mit.edu to see the research we're cba.mit.edu to see the research we're cba.mit.edu to see the research we're talking about talking about talking about um there's a Fab Foundation run by um there's a Fab Foundation run by um there's a Fab Foundation run by Sherry Lasseter at fabfoundation.org Fab

Sherry Lasseter at fabfoundation.org Fab Sherry Lasseter at fabfoundation.org Fab Labs IO is a portal into the slab Labs IO is a portal into the slab Labs IO is a portal into the slab Network Network Network um uh Fab academy.org is this um uh Fab academy.org is this um uh Fab academy.org is this distributed Hands-On educational program distributed Hands-On educational program distributed Hands-On educational program fab.city is the platform of cities fab.city is the platform of cities fab.city is the platform of cities producing what they consume those are producing what they consume those are producing what they consume those are all nodes in this network so you can all nodes in this network so you can all nodes in this network so you can learn with Fab Academy and you can learn with Fab Academy and you can learn with Fab Academy and you can perhaps launch or help launch or perhaps launch or help launch or perhaps launch or help launch or participate in launching a Fab Lab well participate in launching a Fab Lab well participate in launching a Fab Lab well an in particular an in particular an in particular um from one to a thousand we carefully um from one to a thousand we carefully um from one to a thousand we carefully counted Labs now we're going from a counted Labs now we're going from a counted Labs now we're going from a thousand to a million where it ceases to thousand to a million where it ceases to thousand to a million where it ceases to become interesting to count them and in become interesting to count them and in become interesting to count them and in the Thousand to the million the Thousand to the million the Thousand to the million uh what's interesting about that stage uh what's interesting about that stage uh what's interesting about that stage is uh technologically you go to a lab is uh technologically you go to a lab is uh technologically you go to a lab not to get access to the machine but you not to get access to the machine but you not to get access to the machine but you go to the lab to make the machine go to the lab to make the machine go to the lab to make the machine but the other thing interesting in it but the other thing interesting in it but the other thing interesting in it is we have an interesting collaboration is we have an interesting collaboration is we have an interesting collaboration on a a Fab Lab in a box on a a Fab Lab in a box on a a Fab Lab in a box and and and um this came out of a collaboration with um this came out of a collaboration with um this came out of a collaboration with SolidWorks on how you can put a Fab Lab SolidWorks on how you can put a Fab Lab SolidWorks on how you can put a Fab Lab in a box which is not just the tools but in a box which is not just the tools but in a box which is not just the tools but the knowledge so you open the box and the knowledge so you open the box and the knowledge so you open the box and the box contains the knowledge of how to the box contains the knowledge of how to the box contains the knowledge of how to use it as well as the tools within it use it as well as the tools within it use it as well as the tools within it so that the knowledge can propagate and so that the knowledge can propagate and so that the knowledge can propagate and so we have an interesting group of so we have an interesting group of so we have an interesting group of people working on you know the original people working on you know the original people working on you know the original Fab Labs which have a whole team to get Fab Labs which have a whole team to get Fab Labs which have a whole team to get involved in the setting up and training involved in the setting up and training involved in the setting up and training and the Fab Academy is a real in-depth and the Fab Academy is a real in-depth and the Fab Academy is a real in-depth deep technical program in the training deep technical program in the training deep technical program in the training but in this next phase how sort of the

but in this next phase how sort of the but in this next phase how sort of the lab itself knows how to do the lab that lab itself knows how to do the lab that lab itself knows how to do the lab that that it's you know it we've talked that it's you know it we've talked that it's you know it we've talked deeply about the intelligence in deeply about the intelligence in deeply about the intelligence in fabrication but in a much more fabrication but in a much more fabrication but in a much more accessible one about how the the the the accessible one about how the the the the accessible one about how the the the the AI in the lab in effect becomes a AI in the lab in effect becomes a AI in the lab in effect becomes a collaborator with you in this nearer collaborator with you in this nearer collaborator with you in this nearer term to help get started and for for term to help get started and for for term to help get started and for for people people people wanting to connect it can seem like a wanting to connect it can seem like a wanting to connect it can seem like a big step a big threshold but we've big step a big threshold but we've big step a big threshold but we've gotten to thousands of these and they're gotten to thousands of these and they're gotten to thousands of these and they're doubling doubling doubling exactly that way just from people opting exactly that way just from people opting exactly that way just from people opting in in in and uh in so doing driving towards this and uh in so doing driving towards this and uh in so doing driving towards this kind of idea of uh personal digital kind of idea of uh personal digital kind of idea of uh personal digital fabrication yeah and it's not Utopia fabrication yeah and it's not Utopia fabrication yeah and it's not Utopia it's not free but come back to today it's not free but come back to today it's not free but come back to today we separately have education we separately have education we separately have education we have big business we have startups we have big business we have startups we have big business we have startups we have entertainment sort of each of we have entertainment sort of each of we have entertainment sort of each of these things are segregated when you these things are segregated when you these things are segregated when you have Global Connection to one of these have Global Connection to one of these have Global Connection to one of these local facilities in that you can do play local facilities in that you can do play local facilities in that you can do play and art and education and create and art and education and create and art and education and create infrastructure infrastructure infrastructure um you can make many of the things you um you can make many of the things you um you can make many of the things you consume you could make it for yourself consume you could make it for yourself consume you could make it for yourself it could be done on a community skull it it could be done on a community skull it it could be done on a community skull it could be done on a regional scale could be done on a regional scale could be done on a regional scale um it really I'd say um it really I'd say um it really I'd say the research we spent the last few hours the research we spent the last few hours the research we spent the last few hours talking about I thought was hard and in talking about I thought was hard and in talking about I thought was hard and in a sense a sense a sense I mean it's it I mean it's it I mean it's it it's non-trivial but in a sense it's

it's non-trivial but in a sense it's it's non-trivial but in a sense it's just sort of playing out we're turning just sort of playing out we're turning just sort of playing out we're turning the crank what I didn't think was hard the crank what I didn't think was hard the crank what I didn't think was hard is is is if anybody can make almost anything if anybody can make almost anything if anybody can make almost anything anywhere anywhere anywhere how do you live how do you learn how do how do you live how do you learn how do how do you live how do you learn how do you work how you play these very basic you work how you play these very basic you work how you play these very basic assumptions about how Society functions assumptions about how Society functions assumptions about how Society functions there's a way in which it's kind of Back there's a way in which it's kind of Back there's a way in which it's kind of Back to the Future to the Future to the Future in that in that in that this mode where work is money is this mode where work is money is this mode where work is money is consumption and consumption is shopping consumption and consumption is shopping consumption and consumption is shopping by selecting is only a kind of a few by selecting is only a kind of a few by selecting is only a kind of a few decade old stretch decade old stretch decade old stretch um in some ways we're getting back to um in some ways we're getting back to um in some ways we're getting back to you know a a Sami Village in North you know a a Sami Village in North you know a a Sami Village in North Norway is deeply sustainable Norway is deeply sustainable Norway is deeply sustainable but rather than just reverting to living but rather than just reverting to living but rather than just reverting to living the way we did a few thousand years ago the way we did a few thousand years ago the way we did a few thousand years ago being connected globally having the being connected globally having the being connected globally having the benefits of modern society but benefits of modern society but benefits of modern society but connecting it back to older Notions of connecting it back to older Notions of connecting it back to older Notions of sustainability sustainability sustainability um I I hadn't remotely anticipated um I I hadn't remotely anticipated um I I hadn't remotely anticipated just how fundamentally that challenges just how fundamentally that challenges just how fundamentally that challenges how a society functions and how how a society functions and how how a society functions and how interesting and how hard it is to figure interesting and how hard it is to figure interesting and how hard it is to figure out how we can make that work and it's out how we can make that work and it's out how we can make that work and it's possible that this kind of process possible that this kind of process possible that this kind of process will give a deeper sense of meaning to will give a deeper sense of meaning to will give a deeper sense of meaning to each person let me violently agree in in each person let me violently agree in in each person let me violently agree in in two ways one way is uh two ways one way is uh two ways one way is uh this community making

this community making this community making crosses many sensitive sectarian crosses many sensitive sectarian crosses many sensitive sectarian boundaries in many parts of the world boundaries in many parts of the world boundaries in many parts of the world where there's just you know implicit or where there's just you know implicit or where there's just you know implicit or explicit conflict but sort of this act explicit conflict but sort of this act explicit conflict but sort of this act of making of making of making seems to transcend a lot of historical seems to transcend a lot of historical seems to transcend a lot of historical divisions I don't say that divisions I don't say that divisions I don't say that philosophically I just say that as an philosophically I just say that as an philosophically I just say that as an observation and I think observation and I think observation and I think there's something really fundamental in there's something really fundamental in there's something really fundamental in what you said which is you know deep in what you said which is you know deep in what you said which is you know deep in our brain is shaping our environment um um a lot of what's strange about our a lot of what's strange about our a lot of what's strange about our society is the way that we can't do that society is the way that we can't do that society is the way that we can't do that the act of shaping our environment the act of shaping our environment the act of shaping our environment touches something really really deep touches something really really deep touches something really really deep that gets to the essence of Who We Are that gets to the essence of Who We Are that gets to the essence of Who We Are you know that's again why I say that in you know that's again why I say that in you know that's again why I say that in a way the most important thing made in a way the most important thing made in a way the most important thing made in made in these Labs is making itself made in these Labs is making itself made in these Labs is making itself what do you think what do you think what do you think if the shaping of our environment gets if the shaping of our environment gets if the shaping of our environment gets something deep what do you think is the something deep what do you think is the something deep what do you think is the meaning of it all what's the meaning of meaning of it all what's the meaning of meaning of it all what's the meaning of life now life now life now I can tell you I can tell you I can tell you my insights into how life works my insights into how life works my insights into how life works I can tell you in my insights and how to I can tell you in my insights and how to I can tell you in my insights and how to make life meaningful and fulfilling make life meaningful and fulfilling make life meaningful and fulfilling and sustainable and sustainable and sustainable um um um I have no idea what the meaning of life I have no idea what the meaning of life I have no idea what the meaning of life is but maybe that's the meaning of life is but maybe that's the meaning of life is but maybe that's the meaning of life now the uncertainty the confusion of

now the uncertainty the confusion of now the uncertainty the confusion of um because there's a magic to it all um because there's a magic to it all um because there's a magic to it all everything you've talked about from everything you've talked about from everything you've talked about from starting from the basic elements with starting from the basic elements with starting from the basic elements with the big bang that somehow created the the big bang that somehow created the the big bang that somehow created the Sun that somehow uh said Fu to uh Sun that somehow uh said Fu to uh Sun that somehow uh said Fu to uh thermodynamics and created life and all thermodynamics and created life and all thermodynamics and created life and all the ways that you've talked about from the ways that you've talked about from the ways that you've talked about from ribosomes that created the Machinery ribosomes that created the Machinery ribosomes that created the Machinery that created the machine and then now that created the machine and then now that created the machine and then now the biological machine creating the biological machine creating the biological machine creating through digital fabrication more complex through digital fabrication more complex through digital fabrication more complex artificial machines all of that there's artificial machines all of that there's artificial machines all of that there's a magic to that creative process and we a magic to that creative process and we a magic to that creative process and we notice we humans are smart enough to notice we humans are smart enough to notice we humans are smart enough to notice the magic so it's you haven't notice the magic so it's you haven't notice the magic so it's you haven't said the s word yet said the s word yet said the s word yet um which one is that singularity um which one is that singularity um which one is that singularity [Laughter] [Laughter] [Laughter] yeah I'm not sure if Ray Kurzweil is yeah I'm not sure if Ray Kurzweil is yeah I'm not sure if Ray Kurzweil is listening if he is high Ray but I have a listening if he is high Ray but I have a listening if he is high Ray but I have a complex relationship with Rey because a complex relationship with Rey because a complex relationship with Rey because a lot of the things he projects I find lot of the things he projects I find lot of the things he projects I find annoying annoying annoying but then he does his homework and then but then he does his homework and then but then he does his homework and then somewhat annoyingly he points out how somewhat annoyingly he points out how somewhat annoyingly he points out how almost everything I'm doing fits on his almost everything I'm doing fits on his almost everything I'm doing fits on his road maps yeah road maps yeah road maps yeah um and so um and so um and so you know the you know the you know the the the the the the the question is are we heading towards the question is are we heading towards the question is are we heading towards the singularity I singularity I singularity I so I'd have to say I lean towards so I'd have to say I lean towards so I'd have to say I lean towards sigmoids rather than exponentials sigmoids rather than exponentials sigmoids rather than exponentials um we've done pretty well with the um we've done pretty well with the um we've done pretty well with the sigmoids yeah so sigmoids are things sigmoids yeah so sigmoids are things sigmoids yeah so sigmoids are things grow and they taper and then there can grow and they taper and then there can grow and they taper and then there can be one after it and one after it so be one after it and one after it so be one after it and one after it so um

um um you know I'll pass on whether there's you know I'll pass on whether there's you know I'll pass on whether there's enough of them that that they diverge enough of them that that they diverge enough of them that that they diverge but you know to but you know to but you know to the selfish Gene answer to the meaning the selfish Gene answer to the meaning the selfish Gene answer to the meaning of life is the meaning of life is the of life is the meaning of life is the of life is the meaning of life is the propagation of life and so propagation of life and so propagation of life and so um um um you know it it it was a step for S atoms you know it it it was a step for S atoms you know it it it was a step for S atoms to assemble into a molecule to assemble into a molecule to assemble into a molecule for molecules to assemble into a for molecules to assemble into a for molecules to assemble into a protocell for the protocell to form to protocell for the protocell to form to protocell for the protocell to form to then form organelles for the organ cells then form organelles for the organ cells then form organelles for the organ cells to form organs the organs to form an to form organs the organs to form an to form organs the organs to form an organism then it was a step for organism then it was a step for organism then it was a step for organisms to form family units then organisms to form family units then organisms to form family units then family units to form Villages you can family units to form Villages you can family units to form Villages you can view you know each of those as a stack view you know each of those as a stack view you know each of those as a stack in the level of organizations so you in the level of organizations so you in the level of organizations so you could view everything we've spoken about could view everything we've spoken about could view everything we've spoken about as as as the imperative of life the imperative of life the imperative of life just the next step in the hierarchy of just the next step in the hierarchy of just the next step in the hierarchy of that and the Fulfillment of the that and the Fulfillment of the that and the Fulfillment of the inexorable Drive of the violation of inexorable Drive of the violation of inexorable Drive of the violation of thermodynamics so you know you could thermodynamics so you know you could thermodynamics so you know you could view you know I'm an embodiment of the view you know I'm an embodiment of the view you know I'm an embodiment of the will of the violation of thermodynamics will of the violation of thermodynamics will of the violation of thermodynamics speaking speaking speaking the two of us having having an old chat the two of us having having an old chat the two of us having having an old chat yes yeah yes yeah yes yeah um and so continues and even then the um and so continues and even then the um and so continues and even then the singularity is just a transition up the singularity is just a transition up the singularity is just a transition up the ladder there's nothing deeper to ladder there's nothing deeper to ladder there's nothing deeper to Consciousness than it it it's a derived Consciousness than it it it's a derived Consciousness than it it it's a derived property of distributed problem solving

property of distributed problem solving property of distributed problem solving um there's nothing deeper to life than um there's nothing deeper to life than um there's nothing deeper to life than embodied AI embodied AI embodied AI in morphogenesis in morphogenesis in morphogenesis so why so much of this conversation in so why so much of this conversation in so why so much of this conversation in my life is my life is my life is involved in these Fab labs and initially involved in these Fab labs and initially involved in these Fab labs and initially it just started as Outreach then it it just started as Outreach then it it just started as Outreach then it started as keeping up with it started as keeping up with it started as keeping up with it then it turned to then it turned to then it turned to uh uh uh it was rewarding then it turned to we're it was rewarding then it turned to we're it was rewarding then it turned to we're learning as much from these labs in as learning as much from these labs in as learning as much from these labs in as goes out to them it began as Outreach goes out to them it began as Outreach goes out to them it began as Outreach but now more knowledge is coming back but now more knowledge is coming back but now more knowledge is coming back from the labs that is going into them from the labs that is going into them from the labs that is going into them um and then finally it ends with um and then finally it ends with um and then finally it ends with um um um you know what I described as competing you know what I described as competing you know what I described as competing with myself at MIT but a better way to with myself at MIT but a better way to with myself at MIT but a better way to say that is tapping the brain power of say that is tapping the brain power of say that is tapping the brain power of the planet the planet the planet and so from I guess for me personally and so from I guess for me personally and so from I guess for me personally that's the meaning of my life that's the meaning of my life that's the meaning of my life and maybe that's the meaning for the and maybe that's the meaning for the and maybe that's the meaning for the universe too it's uh it's using us universe too it's uh it's using us universe too it's uh it's using us humans and our Creations to understand humans and our Creations to understand humans and our Creations to understand itself itself itself in a way it's uh in a way it's uh in a way it's uh whatever the creative process that whatever the creative process that whatever the creative process that created Earth created Earth created Earth is competing with itself is competing with itself is competing with itself yeah so you could take morphogenesis as yeah so you could take morphogenesis as yeah so you could take morphogenesis as a summary of this whole conversation or a summary of this whole conversation or a summary of this whole conversation or you could take recursion you could take recursion you could take recursion that that in a sense what we've been that that in a sense what we've been that that in a sense what we've been talking about is recursion all the way talking about is recursion all the way talking about is recursion all the way down and in the end I think uh this down and in the end I think uh this down and in the end I think uh this whole thing is pretty fun it's short whole thing is pretty fun it's short whole thing is pretty fun it's short life is but it's pretty fun and so is

life is but it's pretty fun and so is life is but it's pretty fun and so is this conversation you know I mentioned this conversation you know I mentioned this conversation you know I mentioned you offline them going through some you offline them going through some you offline them going through some difficult stuff personally and your difficult stuff personally and your difficult stuff personally and your passion for what you do is just really passion for what you do is just really passion for what you do is just really inspiring and it just uh lights up my inspiring and it just uh lights up my inspiring and it just uh lights up my mood and lights up my heart and your mood and lights up my heart and your mood and lights up my heart and your inspiration for I know inspiration for I know inspiration for I know thousands of people that work with unmit thousands of people that work with unmit thousands of people that work with unmit and millions people across the world and millions people across the world and millions people across the world it's a big honor to use it with me today it's a big honor to use it with me today it's a big honor to use it with me today this is really fun this was a pleasure this is really fun this was a pleasure this is really fun this was a pleasure thanks for listening to this thanks for listening to this thanks for listening to this conversation with Neil gershenfeld to conversation with Neil gershenfeld to conversation with Neil gershenfeld to support this podcast please check out support this podcast please check out support this podcast please check out our sponsors in the description and now our sponsors in the description and now our sponsors in the description and now let me leave you with some words from let me leave you with some words from let me leave you with some words from Pablo Picasso Pablo Picasso Pablo Picasso every child is an artist a challenge is every child is an artist a challenge is every child is an artist a challenge is staying an artist when you grow up staying an artist when you grow up staying an artist when you grow up thank you for listening and hope to see thank you for listening and hope to see thank you for listening and hope to see you next time