Rodney Brooks: Robotics | Lex Fridman Podcast #217

the following is a conversation with. rodney brooks one of the greatest. roboticists in history he led the. computer science and artificial. intelligence laboratory at mit then. co-founded irobot which is one of the. most successful robotics companies ever. then he co-founded rethink robotics that. created some amazing collaborative. robots like baxter and sawyer. finally he co-founded robust.ai. whose mission is to teach robots common. sense which is a lot harder than it.

sounds. to support this podcast please check out. our sponsors in the description. as a side note let me say that rodney is. someone i've looked up to for many years. in my now over two decade journey in. robotics because. one he's a legit great engineer of real. world systems and two he's not afraid to. state controversial opinions that. challenge the way we see the ai world. but of course while i agree with him on.

some of his critical views of ai i don't. agree with some others. and he's fully supportive of such. disagreement. nobody ever built anything great by. being fully agreeable. there's always respect and love behind. our interactions and when a conversation. is recorded like it was for this podcast. i think a little bit of disagreement. is fun. this is the lex friedman podcast and. here is my conversation with rodney. brooks.

what is the most amazing or beautiful. robot that you've ever had the chance to. work with. i think it was domo. which was made by one of my grad. students aaron ed singer. it now sits in daniela roose's office uh. director of sea sale. and it was just a beautiful robot and. aaron was really clever. he didn't give me a budget ahead of time. he didn't tell me what he was going to. do. he just started spending money he spent. a lot of money he and jeff webber who um.

as a mechanical engineer who. aaron insisted he bring with him when he. became a grad student built this. beautiful gorgeous robot domo which is a. upper torso humanoid. two two arms uh. with things three fingered hands um. and. face eyeballs um all uh not the not the. eyeballs but everything else series. elastic actuators uh you can interact. with it um. cable driven all the motors are inside.

and it's just. gorgeous the eyeballs are actuated too. or no oh yeah the eyeballs are actuated. with cameras and you know so it had a. visual attention mechanism you know wow. looking when people came in and looking. in their face and. talking with them. why was it amazing. the beauty of it you said you said what. was the most beauty what is the most. beautiful it's just mechanically. gorgeous as as everything aaron builds. has always been mechanically gorgeous. it's just exquisite in the detail.

we're talking about mechanically like. literally the amount of actuators. the actuators the cables he. anodizes different parts different. colors and it's just. looks like a work of art what about the. face is that do you find the face. beautiful in robots um when you make a. robot. it's making a promise for how well it. will be able to interact so i always. encourage my students. not to over promise you know even with. its essence like the thing it presents.

it should not over promise yeah so i the. the joke i make which i think you'll get. is if your robot looks like albert. einstein it should be as smart as albert. einstein so. the only thing in in. domo's face is. the eyeballs um and because that's all. it can do it can look at you and pay. attention um and. so there is no. it's not like one of those. um japanese robots that looks exactly. like a person at all. but see the thing is us humans and dogs.

too. don't just use eyes for it as. attentional mechanisms they also use it. to communicate it's part of the. communication like a dog can look at you. look at another thing and look back at. you and that designates that we're going. to be looking at that thing yeah or. intent you know in on on both baxter and. sawyer at rethink robotics they had a. screen with you know graphic eyes so it. wasn't actually where the cameras were. pointing but it. the the eyes would look in the direction.

it was about to move its arm so people. in the factory nearby were not surprised. by its motions because it gave that. intent away. before we talk about baxter which i. think is a beautiful robot. let's go back to the beginning when did. you first. fall in love with robotics we're talking. about beauty and love to open the. conversation this is great. i've got these i was born in the end of. 1954 and i grew up in adelaide south. australia and i have these two books.

that are dated 1961. so i'm guessing my mother found them in. a store in 62 or 63. how and why wonder books um. how am i on the book of electricity. and how i won the book of. giant brains and robots. and. i learned how to build circuits you know. when i was eight or nine simple circuits. and i and i read you know learned the. binary system and. um saw all these.

drawings mostly uh of robots and um then. i tried to build them for the rest of my. childhood wait. 61 you said this was when the two books. i've still got the at home what what. does the robot mean in that context no. they they were some of the robots that. they had were. arms you know big arms to move nuclear. material around but they had pictures of. welding robots that looked like humans. under the sea welding stuff underwater.

um so they weren't real robots uh but. they were you know what people were. thinking about for robots. what were you thinking about were you. thinking about humanoids were you. thinking about arms with fingers were. you thinking about faces or cars no. actually to be honest i realized my. limitation on building mechanical stuff. so i just built um the brains. mostly i out of. different technologies as i got older. i built a.

learning system which. was chemical based and i had this ice. cube tray each. well was a cell. and by applying voltage to the two. electrodes it would build up a copper. bridge so over time it would it would. learn. a simple network um so i could teach it. stuff and that was. mostly things were driven by my budget. and nails as electrodes and a an ice. cream i mean an ice cube tray was was.

about my budget at that stage later i. managed to buy transistors and then i. could build gates and flip-flops and. stuff so so one one of your first robots. was an ice cube tray yeah. and it was very cerebral because it. learned to add. very nice uh well. just a decade or so before in 1950 alan. turing. wrote the paper that formulated the. touring test and he opened that paper. with the question. can machines.

think. so let me ask you this question can. machines. think can your ice cube tray. one day think. um certainly machines can think because. i believe you're a machine and i'm a. machine and i believe we both think um i. think it's a big fear i think any other. philosophical position is sort of a. little ludicrous what does think mean if. if it's not something that we do. um and we and we are machines. so yes machines can but do we have a.

clue how to build such machines that's a. very different question are we capable. of building such machines uh you know. are we smart enough we think we're smart. enough to do anything but. maybe we're not maybe you know we're. just not bad enough to build. stuff like us the kind of computer that. uh alan turing was thinking about do you. think there is something. fundamentally. uh or significantly different between. the computer between our ears the the. biological computer that humans use. and uh the computer that he was thinking.

about from a from a sort of high level. philosophical yeah i believe it uh. that's very wrong in fact i'm. halfway through a. i think it'll be about a 480 page book. um. titled the working title is not even. wrong. and if i may i'll tell you a bit about. that book. so there's two two well three thrusts to. it. um. one is the history of computation what. we call computation it goes all the way. back to uh uh.

some manuscripts in latin from 1614 and. 1620 by napier and kepler through. babbage and lovelace and then. turing's 1936 paper uh is you know where. what we think of as the invention of of. of modern computation. and that paper by the way did not set. out to. you know invent computation it set out. to uh negatively answer one of uh. hilbert's three later set of problems he.

called it um. as an effective way of of of getting. answers and and hilbert. hilbert really worked with rewriting. rules as did um. um. a church who also at the same time a. month earlier insuring disproved. hilbert's one of these three hypotheses. the other two had been already been. disproved by godel so turing set out to. disprove it because that's it's always. easier to disprove these things than to.

prove that there is an answer and um. so he needed um. and it really came from his his uh. professor well as an undergrad at. cambridge who said who turned it into is. there a mechanical process so he wanted. to have a show a mechanical process that. could. calculate numbers. because that was a mechanical process. that people used to generate tables they. were called computers the people at the.

time and they followed a set of rules. where they had paper and they would. write numbers down and based on the. numbers that keep writing other numbers. and they would produce. numbers for these tables engineering. tables. that the more the more iterations they. did the more significant digits came out. and so turing. in that paper set out to. define what sort of machine could do. that mechanical machine. where it could produce an arbitrary.

number of digits. in the same way a human computer did. and he came up with a very simple. set of constraints where there was an. infinite supply of paper this is the. tape of the turing machine. and each turing machine had a. set of it came with a set of. instructions that. as a person could do with pencil and. paper write down things on the tape and. erase them and put new things there.

and. he was able to show that that system was. not able to do something that hilbert. hypothesized so he disproved it but he. had to show this was this this system. was. good enough to do. whatever could be done but couldn't do. this other thing yeah and there he said. and he says in the paper i don't have. any real arguments for this but based on. intuition. so that's how he defined computation. and then if you look over the next from.

1936 up until really around 1975. you see people struggling with. is this really what computation is and. so marvin minsky very well known in ai. but also. a fantastic mathematician. uh in his book finite and infinite. machines from the mid 60s which is a. beautiful beautiful mathematical book. um says says at the start of the book. well what is computation turing says. it's this and yeah i sort of think it's. that it doesn't really matter whether.

stuff's made of wood or plastic it's. just you know that relatively cheap. stuff can do this stuff and so yeah. seems like computation. and donald knuth uh in his first volume. of his. you know art of computer programming. in around 1968. says well what's computation. it's this stuff like turing says that a. person could do each step without too. much trouble. and so one of his examples of what would.

be too much trouble was. a step which required knowing whether. fermat's last theorem was true or not. because it was not known at the time and. that's too much trouble for a person to. do as a step. and um. hop craft and allman. sort of said a similar thing. later that year and by nineteen seventy. five. in the a ho up crop the norman book. they're saying. well you know we don't really know what. computation is but intuition says this. is sort of about right and this is what.

it is. that's computation it's a sort of. agreed-upon thing which happens to be. really easy to implement in silicon and. then we had moore's law which took off. and it's been an incredibly powerful. tool. i certainly wouldn't argue with that the. version we have a computation incredibly. powerful can we just take a pause so. what we're talking about is there's an. infinite tape with some simple rules on. how to write and on that tape and that's. that's what we're kind of thinking about.

this is computation yeah and it's. modeled after humans how humans do stuff. and. i think it's a. curing says in the 36 paper one of the. critical facts here is that a human has. a limited amount of memory. so that's what we're going to put onto. our. mechanical computers so. so you know unlike mass. unlike mass or charge or yeah it's not. it's it's not given by the universe it. was. this is what we're going to call. computation yeah and then it has this.

really. you know it had this really good. implementation which has completely. changed our technological world that's. computation. second part of the book. i uh or argument in the book i have this. two by two matrix. with. um science. in the top row engineering in the bottom. row. left column is intelligence right column. is. life so in the bottom row the. engineering there's artificial.

intelligence and there's artificial life. in the top row there's neuroscience. and abiogenesis how does living matter. turn in how does non-living matter. become living better yes four. disciplines these four disciplines all. um. came into the current form in the period. 1945 to 1965. um. that's interesting there was. neuroscience before but it wasn't. effective neuroscience it was you know. these ganglia and there's electrical.

charges but no one knew knows what to do. with it. and furthermore. there were a lot of players who are. common across them. i've. identified common players except for. artificial intelligence and habiogenesis. i don't have but for any other pair i. can point to people who work them and a. whole bunch of them by the way we're at. the research lab for electronics at mit. um where uh warren mcculloch uh held. held held forth. and in fact mcculloch pitts um letven.

and maturana wrote the first paper on. functional neuroscience called what the. frog's eye tells the frog's brain where. instead of it just being this bunch of. nerves they sort of showed what. different. anatomical components were doing and. telling other anatomical components. and. you know generating behavior in the. front would you put them as basically. the fathers or the one of the early. pioneers of what are now called.

artificial neural networks. yeah i mean mcculloch and pitts. pitts was. much younger than him in 1943 had. written a paper inspired by bertrand. russell. on. a calculus for the ideas imminent in. neural systems. where they had tried to without any real. proof. they had tried to give a formalism for. neurons.

basically in terms of logic and gates or. gates and not gates with with no. real. evidence that that was what was going on. but they they talked about it and that. that was picked up by minsky for his. 1953. uh dissertation on which was a was a. neural network we would call it today. it was picked up by um. john von neumann when he was designing. the edvac computer in 1945 he talked. about its components being neurons. based on and in references he's only got.

three references and one of them is the. mcculloch pitts paper. so all these people and then the ai. people and the artificial live people. which was john von neumann originally it. was like overlap because you know. they're all going around the same time. and three of these four disciplines turn. to computation as their primary metaphor. so i i've got a couple of chapters in. the book one is titled wait. computers are people because that's. where our computers came from yeah.

and you know from people who are. computing stuff and then i've got. another chapter. wait people are computers which is about. computational neuroscience yes so. there's this whole circle here and that. competition is it and you know i have. talked to to people about well maybe. it's not computation that goes on in the. head of course it is yeah okay well. when elon musk's rocket goes up is it.

computing. is that how it gets into orbit by. computing. but we've got this idea if you want to. build an ai system you write a computer. program. yeah in a sense so the word computation. very quickly starts doing a lot of work. that it was not initially intended to to. do it's the second saying if you talk. about the universe as essentially. performing a computation yeah right. wolfram does this he turns it into. computation. you don't turn rockets into computation. yeah by the way when you say computation.

in our conversation do you tend to think. of computation narrowly in the way. touring thought of computation. it's it's gotten very. okay. you know. squishy yeah squishy okay. um. but. computation in the way turing thinks. about it and the way most people think. about it. actually fits very well with. thinking like a hunter-gatherer. there are places and there can be stuff.

in places and the stuff in places can. change and it stays there until someone. changes it. and it's this. metaphor of place and container which. you know is a combination of our. place cells in our hippocampus and uh. cortex. but this is this is how we use metaphors. for mostly to think about and when we. get outside of our metaphor range we. have to invent tools which we can sort. of. switch on to you so calculus is an.

example of a tool it can do stuff that. our raw reasoning can't do and we've got. conventions of when you can use it or. not. but sometimes um. you know people try to all the time we. always try to get physical metaphors for. things which is why. quantum mechanics has been such a. problem for a hundred years because it's. a particle no it's a wave it's got to be. something we understand and i say no. it's some weird mathematical logic. that's different from those but we want.

that metaphor. well you know i i suspect that that you. know 100 years or 200 years from now. neither quantum mechanics nor nor dark. matter will be talked about in the same. terms you know in the same way that um. lodges theory eventually went away. because. it just wasn't an adequate explanatory. metaphor you know that metaphor was the. stuff. there is stuff in. the burning the burning is in the matter. because it turns out the burning was. outside the matter it was the oxygen.

so our desire for metaphor and combined. with our limited cognitive capabilities. gets us into trouble that's my argument. in this book now and people say well. what is it then and i say well i wish i. knew that i tried to talk about that but. i you know give some ideas but so so. this is the three things computation is. sort of a particular thing we use. um. uh. oh can i tell you one beautiful thing. one yes. so you know i used an example of a thing. that's different from computation you.

hit a drum and it vibrates and there are. some some stationary points on the drum. surface you know because the waves are. going up and down the stationary points. now. you could compute them. to arbitrary position. um but the drum just knows them the drum. doesn't have to compute. what was the very first computer program. ever written by ada lovelace. to compute bernoulli numbers and. bernoulli numbers are exactly what you. need to find those stable points in the.

drum's surface wow anyway and there was. a bug in her program. the arguments to divide were reversed in. one place. and it still worked well she never got. to run it they never built the. analytical engine she wrote the program. without without it you know. uh so so computation computation is sort. of you know a thing that's. become dominant as a metaphor but yeah. is it the right metaphor. um. all three of these four fields adopted.

computation and. you know the. a lot of it swirls around warren. mcculloch and his all his students and. he funded a lot of people. um. and uh. and and our human metaphors our. limitations to human thinking will play. into this. the. three themes of the book. so i have a little to say about. computation. so. uh. so you're saying that there is a gap.

between the computer or the the the. machine that performs computation. and this machine. that appears to have consciousness. and intelligence yeah can we um that. piece of meat in your head piece of meat. and maybe it's not just the meat in your. head it's the rest of you too i mean you. have you have you actually have a neural. system in your gut um i tend to also. believe. not believe but. we're now dancing around things we don't.

know but. i tend to believe other humans are. important. like so we're almost like. i i just don't think we would ever have. achieved the level of intelligence we. have with other humans. i'm not saying so confidently but i have. an intuition that some of the. intelligence is in the interaction yeah. and and i think you know i think it it. seems to be very likely again we you. know this is speculation but. we our species and probably um.

probably in the end those to some extent. because you can find uh old bones where. they seem to be counting on them by. putting notches. um that when the in the neanderthals are. done we are able to put um. some of our stuff outside our body into. the world and then other people can. share it. and then we get these tools that become. shared tools and so there's a whole. coupling that. would not occur in you know. the single. deep learning network which was fed you.

know all of literature or something. yeah the the the neural network can't um. step outside of itself but is there. is there some um. can we explore this dark room. a little bit and try to get at something. what what is the magic where does the. magic come from in the human brain that. creates the mind. what's your sense. as scientists. that try to understand it and try to. build it what are the directions that.

if followed might be productive is it. creative interactive robots. is it creating large deep neural. networks that. do like self-supervised learning and. just like we'll we'll. we'll discover that when you make. something large enough some interesting. things will emerge is it through physics. and chemistry biology like artificial. life angle like we'll sneak up in this. four quadrant matrix that you mentioned. is there anything.

you're you're most if you had to bet all. your money. financial advice i wouldn't. okay so. every intelligence we know and includes. you know animal intelligence dog. intelligence you know. octopus intelligence which is very. different sort of architecture from from. us. all the intelligences we know. perceive the world. in some way. and then have action. in the world.

but they're able to. perceive objects. in a way which is actually pretty damn. for not phenomenal. and surprising. you know we tend to think you know that. that that uh. the box over here between us which is a. sound box i think is a blue box but. blueness. is something that we construct with with. um. color constancy it's not a it's not a.

it's not the blueness is not a direct. function of the. photons we're receiving it's actually. context you know which is why. um. you can turn. you know. maybe seen the examples where. um. someone turns a stop sign into a. some other sort of sign by just putting. a couple of marks on them and the deep. learning system gets it wrong everyone. says but the stop sign is red you know. why is it why is it think it's the other. sort of science because redness is not.

intrinsic in just the photons it's. actually a construction of an. understanding of the whole world and the. relationship between objects to get. color constancy. um but our tendency in order that we get. an archive paper really quickly is you. just show a lot of data and give the. labels and hope it figures it out but. it's not figuring it out in the same way. we do we have a very complex perceptual. understanding of the world dogs have a. very different perceptual understanding. based on smell they go smell smell a. post they can tell.

how many. you know different dogs have visited in. the last 10 hours and how long ago. there's all sorts of stuff that we just. don't perceive about the world and just. taking a single snapshot is not. perceiving about the world it's not. seeing that the registration between us. and the object. and registration is a. a philosophical concept brian cantwell. smith talks about a lot very difficult. squirmy thing to understand. but i think none of our systems do that.

we've always talked about nai about the. symbol grounding problem how our symbols. that we talk about are grounded in the. world. and when deep learning came along and. started labeling images people said ah. the grounding problem has been solved no. the labeling problem was solved with. some percentage accuracy which is. different from the grounding problem. so you uh. there's uh you agree with hans marvik. and. what's called the marvex paradox. that highlights this counterintuitive.

notion that. reasoning. is easy. but perception. and mobility are hard. yeah we shared an office when um when i. was working on computer vision and he. was working on his first mobile robot. what was those conversations like they. were great. so do you still kind of maybe you can. elaborate and do you still believe this. kind of notion that. perception is really uh hard and like.

can you make sense of why we humans have. this poor intuition about what's hard. and not well well let me let me give us. sort of a. an another another story sure. if you go back to you know the original. um you know teams working on ai um. from the late 50s into the 60s you know. and you go to the ai lab at mit. um who was it that was doing that was a. bunch of really smart kids who got into.

mit. and they were intelligent. so what's intelligence about well the. stuff they were good at playing chess. doing integrals. that was that was hard stuff. yeah but you know a baby could see stuff. that wasn't that wasn't intelligent. anyone could do that that's not. intelligence. and so it you know this there was this. intuition that the hard stuff is the. things they were good at. and the easy stuff was the stuff that. everyone could do. yeah and maybe i'm overplaying it a. little bit but i think there's an.

element of that yeah i mean there. i don't know how much truth there is to. uh like chess for. example has was for the longest time. seen as the highest um. level of intellect. right. until we got computer so we're better at. it than people and then. we realized you know if you go back to. the 90s you'll see you know the stories. in the press around when when. kasparov was beaten by deep blue oh this. is the end of all sorts of things. computers are going to be able to do. anything from now on and we saw exactly.

the same stories with alpha zero the. go playing program yeah. but still to me. reasoning is a special thing. and perhaps no way we actually we're. really bad at reasoning we just use. these analogies based on our how to. gather intuitions but why is that not. don't you think the ability to construct. metaphor is a really powerful thing oh. yeah it is stories it is that's it's the. constructing the metaphor and. registering that yeah something. complicated.

is not what we're doing with. with vision too and. we're telling our stories we're. constructing good models of the world. yeah yeah but but um. i think we we jumped between what we're. capable of and how we're doing it right. there there was a little confusion that. went on um. uh as we were telling each other stories. yes exactly. trying to dilute each other no i i just. think uh i'm not exactly so i'm trying. to pull apart this marvex paradox.

i don't view it as a paradox. what did evolution what did evolution. spend its time on yes it spent its time. on getting us to perceive and move in. the world that was you know 600 million. years as multi-cell creatures doing that. and then it was you know relatively. recent that we that we you know were. able to. um hunt or or gather or you know even. even animals hunting that's much more. recent and then and then anything that. we you know speech uh language those.

things are you know a couple of hundred. thousand years probably if if if that. long and then agriculture 10 000 years. you know. all that stuff was built on top of those. earlier things which took a long time to. develop so if you then look at the. engineering of these things so. building it into robots. what's the hardest part of robotics do. you think. as uh through the decades that you. worked on robots.

in the context of what we're talking. about vision you know perception. the actual sort of the. the biomechanics of movement. i'm kind of drawing parallels here. between humans and machines always like. uh what do you think is the hardest part. of robotics. i sort of think all of them. there are no easy parts to do well um. we we sort of go reductionist and we. reduce it to if only we had all the. location of all the points in 3d yeah. things would be great.

you know if only we had labels on the on. the images you know. things would be great but you know as as. we see that's not good enough. some deeper. understanding but if you if i came to. you and i could solve. one category of problems in robotics. uh instantly. what would give you uh the greatest. pleasure. [Laughter]. i mean is it uh.

you know you you look at robots that. manipulate objects. uh. what's hard about that. you know is it uh the perception. is it the uh the reasoning about the. world like common sense reasoning. is it the actual. building a robot that's able to interact. with the world. is it like human aspects of a robot. that's interacting with humans and that. that game theory of how they work well. together well let's talk about. manipulation for a second because i had. this really blinding.

moment. uh uh you know i'm a grandfather so. grandfather's had blinding moments yes. just a. three or four miles from here. last year my. 16 month old grandson was in his new. house first time right. first time in this house and he'd never. been able to get to a window before but. this had some low windows and he goes up. to this window with a handle on it that. he's never seen before and he's got one.

hand pushing the window and the other. hand turning the handle to open the. window. he he knew he two different hands. two different things he knew how to how. to put together yeah. and he's 16 months old and there you are. watching an awesome. in an environment environment he'd never. seen before mechanism how did he do that. yes that's a good question how did he do. that that's why.

it's like okay like you could see the. the leap of genius from using one hand. to perform a task to combining. doing i mean first of all in. manipulation that's really difficult. it's like two hands. both necessary to complete the action. and completely different and he'd never. seen a window open before. but in third somehow handle opened. something yeah there may have been a lot. of. slightly different failure cases that.

you didn't see. yeah not with a window but with other. objects of turning and twisting in. handles. you know there's a there's a great. counter to. um you know. reinforced reinforcement learning will. just give you know the robot um. or you give the robot plenty of time to. try everything yes actually. can i tell a little side story here. so i'm in um. deep mind in london. is. three four years ago where.

um you know there's a big google. building. and then you go inside and you go. through there's more security and then. you get to deep mind where the other. google employees can't go yeah and i'm. in a. i'm in a conference room bayer. conference room with some of the people. and. they tell me about their reinforcement. learning experiment with uh robots um. um which um. are just trying stuff out and they're my. robots they're they're sawyers that we. sold them um. uh and they really like them because.

sawyers are compliant and can sense. forces so they don't break when it's. bashing into walls they they stop and. they do stuff. and you know so you just let the robot. do stuff and eventually it figures stuff. out by the way so. we're talking about robot manipulation. so robot arms and so on yeah so he's a. robot yeah um just to go what's sawyer. so here's a robot arm that my company. rethink robotics yeah built thank you. for the context. sorry okay cool so we're indeed mine. and the you know it's in the next room.

these robots are just bashing around to. try and use reinforcement learning to. learn how to act and can i go see them. oh no they're secret they're all my. robots they're a secret. that's hilarious okay anyway the point. is you know this idea that you just let. uh reinforcement learning figure. everything out is so counter to how a. kid does stuff. so. again story about my grandson i gave him. this this uh. box that had lots of different lock. mechanisms he didn't randomly you know.

when he was 18 months old he didn't. randomly try to touch every surface or. push everything he found he could see. what where the mechanism was and he. started exploring the mechanism for each. of these different lock mechanisms. and there was reinforcement no doubt of. some sort going on there but he applied. a pre-filter which cut down the search. space dramatically. i i. i wonder to what level we're able to. introspect what's going on. because what's also possible is you have.

something like reinforcement learning. going on in the mind in the space of. imagination so like you have a good. model of the world you're predicting and. you may be running those tens of. thousands of like. loops but you're like as a human you're. just looking at yourself trying to tell. a story of what happened and it might. seem simple but maybe there's a lot of. computation going on. whatever it is but there's also a. mechanism that's being built up it's not. just. random search.

mechanism prunes it. dramatically yeah that that pruning. uh that pruning stuff but it doesn't. it's possible that that's. so you don't think that's akin to a. neural network inside of reinforcement. learning algorithm. is it possible. it's. yeah until it's possible i uh but but i. you know. um. i. i i'll be incredibly surprised if that.

happens i'll also be incredibly. surprised that you know after all the. decades that i've been doing this where. every every few years someone thinks now. we've got it. now we've got it. you know. four or five years ago i was saying i. don't think we've got it yet and. everyone was saying no you don't. understand how powerful hey i had people. tell me you don't understand how. powerful it is. um i you know i i i sort of had a a. a track record of what the world had. done to think well this is no different.

from before oh we have bigger computers. we had bigger computers in the 90s and. we could do more ship stuff. but okay so let me let me let me push. back because i i'm i'm generally sort of. optimistic and tried to find the beauty. in things. i think there's a lot of uh. surprising and beautiful things that. neural networks this new generation of. deep learning revolution has revealed to. me it has continually been very. surprising the kind of things it's able.

to do now generalizing that over saying. like this we've solved intelligence. that's another. uh big leap but is there something. surprising and beautiful to you about. neural networks that where actually you. set back and said. i i did not expect this. oh i think i think their performance. their performance on imagenet was. shocking the computer vision those early. days was just very like wow okay. that doesn't mean that they're solving.

everything in computer vision. we need to solve or in. vision for robots what about alpha zero. and self-play mechanisms and. reinforcement learning isn't that yeah. that was all in in donald mickey's 1961. paper um. everything that was there which. introduced reinforcement learning um no. but come on so no you're talking about. the actual techniques but isn't it. surprising to you the level it's able to. achieve with no human supervision. of chess play like.

to me there's a big big difference maybe. blue and maybe what that's saying. is how overblown our view of ourselves. is. you know we that chest is easy. yeah i mean i i. i came across this. 1946 report. that um and i'd seen this as a kid in. one of those books that my mother had. given me actually.

um 1946 report which pitted uh. someone with an abacus against an. electronic calculator. and he beat the electronic calculator. you know so there at that point was well. humans are still better than. machines are calculating are you. surprised today that a machine can you. know do a billion floating point. operations a second and you know you're. you're puzzling for for minutes through. one. so you know.

i i am i mean i i don't know but i am. certainly surprised. there's something. uh to me different about learning. so system that's able to learn learning. now see now you get into one of the. deadly sins. because. of using terms uh overly broadly yeah i. mean there's so many different forms of. learning yeah and so many different. forms you know i learned my way around. the city i learned to play chess i. learnt latin um i learned to ride a.

bicycle all of those are. you know are very different capabilities. yeah and. if someone. you know has a. in you know in the old days people would. write a paper about learning something. now the corporate. press office puts out a press release. about how. company x has. has. is leading the world because they have a. system that can. yeah but here's the thing okay so what. is learning.

what i'm referring to there's many. things but. a suitcase would it's a suitcase word. but. loosely. there's a dumb system. and over time. it becomes smart. well it becomes less dumb at the thing. that it's doing yeah smart is a. different mass is a loaded word yes less. less dominant thing is again it gets. better performance under some measure. yeah and some set of conditions at that. thing and and most of these learning. algorithms.

um. uh learning systems. fail when you change the conditions just. a little bit in a way that humans don't. so right i was at deepmind. um. the alphago had just come out. and i said what would have happened if. you'd given a 21 by 21 board instead of. a 19 by 19 board they said fail totally. but a human player would actually you. know well. would actually have to play again and. actually funny enough if you look at. deepmind's work uh since then. uh they are pers.

uh. they're presenting a lot of algorithms. that would do uh. that would do well at the at the bigger. board so they're slowly expanding this. generalization i mean to me there's a. core element there i think it is very. surprising to me that even in a. constrained game of chess or go. that through self-play by a system. playing itself. that can it can achieve. super human level performance. through learning alone so like okay so.

so you know you you don't find them as. you did it in search of that you didn't. you didn't like it when i referred to. donald mickey's 1961 paper. there in the second. part of it it came a year later they had. self-play on an electronic computer. at. it's tic-tac-toe okay. but it learned to play tic-tac-toe. through self-play that's not and i've. learned to play optimally what i'm. saying is. uh i. okay i have a little bit of a bias but.

i i find ideas beautiful but only when. they actually. realize the promise that's another level. of beauty like for example. uh what the. bezos and elon musk are doing with. rockets we've had rockets for a long. time but doing reusable cheap rockets. it's very impressive. in the same way. i okay yeah i would have not predicted. first of all when i was uh. started and fell in love with ai. the game of go was seemed to be.

impossible to solve okay so i thought. maybe you know i. maybe it'd be possible to maybe have big. leaps in a moore's law style of way in. computation i'll be able to solve it but. i would never have guessed that you. could learn your way. however. i mean. in the narrow sense of learning learn. your way to. to to beat the best people in the world. at the game of go without human. supervision not studying the game of. experts.

but okay so so that's just. using a different learning technique yes. arthur samuel. in the early 60s and he was the first. person to use machine learning. got had a program that could beat the. world champion at checkers. now yes so and that at the time was. considered amazing by the way arthur. samuel had some fantastic advantages. yeah do you want to hear arthur samuel's. advantage. two things one he was at the 1956 um ai.

conference i knew arthur later in life. he was at stanford when i was scratching. there he wore a tie and a jacket every. day the rest of us didn't. um he's a. delightful man delightful man um. it turns out claude shannon in a 1950. scientific american article. uh outlined. on chess playing outline the learning. mechanism that arthur samuel used. and they had met in 1956. i assume there. was some communication but i don't know. that for sure.

but arthur samuel. had been a vacuum tube engineer on. getting reliability of vacuum tubes and. then had overseen. the first transistorized computers at. ibm and in those days. before you shipped a computer you ran it. for a week to see to get early failures. so he had this whole farm of computers. running random code. for hours and hours. a week for each computer we had a whole. bunch of them so he ran his chess.

learning program. with self play. on on ibm's production line he had more. computation available to him than anyone. else in the world and then he was able. to produce a chess playing program i. mean a checkers playing program that. could beat the world champion. so that's amazing the question is. what i mean surprised i don't just mean. it's nice to have that accomplishment. is there is. a stepping.

towards something that feels. uh. more intelligent. than before yeah and the question is. that's in your view of the world okay. well let me then. it doesn't mean i'm wrong. no no it doesn't. so the question is if we keep taking. steps like that how far that takes us. are we going to build a better. recommender systems are we going to. build better robots or will we solve. intelligence so. you know i'm putting my bet on.

um. but still missing a whole lot a lot um. and and why would i say that well in. these games they're all um you know 100. information games. but again but. each of these systems is a very uh short. description of the current state. um. which is different from registering and. perception in the world okay which gets. back to my rfx paradox i'm definitely. not saying that uh. chess is somehow harder than uh.

perception. or. any kind of even any kind of robotics in. the physical world i i definitely think. is is way harder than the game of chess. so i was always much more impressed by. like the workings of the human mind. that's incredible the human mind is. incredible i've i believe that from the. very beginning i want to be a. psychiatrist for the longest time i. always thought that's way more. incredible in the game of chess i think. the game of chess is uh. i love the olympics it's just another. example of us humans picking a task and. then agreeing that a million humans will.

dedicate their whole life to that task. and that's the cool thing that the human. mind is able to focus on one task and. then compete against each other and. achieve like weirdly incredible levels. of performance. that's the aspect of chess that's super. cool not that chess in itself uh is. really difficult it's it's it's like the. framazlast theorem is not in itself to. me that interesting the fact that uh. thousands of people have been struggling. to solve that particular problem is. fascinating so can i tell you my disease. in this way sure.

which actually is closer to what you're. saying so as a child you know i was. building various i called them computers. they weren't general purpose computers. ice cube tray the ice cube tray was one. but i built other machines and what i. like to build was machines that could. beat adults at a game and they couldn't. they adults couldn't beat my machine. yeah so you were like uh that's powerful. like that's uh that's a way to rebel. yeah i i by the way um. did you. when was the first time you built.

something that outperformed you do you. remember like well. i knew how it worked i was probably nine. years old and i built a thing that. it was a game where you you take turns. in taking. matches from a pile and. either the one who takes the last one or. the one who doesn't take the last one. wins i forget and so it was pretty easy. to build that out of wires and nails and. little coils that were like plugging in. the number and a few light bulbs. um the one the one i was prouder of i.

was 12 when i built a. a thing out of old. telephone switchboard switches that. could uh always uh win at tic-tac-toe. and that was a much harder circuit. to design but again it was just. it was no active components it was just. three position switches empty x. zero. and um. and nine of them and and a light bulb. one which which move it wanted next and. then yeah the human would go and move.

that see there's magic in that creation. yeah yeah i tend to uh i tend to see. magic in robots that. like i i also think that intelligence is. uh is a little bit overrated i think we. can have deep connections with robots. very soon. and well we'll come back to connections. robot sure but but i do want to say. i i don't i i think people too many. people make the mistake of seeing that. magic and thinking well we'll just.

continue you know but each each one of. those is a hard-fought battle for the. next step the next step yes i mean the. open question here is and this is why. i'm playing devil's advocate but i often. do when i read your blog post in my mind. because i have like this eternal. optimism is it's not clear to me. so i don't do what obviously the. journalists do or they give into the. hype but it's not obvious to me how many. steps away we are from. from uh.

truly. transformational. understanding of what it means um. to build intelligence systems like or. how to build intelligence systems. i'm also aware of the whole history of. artificial intelligence which is where. your deep grounding of this is. is there's been an optimism for decades. and that optimism just like reading old. optimism is absurd because people were. like. this is they were saying things are. trivial for decades since the 60s.

they're saying everything is true. computer vision is trivial but. i think my mind is. working crisply enough to where i mean. we can dig into if if you want i'm. really surprised by. the things deepmind has done i don't. think they're so they're. yet. um. close to solving intelligence but i'm. not sure it's not 10 to 10 years away. what i'm referring to is interesting to. see when the engineering. um. it takes that idea to scale.

and this and the idea what and no it. fools people. okay honestly rodney if it was you me. and demis inside a room forget the press. forget all those things just as a. scientist as a roboticist you know that. wasn't surprising to you that at scale. so we're talking about very large now. okay okay let's pick one that's the most. surprising to you okay please don't yell. at me. gpt3. okay. i was honestly gonna bring that out okay. thank you.

alpha zero alpha go alpha go zero alpha. zero and then alpha fold one and two. so are any do any of these kind of have. this core of uh. no forget usefulness or application or. so on which you could argue for alpha. fold like as a scientist. was doors surprising to you that it. worked. as well as it did. okay so if we're going to make the. distinction between. surprise and usefulness and and and.

i'll i have to explain. this i would say alpha fold. and. one of the problems at the moment with. alpha fold is. you know it gets a lot of them right. which is a surprise to me because. they're a really complex thing uh but. you don't know which ones it gets right. which. then is a bit of a problem now they've. come out with a reason you mean the. structure of the protein it gets a lot. of those right yeah it's a it's. surprising right yeah it's been a really. hard problem so that was a surprise how.

many it gets right. so far the usefulness is limited because. you don't know which ones are right or. not and now they've come out with a. thing in the last few weeks which is. trying to get a useful tool out of it. and they may well do it um in that sense. the least alpha fold is different. because your. alpha fold two is different. because now it's producing data sets. that are actually uh. you know potentially revolutionizing. computational biology like they will. actually help a lot of people.

but you would say. uh potentially revolutionizing we don't. know yet but yeah that's true yeah but. they're you know but i i i got you i. mean this is okay so you know what this. is gonna be so fun so let's go. right into it speaking of robots that. operate in the real world. let's talk about self-driving cars. oh. okay. because you do you have built robotics. companies you're one of the greatest. roboticists in history and that's not in.

space. just in the space of ideas we would also. probably talk about that but in the. actual. building and execution of businesses. that make robots that are useful for. people and that actually work in the. real world and make money. you also sometimes are critical. of mr elon musk or less more. specifically focused on this particular. technology which is autopilot inside. teslas. what are your thoughts about tesla.

autopilot or more generally vision-based. machine learning approach to. semi-autonomous driving. uh these are robots they're being used. in the real world by hundreds of. thousands of people. and. if you want to go there i can go there. but that's not too much which there is. let's say they're on par safety wise as. humans currently meaning. human alone versus human plus robot okay. so first let me say i really like the.

car i came here in here today which is. um a. 2021 um. model uh mercedes e450. i am impressed by the um. machine vision. sonar other things i'm impressed by what. it can do i'm really impressed um. with. many aspects of it and and i'm. um it's able to stay in lane is it uh oh.

yeah it does it does the lane stuff um. it uh. you know it's it's looking up on either. side of me it's telling me about nearby. cars or blind spots and so on yeah when. i when i when i'm when i'm. going in close to something in the park. i get this beautiful gorgeous top-down. view of the world i am impressed. up the wazoo of how you know. registered and metrical so it's like. multiple cameras and it's all right. together to produce the 360 view kind of.

360 view you know synthesized as though. it's above the car and it is. unbelievable. i got this car in january it's the. longest i've ever owned a car without. digging it um so it's better than me. for me and it together uh better so i'm. not saying technology's um. uh bad or not useful. but here's my point yes. it's. it's a replay of the same movie.

okay. so maybe you've seen me ask this. question before but um. when um. when when did the first car go over 55. miles an hour. for over. um. over 10 miles. on a public freeway with other traffic. around driving completely autonomously. when did that happen. was it cmu in the 80s or something it.

was a long time ago it was actually in. 1987. in in munich um munich uh at the. bundesvar yeah um. so they they had it running in 1987. when do you think and elon has said he's. going to do this. when do you think we'll have the first. car drive coast to coast in the us. hands off the wheel hands off the wheel. feet off the pedals coast to coast. as far as i know a few people have. claimed to do it 1995. that was comedy i.

didn't know about oh that was the code. yeah uh they didn't claim did they claim. a hundred percent not a hundred percent. not a hundred percent but. and then there's a few marketing people. who have claimed 100 percent since but. my point is that. you know i i what i see happening again. is someone sees a demo. and they over generalize and say we must. be almost there well we've been we've. been working on it for 35 years because. that's demos but this is going to take. us back the same conversation with alpha. zero are you not.

okay i'll just say what i am. because i thought okay when i first. started interacting with the. with the mobile eye implementation tesla. autopilot. i've. driven a lot of car you know i have been. in the google self-driving car since the. beginning. um. i thought there was no way before i sat. and used mobileye i thought they're just. knowing computer vision i thought. there's no way it could work as well as. it was working so. my model of the limits of computer.

vision. uh was. was way more limited than the actual. implementation of mobile eye i was so. that's one example i was really. surprised i was like wow that was that. was incredible. the second surprise came. when tesla threw away mobileye. and started from scratch. uh i thought there's no way they can. catch up to mobile eye i thought what. mobile i was doing was kind of. incredible like the amount of work and. the annotation yeah well mobile i was. started by amazon structure and and used.

a lot of traditional you know. hard-fought computer vision techniques. but they also did a lot of good sort of. uh like non-research stuff like actual. like. uh just good like what you do to make a. successful product right scaled all that. kind of stuff and so i was very. surprised when they from scratch were. able to catch up to that. uh that's very impressive and i've. talked to a lot of engineers though i. was involved this is. that was impressive uh and. the recent progress especially under um.

well with the involvement of andre. kupathi. the what they were. what they're doing with the data engine. which is converting into the driving. task into these multiple tasks and then. doing this edge case discovery when. they're pulling back like the level of. engineering. made me rethink what's possible i don't. i still you know um. i don't know to that intensity but i. always thought it was very difficult to. solve anton was driving with all the. sensors with all the computation i just.

thought it was a very difficult problem. but i've been. continuously surprised. how much you can engineer first of all. the data acquisition problem because i. thought. you know just because i worked with a. lot of car companies. and. they're. they're so a little. a little bit old school to where i. didn't think they could do this at scale. like aws style. data collection so when tesla was able. to do that. i started to think okay so what are the.

limits of this. i still believe. that um. driver like sensing and the interaction. with the driver and like studying the. human factors psychology problem is. essential it's it's always going to be. there. it's always going to be there even with. fully autonomous driving. but i've been surprised what is the. limit especially of vision based alone. how far that can take us. um so that's my level is a surprise.

now. okay uh can can you explain. in the same way you said like alpha zero. that's a homework problem that scaled. large in his chest like who cares go. with here's actual people using an. actual car and driving many of them. drive more than half their miles. using the system right. so and. yeah they're doing well with with pure. vision without your vision yeah and you. know. and now no radar which is i suspect that.

can't go all the way and one reason is. without without new cameras that have a. dynamic range closer to the human eye. because human eye has incredible dynamic. range and we make use of that dynamic. range in its uh 11 orders of magnitude. or some crazy number like that. the cameras don't have that which is why. you see the the the the bad cases where. the sun on a white thing and the blinds. are. in a way it wouldn't apply in the person.

i think there's. a bunch of things. to think about before you say this is so. good it's just gonna work. okay. um. and i'll come at it from multiple angles. and i know you've got a lot of time yeah. okay let's. have thought about these things yeah i. know you've been writing a lot of great. blog posts about it for a while before. tesla had autopilot right so you've been. thinking about autonomous driving for a. while from every angle.

so so a few things. you know in the us um i think that the. the death rate from. motor vehicle accidents. is about 35. 000. a year. which is an outrageous number. not outrageous compared to covert deaths. but you know there is no rationality. and that's part of the thing people have. said engineers say to me well if we cut. down the number of deaths by 10 by. having autonomous driving that's going.

to be great everyone will love it. and. my prediction is. that if autonomous vehicles kill more. than 10 people a year they'll be. screaming and hollering even though 35. 000 people a year have been killed by. human drivers. it's not rational. it's a different set of expectations. and that will probably continue. so there's that aspect of it. the other aspect of it is that.

when we introduce new technology. we often change the rules of the game. so when we introduced. cars. first. you know into our daily lives we. completely rebuilt our cities and we. changed all the laws yeah jaywalking was. not an offense. that was pushed by the car company so. that people would stay off the road so. there wouldn't be deaths from. pedestrians getting hit. we completely changed the structure of.

our cities and had these foul smelling. things you know everywhere around us and. you know and now you see pushback in. cities like barcelona is really trying. to exclude cars. etc. um. so. i think that. to get to self-driving. we will. um. large adoption. it's not going to be just take the. current situation take out the driver. and put the same car doing the same.

stuff because the end case is too many. um. here's an interesting question. how many. fully autonomous. train systems do we have in the us. i mean do you count them as fully. autonomous i don't know because they're. usually as a driver but they're kind of. autonomous right no well. let's get rid of the driver. okay i don't know. it's either 15 or 16. most most of them.

are in airports. okay um there's a few that go about five. two that go about five kilometers out of. airports yeah. um. uh. when do when is the first. fully autonomous train system for mass. transit expected to operate fully. autonomously. with no driver uh in the u.s city. it's expected to operate in 2017. in honolulu. it's delayed but they will get there but.

by the way it was originally going to be. autonomous uh here in the bay area i. mean they're all. very close to fully autonomous right. yeah but. getting the clues is the thing and i. have i have i've often gone on a fully. autonomous train in japan um one that. goes uh out to that fake island in the. middle of tokyo bay i forget the name of. the. and. and what do you what do you see when you. look at that what do you see when you go. to a fully autonomous train in a in a in. a.

um an airport. it's not like regular trains there's. at every station there's a double set of. doors um so that there's the door of the. train and this door off the the um. off the the platform yeah um. and it's really visible in this japanese. one because it goes out in in amongst. buildings. the whole track is built so that people. can't climb onto it yeah. so there's engineering that then makes.

the system safe and makes them. acceptable i think we'll see. similar sorts of things happen. in the u.s what surprised me. i thought. wrongly. that we would have. special purpose lanes on 101. in the bay area the the leftmost lane. so that it would be normal for. teslas or other cars to move into that. lane and then say okay now it's.

autonomous and have that dedicated lane. i was expecting. movement to that you know five years ago. i was expecting we'd have a lot more. movement towards that we haven't and it. may be because. tesla's been over promising by saying. this you know calling their system fully. self-driving um i think they may have. been gotten there quicker by. collaborating. to change the infrastructure. this is one of the problems. with. long-haul trucking.

being autonomous i think it makes sense. on freeways. at night for the trucks to go. autonomously um. but then is that how to get onto and off. of the freeway what sort of. infrastructure do you need for that. um do you need to have the human in. there to do that. or can you get rid of the human so i. think there's ways to get there but it's. an infrastructure. argument. because the long tail of cases. is very long.

and the acceptance of it will not be at. the same level as human drivers. so i'm. i'm with you still and i was with you. for a long time but i am surprised how. how well how many edge cases. of machine learning and vision based. methods can cover. this this is what i'm trying to get get. at is um. i think there's something fundamentally. different with vision based methods and. tesla autopilot and any company that's.

trying to do the same. okay well i'm not i'm not going to argue. with you because you know i. i. was speculating yes but i. you know my gut feeling tells me. it's going to be. things will things will speed up when. there is engineering of the environment. because that's what happened with every. other technology. i'm a bit i don't know about you but i'm. a bit cynical that. infrastructure which relies on on. government.

to help out. in these cases um. if you just look at infrastructure in. all domains it's just a government. always drags behind on infrastructure. there's like there's so many just well. in this country in the future sorry yes. in the in this country and and of course. there's many many countries that are. actually much worse on infrastructure oh. yes there's nothing many are much worse. and there's some that you know like. high-speed rail the. other countries have done much better i. guess uh my question is like.

which is at the core what i was trying. to think through here and ask is like. how hard is the driving problem. as it currently stands so you mentioned. like we don't want to just take the. human out and duplicate whatever the. human was doing but if we were to try to. do that. what. how hard is that problem. because i used to think is way harder. like i i used to think it's uh. with vision alone it it it would be.

three decades four decades okay so i i. don't know the answer to this thing i'm. about to pose. but i do notice that on highway 280 here. in the bay area which largely has. concrete. surface rather than blacktop surface. the white lines that are painted there. now have black boundaries around them. and. my. lane drift system in my car would not. work without those black boundaries.

interesting so i don't know whether. they've started doing it to help the. lane drift whether it is an instance of. infrastructure. following the technology but but it. my car would not perform as well without. that change in the way they paint the. line unfortunately really good lane. keeping. is not as valuable. like. it's orders of magnitude more valuable. to have a fully autonomous system like. yeah but but for me. lane keeping is really helpful because. i'm busy at it but you wouldn't pay.

10 times like. um. the problem is there's not financial. like it doesn't make sense to to to uh. revamp the infrastructure to make lane. keeping easier. it does make sense to prevent the. infrastructure if you have a large fleet. of autonomous vehicles now you change. what it means to own cars you change the. nature of transportation i mean but that. that for that you need. uh autonomous vehicles let me ask you.

about waymo then i've gotten a bunch of. chances to to ride in in a waymo um. self-driving car and they're. i don't know if you'd call them. self-driving but well i mean i i wrote. in one before they were called waymo. yeah still at x so there's currently. this was a big another surprising leap i. didn't think it would happen which is. they have no driver. currently yeah in chandler in chandler. arizona and i think they're thinking of. doing that in austin as well but they're. they're expanding.

although although you know and i i do an. annual uh checkup on this so as of late. last year they were aiming for hundreds. of rides. a week. not thousands and. um there is still no one in the car but. there's certainly uh. um safety uh people in the loop and it's. not clear how many you know what the. ratio of cars to safety people is. i it wasn't. uh obviously they're not 100 transparent.

about this no none of them are 100. transfers. but i'd. at least the way they're. i don't want to make definitely but. they're saying there's no tele operation. um. like they're i mean. okay and and and that sort of fits with. with um youtube videos i've seen of. people being trapped in the car yeah um. by a red cone on the on the street and. they do they do have rescue vehicles. that come yeah and then a person gets in.

and drives it yeah. but isn't it incredible to you. it was to me to get in a car with no. driver and watch the steering wheel turn. like for somebody who has been studying. at least certainly the human side of. autonomous vehicles for many years and. you've been doing it for way longer like. it was incredible to me that this was. actually could happen i don't care if. that scale is a hundred cars this is not. a demo this is not this is me as a. regular the argument i have is that.

people make interpolations from that. interpolation that you know it's here. it's done. um you know it's just you know we've. solved that no we haven't yet. and and that's my argument okay so i'd. like to go to. you uh you keep a list of predictions. on your amazing blog post it'd be fun to. go through them but before then let me. ask you about this. you have um. you have a. harshness to you sometimes in your. criticisms of what is.

and so like because people extrapolate. like you said and they they kind of buy. into the hype and then they. they kind of start to think that um. uh the technology is way better than it. is but. let me ask you maybe a difficult. question. sure. do you think if you look at history of. progress. don't you think to achieve the quote.

impossible you have to believe that it's. possible. absolutely yeah like his his his his. two great runs. great unbelievable. 1903. first human. um power human uh. you know heavier than their flight yeah. 1969 we land on the moon that's 66 years. i'm 66 years old in my lifetime that. span of my lifetime. barely get you know flying i don't know.

what it was 50 feet or the length of the. first flight or something to landing on. the moon. unbelievable. fantastic but that requires by the way. one of the wright brothers. both of them but one of them didn't. believe it's even possible like a year. before. right so. like not just possible soon but like. yeah ever so so so you know how. important is it to believe and be. optimistic is what i guess oh yeah it is. important it's when it goes crazy when. when i you know you said what was the.

word you used for my bad harshness. harshness yes. i just get so frustrated yes when when. people make these leaps. and tell me that i'm that i don't. understand right i. you know yeah. there's. just from. irobot which i was co-founder of yeah i. don't know the exact numbers now because. i haven't it's 10 years since i stepped.

off the board but i believe it's well. over 30 million. robots cleaning houses from that one. company and now there's lots of other. companies yesterday. was that a crazy idea that we had to. believe. uh. in 2002 when we released it. yeah that was we we had we had uh you. know. believed that it could be done let me. ask you about this so irobot one of the. greatest robotics companies ever. in terms of manufacturing. creating a robot that actually works in.

the real world probably the greatest. robotics company ever. you're the co-founder of it. um. if if the rodney. brooks of today talked to the rodney of. back then. what would you tell him because i have a. sense that. would you pet him on the back and say. what you're doing is going to fail. but go at it anyway. that's what i'm referring to is with the. harshness you've accomplished. an incredible thing there one of several.

things we'll talk about. what like that's what i'm trying to get. at that line. no it's it's when. my harshness is reserved for people who. are not doing it. who claim it's just well this shows that. it's just gonna happen but here here's. the thing. this shows but you have that harshness. for elon too. and no no it's a different harshness no. it's it's a. different um. argument with yuan you know i.

i think spacex is an amazing company on. the other hand you know i in one of my. blog posts i said what's easy and what's. hard i said space x. vertical landing rockets it had been. done before. grid fins have been done since the 60s. every soyuz has them um. reusable space. dcx reused those rockets that landed. vertically. there's a whole insurance industry in.

place for. rocket launchers so all sorts of. infrastructure. that. was doable it took a great entrepreneur. a great personal. expense he almost drove himself you know. bankrupt doing it. um a great belief to do it. whereas. hyperloop. there's a whole bunch more stuff that's. never been thought about never been. demonstrated so my estimation is.

hyperloop is a long lot long a lot. further off. and and if i've got a criticism of of of. elon it's that he doesn't make. distinctions. between. when the technology's. coming along and ready and then he'll go. off and and. mouth off about other things which then. people go and compete about and try and. do. and. so. this is where i um i i understand what. you're saying i tend to draw a different. distinction i.

i have. a similar kind of harshness towards. people who are not telling the truth. who are. basically fabricating stuff to make. money or to well he believes what he. says i just think to me that's a very. important difference yeah i'm not. because i think uh in order to fly. in order to get to the moon you have to. believe um even when uh most people tell. you you're wrong and most likely you're. wrong but sometimes you're right i mean. that's the same thing i have with tesla. autopilot i i think that's an.

interesting one i was especially when i. was you know um at mit and just the. entire human factors in the robotics. community were very negative towards. elon it was very interesting for me to. observe colleagues at mit. i wasn't sure what to make of that that. was very upsetting to me. because i understood where that where. that's coming from. and i agreed with them and i kind of. almost felt the same thing in the. beginning until i kind of opened my eyes. and and. realized there's a lot of interesting.

ideas here there might be over hype you. know. if if you focus yourself on the idea. that. you shouldn't call a system full. self-driving. when it's obviously not. autonomous fully autonomous. you're going to miss the magic oh. yeah you are going to miss the magic but. at the same time there are people who. buy it. literally pay money for it yeah and take. those words as given so it's that's uh. but i haven't so.

that i. take words as given as one thing i. haven't actually seen people that use. autopilot that believe that the behavior. is really important like the actual. action so like this is like to push back. on the very thing that you're frustrated. about which is like journalists in. general people. uh. buying all the hype and going on. in the same way. i think there's a lot of hype about. the the negatives of this too that. people are buying without using people. used the way this is what this was this.

opened my eyes actually. the way people use the product is very. different than the way. they talk about it this is true with. robotics with everything everybody has. dreams of how a particular product might. be used or so on this and then when it. meets reality there's a lot of fear of. robotics for example that robots are. somehow dangerous and all those kinds of. things but when you actually have robots. in your life whether it's in the factory. or in the home making your life better. that's going to be. that's way different your perceptions of. it are going to be way different and so.

my. just tension was was like here's an. innovator. um. uh like uh. uh what is it sorry super cruise from. cadillac was super interesting too. that's a really interesting system. there's we should like be excited by. those innovations okay so let me can i. tell you something that's really annoyed. me recently. it's really annoyed me that. the press and friends of mine on. facebook are going these billionaires. and their space games you know why are.

they doing that yeah that's been very. frustrating really pisses me off i i. must say i i applaud that yeah i applaud. it yeah it's the taking. and not necessarily the people who are. doing the things but. you know like that i keep having to push. back against. unrealistic expectations when. these things can become real yeah i. this was interesting ana because there's. been a particular focus for me is. autonomous driving. elon's prediction of when certain.

milestones would be hit. there's several things to be said there. that i always i thought about because. whenever you said them it was obvious. that's not going to me as a person that. kind of. not inside. the system it was obvious it's unlikely. to hit those. there's two comments i want to make one. he legitimately believes it. and two much more importantly. i think.

that. having ambitious deadlines drives people. to do the best work of their life even. when the odds of those deadlines are. very um. to a point and i'm not killed i'm not. talking about anyone yeah i'm just. saying so there's a line there right you. have to have a line because. you over extend and it's. it's demoralizing yeah. [Music]. but i will say that there's an. additional thing here. that those words. also.

um. drive the stock market. yeah and you know we have because of the. way that rich people in the past have. manipulated. the rubes. through investment we have. um um we have. developed laws about what you know what. you're allowed to say and yeah i promise. and you know there's an area here which. is. i i i tend to be. maybe i'm naive but i i tend to believe.

uh that. like engineers innovators people like. that they're not. they're my they don't think like that. like manipulating the price at the stock. price but. it's possible that i'm uh i'm certain. it's possible that i'm wrong. i it's a very cynical view of the world. because i don't i think most people that. run companies and build like especially. original founders. they um yeah i'm not saying that's the. intent i'm saying it's a eventually it's.

kind of you uh yeah. you you fall into that kind of a. behavior pattern i don't know i i tend. to. i wasn't saying i wasn't saying it's. falling into that intent it's just a. you also have to protect investors in. this in this market yeah. okay so you have first of all you have. an amazing blog that people should check. out but you also have this in that blog. a set of predictions. it's such a cool idea i don't know how. long ago you started like three four. years ago it was um january 1st.

2018 18. yeah and i made these. predictions and i said that every. january 1st i was going to check back on. how my predictions would be that's such. a great orthodontics for 32 years. oh you said 32 years i said 32 years. because it's still that'll be january. 1st 2050 yeah i'll be. i will just turn 95. um. you know so. and so people know that your predictions. at least for now are in the space of.

artificial intelligence yeah i didn't. say i was going to make new predictions. i was just going to measure this set of. predictions that i made because yeah it. was sort of i was sort of annoyed that. everyone could make predictions they. didn't come true and everyone forgot so. i should hold myself to a high standard. yeah but also just putting years and. like date rangers on things it's a good. thought exercise yeah like and like. reasoning your thoughts out and so the. topics are. uh artificial intelligence autonomous. vehicles and space yeah. um.

i was wondering if we could just go. through some that stand out maybe from. memory i can just mention to you some. let's talk about self-driving cars like. some predictions that you're. particularly proud of or are. particularly interesting uh from flying. cars to the the other element here is. like how widespread the location. where the deployment of the autonomous. vehicles is. and there's also just a few fun ones is. there something that jumps to mind that. you remember from the predictions.

well i did i think i did put in there. that there would be a. dedicated self-driving lane on 101 by. some year and i think i was over. optimistic on that one yeah actually. yeah i actually do remember that but you. uh i think you were mentioning like. difficulties in different cities. yeah yeah so. cambridge massachusetts i think was an. example yeah like in cambridge port you. know yeah i lived in cambridge port for. a number of years and. you know the roads are narrow and.

getting getting anywhere as a human. driver is incredibly frustrating when. you start to put and people drive the. wrong way on one-way streets there it's. just. your prediction was. driverless taxi services operating on. all streets in cambridgeport. massachusetts. in uh. 2035. yeah and. that may have been too optimistic you. think so you know i've gotten a little. more pessimistic since i made these. internally on some of these things so.

what uh. can you put a year to a major milestone. of deployment of a taxi service. in um. in a few major cities like something. where you feel like yeah so autonomous. vehicles are here so let's let's take um. the grid streets of san francisco north. of market okay okay. um.

relatively benign. um. uh environment the streets are wide. the major. problem is. delivery trucks stopping everywhere. which made things more complicated. a taxi system there with um. somewhat designated pickup and drop-offs. unlike with uber and lyft where you can. sort of get to any place and the drivers. will figure out how to.

get in there um. we're still a few years away i you know. i live in that area so i see. you know the self-driving car companies. cars. multiple multiple ones every day i'll. say. cruise. zooks less often. way more all the time. different and different ones come and go. and there's always a driver. there's always a driver at the moment.

although i have noticed. that um. sometimes the driver does not have the. authority to take over without talking. to the home office because they will sit. there waiting for a long time. and clearly something's going on where. the home office is making a decision um. so they're you know and and so you can. see whether they've got their hands on. the wheel or not and and it's the. incident resolution time that tells you. gives you some clues.

so what year do you think what's your. intuition what date range are you. currently thinking. san francisco would be autonomous. uh. taxi service from. any point a to any point b without a. driver. are you are you still. are you thinking uh 10 years from now 20. years from now 30 years from now. certainly not 10 years from now. it's going to be longer if you're. allowed to go south of market way longer. um.

and unless it's re-engineering of course. roads. by the way what's the biggest challenge. you can mention a few is that the the is. it the delivery trucks is it the edge. case is the computer perception. uh well. here's a case that i saw outside my. house a few weeks ago. um about 8 pm on a friday night it was. getting dark it was before the solstice. it was a. cruise vehicle come down the hill uh. turned right um. and.

stopped dead. covering the crosswalk why did it stop. dead because there was a. human just two feet from it. now i just glanced i knew what was. happening the human. was it was a woman was at the door of. her car trying to unlock it with one of. those things that yeah you know when you. don't have a key yes. that car. thought. oh she could jump out in front of me any. second yeah as a human i could tell no. she's not going to jump out she's busy. trying to unlock her she's lost her keys.

she's trying to get in the car. and it it stayed there for. until i got bored um yeah. and so the the human driver in there did. not take over. but here's the kicker to me. a guy comes down the hill. with a stroller i assume there's a baby. in there. and. now the crosswalk's blocked by this. cruise vehicle. what's he going to do. cleverly i think he decided not to go in.

front of the car. he went but he had to go behind it he. had to get off the crosswalk out into. the intersection. to push his baby around this car which. was stopped there and no human driver. would have stopped there for that length. of time um they would have gotten out of. the way. and that's another one of. my pet peeves. that. safety has been compromised. for individuals who didn't sign up for. having this happen in their. neighborhood.

yeah but. now you can say that's an edge case but. yeah well i'm in general. not a fan which. of uh anecdotal evidence for stuff like. this is one of my biggest problems. with the discussion of autonomous. vehicles in general people that. criticize them or support them by using. cases okay uh uh aren't using anything. so so let me but i got you you know you. know your question is when is it going. to happen in san francisco i say not. soon but now it's going to be one of. them but when where it is going to.

happen. is in. limited domains. campuses of various sorts. gated communities. where the other drivers. are. are not arbitrary people. they're people who know about these. things they you know it's been warned. about them. and at velocities where it's always safe. to stop dead yeah um you can't do that. on the freeway that i think we're going. to start to see.

and they may not be shaped like. you know current cars they may be you. know things like you know may mobility. has those. things and various companies have these. yeah i wonder if that's a compelling. experience to me it's always important. it's not just about automation it's. about creating a product that like. that makes your it's not just cheaper. but it makes your this fun to ride one. of the most. one of the least fun things is for a car. that stops. and like waits there's something deeply.

frustrating for us humans. for the rest of the world to take. advantage of us as we wait but. um. think about uh you know not you as the. customer but someone who's in their. 80s. in an uh you know a retirement village. whose kids have said you're not driving. anymore. and this gives you the freedom to go to. the market more that's a hugely. beneficial thing but. it's a very uh few orders of magnitude. less impact on the world it's not it's.

just a few people in a small community. using cars as opposed to the entirety of. the world uh. i like that uh the first time that a car. equipped with some version of a solution. to the trolley problem is uh what's niml. stand for like not in my life not in my. life i define my lifetime as up 2050. 2015. yeah. uh you know and then i ask i ask you. when when have you had to decide which. person should i kill um no you put the.

brakes on and you break as hard as you. can. i mean uh making that decision it is uh. you know i do think autonomous vehicles. or semi-autonomous vehicles do need to. solve the whole pedestrian problem that. has elements of the trolley problem. within it but it's not yeah well so. here's a and i talked about it in one of. the articles or blog posts that i wrote. his his and people have told me i one of. my co-workers has told me he does this. he. he tortures. autonomously driven vehicles and. pedestrians will will torture them.

they'll you know once they realize that. you know putting one foot off the curb. makes the car think that they might walk. into the road kids teenagers will be. doing that all the time they will. i by the way one of my that's a whole. nother discussion because my main issue. with robotics is hri human robot. interaction. i believe that robots that interact with. humans will have to. push back. like they can't just be. bullied because that creates a very.

uncompelling experience for the humans. yeah well you know waymo before it was. called waymo discovered that you know. they had to do that at four-way. intersections they had to they had to. nudge forward to give the cue that they. were going to go because otherwise the. other drivers would just. beat them all the time. so you co-founded irobot as we mentioned. uh one of the most successful. robotics companies ever. what are you most proud of with that. company and uh the approach. you uh.

took to robotics well like there's. something i'm quite proud of there. which may be a surprise. but um. i was still on the board when this. happened it was march 2011. and we. sent robots to japan and they were used. to. uh shut help shut down. the fukushima fukushima daiichi nuclear. power plant. um which was everything i've been there. since i was there in 2014 to the robots.

some of the robots were still there i. was i was proud that we were able to do. that. why were we able to do that. and and you know people have said well. you know japan is so good at robotics. it was because we had had. about 6 500 robots. uh deployed. in iraq and afghanistan. teleopt but with intelligence. dealing with. roadside bombs. so we had uh i think it was at that time. nine years of.

in-field. experience with the robots in harsh. conditions. whereas the japanese robots which were. you know getting you know just goes back. to what. annoys me so much getting all the hype. look at that look at that honda robot it. can walk well the future's here. um couldn't do a thing uh because they. weren't deployed but we had deployed in. really harsh conditions for a long time. and so we're able to. to. do something very positive in a very bad. situation what about.

just the simple. and for people who don't know one of the. things that irobot has created is the. roomba. uh vacuum cleaner. what about the simple robot. that that is the room bus quote-unquote. simple. that's deployed in. tens of millions of in tens of millions. of homes. what do you think about that. well i make the joke that i started out. life as a. pure mathematician and turned into a.

vacuum cleaner salesman so. if you're going to be an entrepreneur be. ready for that. be ready. um but i was you know. there was a there was a. wacky uh lawsuit that i got posed for uh. not too many years ago. and i was the only one who had emailed. from the 1990s. and. no one in the company had it so i went. and went through my email and and it. reminded me of.

you know the joy of what we were doing. and and what what was i doing what was i. doing at the time we were building. um. building. uh the roomba. one of the things was we had this. incredible incredibly tight budget. because we wanted to to put it on the. shelves at. 200. there was another home cleaning robot at. the time it was the. electrolux. trilobite.

which sold for 2 000 euros. and to us that was not going to be a. consumer product. so we. had reason to believe that 200 was a was. a thing that people would buy at. that was our aim but that meant we had. you know that's that's. on the shelf making profit. uh that means the cost of goods has to. be minimal. so i find all these emails of me. going you know i'd be in um. taipei for a mit meeting and i'd stay a.

few extra days and go down to shinshu. and talk to these little tiny companies. lots of little tiny companies outside of. uh tsmc taiwan semiconductor. taiwan semiconductor manufacturing. corporation which. let all these little companies be. fabulous they didn't have to have their. own fab so they could innovate. and then. um. they were building their innovations. were built stripped down 6802s. 1682 was what was in an apple one get. rid of half the silicon still have it be. viable.

and i'd i'd previously got. some of those for some earlier failed. products of of a robot and um and that. was um in hong kong. going to all these um. companies that built you know. they weren't gaming in the current sense. there were these handheld games that you. would play. um or. or birthday cards because we had about a. 50 cent budget for computation. so i'm trekking from place to place. looking at their chips.

looking at what they'd removed. their interrupt their interrupt handling. is too weak for a general purpose so i. was going deep technical detail and then. i found this one from a company called. winbond which had. and i'd forgotten it had this much ram. it had 512 bytes of ram and it was in. our budget and it had all the. capabilities we needed yeah so and. you're excited yeah and i i was reading. all these emails calling i found this.

so did you think did you ever think that. you guys could be so successful. like eventually this company would be so. successful did you could you possibly. have imagined. um no we never did think that we had 14. failed business models up till 2002 and. then we had two winners the same year um. uh. no and then you know. we i remember the board. um because by this time we had some uh.

venture capital in. the board went along with us building um. some robots for you know aiming at the. christmas 2002. market and. we went three times over what they. authorized and built 70 000 of them and. sold them all. in that first because we released on. september 18th. and uh. all sold by christmas so it was uh. so we were gutsy but.

but yeah you didn't think this will take. over the world well this is uh. so a lot of amazing robotics companies. have gone under over the past few. decades. why do you think it's so damn hard. to uh run a successful well there's a. robotics company there's a few things um. one is. expectations of capabilities by the. founders that are.

off base. the founders not the consumer and the. founders yeah expectations what what can. be delivered sure. mispricing. and. what a customer thinks is a valid price. is not rational. necessarily yeah. and expectations of customers. and. just the. sheer hardness of getting people. to adopt a new technology and i've.

suffered from all three moons. uh you know i've had. more failures and successes. in terms of companies i've suffered from. all three. um. so. do you think. one day there will be a robotics company. and by robotics company i mean where. your primary source of income is. from robots. that will be a trillion plus dollar. company. and it's so what come what would that.

company do. i can't you know because i'm still. starting robot companies yeah. i'm not making any such predictions in. my own mind i'm not thinking about a. trillion dollar company and by the way i. don't think you know in the 90s anyone. was thinking that apple would ever be a. trillion-dollar company so these are. these are very hard to to predict but. sorry to interrupt but don't you because. i kind of have a vision.

in this in a small way and it's a big. vision in a small way that. i see that there would be robots in the. home. at scale like roomba but more. and that's trillion dollar. right and i and i think there's a. there's a real market pool for them. because of the um um demographic. inversion you know who's who's going to. do all the stuff for the older people um. there's too many i'm you know i'm i'm. leading here.

it's going to be too many of us. and. um. but. we don't have capable enough robots to. to make that economic argument at this. point. do i expect that that will happen yes i. expect it will happen but i gotta tell. you we introduced the roomba in 2002 and. i stayed another. nine years we were always trying to find. what the next home robot would be and. still today the primary product of 20.

years almost 20 years later 19 years. later the primary product is still the. roomba so irobot hasn't found the next. one do you think it's possible for one. person in the garage to build it versus. like google. launching google self-driving car that. turns into waymo you think it's pos. this is almost like what it takes to. build a successful robotics company do. you think it's possible to go from the. ground up or is it just too much capital. investment. yeah so. it's very hard to get there.

um. without a lot of capital and we're. starting to see. you know a fair chunks of capital uh for. some robotics companies um you know. series b's because i saw one yesterday. for 80 million dollars i think it was. for co-variant um. [Music]. but it can take real money to. to get into these things and you may. fail along the way i've certainly failed. at rethink robotics um and we've all. lost 150 million dollars in capital.

there so okay so rethink robotics is. another amazing robotics company you. co-founded. so what was the vision there. what was. the dream and. what what are you most proud of with. rethink robotics i'm most proud of the. fact that we got um. robots out of the cage in factories. that were safe absolutely safe for. people and robots to be next to each. other so these are robotic arms robotics. arms they're able to pick up stuff and. interact with humans yeah and that.

humans could re-task them. without writing code. right and and now that sort of become an. expectation for a lot of other little. companies and big companies are. advertising they're doing that's both an. interface problem and also safety. problem yeah. yeah. so i'm most proud of that. i completely i let. myself be talked out of. what i wanted to do and you know you've.

always got you know i can't replay the. tape. you know i can't replay it maybe. maybe i you know if i'd been stronger um. and i remember the day i remember the. exact meeting. um can you take me through that meeting. yeah. um. so i said that i'd set as a target for. the company that we were going to build. three thousand dollar robots with force. feedback. um that was safe for people to be around. wow that was my goal.

and we built uh so we started in 2008. and we had prototypes built of plastic. plastic gearboxes. and at a three thousand dollar you know. uh. lifetime of three thousand dollar. i was. saying we're going to go after not the. people who already have robot arms in. factories the people who never have a. robot arm we're going to go after a. different market so we don't have to. meet their expectations. um.

and and so we're going to build it out. of plastic it doesn't have to have a 35. 000 lifetime it's going to be so cheap. that it's opex not capex. and so we had we had a prototype that. worked reasonably well. but. the control engineers were complaining. about these plastic gearboxes with a. beautiful little planetary gearbox um. but. we could. use something called serious elastic. actuators we embedded them in there we.

could measure forces we knew when we hit. something et cetera. the control engineers were saying yeah. but this is torque ripple because these. plastic gears they're not great gears. and there's this ripple and trying to do. force control around this ripple is so. hard. and i'm not going to name names but i. remember one of the mechanical engineers. saying we'll just build a metal gearbox. with spur gears. and it'll take six weeks we'll be done.

problem solved. two years later we got to get the spur. gearbox working yeah um we we cost. reduced it every possible way we could. um yeah but. now the price went up to and then the. ceo at the time said well we have to. have two arms not one arm so our first. robot product baxter now cost 25 000. and the only people who are going to. look at that were people who had arms in. factories because that was somewhat.

cheaper for two arms than arms and. factories but they were used to. 0.1 millimeter reproduce. reproducibility of motion. and certain velocities. and we i kept thinking but that's not. what we're giving you you don't need. position repeatability use force control. like a human does no. no but we want we want that. repeatability we want that repeatability. yes all the other robots have that. repeatability why don't you have that. repeatability so. you clarify force controls you can grab.

the arm and you can move it or you can. move it around but but suppose you um. can you see that yes suppose you want to. yes. suppose this this thing is a you know. precise thing that's going to fit here. in this right angle. um under position control you sent your. you you have fixtured where this is you. know where this is precisely and you. just move it open you know and it goes. there if force control you would do. something like. slidell over here till we feel that and.

slide it in there and that's how a human. gets precise precision yeah they use. force feedback yes and get the things to. mate rather than just. go straight to it yeah. couldn't convince couldn't convince our. customers who were in factories and were. used to thinking about things a certain. way. and they wanted that one wonder so then. we said okay we're going to build an arm. that gives you that. so now we ended up building a 35 000. robot with one arm with um.

um. oh what are they called um. um. a certain sort of gearbox made by a. company whose name i can't remember. right now but it's the name of the. gearbox. um. and um but it's it's. got torque ripple in it. so now there was an extra two years of. solving the problem of doing the force. with the talk ripple so we had to do the. the thing we had avoided. and. for the plastic gearboxes we ended up.

having to do the robot was now. overpriced and. um they and that was your intuition from. the very beginning kind of that this is. not. you're opening a door to to solve a lot. of problems that you're you're. eventually going to have to solve this. problem anyway yeah and also i was. aiming at a low price to go into a. different market price that that didn't. have a thousand dollars would be amazing. yeah i think we could have done it for. five um but. you know you said talked about. setting the goal a little too far for. the engineers exactly.

so why would you say that company um. not failed but went under. we had buyers and um. there's this thing called the committee. on foreign investment in the us cyphus. and um that had previously been invoked. twice. around where the government could stop. foreign money coming into a u.s company.

based on. defense requirements. went through due diligence multiple. times we were going to get acquired. um but every consortium had chinese. money in it. and all the bankers would say at the. last minute you know this isn't going to. get past cyphus. and the investors would go away. and then we had two buyers. once we were about to run out of money. two buyers. and one used heavy-handed legal stuff. with the other one.

said they were going to take it. and pay more. dropped out when we were out of cash and. then bought the assets. at 1 30th of the price they had offered. a week before. it was a tough week. do you um does it hurt to think about. like an amazing company that didn't. you know like a robot didn't find a way. yeah it was tough um i said i was never.

going to start another company i was. pleased that everyone liked what we did. so much that the teams. the. team was hired by um. three companies within a week everyone. had a job in one of these three. companies some stayed in their same. desks because the com another company. came in and rented the space. so i felt good about people not being. out on the street. so baxter has a screen with a face. what uh.

that's the revolutionary idea for a uh. robot manipulation like for a robotic. arm. uh what. proposition did you get well first the. screen was also used during um. codeless programming where you taught by. demonstration it showed you what its. understanding of the task was. so it had two roles. um. some customers hated it. and so we made it so that when the robot. was running it could be showing graphs. of what was happening i'm not sure the. eyes other comp and other.

people and some of them surprised me who. they were saying. well this one doesn't look as human as. the old one we like the human looking. yeah so there was a mixed bag. but do you think that's uh. i don't know i i'm kind of disappointed. whenever i talk to um. to roboticists. like the best robotics people in the. world they seem to not want to do the. eyes type of thing like they seem to see. it as a machine. as opposed to a machine that can also.

have a human connection. i'm not sure what to do with that it. seems like a lost opportunity i think. the trillion dollar company. will have to do the human connection. very well no matter what it does yeah i. agree. can i ask you a ridiculous question sure. i want to give a ridiculous answer. uh do you think uh. well maybe by way of asking the question. let me first mention that you're kind of. critical of the idea of the touring test. as a test of intelligence.

let me first ask this question. do you think we'll be able to build. an ai system. that humans fall in love with and it. falls in love with the human. like romantic love. but we've had that with humans falling. in love with cars even back in the 50s. it's a different love right well i think. i think there's a lifelong partnership. where you uh. can communicate and grow like.

i think we're a long way from that. i think we're a long long way i think. uh. blade runner was you know had the time. scale totally wrong. um. yeah but. do you so uh. to me honestly the most difficult part. is the thing that you said with the. marvex paradox is to create a human form. that interacts and perceives the world. but if we just look at a voice. like the movie her or just like an alexa. type voice. i tend to think we're not that far away.

well for some for some people maybe not. but i i. you know i i. you know as humans as we think about the. future we always try to. and this is the premise of most science. fiction movies you've got the world. justices today and you change one thing. right but that's not how and it's the. same with a self-driving car you change. one thing no you everything changes yes.

everything. grows together so surprisingly i might. be surprising to you or might not i. think the best movie about this stuff. was bicentennial man. and what was happening there um it was. schmaltzy and you know but what was. happening there. as the robot was trying to become more. human. the humans were adopting the technology. of the robot and changing their bodies. yeah so there was a convergence. happening. in.

so we will not be the same you know. we're already talking about uh. genetically modifying our babies you. know there's a. there's. more and more stuff happening around. that. we will we will want to modify ourselves. even more for all sorts of of things. we put. all sorts of technology in our bodies um. to improve it you know. i've got. i've got things in my ears so that i can. sort of hear you yeah. yeah so we're always modifying our.

bodies so. so you know i think it's hard to imagine. exactly what it would be like in the. future. but on the turin test side. do you think uh so forget about love for. a second. let's talk about just uh like the elect. surprise actually i was invited to be uh. what is the interviewer for the alexa. prize or whatever um. that's in two days. their idea. is uh. success. looks like a person wanting to talk to.

an ai system for a prolonged period of. time like 20 minutes. how far away are we. and why is it difficult to build an ai. system with which you'd want to have a. beer and talk for an hour or two hours. like. not for to check the weather or to check. music but just like to. uh to talk as friends yeah well you know. we saw we saw um weisenbaum uh back in. the 60s with his programmer eliza yeah. um.

being shocked at how much people would. talk to eliza and i i remember you know. in the 70s typing you know stuff to. eliza see what it would come back with. um. you know i think right now. and. this is a thing that um. uh. amazon's been trying to improve with the. like so there is no continuity of of of. topic. there's not. you can't refer to what we talked about. yesterday. it's not the same as talking to a person.

where there seems to be an ongoing. existence where it changes. we share moments together and they last. in our memory together yeah and there's. none of that and there's no. um. sort of intention of these systems that. they have any. goal in life even if it's to be happy. you know they don't they don't. even have a semblance of that now i'm. not saying this can't be done i'm just. saying i think this is why we don't feel. that way. about them well that's a that's a a i'm.

sort of a minimal requirement if you. want the sort of. interaction you're talking about it's a. minimal requirement whether it's going. to be sufficient. i don't know. we haven't seen it yet we don't know. what it. feels like i tend to be i tend to think. it's uh it's not. as difficult as solving intelligence for. example. and i think it's achievable in the near. term. but on the touring test. why don't you think the turing test is a.

good test of intelligence oh i i because. you know. again the turing if you read the paper. turing wasn't saying this is a good test. he was using as a rhetorical device to. argue. that if you can't tell the difference. between a computer and a person. you must say that the computer's. thinking because. you can't tell the difference you know. when it's thinking you can't you can't. say something different. um what it has become as this sort of.

weird game of fooling people um. so. back at the. uh ai lab in the late 80s we had this. thing that still goes on called the ai. olympics and one of the events we had. one year. was um. the original imitation game as turing. talked about because he starts by saying. can you tell whether it's a man or a. woman. so we did that at the at the lab we had. you know you'd go and type and.

the thing would. come back and you had to tell whether it. was a man or a woman um. and um. the. the uh. one of the one of the one of the. uh. one man. came up with a question that he could. ask. which was. always a dead giveaway over whether the. other person was really a man or a woman. you know what he would ask them did you. have um. green plastic toy soldiers as a kid yeah.

what do you do with them and a woman a. woman trying to be a man would say i. lined them up we had wars we had battles. and the man just bringing them out and. said i stomped on them. so you know. that's what that's what the turing test. the turing test with. computers has become what's the trick. question that's that's that's why that's. right it's sort of that's right devolved. into this. weirdness. nevertheless conversation not formulated.

as a test is a pretty. it's a fascinatingly challenging dance. uh that's a really hard problem to me. conversation when nan poses a test. is a. is a more intuitive illustration how far. away we are from solving intelligence. than like computer vision it's hard. computer vision is harder for me to pull. apart. but with language with conversation you. could see because language is so human. we can so we can still clearly.

uh. see it. you mentioned something i was gonna. go on off on okay um. i mean i have to ask you because you. you were the. head of csail ai left for a long time. you're i don't know uh to me when i came. to mit you're like one of the greats at. mit so what was that time like what. and and plus you uh. you're. i don't know friends with but you knew.

minsky and all the all the folks they're. all the legendary ai uh people of which. you're one. so what was that time like what what are. memories that um stand out to you from. that time. from your time at mit from the ai lab. from the dreams that they are lab. represented to the actual like. revolutionary work well let me tell you. first a disappointment in myself. you know as i've been researching this. book um and so many of the players you.

know were active in the 50s and 60s i. knew many of them when they were older. and i didn't ask them all the questions. now i wish. i had asked. i'd sit with them at our thursday. lunches which we had a faculty lunch and. and i didn't ask him so many questions. that now i wish i had he asked you that. question because because you wrote that. you wrote that you were fortunate to. know and rub shoulders with many of the. greats. those who founded ai robotics and. computer science and the world wide web.

and you wrote that your big regret. nowadays is that often i have questions. for those who have passed on yeah and i. didn't think to ask them any of these. questions right. even as i saw them and said hello to. them on a daily basis so. maybe also another question i want to. ask. if you could talk to them today what. question would you ask what questions. would you ask well rick lyder i i would. ask him you know he had the vision for. humans and and.

computers working together and he really. founded that at darpa and he. gave the money to mit which started. project mac in 1963. and i i would have talked about what. what the successes were what the. failures were what he saw as progress. et cetera. i would have asked him more more. questions about that because now i could. use it in my book. but i think it's lost it's lost forever. a lot of the motivations are lost.

um. uh. i i should have asked marvin why he he. and seymour pappet. came down so hard on neural networks in. 1968 in their book perceptrons because. marvin's phd thesis was on neural. networks yeah how do you make sense of. that that destroyed the field for he. probably do you think he knew that the. effect that book would have.

all the theorems and negative theorems. yeah. um yeah. so. yeah that's just the way of. that's the way of life. yeah but still it's kind of tragic that. he was both the proponent and the. destroyer of neural networks yeah. um is there other memory standouts. from the. the robotics and the ai work at mit. well. yeah but you gotta be more specific.

well i mean like it's such a magical. place i mean to me it's a little bit. also heartbreaking. that. you know with google and facebook like. deepmind and so on so much of the talent. you know doesn't stay necessarily for. prolonged periods of time in these. in these universities oh yeah i mean. some of the companies are more guilty. than others are paying. fabulous salaries to some of the highest. you know producers and then just.

you never hear from them again they're. not allowed to give public talks they're. sort of locked away. and it's sort of like collecting. collecting you know hollywood stars or. something and i'm not allowed to make. movies anymore i own them um yeah that's. tragic because i mean the there's an. openness to the university setting where. you do research to uh both in the base. of ideas and space like publication all. those kinds of things yeah you know and. you know there's the publication and all. that and often you know although these.

places say they publish. yeah there's pressure. and um. but i think for instance um. you know on net net i think. google buying those 809 robotics company. was bad for the field because it locked. those people away. they didn't have to make the company. succeed anymore locked them away for. years and then. sort of all threaded away yeah.

so um. do you have hope for mit. for him for mit yeah why shouldn't i. well i could be harsh and say that. i'm not sure i would say mit is leading. the world in ai. or even stanford. or berkeley i would say. i would say um deepmind google ai. facebook ai say i would take a slightly. different approach. or a different answer.

i'll leave i'll come back to facebook in. a minute but i think those other places. are. following a dream of. one of the founders uh and i'm not sure. that it's well founded. the dream. and i'm not sure that it's going to have. the impact that. he believes it is. um. you're talking about facebook and google. and so on i'm talking about google. google but the thing is those research.

labs aren't. there's the big dream. and i'm i'm usually a fan of no matter. what the dream is a big dream is a. unifier because what happens is you have. a lot of bright minds. working together. uh on a dream what results is a lot of. like adjacent ideas i mean yeah so much. progress is made yeah i'm i'm so i'm not. saying they're actually leading i'm not. i'm not saying that the universities are. leading yeah but i don't think those. companies are leading in general because. they're.

you know and we saw this. incredible spike in you know. attendees at europe's and. as i said in my january first review. this year for. 2020. 2020 will not be remembered as a. watershed year for machine learning or. ai. you know there was nothing. surprising happened anyway unlike when. deep learning hit. imagenet. that was a that was a.

shake. and there's a lot more people writing. papers but the papers are. fundamentally boring yeah and. uninteresting incremental work. is there a particular memories you have. with minsky or somebody else at mit that. stand out. my funny stories. i mean unfortunately he's another one. that's passed away. you've known some of the biggest minds. in ai. yeah and you know they they did amazing.

things and some sometimes they were. grumpy um. [Laughter]. well he was uh he was interesting. because he was very grumpy but that. that was. uh. i remember him saying in an interview. that the key to success. or being to keep being productive is to. hate everything you've ever done in the. past. maybe maybe that explains the perceptron. book. and there it was he told you exactly. but he meaning like just like.

i mean maybe that's the way to not treat. yourself too seriously just uh always be. moving forward. uh that was this idea i mean that. crankiness i mean doesn't. yeah. so let me let me let me tell you you. know what really. um. you know the joy memories are about. having access to technology before. anyone else has seen it so so you know i. i got to stanford in 1977.

and we had um. you know we had terminals that could. show live video on them um. digital digital sound system we had um. as. xerox graphics printer we could print. um. uh it wasn't you know it wasn't like a. typewriter ball hitting in with. characters it could print arbitrary. things only in you know one bit you know. black or white but arbitrary pictures. this was science fiction sort of stuff.

um at mit the uh. the list machines which. you know they were the first personal. computers and you know they cost a. hundred thousand dollars each and i. could you know i got there early enough. in the day i got one for the day. couldn't couldn't stand up let's keep. working. um having that like direct glimpse into. the future yeah and you know i've had. email every day since 1977 um. and uh you know.

the the host field was only eight bits. you know that many places but we i could. send email to other people at a few. places so. that was that was pretty exciting to be. in that. world so different from what the rest of. the world knew um. and uh. let me ask you probably edit this out. but just in case you have a story uh. i'm hanging out with don knuth. uh for a while tomorrow.

did you ever get a chance to such a. different world than yours. he's a very kind of theoretical computer. science the puzzle of. of uh computer science and mathematics. and you're so much about the magic of. robotics like the practice of it did you. mention him earlier for like. not you know about computation did your. worlds cross they did enough you know i. i know him now we talked you know but. let me tell you my my donald canoe story. okay. so um. you know besides you know analysis of.

algorithms he's well known for. writing tech yeah which is in latex. which is the academic publishing system. so he did that at the ai lab. and he would do it he would work. overnight at the air lab. and one. one day the. one night the uh. the mainframe computer went down. and um. a guy named robert paul was there he. only did his phd at the media lab at mit.

and he was a you know. engineer uh and so. i and he and i you know tracked down. what were the problem was it was one of. those big refrigerator size or washing. machine size disk drives unveiled and. that's what brought the whole system. down so we got panels pulled off and. we're pulling you know circuit cards out. and donald knuth who's a really tall guy. he walks in and he's looking down and. says when will it be fixed you know does. he want to get back to write his tax. system.

we figured out you know it was a. particular chip 7400 series chip which. was socketed we popped. it out we put a replacement in put it. back in smoke comes out because we put. it in backwards because we're so nervous. that donald knuth was standing over us. anyway we eventually got it fixed and. got the mainframe running again. so that was your little when was that. again that well that must be before. october 79 because we moved out of that. building then so sometimes probably 78.

sometime or early 79. yeah those all those figures is just. fascinating all the people with past. pass through mit is really fascinating. is there uh. let me. ask you to put on your big wise. man hat. is there advice that you can give to. young people today whether in high. school or college who are thinking about. their career. or thinking about life. how how to live uh.

a life they're proud of a successful. life. yeah so so many people ask me for advice. and have asked when i give i talk to a. lot of people all the time. and there is no one way. um. you know there's a lot of pressure. to. produce papers. um that would be acceptable and be. published.

uh maybe i was maybe i come from an age. where i would i could be a rebel against. that and and still succeed maybe it's. harder today. but i think it's important not to get. too caught up. with. what everyone else is doing. and. if you if a lot depends on what you want. of life if you want to. have real impact. you have to be ready to fail. a lot of times so you have to make a lot.

of unsafe decisions. and the only way to make that work is to. make keep doing it for a long time and. then one of them will be work out and so. that that that will make something. successful or not. or you may or you just may you know end. up you know not having a you know having. a lousy career i mean it's certainly. possible taking the risk is the thing. yeah so but it it. but there's no way to to. make all safe decisions and actually.

really contribute. do you um. think about your death by your mortality. i gotta say when covert hit. i did because we did you know in the. early days we didn't know how bad it was. going to be and i that that made me work. on my book harder for a while but then. i'd started this company and now i'm. doing full-time more than full-time in. the company so the books on hold but i. do want to finish this book um when you.

think about it are you afraid of it. i'm afraid of dribbling. you know. i'm losing it the details of okay yeah. yeah but the fact that the ride ends. i've known that for a long time um so. it's. yeah but there's knowing and knowing. it's such a. yeah and it really sucks it feels it. feels a lot closer.

so my in in my my blog with my. predictions my sort of pushback against. that was. i said. i'm going to review these every year for. 32 years and that puts me into my. mid-90s so you know it's my whole every. every time you write the blog post. you're getting closer and closer to your. own prediction that's that's true of. your death yeah. what do you hope your legacy is. you're one of the greatest roboticist ai.

researchers of all time. um. what i hope is that i actually. finished writing this book. and that. there was a there's one person. who reads it. and sees something. about changing the way they're thinking. and that leads to. the next. big. and then there'll be. on a podcast a hundred years from now. saying i once read that book.

and that changed everything. uh what do you think is the meaning of. life. this whole thing the existence the the. the all the hurried things we do on this. planet. what do you think is the meaning of it. all yeah well you know i think we're all. really bad at it. life or finding meaning or both yeah we. get caught up in in in the it's easier. to get easier to do the stuff that's. immediate and not through the stuff it's. not immediate. um so the big picture we're batting yeah.

yeah do you have a sense of what that. big picture is like why you ever look up. to the stars and ask why the hell are we. here. you know my my. my my atheism tells me it's just random. but you know i want to understand the. way random in the and that's what i talk. about in this book how order comes from. disorder yeah um. [Music].

but it kind of sprung up like most of. the whole thing is random but this. little. pocket of complexity they would call. earth. that like why the hell does that happen. and and what we don't know. is how common that. pop those pockets of complexity are or. how often. um because they may not last. forever. which is uh more exciting slash sad to. you if we're alone or if there's. infinite number of oh i i i think i.

think it's impossible for me to believe. that we're alone um. that would just be too horrible too. cruel. it could be like the sad thing it could. be like a graveyard of intelligent. civilizations oh everywhere yeah that. might be the most likely outcome. and for us too yeah exactly yeah and all. of this will be forgotten yeah. including all the robots you build. everything forgotten.

well on average. everyone has been forgotten in history. yeah right. yeah most people are not remembered. beyond a generation or two. um. i mean yeah well not just on average. basically. very close to 100 percent of people. who've ever lived or forgotten yeah i. mean. no longer i don't know anyone alive who. remembers my great grandparents because. we didn't meet them. so. still this fun this. this life is pretty fun somehow.

yeah. even the. immense absurdity and uh at times. meaninglessness of it all it's pretty. fun and one of the for me one of the. most fun things is. robots and i've looked up to your work. i've looked up to you for a long time. that's right. ron it it's it's an honor that uh. you would spend your valuable time with. me today talking it was an amazing. conversation thank you so much for being. here well thanks for thanks for talking. with me i've enjoyed it.

thanks for listening to this. conversation with rodney brooks to. support this podcast please check out. our sponsors in the description. and now let me leave you with the three. laws of robotics from isaac asimov. one a robot may not injure a human being. or through inaction allow a human being. to come to harm. two a robot must obey the orders given. to it by human beings except when such. orders would conflict with the first law. and three a robot must protect its own.

existence. as long as such protection does not. conflict. with the first or the second laws. thank you for listening i hope to see. you next time.