Speaker 1: All right, should I try to put something up so
Speaker 1: I would like so if you hit the space bar,
Speaker 1: you will be in control of the arm. You'll see
Speaker 1: your grippers now green. Um. You may need to move
Speaker 1: down a little farther because once you get to a
Speaker 1: certain height, it will refuse to follow you. Okay, um,
Speaker 1: and you would need your right hand on the mouse. Yes,
Speaker 1: that's me getting a lesson in a computerized version of
Speaker 1: a game I remember playing at truck stops when I
Speaker 1: was a kid. I'm sitting in front of a laptop,
Speaker 1: using a mouse in each hand to steer a claw
Speaker 1: around my screen and trying to grab plush objects. This
Speaker 1: is where your pointer is. Make sure you're not clicking
Speaker 1: buttons and so what you've just done is caused a
Speaker 1: protective stop. That was quick. Yeah, that's because I'm ran
Speaker 1: into the clothing. Yeah, you just basically impaled a stack
Speaker 1: of clothing. I always knew this game is big choice.
Speaker 1: But Chris Hayes, who's the guy with me here, has
Speaker 1: never heard that name. I'm not recognizing the name of
Speaker 1: glass In case, like a glass cub with a bunch
Speaker 1: of stuffed animals in the bottom, and then you have
Speaker 1: like two buttons and there's like a little river, and
Speaker 1: you like, oh, it's just like crane games. You could
Speaker 1: see it as an ultimate like virtual crane game in
Speaker 1: which you are trying well, it's not stacked against you,
Speaker 1: and which is stacked in your favor to try and
Speaker 1: help you actually grab something instead of drop everything you
Speaker 1: try to grab. My wife's actually really good at crane games.
Speaker 1: One's stuff from them all the time. Chris works for
Speaker 1: a startup called Kindred, which isn't quite in the business
Speaker 1: of producing nostalgically themed video games, and the game Chris
Speaker 1: is guiding me through isn't actually a game at all.
Speaker 1: It's the job of a dozen or so employees at Kindred.
Speaker 1: As they steer these robotic clause in a computer screen,
Speaker 1: they're teaching robots how to do things in the physical world.
Speaker 1: Kindred calls these employees pilots. Grabbing different objects and moving
Speaker 1: them around might sound like a simple task, but it's
Speaker 1: actually a lot harder than what most robots have been
Speaker 1: able to handle in the past. It requires them to
Speaker 1: constantly take in new information about their environments and adjust accordingly.
Speaker 1: The robots that can do this are just now emerging,
Speaker 1: and the better they get the more uncomfortable they make us.
Speaker 1: How many of our jobs are going to become obsolete?
Speaker 1: The short answer is, we don't know, but we do
Speaker 1: know something more about a related question, how are these
Speaker 1: robots going to learn to do our jobs? Anyway? Hi,
Speaker 1: I'm Akio and I'm Joshua Freusting, and this week on Decrypted,
Speaker 1: we're taking you inside the race to teach robots how
Speaker 1: to do the things that only humans could do before.
Speaker 1: Some of these robot trainers work at startups trying to
Speaker 1: sell robotics services to companies like The Gap. Some spend
Speaker 1: their time in academic slobs. Some do both. There's something
Speaker 1: ironic and bitter sweet about these people who are automating
Speaker 1: away their own usefulness. How they're approaching their work today
Speaker 1: might have lessons for all of us in the future.
Speaker 1: Stay with us. I walked right by the first time.
Speaker 1: There is not a sign on this door. Back in March,
Speaker 1: I visited Kindred's California office. It's in one of those
Speaker 1: charming San Francisco neighborhoods, you know, the ones under the
Speaker 1: highway over passes where you can't hear yourself think. And
Speaker 1: most of the businesses are auto repair shops. Hey, are
Speaker 1: you guys with Kindred? Hey, I'm looking for Chris some Josh.
Speaker 1: Chris is the guy who heard me talking to earlier.
Speaker 1: He was the very first pilot that Kindred hired. When
Speaker 1: I found him, he was sitting in a conference room
Speaker 1: on his laptop. The only thing that looked different from
Speaker 1: the setup that I used for my job was that
Speaker 1: he had two mice. There was the standard one your
Speaker 1: right hand, and a weird looking one in the left
Speaker 1: hand that he called us Space Navigator. He used both
Speaker 1: mice simultaneously to control a robot that the company had
Speaker 1: set up in its basement. So what did you see
Speaker 1: on his laptop? There were a few different video feeds
Speaker 1: of a pile of clothing laid out under the robotic claw.
Speaker 1: One was a standard video, the other one was what
Speaker 1: Chris called a polygon cloud, which was a three D
Speaker 1: image that showed the same thing but gave you a
Speaker 1: sense of depth. And a dashboard tracked your stats along
Speaker 1: the sides. You can tell how quickly we're picking things
Speaker 1: up on average, and things like that. And you said,
Speaker 1: there's an actual robot in the basement that was responding
Speaker 1: to his commands. Yeah, Kindred has a little workshop down
Speaker 1: there in the basement with a few engineers and the
Speaker 1: robot that it calls Kindred Sort. It's a yellow arm
Speaker 1: with a claw at the end, and it's an a
Speaker 1: round metal enclosure. The office has a few bins filled
Speaker 1: with stuff. It looks like someone just wandered around Kmart
Speaker 1: with a shopping cart, throwing random things in as they
Speaker 1: happened to walk past them. Protective stop. Just clothes and stuff.
Speaker 1: Somebody's in here. Um, let's see if they're the easier
Speaker 1: thing to try and grab you. We can try doing
Speaker 1: some training and if you feel like that's easy, we
Speaker 1: can throw some general merchandise in there as well. Okay,
Speaker 1: Then we walked back upstairs to Chris's computer. I sat
Speaker 1: in front of a program that showed several feeds from
Speaker 1: downstairs and tried to follow Chris's directions to move things around.
Speaker 1: It felt like a video game. Chris actually used to
Speaker 1: be a game designer, I should say. Since I was
Speaker 1: a kid, one of my biggest fears has been playing
Speaker 1: video games in front of people I don't really know,
Speaker 1: and so I wanted Chris to tell me how hard
Speaker 1: it was going to be. There's a trend in gaming
Speaker 1: that you want to make your game like easy to
Speaker 1: pick up, difficult to master, right, And I'd say it
Speaker 1: follows that pattern. It seems like it takes longer than
Speaker 1: twenty minutes to be competent at this, or at least
Speaker 1: I certainly wasn't teaching any robots anything of value after
Speaker 1: my twenty minutes test drive, so it feels like I
Speaker 1: could probably just grip it here you can try. Nope,
Speaker 1: there you go. There was your your claw game that
Speaker 1: felt very much like the log game. Actually, that sense
Speaker 1: of humiliation and disappointment I remember that, Josh, you have
Speaker 1: to ask, are you just a really bad gamer? So
Speaker 1: tell me straight, I'm not very good at this, not yet,
Speaker 1: but like on the average of someone who's spent fifteen
Speaker 1: minutes trying to standard Chris told me most people reach
Speaker 1: competence in a few hours, and then they can get
Speaker 1: about twice that good over a few days or whatever.
Speaker 1: And what do they do once they're good enough? They'll
Speaker 1: be in charge of driving robots operating in some distant
Speaker 1: warehouse somewhere. Kindred as a handful of clients. So far,
Speaker 1: the only one that's announced publicly is the Gap, which
Speaker 1: has a few of its robots running at a facility
Speaker 1: in Tennessee. Over time, as they learned from making my pilots,
Speaker 1: I would imagine that these robots become more capable of
Speaker 1: doing things on their own. That's the idea to teach
Speaker 1: a robot a task. The pilot starts off by mostly
Speaker 1: doing it himself. Then over time the robot takes over
Speaker 1: and just asks the pilot when they get stuck. Do
Speaker 1: we know how long it takes for them to reach
Speaker 1: this fully autonomous state where the robotsn't longer need the pilots? Now?
Speaker 1: This is a sense of question, of course, and robotics
Speaker 1: companies always tie themselves into pretzels trying to talk about
Speaker 1: their impact on labor. On the one hand, they want
Speaker 1: to boast about how quickly they're making progress, but they'll
Speaker 1: also object to any suggestion that they're quote unquote replacing
Speaker 1: jobs in these places. Here's George Bebow, a co founder
Speaker 1: of Kindred who's now the company's chief product officer. Jobs
Speaker 1: are going away. Nobody's seeing or talking about the jobs
Speaker 1: that are coming right, the annotators, the pilots and supervisors,
Speaker 1: the designers. There's a lot of work to be done,
Speaker 1: you know. There have been some pretty scary projections about
Speaker 1: job blasts. I'm thinking in particular about a McKinsey report
Speaker 1: from last year that predicted that automation could displace as
Speaker 1: many as an eight hundred million jobs by twenty Yeah,
Speaker 1: there's been a lot of bracing reports along those lines. Now,
Speaker 1: those numbers always do come with a lot of caveats.
Speaker 1: There could be fewer jobs that end up being automated away,
Speaker 1: and then there's also the question of how many new
Speaker 1: jobs could be created. And that same report that you cited,
Speaker 1: mackenzie said the newly created jobs might actually offset job losses.
Speaker 1: So the transition towards more automation could be disastrous. It
Speaker 1: could end up being much more benign, or it could
Speaker 1: end up being traumatic for many people, with the real
Speaker 1: issue being how to prepare workers for that shift. We
Speaker 1: just don't know. So it's being a robot pilot a job,
Speaker 1: you know. I asked everyone that kin Dre that question,
Speaker 1: and I have to say they didn't even steam sure
Speaker 1: how to answer it. As of now, the company doesn't
Speaker 1: actually have people who show up in the morning, drive
Speaker 1: a robot all day, and then head home at night. Instead,
Speaker 1: it's pilots, pilot for part of the day, then do
Speaker 1: something else for the rest of the time. Chris, their
Speaker 1: first pilot, said he's never done it for more than
Speaker 1: two hours at a stretch, and today he now spends
Speaker 1: his time working on the off where that other pilots use,
Speaker 1: So he's moved out of the robot piloting job altogether.
Speaker 1: Sounds like a smart move if you're kind of automating
Speaker 1: yourself out of a job anyway. Yeah, Well, the folks
Speaker 1: that kindred say the demand for pilots will actually grow significantly.
Speaker 1: That's because the number of robots in the field should
Speaker 1: grow faster than the rate at which each robot becomes
Speaker 1: completely autonomous. So I'm imagining these pilots maybe controlling twenty
Speaker 1: robots at a time instead of maybe just one or two. Yeah,
Speaker 1: that's where they want to end up. And one of
Speaker 1: the challenges Chris is dealing with now actually relates to
Speaker 1: what it's like for a single pilot to be in
Speaker 1: charge of so many robots. He says it can be
Speaker 1: jarring to finish helping one machine pick up a shirt
Speaker 1: and then suddenly be transported to another robot that's right
Speaker 1: in the middle of trying to do something else. The
Speaker 1: more machines you switch between, the more I guess that
Speaker 1: kind of impacts you mentally. Is far as like contact switching.
Speaker 1: When you have you switch over to another machine, you
Speaker 1: it takes little bit of time to get the context
Speaker 1: of like what's going on here, like what is the
Speaker 1: best I'm to grab and like go for it right,
Speaker 1: And the more different machines you have, the more that
Speaker 1: kind of impacts things. Okay, before I described my next stop,
Speaker 1: I think it would be helpful to run through some
Speaker 1: basic FOCAB. So let's start with what we've described so far.
Speaker 1: When I was driving that claw at Kindred, the robotic
Speaker 1: system was tracking my movements and learning exactly how I
Speaker 1: picked up a shirt. That technique is called imitation learning. Okay,
Speaker 1: so this is like showing your kids how to tie
Speaker 1: their shoes by having them watch you do it first exactly. Now,
Speaker 1: there's another strategy for teaching robots that's called reinforcement learning.
Speaker 1: Let's say you're trying to teach a robot how to
Speaker 1: do a backflip. It gives it a shot, but a
Speaker 1: robot doesn't even know what a backflip is, so you
Speaker 1: have to tell it whether it's successful or not. You'd
Speaker 1: also tell at how close it got to doing it right,
Speaker 1: and maybe just how it went wrong. Then the robot
Speaker 1: tries another backflip, and another one and another one, and
Speaker 1: through trial and error, it learns exactly what a backflip
Speaker 1: is and how to successfully carry one out. So this
Speaker 1: might be more like giving your kids some candy when
Speaker 1: they do their homework on time exactly, And just like
Speaker 1: with teaching children, when you're teaching robots, it can help
Speaker 1: to use a combination of different teaching techniques. Reinforcement learning
Speaker 1: has been the dominant technique and a lot of artificial
Speaker 1: intelligence research in recent years, and it works best when
Speaker 1: you have a huge amount of data, but imitation learning
Speaker 1: can fill in some key gaps, so places like Kindred
Speaker 1: that are focusing on it use a combination of both techniques.
Speaker 1: Some of the most advanced academic research on these techniques
Speaker 1: is going on just across the bay from Kindred at
Speaker 1: the Robotics Lab at Berkeley h I was there to
Speaker 1: meet Chelsea Finn. She's a pH d student there. Chelsea
Speaker 1: is a quiet, friendly type who was happy to walk
Speaker 1: me through what she did and was really good at
Speaker 1: explaining things. But I also got the feeling that she
Speaker 1: was a bit embarrassed by the attention. Chelsea's research is
Speaker 1: kind of like the graduate level coursework for robots. The
Speaker 1: ideas that robots should be able to learn things progressively
Speaker 1: faster than the last ones by building on the knowledge
Speaker 1: that they've picked up from all the other things they've
Speaker 1: learned to do well. We've been building on techniques which
Speaker 1: are called metal learning or learning to learn, so that
Speaker 1: you can learn from a new demonstration with a very
Speaker 1: small amount of data. When you see a new demonstration,
Speaker 1: a single new demonstration for a new task, you can
Speaker 1: learn that just from that task. A big goal for
Speaker 1: Chelsea and her colleagues is to have robots learned from
Speaker 1: less and less exact demonstrations. Give us an example. Okay,
Speaker 1: this is gonna sound very elementary about human standards, but
Speaker 1: here goes. Let's say you have a robot and you
Speaker 1: wanted to pick up a ball and put it in
Speaker 1: a red bowl. You've already taught this robot how to
Speaker 1: pick things up, and you've taught it how to put
Speaker 1: things two containers, but it's never seen a red bull before.
Speaker 1: So pilots like Chris and the folks at Kindred would
Speaker 1: do this in a direct way. They'd steer the robot
Speaker 1: through the task while the censors record exactly what this means.
Speaker 1: But Chelsea's team would just showed a video of someone
Speaker 1: putting a ball in a red bowl. At first, the
Speaker 1: person would have to be in exactly the same environment
Speaker 1: the robot already knew about same background, same table, same ball.
Speaker 1: But over time it had learned to do things from
Speaker 1: videos that were less and less similar to its own environment,
Speaker 1: and eventually robots were able to pick up new skills
Speaker 1: just by searching for videos of people, say, putting balls
Speaker 1: into red bulls. It's a big genre on YouTube, and
Speaker 1: the endgame would be for robots to just search YouTube
Speaker 1: for videos it needed, just the way that you might
Speaker 1: learn how to braid hair or change a light switch.
Speaker 1: I'm imagining Chelsea's robot watching hours of makeup tutorials. But
Speaker 1: Chelsea and her colleagues are slowly working their way out
Speaker 1: of the lab. At a big machine and learning expo
Speaker 1: called the Conference on Neural Information Processing Systems, or NIPS,
Speaker 1: they recently showed off the robots flexibility by having people
Speaker 1: at the conference record themselves doing some simple task a
Speaker 1: single time, and then showing how the robot could do
Speaker 1: the same thing by watching them. I mean, this is
Speaker 1: kind of a step between the lab and the real world,
Speaker 1: and that you're actually moving off of the lab that
Speaker 1: it was trained on into this other space and isn't
Speaker 1: the real world. No, it's it's not not. Yes, it's
Speaker 1: definitely not the real world. Although I will say that
Speaker 1: the researchers really like to try to trick the robot.
Speaker 1: You know, I can see how this would really speed
Speaker 1: things up. If you didn't have to have someone like
Speaker 1: Chris painstakingly show how to do each and everything, these
Speaker 1: robots could learn a lot faster. Yeah, And the point
Speaker 1: of all this is that robots don't have to do
Speaker 1: just a single type of job. You have to be
Speaker 1: prepared to do all kinds of things depending on what
Speaker 1: people need from them at any specific time, which is
Speaker 1: probably what most of our jobs are like. Right. Think
Speaker 1: about what makes a good human colleague. It's based really
Speaker 1: the ones who are the most flexible at doing all
Speaker 1: kinds of things and the ones that pick up things
Speaker 1: with these amount of handholding. Right. But Chelsea thinks there's
Speaker 1: a long way to go before robots are really able
Speaker 1: to be this kind of generalist usually a long way.
Speaker 1: Is it like it's so far in the future that
Speaker 1: it's hard to even imagine? Is that like five years,
Speaker 1: that ten years? I would say that it's more than
Speaker 1: five years, and that beyond five years, it's uh, it's
Speaker 1: hard to make accurate predictions. Right down the hall from
Speaker 1: Chelsea is someone who's trying to bridge the gap between
Speaker 1: the academic and commercial worlds of robotics. His name is
Speaker 1: Peter Abiel. He recently started a company called Embodied Intelligence,
Speaker 1: but keeps an opposit at Berkeley. So I swung by
Speaker 1: after saying goodbye to Chelsea. Peter just returned from a
Speaker 1: private event that Jeff Bezos throws every year for researchers
Speaker 1: and AI, robotics and space so they can compare notes. Yeah,
Speaker 1: I've read about the party's I think Dasis calls them
Speaker 1: summer camp for geeks. Well, Peter definitely meets that description.
Speaker 1: When I found him, he was unwrapping random pieces of
Speaker 1: computer equipment that had showed up in the mail while
Speaker 1: he was away. Peter thinks the most important thing to
Speaker 1: make progress on is accelerating the pace at which robots
Speaker 1: to learn new tasks. Hundred and first skills should be
Speaker 1: faster than the hundred skill to learn, and then as
Speaker 1: you keep going, the next one should be even faster
Speaker 1: than faster, even faster. And that's where things get very interesting.
Speaker 1: Is at some point is going to hit a very
Speaker 1: low number of demonstrations, maybe only one or two to
Speaker 1: learn something new. Peter's business, embodied intelligence is super early.
Speaker 1: At this point. He isn't willing to reveal much about
Speaker 1: it except that it exists and he's been talking to
Speaker 1: potential clients. Some of his more recent work at Berkeley
Speaker 1: focused on robot pilots who wore virtual reality headsets. The
Speaker 1: researchers were surprised that it took less than thirty minutes
Speaker 1: of demonstration for robots to learn most new tasks. So
Speaker 1: Josh is he in Chelsea's camp that it will take
Speaker 1: at least five years for robots to be able to
Speaker 1: handle the wide range of tasks that we do well.
Speaker 1: I think the main difference between Peter and Chelsea is
Speaker 1: almost want of tone. Chelsea is speaking as the cautious
Speaker 1: academic and Peter is more of the excitable entrepreneur. They
Speaker 1: both agree that robots that are just sort of generally
Speaker 1: competent and everything that's more than five years out and
Speaker 1: kind of hard to tell. But Peter focus is more
Speaker 1: on what they will be able to do in five years,
Speaker 1: and he thinks they'll be good enough to try a
Speaker 1: lot of things that they can't do today. The longer
Speaker 1: timeline is can we get anybody to teach a robot
Speaker 1: and new skill? The shorter timeline is, can we ourselves
Speaker 1: at embodied intelligence teach robots new skills that robots haven't
Speaker 1: done before. So if you can do that, then those
Speaker 1: robots could be doing things that currently are not possible.
Speaker 1: You know. It sounds like the speed that robots can
Speaker 1: learn these new tasks and switch from task to task
Speaker 1: could be a really critical point to how they would
Speaker 1: be used. Yeah, take the example of a company that
Speaker 1: runs huge logistics facilities. They need a lot of robots
Speaker 1: to operate really quickly, But what they're gonna do from
Speaker 1: day to day isn't going to change that much. If
Speaker 1: it's an expensive and time consuming process for teaching those
Speaker 1: robots their jobs in the first place, it might not
Speaker 1: matter that much because once they're set up they can
Speaker 1: be very efficient. But I would imagine at a smaller
Speaker 1: business the needs would change a lot more than a
Speaker 1: big industrialized setting. Yeah, the way that smaller operations are
Speaker 1: going to benefit from robots is probably if they can
Speaker 1: be really customizable and if they can be trained quickly
Speaker 1: to do whatever the task is at hand. And that's
Speaker 1: really where Peter is hoping to get to. He wants
Speaker 1: to change the dynamic of what it means to have
Speaker 1: a robot. Do Do I need help? Do I need
Speaker 1: physical help moving things around? Putting things together? Because if
Speaker 1: I do whatever it is I need today, it's fun.
Speaker 1: I can need something else tomorrow. I can just reteach
Speaker 1: the robot every single time, and so that will open
Speaker 1: up a lot of opportunities and people will need far
Speaker 1: less capital investment, far less scale be able to take
Speaker 1: advantage of having a robot helped them out. Okay, going
Speaker 1: back to Kindred, the job of a rope but pilot
Speaker 1: didn't actually always look like what I tried sitting in
Speaker 1: front of a laptop and playing what seemed like a
Speaker 1: pretty simple video game. In fact, the original mission of
Speaker 1: the company was to create robots that could do anything
Speaker 1: a human could do physically. At least according to George,
Speaker 1: the co founder who we talked earlier, the job of
Speaker 1: robot training used to involve wearing what's called an exoskeleton.
Speaker 1: Is that what you can capture all the motions from
Speaker 1: all the joints, all the fingers, elbose shoulders, you can
Speaker 1: capture all the informations basically a person in a robots
Speaker 1: the person in robots. For listeners who have never seen
Speaker 1: an exo skeleton, they look truly crazy, like Iron Man.
Speaker 1: It really looks like you're wearing a robot on your body.
Speaker 1: And this is really exciting for scientists. But it turns
Speaker 1: out it was overkill for what most of Kindred's potential
Speaker 1: customers said they wanted, like placing boxes on their right
Speaker 1: shelves right. So the company set the exo skeletons aside
Speaker 1: and focused on doing this one rather mundane thing that
Speaker 1: would win them contracts with companies that were operating warehouses.
Speaker 1: That does feel like a little bit of a letdown.
Speaker 1: The company actually split over it. Two of its founders
Speaker 1: left at the beginning of this year to start a
Speaker 1: new company called Sanctuary dot ai. I talked to one
Speaker 1: of them. Her name is Suzanne Gildert, and she said
Speaker 1: that she had wanted to do work on training robots
Speaker 1: to get all the way to human like intelligence. But
Speaker 1: she also knew that this was going to take a
Speaker 1: lot of time toiling without any real business model in
Speaker 1: the short term, and when Kindred discovered something it could
Speaker 1: sell immediately, she decided to part ways. And is it
Speaker 1: selling well? Kindred does say it is, but they're being
Speaker 1: pretty vague about it. The company's testing period at the
Speaker 1: Gap ended earlier this year, and they decided to expand it.
Speaker 1: I caught up with Jim Liefer, Kindred CEO, on the
Speaker 1: phone a few weeks ago. He was at a supply
Speaker 1: chain conference in Atlanta and said his voice was sore
Speaker 1: from all the pitching. We have a very full dance
Speaker 1: card after these four days. I'm pretty excited about it.
Speaker 1: Jim says that Kindred is really going to ramp up
Speaker 1: its operations over the course of and will operate on
Speaker 1: a far larger commercial scale. I wanted to know whether
Speaker 1: the company's view on the roll of the robot pilot
Speaker 1: had devolved. It employs about a dozen people as pilots,
Speaker 1: but as you said earlier, none of them do it
Speaker 1: full time. Instead, Jim says, they can use the job
Speaker 1: as a way to get into the company and then
Speaker 1: move on to do something else. The current facility for
Speaker 1: pilots is just a corner of its Toronto office. Our
Speaker 1: next one will probably be in place like Mexico, and
Speaker 1: so it might be that there's more of sort of
Speaker 1: like a labor farm of people that will do that.
Speaker 1: And I'm not sure if we if we do, If
Speaker 1: we stand this up in Mexico as I'm as I'm
Speaker 1: at least thinking about doing, then um it would there
Speaker 1: would be less opportunity for them to transition into other roles.
Speaker 1: But just like Peter, Jim says he's been surprised that
Speaker 1: the robots are learning faster than they've predicted. The company
Speaker 1: recently set up a robot at its trade show in Atlanta.
Speaker 1: When Jim first explained it to me, he didn't even
Speaker 1: mentioned there was a pilot involved. He just said it
Speaker 1: was autonomous. But then I asked him about it and
Speaker 1: he corrected self, and so there was a pilot there.
Speaker 1: It was just that the robot was so good at
Speaker 1: doing everything itself that it barely needed him to intervene.
Speaker 1: You know, I've been thinking about what I took away
Speaker 1: from my visits to these robotics labs, and I can't
Speaker 1: help but come back to this story of the mechanical
Speaker 1: turk Um if you don't know that one. This is
Speaker 1: this famous eighteenth century chess playing machine. It appeared to
Speaker 1: be automated, but actually there was a person inside actually
Speaker 1: making the moves by hand. Yeah, that's a great metaphor.
Speaker 1: I mean, watching Kindred's robots in these factories, you'd probably
Speaker 1: think that they're doing all this on their own, but
Speaker 1: it's possible that they're actually being steered by a human
Speaker 1: in an office very far away. Yeah, and Kindred actually
Speaker 1: adds an interesting twist to that story. It starts off
Speaker 1: just like the mechanical turk with someone controlling it from
Speaker 1: far way, but then over time it evolves into what
Speaker 1: the machine was actually pretending to be, and then the
Speaker 1: chess player who cramped himself inside is out of a job. Exactly.
Speaker 1: I took one other thing away. I think at first,
Speaker 1: it seems like there's this really straight line between where
Speaker 1: we are now and some future where robots are doing
Speaker 1: everything that you or I can do, or at least
Speaker 1: everything we can do physically. But Kindred set that original
Speaker 1: vision aside, and it's not clear that once the company
Speaker 1: masters warehouse robots that it will then train robots to
Speaker 1: do the next thing and the next thing, until we
Speaker 1: end up with some sort of autonomous humanoids out of
Speaker 1: a sci fi film. So what you're saying is that
Speaker 1: what businesses need in the short term or maybe even
Speaker 1: the long term, isn't these humanoid machines that can do
Speaker 1: everything we can do, but probably something a lot simpler. Yeah.
Speaker 1: I think it's really tempting to think that when we're
Speaker 1: making robots, were designing them in our own image. But
Speaker 1: maybe it just never makes sense to get there. I
Speaker 1: guess it just depends on what we decide to teach them.
Speaker 1: And that's it for this week's to cooked in. Thanks
Speaker 1: for listening. Let us know what you thought of today's show.
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