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Speaker 1: Welcome to tech Stuff, a production from I Heart Radio.

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Speaker 1: Hey there, and welcome to tech Stuff. I'm your host

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Speaker 1: job in Strickland. I'm an executive producer with I Heart Radio,

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Speaker 1: and I love all things tech and it is time

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Speaker 1: for a classic episode of tech Stuff. This episode originally

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Speaker 1: published way back on October fifteenth, two thousand fourteen. It

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Speaker 1: is titled do we need humanoid Robots? An interesting question

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Speaker 1: because we often, at least I don't know, I can't

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Speaker 1: say we. I often think of robots as sort of

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Speaker 1: anthropomorphic robots, like I think of droids in the Star Wars,

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Speaker 1: like C three b O that kind of droid. Not

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Speaker 1: are two. He's clearly not humanoid. But you know, I

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Speaker 1: think of humanoid robots more frequently than other types, despite

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Speaker 1: the fact that the vast majority of robots I have have

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Speaker 1: encountered have not been humanoid robots. So that does raise

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Speaker 1: a question does a humanoid robot even makes sense? We

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Speaker 1: try and answer that in this particular episode, Enjoy. I

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Speaker 1: asked Josh what he would like to cover, because with

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Speaker 1: the fact that I've got all these guests coming in

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Speaker 1: to sit down with me, um, you know, some people

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Speaker 1: like to come up with their own suggestions. Some people

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Speaker 1: preferred if I pick a topic and then they research it.

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Speaker 1: I asked Josh what he would like to talk about,

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Speaker 1: and you were really interested in the idea of humanoid robots. Well,

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Speaker 1: you have this awesome spreadsheet of um of listeners suggestions,

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Speaker 1: and it might as well been a neon when it

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Speaker 1: was going down the sheet. I'm like humanoid robots, of course,

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Speaker 1: and this is a great topic. In order to really

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Speaker 1: get into it, I was going to define a few terms,

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Speaker 1: even though a lot of these are ones that I

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Speaker 1: think most of us just kind of understand just from

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Speaker 1: the fact that this is in our culture now. It's

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Speaker 1: not just not just a reality as far as technology goes,

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Speaker 1: but it plays a large part in fiction. In fact,

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Speaker 1: that's where the term robot comes from, is from fiction. Uh.

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Speaker 1: It was from Carrel Chopeck, a Czechoslovakian playwright. I did,

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Speaker 1: in fact listen to it a couple of times. Carrel Chopec, Yeah,

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Speaker 1: because it's his last name is spelled c A p

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Speaker 1: e K. And it includes uh symbols that are not

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Speaker 1: in the English alphabet, like squiggly lines and little UFOs

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Speaker 1: and things he wrote. Are you are also known as

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Speaker 1: Rossum's Universal robots, and the word robot comes from the

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Speaker 1: check word robot to which means forced labor. Yeah, so

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Speaker 1: a robot is a an entity, a synthetic construct that

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Speaker 1: is forced to do work. Then we have humanoid, which

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Speaker 1: just means resembling a human being. That's a term that

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Speaker 1: is relatively young. It started showing up around the turn

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Speaker 1: of the twentieth century. And uh, it started I think

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Speaker 1: the first few times it was ever mentioned was around

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Speaker 1: nineteen twelve, and it was mostly used then to describe fossils,

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Speaker 1: saying these are humanoid fossils, like yeah. And then we

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Speaker 1: have android, which is we're probably not gonna be using

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Speaker 1: that word very often, but android is a robot that's

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Speaker 1: in the form of a human. So all androids or robots,

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Speaker 1: but not all robots are androids. And you know, I

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Speaker 1: ran into I looked up android as well. Yeah, and

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Speaker 1: apparently that's from like the early eighth century. It's a

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Speaker 1: little odd that it actually predates robots. Yeah, but uh,

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Speaker 1: when we look at myths and legends, there's so many

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Speaker 1: stories that involve a human like entity that's not actually

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Speaker 1: a person that you can see where it gets translated

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Speaker 1: in there. This gets a little confusing too, because Star

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Speaker 1: Wars they called all their robots droids, but there they

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Speaker 1: aren't androids are two? T two is not an android

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Speaker 1: because he's not um or it's not human shaped. You

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Speaker 1: could even argue that C three Po is not a

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Speaker 1: true android because some people say to be an android

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Speaker 1: you have to appear, at least on casual glance to

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Speaker 1: be human. He's way too shiny, way too shiny. Data

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Speaker 1: from Star Trek Next Generation might be uh android, but

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Speaker 1: he's an android who's had, you know, a long time

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Speaker 1: in the man cave. He hasn't seen much sun, right, Yeah,

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Speaker 1: because he's definitely got a weird complexion. Thing goes the

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Speaker 1: kid in a I would be an android David, yes,

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Speaker 1: which turns out to be a popular name for droids,

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Speaker 1: because there was well there's David in AI, and there

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Speaker 1: was also David and Prometheus. Oh you know, I never

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Speaker 1: saw Prometheus. I don't know if your listeners are going

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Speaker 1: to agree with me or not, because I could see

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Speaker 1: um getting shouted down. But I thought Prometheus was a

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Speaker 1: great movie. Even upon second viewing, I thought it was good.

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Speaker 1: You know, I know that the from the artistic level,

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Speaker 1: A lot of people really loved it, And then there

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Speaker 1: were some people who said, how can you get lost

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Speaker 1: if you have a three dimensional map with you at

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Speaker 1: all time? But you know, plot versus artist, I don't know.

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Speaker 1: Then you've got replicants from Blade Runner. These are more

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Speaker 1: like cyborgs because they have some sort of organic material

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Speaker 1: attached to them. They're not completely you know, synthetic material.

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Speaker 1: So Terminator is another example. They have a fleshy over

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Speaker 1: skin on top of their metallic bodies. But was that

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Speaker 1: real skin that he had there was a synthetic skin,

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Speaker 1: because that would make a difference. That's a good question.

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Speaker 1: And I uh, you know, I know that they refer

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Speaker 1: to them as cyborgs at least a few times in

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Speaker 1: the movies, which would suggest that it's actual skin. Maybe

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Speaker 1: it's lab grown human skin. So you know, there's some

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Speaker 1: fuzzy lines around these definitions. I would think that cyborgs

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Speaker 1: would be the hardest of all of the the um

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Speaker 1: humanoid robots to make, because the flesh would just wrought

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Speaker 1: like you have your normal looking humanoid robot your cyborg,

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Speaker 1: and it's ear would just fall off. Yeah, and it's

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Speaker 1: not as easy as you might think to wire together

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Speaker 1: the wet wear that's in our heads, hardware that runs

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Speaker 1: on circuits. We will often think of computers working in

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Speaker 1: a way that's similar to our brains, but in fact

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Speaker 1: the two work in very different ways. Well it seems

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Speaker 1: like running into this though, Strickland um, the the more

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Speaker 1: we got into humanoid robotics, the more we started to

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Speaker 1: understand just how complex we are. Yeah, that's one of

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Speaker 1: the things that I think is a benefit of study

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Speaker 1: of humanoid robotics. The idea of pursuing the goal of

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Speaker 1: creating a humanoid robot is not just that we learn

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Speaker 1: more about all the different areas and robotics and they're

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Speaker 1: a lot, and we'll talk about some of them, but

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Speaker 1: we also learn more about ourselves. We're trying to figure out, Okay,

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Speaker 1: if we're going to make something that is able to

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Speaker 1: perform tasks the way human does, then we really got

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Speaker 1: to take a close look at humans. That's that's the

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Speaker 1: first place to start. So what makes a humanoid robot?

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Speaker 1: And generally speaking, we're talking about a robot that has

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Speaker 1: basic features usually minimum a torso, arms and legs, and

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Speaker 1: is walking up right. Uh, it may have a head

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Speaker 1: or it might not. Early humanoid robots didn't, or at

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Speaker 1: least their sensory uh. Instruments were all located within the

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Speaker 1: top part of the torso there wasn't like a separate head.

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Speaker 1: Did you see a picture of Minerva at the Smithsonian? No?

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Speaker 1: I did not. It's a robot to her guide, but um,

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Speaker 1: she came up in the humanoid robot research and I

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Speaker 1: think she's stretching it a little bit. Yeah, she looks

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Speaker 1: a bit like a washing machine with a couple of

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Speaker 1: UM cameras on top. So just those alone, I guess

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Speaker 1: makes her eligible for the humanoid robot realm. But that

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Speaker 1: that seems like that's a bit of a you know,

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Speaker 1: if it has an appendage, that doesn't necessarily make a humanoid.

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Speaker 1: I mean, you could look at the Mars Curiosity rover,

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Speaker 1: which has several appendages, but I don't think anyone would

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Speaker 1: ever describe it as humanoid. Right, So, uh, ideally a

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Speaker 1: humanoid robe lot would be able to interact with humans

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Speaker 1: within a human environment. Because here's the thing about we,

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Speaker 1: we people. We have defined our environments to a large extent,

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Speaker 1: especially in developed nations, where the stuff that's around us

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Speaker 1: we have shaped so that it works within our capabilities,

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Speaker 1: right with our with our human environment thus far. If

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Speaker 1: you look at technology though, on the whole, we've pretty

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Speaker 1: much been forced to adapt to it. So, for example,

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Speaker 1: like a keyboard, we don't normally naturally, you know, um

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Speaker 1: express ideas through our fingers on a little a little board, right,

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Speaker 1: we don't normally do that, So we had to adapt

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Speaker 1: to the technology and learn to type and get good

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Speaker 1: at it. With humanoid robots, it's basically going the exact opposite.

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Speaker 1: It's saying we already have an environment, we already are um,

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Speaker 1: you know, good at all this other stuff. If we're

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Speaker 1: gonna make humanoid robots, one of the great benefits is

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Speaker 1: they can adapt to us. Right. Yeah, we don't have

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Speaker 1: to uh create a unitask or robe, but that's really

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Speaker 1: good at one thing, um that may or may not

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Speaker 1: be something that humans can do easily. We can make

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Speaker 1: a robot that's good lots of things. Uh. I also

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Speaker 1: think usually when I think of humanoid robots, when I

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Speaker 1: think of robots in general, I normally think that they

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Speaker 1: are at least semi autonomous. That's that's one of the

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Speaker 1: things I usually think of. It doesn't necessarily have to be.

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Speaker 1: You could have a tel operated robot, but I almost

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Speaker 1: think of that closer to the realm of like a

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Speaker 1: remote controlled car or a puppet even. Um So, I

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Speaker 1: often one of the definitions I use is that it's

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Speaker 1: an autonomous or semi autonomous machine in human form. It's

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Speaker 1: mechanical and electronic, and it can thus do the sort

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Speaker 1: of things humans do, but do it in a totally

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Speaker 1: synthetic way. And you have to be careful when you

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Speaker 1: say autonomous or semi autonomous, because the state of the

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Speaker 1: art right now appears to be that robots display autonomy

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Speaker 1: because they just kind of wander off in places they're

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Speaker 1: not supposed But it's not because they want to. It's

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Speaker 1: because their their program just ran a foul of program. Right.

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Speaker 1: There's no determination there on the part of the robot.

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Speaker 1: It's not exploring its environment on its own accord. It's

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Speaker 1: someone made a mistake in the code somewhere and where

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Speaker 1: the robot was supposed to take a left hand turn

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Speaker 1: at this one you know, predetermined spot, and instead continued

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Speaker 1: forward or something exactly. Um So, I wanted to talk

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Speaker 1: a little bit about the history of humanoid robots, and

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Speaker 1: if you wanna look way way way back, I mean

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Speaker 1: we're talking about the first people to really kind of

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Speaker 1: attempt to build a humanoid machine that could mimic or

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Speaker 1: the movements at least of a person. You gotta go

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Speaker 1: all the way back to Greece between the years ten

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Speaker 1: and seventy Common era. That's when Hero of Alexandria started

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Speaker 1: to create various machines. He's also the person who made

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Speaker 1: the first working steam engine style tool, which is pretty impressive. Yeah.

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Speaker 1: He he had come up with a lot of very

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Speaker 1: clever designs. Whether they were built or not depends upon

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Speaker 1: uh certain accounts and and if they're true. But the

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Speaker 1: stuff he designed is completely build a bowl. So he

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Speaker 1: didn't come up with any ideas where it was so

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Speaker 1: you know, outlandish, that was impossible. He wasn't just like

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Speaker 1: drawing in the margins of his diary or something. No, Yeah,

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Speaker 1: he came up with specific plans that people today have recreated.

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Speaker 1: They built their own versions. Yeah. So he created a

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Speaker 1: lot of designs for automata. Although these are things that

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Speaker 1: were controlled by pulleys and ropes and cogged wheels, and

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Speaker 1: uh needed some form of outside influence to make them work,

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Speaker 1: so they're not all fully self contained like the show

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Speaker 1: biz pizza Rocke fire explosion day and yeah exactly, yeah, yeah,

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Speaker 1: it was some one of those audio animatronic figures that

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Speaker 1: that looks very robotic, but you realize it's really just

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Speaker 1: one tiny piece of a giant system. Uh. In four,

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Speaker 1: we get up to Leonardo da Vinci. He designed an

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Speaker 1: automaton in the form of a mechanical night which uh

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Speaker 1: again supposedly he built. There's no actual record of an

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Speaker 1: existing one from history, but they have created ones based

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Speaker 1: on the design since then, and it could do things

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Speaker 1: like move its arms and raise its visor. Who's doing this?

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Speaker 1: Who's doing this? Crazy engineers who are also uh, very

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Speaker 1: excited about history and very wealthy too, I would imagine. Yeah,

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Speaker 1: so in this case, you're talking about Mark Rossheim, who

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Speaker 1: recreated this particular machine, and it it's a night, it's

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Speaker 1: a night in German medieval armor. And it can sit down,

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Speaker 1: it can stand up, it can move its arms, it

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Speaker 1: can raise a visor, it can work its jaw ling,

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Speaker 1: I imagine. So I tried to find video of ross

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Speaker 1: Heims version working and I couldn't find it. He did, however,

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Speaker 1: make another of Da Vinci's inventions, which was a self

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Speaker 1: driving cart that used Yeah you you wound a spring,

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Speaker 1: and it had cam stops that would allow it to

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Speaker 1: steer a predetermined path. You would actually program the cart

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Speaker 1: by putting cam stops in particular locations along the cam

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Speaker 1: and that would tell it when to turn left or

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Speaker 1: when to turn right, so it couldn't it couldn't navigate

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Speaker 1: through uh an obstacle course unless you had already previously

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Speaker 1: seen the obstacle course and you could figure out when

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Speaker 1: it needed to turn ahead of time, so you're essentially

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Speaker 1: programming the device. Um. There are lots of examples in

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Speaker 1: the renaissance of automata and semi automata, things that are

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Speaker 1: really more like puppets. You've heard about the Mechanical Turk

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Speaker 1: have the chess playing robot uh So it looked like

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Speaker 1: it was a robot that could play chess and was

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Speaker 1: really really good at playing chess, and it turned out

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Speaker 1: eventually to be a hoax. It was actually it was

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Speaker 1: actually a puppet, and there was an actual chess master

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Speaker 1: hidden in a cabinet beneath the Mechanical Turk who sat

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Speaker 1: kind of Indian style with a chessboard in front of

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Speaker 1: him and could move the pieces to where it needed

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Speaker 1: to be. But it was all being guided by an

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Speaker 1: actual chess master who was hidden under When was that

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Speaker 1: that was late Renaissance early Enlightenment. That in and of

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Speaker 1: itself is pretty impressive. Yeah, it was. It was neat

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Speaker 1: that people were thinking about these sort of things. Uh.

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Speaker 1: By ninety six we get the first humanoid robot to

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Speaker 1: appear on film, uh, Metropolis, the character of Maria, and

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Speaker 1: at ninety nine at the World's Fair, Westinghouse Electric Corporation

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Speaker 1: showed off a robot called Electro. Now have you ever

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Speaker 1: seen this? So he kind of looks like the ten

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Speaker 1: man from the Wizard of Oz film. Yes, he smokes.

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Speaker 1: I have seen one of the things he can do

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Speaker 1: get a little bellows in his head that allowed him

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Speaker 1: to puff smoke. He could also kind of speak. He

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Speaker 1: had a seventy eight revolution per minute UH record player

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Speaker 1: essentially inside of him. I suppose if the if the

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Speaker 1: needles skipp it would at least sound like it. Um.

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Speaker 1: He repeat himself over. He called the audience twots. That

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Speaker 1: was the name. Yeah, he's apparently the main reason he

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Speaker 1: was retired from being shown off at exhibitions was because

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Speaker 1: it was very dated kind of lingo. But he was

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Speaker 1: used at the nineteen thirty nine World's Fair, which was

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Speaker 1: It's funny because I'll be talking about that again in

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Speaker 1: another episode very shortly. The World's Fair would have been

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Speaker 1: an amazing thing to visit. Um, I'm more I can

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Speaker 1: understand that. But if you're if you're going to get

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Speaker 1: to the point where you're looking at full scale anthropomorphic robots,

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Speaker 1: you gotta get up to about ninety three. That's when

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Speaker 1: the Waybot one from the Waseda University came out. That

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Speaker 1: was the first full scale anthropomorphic robot developed in the

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Speaker 1: world which had limb control, a vision system, so I

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Speaker 1: had an optical system that could recognize its environment and

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Speaker 1: objects and measure distance, and it also had a conversation system. Uh.

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Speaker 1: It was actually a collection of a bunch of very

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Speaker 1: complex machinery. Like its hands had been previously developed independently

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Speaker 1: of the robots, so had its legs. So it's like

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Speaker 1: all these people coming up with these various pieces saying,

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Speaker 1: all right, let's connect all this together and see what happens.

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Speaker 1: So that was a huge, huge leap forward. Yeah, you know,

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Speaker 1: if you'll notice, we went basically from um HOAXI chess

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Speaker 1: playing turks to you know, a robot that could converse

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Speaker 1: and interact with its environment. And then um, it seems

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Speaker 1: like we we kind of went off course for a

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Speaker 1: little bit and now we're coming full circle back to

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Speaker 1: that where like you said, a lot of different disciplines

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Speaker 1: are contributing these different pieces to what will eventually be

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Speaker 1: all of the best practices from each little sub discipline

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Speaker 1: put together in you know, the true humanoid robot. Well yeah,

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Speaker 1: I mean, if we're talking about a humanoid robot that's

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Speaker 1: capable of interacting with people as if they were, you know,

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Speaker 1: their own person, even though maybe an odd person, not

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Speaker 1: like the kind of person you would typically run into.

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Speaker 1: There's it's a multi disciplinary approach. I mean, artificial intelligence

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Speaker 1: by itself is multi disciplinary because you have sensing, you

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Speaker 1: have so all the perception, there's all these different just

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Speaker 1: that's multi disciplinary. Then you've got the processing, the cognition,

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Speaker 1: things like planning, navigation. There are so many things that

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Speaker 1: come together to make a humanoid robot a a possibility.

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Speaker 1: And that's just the the mental side, right. Then you

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Speaker 1: have all the physical side, the how do you make

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Speaker 1: it walk, how do you make it keep its balanced?

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Speaker 1: So lots of stuff to consider there. We're gonna take

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Speaker 1: a quick break but when we come back, we will

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Speaker 1: look more at the issue of humanoid robots. Wybot two

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Speaker 1: came out in that was a specialist robot. It could

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Speaker 1: play a keyboard, trying a keyboard. He could read sheet

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Speaker 1: music and play music. Uh. It was because it was

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Speaker 1: a specialist. It was not able to do the general

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Speaker 1: functions that it's predecessor could do. And that's why the

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Speaker 1: issues in robotics today is that it's very challenging to

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Speaker 1: build a general purpose robot. It's much easier to take

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Speaker 1: a specific task that you need to have done and

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Speaker 1: designed a robot to do that. Yeah, because I mean

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Speaker 1: we already have those rooms and rovers. I mean you know,

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Speaker 1: there's there's also all the robots and manufacturing, all the

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Speaker 1: welding robots things like that. Uh. Nine Pacific Northwest National

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Speaker 1: Laboratory built a robot called Manny that was the first

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Speaker 1: full scale android body and it had forty two degrees

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Speaker 1: of freedom but no AI or autonomy. It was completely teleoperated.

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Speaker 1: And um it took me a couple of times to

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Speaker 1: figure out what degrees of freedom meant. Yeah, Um, I

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Speaker 1: thought it meant like the it could move its arm

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Speaker 1: forty two degrees basically, but a degree of freedom is say,

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Speaker 1: like it can move its wrist, that's a degree of freedom.

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Speaker 1: It can turn its head left and right, that's a

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Speaker 1: degree of freedom right right. And if you look at

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Speaker 1: the human hand, the human hand has about thirty degrees

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Speaker 1: of freedom, meaning that you look at the way each

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Speaker 1: finger and your thumb can move. You look at the

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Speaker 1: way you can clench a fist, you can twist your

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Speaker 1: hand with your wrist um, those are all different degrees

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Speaker 1: of freedom. And uh. In fact, one of the cool

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Speaker 1: things about robots is that as we get better and

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Speaker 1: better at designing them, we can create robots that have

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Speaker 1: far more degrees of freedom than the human body does.

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Speaker 1: So I like the idea of a humanoid robot in

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Speaker 1: the future that has sixty degree motion with its wrist

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Speaker 1: and then just having it changed light bulbs. Is it

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Speaker 1: just spin and not have to do the little twisty

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Speaker 1: turning motion. How many robots does it take to change

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Speaker 1: the light bulbs? Just just the one, just that one,

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Speaker 1: just one billion dollar rob Yeah. Yeah, I'm not saying

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Speaker 1: it's a fishing system. I'm just saying I'm in supremely

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Speaker 1: lazy human being with tall ceilings. Uh. In nine Honda

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Speaker 1: introduced the P two robot, which was a self contained

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Speaker 1: robotic humanoid. It could walk and climb stairs. The P

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Speaker 1: three followed in n and in two thousand two, Honda

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Speaker 1: introduced My good buddy as Emo. As the first article

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Speaker 1: I wrote for How Stuff Works. As Works have an

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Speaker 1: episode on it. I have not done a full episode

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Speaker 1: on Asimo. I even was offered the opportunity to meet

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Speaker 1: as Amo when I first wrote the article, but it

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Speaker 1: would have meant having to travel to Disneyland to do it,

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Speaker 1: and at the time, How Stuff Works was not prepared

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Speaker 1: to do such a I went to Disneyland by myself,

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Speaker 1: uh well with my wife, and we went and saw

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Speaker 1: the Asimo production. And at the end of it, I

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Speaker 1: talked to of the Disney cast members and I said, yeah,

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Speaker 1: I wrote the article about how Asthma works for How

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Speaker 1: Stuff Works, and she said, hang on a minute. And

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Speaker 1: I got to meet Asthmo. And it was a man

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Speaker 1: in a suit, right. It was actually it was actually

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Speaker 1: a collection of cats that the duct taped together and

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Speaker 1: then plastic. No, it was a working robot. That's pretty

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Speaker 1: near how blown away? I was very much blown away.

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Speaker 1: It was cool seeing it up close. I mean, it

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Speaker 1: looks like a little tiny astronaut right because he's got

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Speaker 1: like the face plate especially. I love the people call

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Speaker 1: it he people give And I do this all the

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Speaker 1: time too. With robots, I'll sign a gender even though

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Speaker 1: technically many of them are specifically genderless. As amo is

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Speaker 1: is supposed to be genderless, but I often refer to

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Speaker 1: Asmo as a he as well. Well, you know why.

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Speaker 1: It's the shoulders, I would guess. Yeah, they go straight

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Speaker 1: across and and and far out. That's very masculine no

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Speaker 1: matter what. Yeah, and you need that, I would guess

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Speaker 1: you need shoulders in a humanoid robot with flexible arms. Well,

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Speaker 1: and also, I'm sure every single element of Asimo is

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Speaker 1: built with the balance in mind, because is the first

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Speaker 1: robot that can run. Yes, I've seen him run. It's gawky. Yeah,

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Speaker 1: it kind of look like someone who really needs to

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Speaker 1: get to the bathroom as a little bit of a

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Speaker 1: hoppy kind of run. But the the definition of run

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Speaker 1: here is that there are moments where both feet are

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Speaker 1: off the ground. So walking you always have one ft

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Speaker 1: in contact with the ground, and running both feet at

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Speaker 1: some point are out of contact. And that's a huge

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Speaker 1: deal for robotics, right. I mean, you have a machine

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Speaker 1: that completely separates itself from contact on the ground. It

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Speaker 1: has no propulsion to keep it upright, you know, it

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Speaker 1: doesn't have like propellers or jets or anything like that.

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Speaker 1: So you have to design it so it can it

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Speaker 1: can propel itself off the ground and then catch itself

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Speaker 1: when it comes back down without falling over. And that

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Speaker 1: is a non trivial challenge. No, it's an enormous challenge

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Speaker 1: that robots UM really kind of started to tackle lately. UM.

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Speaker 1: One of the ways that they've overcome it is with

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Speaker 1: rounded feet, which are very helpful in keeping balanced and

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Speaker 1: allowing it to run UM. But there's drawbacks to it

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Speaker 1: as well, Like the robot can't start itself, it also

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Speaker 1: can't stop, so it can't stop moving, which is not

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Speaker 1: something you want. Like, there's still some challenges there. Ahead

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Speaker 1: of the robotists who are learning to teach a robot

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Speaker 1: to walk, and even the ones that have taught robots

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Speaker 1: to walk UM, they typically can just walk over flat

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Speaker 1: surfaces with no obstacles. When they encounter stairs, there in trouble.

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Speaker 1: But then you have robots that know how to go upstairs,

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Speaker 1: but they can't walk on a flat surface. Eventually, all

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Speaker 1: this information, all this knowledge will be brought together and

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Speaker 1: you'll have a robot that can walk no problem. Right.

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Speaker 1: In fact, this kind of transitions nicely into those challenges

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Speaker 1: that face designers of humanoid robots and and locomotion is

417
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Speaker 1: the probably one of the top ones, at least from

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Speaker 1: the physical engineering side. For example, you know, as Amo

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Speaker 1: can can go up and downstairs, but that is a

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Speaker 1: little deceptive because as Amo has to be programmed to

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Speaker 1: go up or down the staircase and know exactly how

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Speaker 1: many stairs are involved. It's not so much it's not

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Speaker 1: a case of Asimo detecting a staircase and then uh,

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Speaker 1: and then navigating through oh up or down it. It's

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Speaker 1: the fact that all right now we're initiating your stair

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Speaker 1: climbing program. Yeah exactly. It's kind of like smoke and

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Speaker 1: mirrors robotics. Basically, it's at But that's you know, those

428
00:24:28,359 --> 00:24:31,119
Speaker 1: are the little no no pun intended, Those are the

429
00:24:31,119 --> 00:24:32,800
Speaker 1: little steps you have to take in order to get

430
00:24:32,800 --> 00:24:35,000
Speaker 1: to the destination. What do you mean, no pun intended.

431
00:24:35,040 --> 00:24:37,600
Speaker 1: I don't buy that at all. I started saying it

432
00:24:37,640 --> 00:24:40,439
Speaker 1: without thinking about it, and then I mean, but then

433
00:24:40,480 --> 00:24:42,000
Speaker 1: I did follow through with it, so I guess there

434
00:24:42,040 --> 00:24:45,040
Speaker 1: was some intention there at the end. But uh yeah.

435
00:24:45,119 --> 00:24:48,480
Speaker 1: They're also not very good at going across any kind

436
00:24:48,640 --> 00:24:54,720
Speaker 1: of uneven terrain, right, So, humanoid robots in particular find

437
00:24:54,720 --> 00:24:57,760
Speaker 1: it very difficult to maintain balance over anything that's not

438
00:24:58,359 --> 00:25:00,800
Speaker 1: either a flat surface or in the case some robots

439
00:25:00,840 --> 00:25:03,600
Speaker 1: that can go up or downstairs and stairs. So if

440
00:25:03,600 --> 00:25:07,200
Speaker 1: you're talking about like a sidewalk that is not completely even,

441
00:25:07,640 --> 00:25:09,960
Speaker 1: that would be enough to give a robot trouble because

442
00:25:10,080 --> 00:25:12,240
Speaker 1: it's going to try and put its foot down to

443
00:25:12,359 --> 00:25:15,399
Speaker 1: where it would believe the ground to be, and if

444
00:25:15,400 --> 00:25:19,240
Speaker 1: the ground is not exactly level, then that's yeah, because

445
00:25:19,320 --> 00:25:22,520
Speaker 1: they can't really catch their balance very well. There are

446
00:25:22,640 --> 00:25:26,479
Speaker 1: robots that can, but they are four legged. Yeah, you've

447
00:25:27,800 --> 00:25:30,800
Speaker 1: you've seen the Big Dog video, No, I saw the Army,

448
00:25:31,960 --> 00:25:34,960
Speaker 1: though it's very similar. Big Dog is essentially a robo

449
00:25:35,040 --> 00:25:39,280
Speaker 1: mule type development. It is a four legged robot that

450
00:25:39,480 --> 00:25:42,560
Speaker 1: is able to maintain its balance even when pushed. And

451
00:25:42,640 --> 00:25:47,080
Speaker 1: the famous video shows the robot dog the Big Dog

452
00:25:48,359 --> 00:25:52,240
Speaker 1: kind of jogging and then a guy just casually lifts

453
00:25:52,280 --> 00:25:56,160
Speaker 1: his leg up and kicks the robot dog like he

454
00:25:56,200 --> 00:25:58,240
Speaker 1: puts essentially puts the bomb of his foot against the

455
00:25:58,280 --> 00:26:01,199
Speaker 1: side of it and pushes really hard, tipping and you

456
00:26:01,240 --> 00:26:04,960
Speaker 1: see the big dog stumble. It actually stumbles and then

457
00:26:05,040 --> 00:26:09,320
Speaker 1: catches itself and then rights itself and continues on. And

458
00:26:09,520 --> 00:26:12,840
Speaker 1: almost everyone has an emotional reaction to this, like how

459
00:26:12,960 --> 00:26:16,760
Speaker 1: dare that evil man kick that poor defenseless robot. The

460
00:26:16,840 --> 00:26:22,520
Speaker 1: robot can't feel anything, but that robot is um gasoline powered.

461
00:26:23,119 --> 00:26:25,440
Speaker 1: So oh yeah, that's right. That's one of the big

462
00:26:25,440 --> 00:26:28,639
Speaker 1: dis keeping it inside you. Yeah, you wouldn't want to

463
00:26:28,680 --> 00:26:31,359
Speaker 1: have one of these indoors. No, you don't bring your

464
00:26:31,400 --> 00:26:35,240
Speaker 1: lam indoors. No, and uh. And the the pistons that

465
00:26:35,400 --> 00:26:38,480
Speaker 1: allow it to do this are quite loud, you know.

466
00:26:38,520 --> 00:26:41,560
Speaker 1: It's not not a subtle system at all. So a

467
00:26:41,600 --> 00:26:44,679
Speaker 1: lot of work has to go into creating better systems

468
00:26:44,800 --> 00:26:47,720
Speaker 1: for robots to maintain their balance in order for the

469
00:26:47,800 --> 00:26:50,840
Speaker 1: locomotion problem to really be solved. And again, I mean,

470
00:26:50,880 --> 00:26:53,480
Speaker 1: like anybody who's seen short Circuit knows that you can

471
00:26:53,480 --> 00:26:56,240
Speaker 1: build a robot like Johnny five with arms in the

472
00:26:56,320 --> 00:27:00,240
Speaker 1: head and a torso and then like traction um wed

473
00:27:00,520 --> 00:27:04,520
Speaker 1: shreds uh and it can go anywhere over terrain, it

474
00:27:04,520 --> 00:27:07,439
Speaker 1: can go up steps. Probably. The thing is is again

475
00:27:07,600 --> 00:27:10,760
Speaker 1: you have to remember when it comes to humanoid robots,

476
00:27:10,800 --> 00:27:12,960
Speaker 1: you're trying to make the robot that can adapt to

477
00:27:13,000 --> 00:27:16,240
Speaker 1: the human world. So if you had somebody like Johnny

478
00:27:16,280 --> 00:27:19,000
Speaker 1: five as your house butler or something, you you couldn't

479
00:27:20,359 --> 00:27:22,560
Speaker 1: You couldn't have an island in your kitchen, and who

480
00:27:22,600 --> 00:27:25,439
Speaker 1: doesn't love an island in their kitchen. Johnny five couldn't

481
00:27:25,440 --> 00:27:27,639
Speaker 1: maneuver around it because he's too wide. Yeah, you wouldn't

482
00:27:27,640 --> 00:27:29,840
Speaker 1: have You wouldn't be able to have any any space

483
00:27:29,840 --> 00:27:33,040
Speaker 1: that would be narrower than the robot's body exactly. That's

484
00:27:33,040 --> 00:27:36,240
Speaker 1: not what you want. Yeah, with humanoid robots, it wouldn't

485
00:27:36,240 --> 00:27:38,199
Speaker 1: work well. In my house. I've got a I've got

486
00:27:38,320 --> 00:27:44,120
Speaker 1: a a flat style house where there's three floors. Yeah,

487
00:27:44,560 --> 00:27:48,080
Speaker 1: it's like flat, like European flat. Uh, not flat as

488
00:27:48,119 --> 00:27:50,720
Speaker 1: in there's only one level. They're actually three of them,

489
00:27:51,160 --> 00:27:53,840
Speaker 1: four if you count the rooftop denck, so that counts.

490
00:27:53,960 --> 00:27:56,280
Speaker 1: It makes it. You know, any robot that would not

491
00:27:56,280 --> 00:27:59,600
Speaker 1: be able to navigate stairs easily would definitely have an issue,

492
00:27:59,600 --> 00:28:01,480
Speaker 1: which is the main reason why I don't have a rumba,

493
00:28:01,560 --> 00:28:03,000
Speaker 1: because I don't want to hear the sound of a

494
00:28:03,040 --> 00:28:07,520
Speaker 1: roomba going falling down a flight up stairs. Um, but

495
00:28:07,600 --> 00:28:10,679
Speaker 1: at any rate, Uh, that's a great point. Moving on

496
00:28:10,720 --> 00:28:14,800
Speaker 1: from locomotion, there's also dexterous manipulation. Yeah, I think we should.

497
00:28:14,840 --> 00:28:18,359
Speaker 1: I think that point bears repeating. What we just talked

498
00:28:18,359 --> 00:28:20,639
Speaker 1: about is locomotion. Yeah, and this is you and I

499
00:28:20,800 --> 00:28:24,240
Speaker 1: a couple of non robot experts talking about the problem

500
00:28:24,280 --> 00:28:29,119
Speaker 1: with locomotion. That's just one of myriad challenges facing humanoid

501
00:28:29,280 --> 00:28:33,720
Speaker 1: robotics designers. Yeah, yeah, exactly. It's It's one that's easy

502
00:28:33,800 --> 00:28:37,760
Speaker 1: to point to because it's something that we all, you know,

503
00:28:38,160 --> 00:28:43,400
Speaker 1: end up at least observing or participating in all the time.

504
00:28:43,640 --> 00:28:46,240
Speaker 1: Can we take it for granted? But then when you think, okay, well,

505
00:28:46,240 --> 00:28:47,800
Speaker 1: how do I make a machine that does that? You

506
00:28:47,840 --> 00:28:50,200
Speaker 1: start to realize this is you know, even if I

507
00:28:50,280 --> 00:28:53,600
Speaker 1: have a leg that has lots of different degrees of

508
00:28:53,600 --> 00:28:56,840
Speaker 1: freedom and points of articulation, I still have to design

509
00:28:56,880 --> 00:28:58,680
Speaker 1: the upper part of the robots so that it does

510
00:28:58,720 --> 00:29:01,840
Speaker 1: not unbalance the lower part, and if it does on balance,

511
00:29:01,920 --> 00:29:04,760
Speaker 1: it's able to catch itself. You know, some people just

512
00:29:04,840 --> 00:29:10,600
Speaker 1: describe walking as falling and catching yourself over and over again. Yeah, yeah,

513
00:29:10,680 --> 00:29:15,440
Speaker 1: they're walking right now. No, I I described walking as

514
00:29:15,480 --> 00:29:18,840
Speaker 1: something that other people do. I like to keep my

515
00:29:18,920 --> 00:29:21,240
Speaker 1: walking to a minimum. I thought you walked alot, Actually

516
00:29:21,280 --> 00:29:24,080
Speaker 1: I do. I just joke about being lazy. I think

517
00:29:24,520 --> 00:29:27,920
Speaker 1: moving forward, falling down and catching your balance every time

518
00:29:27,920 --> 00:29:32,480
Speaker 1: it's lurching. Yeah, that's well. As an Adams Family fan,

519
00:29:32,600 --> 00:29:36,160
Speaker 1: I'm okay with that. Yeah, but uh, Dexter's manipulation would

520
00:29:36,200 --> 00:29:40,160
Speaker 1: be the ability to pick up and manipulate objects. Now,

521
00:29:40,200 --> 00:29:42,120
Speaker 1: we're really good at that, we humans, You know, we

522
00:29:42,200 --> 00:29:45,400
Speaker 1: can we can feel an object and decide at that

523
00:29:45,480 --> 00:29:48,880
Speaker 1: point how to handle it, even if we've never encountered

524
00:29:48,880 --> 00:29:51,560
Speaker 1: that kind of object before. So if I encounter something

525
00:29:51,600 --> 00:29:54,600
Speaker 1: I've never seen before and i've I've ascertained it's safe

526
00:29:54,720 --> 00:29:56,800
Speaker 1: for me to touch it, I can touch it. I

527
00:29:56,840 --> 00:29:58,239
Speaker 1: can feel like, I can get a feel for how

528
00:29:58,280 --> 00:29:59,840
Speaker 1: heavy it is, I can get a feel for how

529
00:30:00,000 --> 00:30:03,360
Speaker 1: eloquant it might be, and then I can adjust on

530
00:30:03,440 --> 00:30:06,360
Speaker 1: the fly so that I can handle it appropriately. Well,

531
00:30:06,360 --> 00:30:08,520
Speaker 1: I'm not gonna hurt myself. I'm not gonna hurt the object.

532
00:30:09,040 --> 00:30:13,480
Speaker 1: Robots are not so good at that. Yeah, exactly, even

533
00:30:13,520 --> 00:30:15,920
Speaker 1: if they don't mean to right, Yeah, if the robots.

534
00:30:15,960 --> 00:30:18,240
Speaker 1: If the robots grip is too strong, it can break

535
00:30:18,280 --> 00:30:20,520
Speaker 1: the object. If it's too weak, the object slips from

536
00:30:20,520 --> 00:30:23,760
Speaker 1: its grip and it falls. Uh. And it may not

537
00:30:23,840 --> 00:30:28,200
Speaker 1: be able to distinguish between different types. So getting those

538
00:30:28,240 --> 00:30:31,880
Speaker 1: tactile sensors where a robot can tell how tightly it's

539
00:30:31,920 --> 00:30:35,360
Speaker 1: gripping something and how much pressure a particular object can

540
00:30:35,360 --> 00:30:39,080
Speaker 1: take before you've reached the failure point is a big deal. Now,

541
00:30:39,120 --> 00:30:41,440
Speaker 1: this is also a big deal for just making robots

542
00:30:41,520 --> 00:30:44,480
Speaker 1: safe for humans to be around. It is a big deal.

543
00:30:44,560 --> 00:30:49,000
Speaker 1: You know that the first fatality by robot occurred um

544
00:30:49,040 --> 00:30:52,240
Speaker 1: at the business end of a robotic arm in a

545
00:30:52,560 --> 00:30:55,719
Speaker 1: flat rock, Michigan in nine a man named Robert Williams

546
00:30:55,720 --> 00:30:59,440
Speaker 1: who's working on a forward line. Yeah, his robot arm

547
00:30:59,560 --> 00:31:02,360
Speaker 1: was moving a little slow for his taste in getting

548
00:31:02,400 --> 00:31:05,200
Speaker 1: supplies down, so he climbed up to where the supplies were.

549
00:31:05,480 --> 00:31:07,760
Speaker 1: The robot arms suddenly sped up and hit him in

550
00:31:07,800 --> 00:31:11,320
Speaker 1: the head and killed him instantly. Wow. Yeah, I've I've

551
00:31:11,640 --> 00:31:15,239
Speaker 1: had a chance to see some of these industrial robots. Uh.

552
00:31:15,320 --> 00:31:17,680
Speaker 1: And I would say up close, but you can't because

553
00:31:18,120 --> 00:31:22,520
Speaker 1: because of instances like that. Industrial robots usually have lots

554
00:31:22,560 --> 00:31:27,200
Speaker 1: of of safety barriers around them because it's not safe

555
00:31:27,200 --> 00:31:31,160
Speaker 1: to be near those robots when they're in operation. They

556
00:31:31,200 --> 00:31:33,880
Speaker 1: they can't react exactly, So you leave it up to

557
00:31:33,920 --> 00:31:36,480
Speaker 1: the humans to stay away from the robots because the road.

558
00:31:36,480 --> 00:31:38,480
Speaker 1: We haven't gotten to the point where the robots no,

559
00:31:38,600 --> 00:31:40,880
Speaker 1: did not crush you or hit you in the head. Right, Yeah,

560
00:31:40,920 --> 00:31:42,760
Speaker 1: I got the robots fall. I'm looking forward to that

561
00:31:42,840 --> 00:31:45,040
Speaker 1: day when they figure out not to crush me. Yes,

562
00:31:45,680 --> 00:31:49,000
Speaker 1: it's been pretty lousy days so far. Uh. Yeah, when

563
00:31:49,000 --> 00:31:52,840
Speaker 1: I when I toured the Georgia Tech Robotics Lab, they

564
00:31:52,920 --> 00:31:55,760
Speaker 1: talked specifically about this. This is a real challenge having

565
00:31:55,840 --> 00:31:58,600
Speaker 1: robots recognize and react in a way that's going to

566
00:31:58,680 --> 00:32:03,440
Speaker 1: be safe around humans. And uh, but dexterous manipulation is

567
00:32:03,440 --> 00:32:05,760
Speaker 1: only that's only a part of dexterous manipulation. Obviously. The

568
00:32:05,840 --> 00:32:09,160
Speaker 1: rest of it is again that object recognition and handling

569
00:32:09,240 --> 00:32:11,120
Speaker 1: so that you're not destroying whatever it is you're trying

570
00:32:11,120 --> 00:32:15,680
Speaker 1: to pick up. Um. Another big challenge in designing robots

571
00:32:15,800 --> 00:32:19,680
Speaker 1: in general, not just humanoid robots, is just the the perception,

572
00:32:19,720 --> 00:32:24,200
Speaker 1: the sensory perception of the robot. Yeah, so you know,

573
00:32:24,280 --> 00:32:28,280
Speaker 1: whether it's optical systems like actual cameras in the place

574
00:32:28,320 --> 00:32:31,520
Speaker 1: of eyes, or infrared so that you can see even

575
00:32:31,520 --> 00:32:35,440
Speaker 1: in low light situations, radar, light ar, I mean, there's

576
00:32:35,480 --> 00:32:40,200
Speaker 1: tons of different ways of sensing. Yep, there's uh, there's

577
00:32:40,640 --> 00:32:42,719
Speaker 1: you know sensing obviously, it's not just site. Then you

578
00:32:42,760 --> 00:32:45,520
Speaker 1: have to have the sound. That's a really tricky one

579
00:32:45,560 --> 00:32:49,600
Speaker 1: actually because for us humans, we we can kind of

580
00:32:49,680 --> 00:32:52,600
Speaker 1: zero in on what's important. Right, So if we're if

581
00:32:52,640 --> 00:32:54,560
Speaker 1: you and I were at a party, which you know,

582
00:32:54,960 --> 00:32:57,720
Speaker 1: someone made a mistake and invited me, Uh, we could

583
00:32:57,720 --> 00:33:01,440
Speaker 1: have a conversation and be able to carry that conversation

584
00:33:01,440 --> 00:33:04,400
Speaker 1: on even within the context of a big, bustling party

585
00:33:04,720 --> 00:33:06,920
Speaker 1: because we can focus on what the other person is saying.

586
00:33:06,960 --> 00:33:10,360
Speaker 1: It's called latent inhibition. Yeah. So when you don't have that,

587
00:33:10,360 --> 00:33:14,280
Speaker 1: that schizophrenia, Yeah, you you can't separate the the signal

588
00:33:14,360 --> 00:33:17,200
Speaker 1: from the noise, and everything either becomes noise or everything

589
00:33:17,240 --> 00:33:20,720
Speaker 1: becomes signal. Um. So for a robot that might for example,

590
00:33:20,880 --> 00:33:25,120
Speaker 1: require verbal commands, that's really tricky. What if you have

591
00:33:25,160 --> 00:33:30,360
Speaker 1: the television on and someone's saying something on TV and uh,

592
00:33:30,640 --> 00:33:33,040
Speaker 1: you are trying to get your robot to do something

593
00:33:33,120 --> 00:33:34,880
Speaker 1: and it's not quite sure what to do because it's

594
00:33:34,880 --> 00:33:38,560
Speaker 1: hearing these different commands and isn't sure who to obey. Yeah,

595
00:33:38,640 --> 00:33:42,200
Speaker 1: you are the person pitching the bacon bowl right right exactly.

596
00:33:42,360 --> 00:33:44,040
Speaker 1: Let's say that you are, you know, trying to get

597
00:33:44,040 --> 00:33:47,040
Speaker 1: some help in the kitchen, but it just keeps hearing U,

598
00:33:47,240 --> 00:33:49,520
Speaker 1: C S I Miami and say kill Billy. And then

599
00:33:49,600 --> 00:33:51,960
Speaker 1: next thing you know, you're like, you're just desperately trying

600
00:33:51,960 --> 00:33:54,960
Speaker 1: to kill the robot. Please don't kill Billy. Um. Yeah,

601
00:33:56,040 --> 00:33:59,120
Speaker 1: well that's a silly example. It's a real problem. Uh.

602
00:33:59,160 --> 00:34:02,960
Speaker 1: And then there's the tactile the responses, the tactile sensors,

603
00:34:03,000 --> 00:34:05,800
Speaker 1: like the making sure you don't crush something that's delicate,

604
00:34:05,840 --> 00:34:10,200
Speaker 1: that that falls into perception. Smell can also fall into perception.

605
00:34:10,719 --> 00:34:13,279
Speaker 1: You might want to have humanoid robots that work in

606
00:34:13,360 --> 00:34:17,480
Speaker 1: areas where the humanoid robot can alert humans to the

607
00:34:17,480 --> 00:34:20,160
Speaker 1: presence of things that might be toxic. You know, this

608
00:34:20,239 --> 00:34:23,840
Speaker 1: isn't necessarily just the robot butler we're talking about. This

609
00:34:23,920 --> 00:34:27,319
Speaker 1: could be robots that work in areas that might be

610
00:34:27,719 --> 00:34:31,239
Speaker 1: dangerous for humans, and that would be an important element too.

611
00:34:31,719 --> 00:34:34,080
Speaker 1: It's not a robot. But NASA already has a sensor

612
00:34:34,320 --> 00:34:38,040
Speaker 1: that senses things like ammonia or smoke. It can actually

613
00:34:38,280 --> 00:34:45,799
Speaker 1: sense smoke artificially smell smoke before the fires actually started ignited. Interesting, yeah,

614
00:34:45,880 --> 00:34:48,839
Speaker 1: because you know it's such a dangerous proposition, right, yes,

615
00:34:48,920 --> 00:34:52,759
Speaker 1: clearly for for anything NASA related. But they can also

616
00:34:52,840 --> 00:34:55,160
Speaker 1: sense ammonia because you know a lot of the refrigeration

617
00:34:55,200 --> 00:34:59,000
Speaker 1: systems run on ammonia and you can't have aneumonia leak

618
00:34:59,239 --> 00:35:02,680
Speaker 1: on the space right right, And I mean the same

619
00:35:02,680 --> 00:35:04,839
Speaker 1: thing is true for I mean I've heard of of

620
00:35:05,520 --> 00:35:09,880
Speaker 1: robots that are used in in mining operations, which you know,

621
00:35:09,920 --> 00:35:12,080
Speaker 1: if you come upon a pocket of natural gas that

622
00:35:12,120 --> 00:35:14,640
Speaker 1: can be a real danger that sort of stuff. Then

623
00:35:14,680 --> 00:35:18,440
Speaker 1: you've got the the back end of the sensory perception.

624
00:35:18,680 --> 00:35:22,360
Speaker 1: That's where you have the actual interpretation of the data.

625
00:35:22,840 --> 00:35:25,120
Speaker 1: Where that's a big one. It's huge because not only

626
00:35:25,120 --> 00:35:27,200
Speaker 1: do you need to have a robot that can have

627
00:35:27,280 --> 00:35:31,720
Speaker 1: that has binocular vision so it has a depth of field, right, um,

628
00:35:31,760 --> 00:35:35,480
Speaker 1: it also has to know what it's doing, what what

629
00:35:35,520 --> 00:35:40,080
Speaker 1: the information means, and how to apply it to adapted changes,

630
00:35:40,360 --> 00:35:43,680
Speaker 1: right right. So So if I were to show you

631
00:35:43,840 --> 00:35:46,960
Speaker 1: josh a series of pictures of various types of dogs.

632
00:35:47,520 --> 00:35:50,400
Speaker 1: You would very quickly pick up on the the things

633
00:35:50,480 --> 00:35:53,880
Speaker 1: that mean mean dog like, you would understand the concept

634
00:35:53,920 --> 00:35:59,120
Speaker 1: of dog pretty quickly. Robots and various other computers machines,

635
00:35:59,600 --> 00:36:02,400
Speaker 1: they have a lot harder problem with this. If whatever

636
00:36:02,480 --> 00:36:06,239
Speaker 1: they're looking at doesn't exactly match the parameters of the example.

637
00:36:06,880 --> 00:36:10,520
Speaker 1: It's very difficult for a machine to extrapolate and say, oh,

638
00:36:10,640 --> 00:36:13,359
Speaker 1: this other thing I'm looking at relates to this thing

639
00:36:13,440 --> 00:36:17,719
Speaker 1: I know, even though the two examples don't aren't identical.

640
00:36:18,360 --> 00:36:20,400
Speaker 1: So the same thing could be true for any object.

641
00:36:20,520 --> 00:36:23,560
Speaker 1: Let's let's just use a coffee mug, and let's say

642
00:36:23,640 --> 00:36:27,560
Speaker 1: that you use a plain white coffee mug of average

643
00:36:27,640 --> 00:36:32,200
Speaker 1: size as the example for the robot, and then the

644
00:36:32,280 --> 00:36:36,440
Speaker 1: robot encounters a larger blue coffee mug and the handle

645
00:36:36,480 --> 00:36:39,200
Speaker 1: has turned the other way. The robot might be completely

646
00:36:39,200 --> 00:36:42,600
Speaker 1: befuddled by this. So this is a real problem in

647
00:36:42,760 --> 00:36:47,960
Speaker 1: artificial intelligence. Is object identification, so that a robot knows

648
00:36:48,000 --> 00:36:51,480
Speaker 1: what it's looking at and also understands the context that

649
00:36:51,480 --> 00:36:56,239
Speaker 1: that object fills within the environment. So it's not just that, oh,

650
00:36:56,280 --> 00:36:59,280
Speaker 1: that's a mug, it's oh, that's a mug. A mug

651
00:36:59,320 --> 00:37:01,640
Speaker 1: is a container. I can put things into that mug.

652
00:37:01,800 --> 00:37:03,279
Speaker 1: You are the things that can go in the mug.

653
00:37:03,320 --> 00:37:05,319
Speaker 1: Here are the things that absolutely should not go in

654
00:37:05,320 --> 00:37:07,840
Speaker 1: the mug, like Billy. Those are the kind of things

655
00:37:07,880 --> 00:37:11,160
Speaker 1: that yeah, Billy, well you know we're gonna need another Billy. Um,

656
00:37:11,640 --> 00:37:14,239
Speaker 1: we'll make a robot Billy. Yeah, which in which case

657
00:37:14,280 --> 00:37:16,560
Speaker 1: you can just turn those suckers out right, mass production

658
00:37:16,600 --> 00:37:20,520
Speaker 1: of Billy. But yeah, artificial intelligence an enormous problem. And that,

659
00:37:20,600 --> 00:37:22,840
Speaker 1: of course is not just with robotics. That's that's a

660
00:37:22,920 --> 00:37:27,200
Speaker 1: field unto itself, and robotics is just one branch that

661
00:37:27,600 --> 00:37:31,920
Speaker 1: relies upon artificial intelligence. And it's from what I came across,

662
00:37:31,960 --> 00:37:34,120
Speaker 1: it looked like just out of the gate, I guess

663
00:37:34,120 --> 00:37:37,440
Speaker 1: that was said a university, they tried to build a

664
00:37:37,560 --> 00:37:41,600
Speaker 1: robot that was just like high functioning, Yeah, and they realized, like,

665
00:37:41,800 --> 00:37:44,960
Speaker 1: we have no idea what we're doing. Yeah, that that

666
00:37:45,000 --> 00:37:48,840
Speaker 1: waybot one was able to converse at the level or

667
00:37:49,000 --> 00:37:53,520
Speaker 1: was able to have a a a cognitive function equivalent

668
00:37:53,560 --> 00:37:56,320
Speaker 1: to a one and a half year old person, which

669
00:37:56,560 --> 00:37:59,160
Speaker 1: I have to say, when you're talking right out of

670
00:37:59,160 --> 00:38:02,879
Speaker 1: the gate, Yeah, impressive, very impressive, because we're not that

671
00:38:02,960 --> 00:38:06,840
Speaker 1: much further along now. But what they found from making

672
00:38:06,840 --> 00:38:10,960
Speaker 1: waybot one was, Okay, this is way more difficult than

673
00:38:10,960 --> 00:38:15,360
Speaker 1: we thought. You can't just program every kind of coffee

674
00:38:15,360 --> 00:38:18,480
Speaker 1: cup in the world, and even if you could, then

675
00:38:18,520 --> 00:38:21,080
Speaker 1: you also have to program every kind of table and

676
00:38:21,080 --> 00:38:23,680
Speaker 1: every kind of light. And we need to come at

677
00:38:23,680 --> 00:38:27,319
Speaker 1: this in a different way. And so they realized number one,

678
00:38:27,560 --> 00:38:32,480
Speaker 1: humans are extraordinarily more complex than we thought before. And

679
00:38:32,520 --> 00:38:35,640
Speaker 1: then number two, humans make a pretty good model for

680
00:38:35,719 --> 00:38:39,480
Speaker 1: a humanoid robot in the realm of things like perception

681
00:38:40,000 --> 00:38:45,520
Speaker 1: and UM information systems and uh learning. So they went

682
00:38:45,640 --> 00:38:52,759
Speaker 1: to these different these different disciplines like neurobiology, neurology, um psychology,

683
00:38:52,800 --> 00:38:55,279
Speaker 1: and they said, what can we learn from you guys

684
00:38:55,320 --> 00:38:58,440
Speaker 1: about how humans do this that we can apply to robots.

685
00:38:58,600 --> 00:39:03,759
Speaker 1: And since they started taking those steps, it seems like, uh,

686
00:39:04,160 --> 00:39:07,920
Speaker 1: humanoid robotics has gotten it's it's footing a little more. Yeah,

687
00:39:08,000 --> 00:39:10,719
Speaker 1: and we're seeing so many developments in other areas of

688
00:39:10,760 --> 00:39:14,120
Speaker 1: AI that are really promising. I always bring up IBM

689
00:39:14,160 --> 00:39:18,880
Speaker 1: S Watson because it's natural language recognition was phenomenal. The

690
00:39:18,920 --> 00:39:23,200
Speaker 1: ability for it to parse clues in jeopardy and come

691
00:39:23,280 --> 00:39:26,600
Speaker 1: up with the appropriate answer, knowing that Jeopardy those clues

692
00:39:26,640 --> 00:39:30,200
Speaker 1: are not always straightforward. Uh. And it again illustrates the

693
00:39:30,239 --> 00:39:34,759
Speaker 1: complexity that we humans navigate without much trouble because this

694
00:39:34,840 --> 00:39:38,080
Speaker 1: is the world we've created. But then we realize if

695
00:39:38,120 --> 00:39:40,480
Speaker 1: we make a machine that's mostly when you get down

696
00:39:40,480 --> 00:39:43,480
Speaker 1: to it based on yes or no, a one or

697
00:39:43,520 --> 00:39:46,360
Speaker 1: a zero, true or false, and you're trying to build

698
00:39:46,400 --> 00:39:49,719
Speaker 1: complex behaviors off of something that is incredibly simple. When

699
00:39:49,719 --> 00:39:52,719
Speaker 1: you boil it down to its basic element, that's where

700
00:39:52,760 --> 00:39:54,800
Speaker 1: you're like, oh, this is this is gonna require a

701
00:39:54,840 --> 00:39:57,680
Speaker 1: lot of work. I mean, IBMS Watson was an enormous

702
00:39:57,719 --> 00:40:02,799
Speaker 1: machine with with thoul of microprocessors just so it could

703
00:40:02,800 --> 00:40:07,080
Speaker 1: be able to play Jeopardy. That's a very specific function too.

704
00:40:07,520 --> 00:40:18,440
Speaker 1: We'll be right back after this next break. So, creating

705
00:40:18,480 --> 00:40:22,279
Speaker 1: a robot that is able to navigate and interact with

706
00:40:22,320 --> 00:40:24,800
Speaker 1: a human environment and be able to interact with humans

707
00:40:24,840 --> 00:40:27,600
Speaker 1: in a way that makes sense is a big challenge. Also,

708
00:40:27,680 --> 00:40:31,120
Speaker 1: just the way that a robot would socialize with humans

709
00:40:31,280 --> 00:40:33,480
Speaker 1: is a huge challenge. How do how do you make

710
00:40:33,480 --> 00:40:38,000
Speaker 1: a robot that is able to respond to commands and

711
00:40:38,200 --> 00:40:41,400
Speaker 1: cues in an appropriate way? Uh? An appropriate way is

712
00:40:41,440 --> 00:40:44,360
Speaker 1: the key there, because there are humans are pretty complex

713
00:40:44,360 --> 00:40:46,839
Speaker 1: and we can be very subtle in many ways. Yeah,

714
00:40:47,000 --> 00:40:52,399
Speaker 1: we speak unplainly, we use sarcasm, um, we we uh yeah,

715
00:40:52,480 --> 00:40:57,080
Speaker 1: we use a lot of gestures rather than just words. Yep.

716
00:40:57,280 --> 00:40:59,759
Speaker 1: There's a lot that goes into human communication that, if

717
00:40:59,800 --> 00:41:03,080
Speaker 1: you are a human, is pretty much natural, especially I

718
00:41:03,080 --> 00:41:05,680
Speaker 1: mean if you're a human within that particular culture and

719
00:41:05,719 --> 00:41:08,600
Speaker 1: you're familiar with that culture, Because anyone who has traveled

720
00:41:08,600 --> 00:41:11,839
Speaker 1: extensively knows there are cultures where things that would be

721
00:41:12,000 --> 00:41:15,600
Speaker 1: commonplace at home are very different in the place where

722
00:41:15,640 --> 00:41:17,640
Speaker 1: you happen to be, right then, and it may be

723
00:41:17,800 --> 00:41:22,240
Speaker 1: that something that is completely innocent at home is an uh,

724
00:41:22,280 --> 00:41:25,200
Speaker 1: offensive gesture in the place where you are. Now, we'll

725
00:41:25,239 --> 00:41:29,520
Speaker 1: imagine a robot that is not programmed to handle these

726
00:41:29,560 --> 00:41:38,120
Speaker 1: kind of subtle uh communication methods, and yeah, he doesn't know,

727
00:41:38,480 --> 00:41:41,560
Speaker 1: he's just he's just doing as he was programmed. But

728
00:41:41,680 --> 00:41:44,319
Speaker 1: even even beyond that, something that you brought up in

729
00:41:44,360 --> 00:41:48,120
Speaker 1: our research when we were planning this was the Uncanny Valley. Yeah,

730
00:41:48,160 --> 00:41:50,680
Speaker 1: that's it's a big one. Yeah, I've never read that

731
00:41:50,680 --> 00:41:54,000
Speaker 1: paper before, and I'm glad I did. It's really interesting, right, Yeah.

732
00:41:54,040 --> 00:41:56,240
Speaker 1: So the uncanny Valley, for those who are not familiar

733
00:41:56,239 --> 00:41:59,359
Speaker 1: with the term, uh it's it describes when we start

734
00:41:59,440 --> 00:42:04,680
Speaker 1: to appre coach artificial humans that look almost, but not

735
00:42:04,800 --> 00:42:07,759
Speaker 1: quite like real humans. And and by look, I don't

736
00:42:07,800 --> 00:42:11,160
Speaker 1: necessarily just mean the physical appearance. I also mean their behaviors,

737
00:42:11,160 --> 00:42:14,239
Speaker 1: their movements. So if you are if you were to

738
00:42:14,360 --> 00:42:18,279
Speaker 1: look out and see a figure that from the from

739
00:42:18,280 --> 00:42:20,560
Speaker 1: a distance looked like it was a human figure, and

740
00:42:20,600 --> 00:42:22,880
Speaker 1: you start walking towards it just thinking this is another person,

741
00:42:23,040 --> 00:42:25,400
Speaker 1: and then they start moving in a very herky jerky motion,

742
00:42:25,719 --> 00:42:30,200
Speaker 1: very mechanical motion, then you're likely going to have a

743
00:42:30,280 --> 00:42:34,799
Speaker 1: negative emotional response. Um often revulsion is one of the

744
00:42:34,800 --> 00:42:39,560
Speaker 1: words used us. Yeah, I mean I remember the C

745
00:42:39,719 --> 00:42:43,920
Speaker 1: G I movies that would that were almost to the

746
00:42:44,000 --> 00:42:47,840
Speaker 1: point of photo realism, where they look like people for

747
00:42:47,880 --> 00:42:50,399
Speaker 1: the most part, but there's not something's just not quite

748
00:42:50,440 --> 00:42:56,800
Speaker 1: right with the eyes. There's a good example, and Polar

749
00:42:56,800 --> 00:43:00,120
Speaker 1: express polar expresses the way everyone very well because the

750
00:43:00,239 --> 00:43:02,359
Speaker 1: uncanny Valley, that's what they blame it on. Yeah, and

751
00:43:02,440 --> 00:43:05,120
Speaker 1: the same thing applies to robots. So in fact, I

752
00:43:05,160 --> 00:43:08,840
Speaker 1: saw a robot that was really disturbing to me, it

753
00:43:08,880 --> 00:43:13,719
Speaker 1: was really an art exhibit, an installation, and um it

754
00:43:13,800 --> 00:43:18,239
Speaker 1: was a robot of Confucius in a cell filled with monkeys,

755
00:43:19,080 --> 00:43:22,720
Speaker 1: and uh they're live monkeys, real monkeys, and the robot

756
00:43:22,800 --> 00:43:28,239
Speaker 1: would just thrash around wildly. It was it was the

757
00:43:28,239 --> 00:43:30,920
Speaker 1: stuff of nightmares that I'll show it to you after

758
00:43:30,960 --> 00:43:35,000
Speaker 1: the show. Yeah. So these are all big challenges, and

759
00:43:35,040 --> 00:43:37,440
Speaker 1: some of them are going to be harder for us

760
00:43:37,440 --> 00:43:39,920
Speaker 1: than others. It may be that the engineering challenges of

761
00:43:40,000 --> 00:43:43,879
Speaker 1: locomotion are solved well before we ever get a real

762
00:43:43,920 --> 00:43:46,520
Speaker 1: grip on all the artificial intelligence problems. Or it could

763
00:43:46,520 --> 00:43:50,120
Speaker 1: be the other way around. Uh we but it is multidisciplinary.

764
00:43:50,320 --> 00:43:53,720
Speaker 1: It's a big, big issue. So there are some people

765
00:43:53,719 --> 00:43:57,400
Speaker 1: who argue for humanoid robots, there are people who argue

766
00:43:57,440 --> 00:44:02,200
Speaker 1: against humanoid robots. Where do you, um, I think. I

767
00:44:02,200 --> 00:44:06,240
Speaker 1: think research and development with humanoid robots is important because

768
00:44:06,280 --> 00:44:09,680
Speaker 1: by having the goal of creating a humanoid robot, you

769
00:44:09,800 --> 00:44:13,320
Speaker 1: drive the research and development process. You have a specific

770
00:44:13,320 --> 00:44:15,640
Speaker 1: goal in mind, and in order to achieve that goal,

771
00:44:15,880 --> 00:44:18,200
Speaker 1: you know what sort of problems you have to solve.

772
00:44:18,760 --> 00:44:21,880
Speaker 1: And even if we never enter a future where humanoid

773
00:44:21,960 --> 00:44:27,080
Speaker 1: robots are a common thing, even if they are mostly

774
00:44:27,200 --> 00:44:31,920
Speaker 1: used as something in an exhibition or uh for pr

775
00:44:32,040 --> 00:44:34,439
Speaker 1: or whatever. Even if that's the only use for them,

776
00:44:34,920 --> 00:44:38,359
Speaker 1: We're going to benefit from the research and development of

777
00:44:38,440 --> 00:44:42,399
Speaker 1: making that possible in ways we can't anticipate. Well. Yeah,

778
00:44:42,400 --> 00:44:45,560
Speaker 1: and then the more we get into humanoid robotics, the

779
00:44:45,600 --> 00:44:50,000
Speaker 1: more we understand humans, which is pretty much the only

780
00:44:50,360 --> 00:44:54,120
Speaker 1: argument I've seen that stands up in favor of doing

781
00:44:54,200 --> 00:44:57,640
Speaker 1: humanoid robots. Yeah, because it's it's expensive, and it's hard.

782
00:44:57,960 --> 00:45:00,719
Speaker 1: I mean, it's it's really a different cult problem, and

783
00:45:00,760 --> 00:45:03,239
Speaker 1: it's and and to build a humanoid robot something that

784
00:45:03,360 --> 00:45:07,960
Speaker 1: is capable of being a general purpose robot, it's you know,

785
00:45:08,480 --> 00:45:10,680
Speaker 1: it's hard to anticipate all the things you're going to

786
00:45:10,719 --> 00:45:12,960
Speaker 1: need to be able to do. If you're talking general

787
00:45:13,040 --> 00:45:16,279
Speaker 1: purpose and adaptable, that's really tough. I mean, we didn't

788
00:45:16,280 --> 00:45:19,239
Speaker 1: even talk about the adaptability problem very much. We talked

789
00:45:19,239 --> 00:45:21,719
Speaker 1: a little bit about a robot capable of learning from

790
00:45:21,719 --> 00:45:25,399
Speaker 1: other people, which I find fascinating. Um, you know, that's

791
00:45:25,440 --> 00:45:29,640
Speaker 1: one way, just watching humans and then mimicking humans, that's

792
00:45:29,680 --> 00:45:34,759
Speaker 1: one way of robot learning. There's also, um the way

793
00:45:34,760 --> 00:45:38,319
Speaker 1: where it's controlled through virtual reality by a human and

794
00:45:38,360 --> 00:45:41,040
Speaker 1: it just kind of logs the motions the humans making

795
00:45:41,120 --> 00:45:43,320
Speaker 1: it do. Like there's a NASA has a robot not

796
00:45:43,520 --> 00:45:48,080
Speaker 1: that learns like that. So I think I think there's

797
00:45:48,120 --> 00:45:52,960
Speaker 1: a lot of benefit to investigating artificial intelligence. Like you

798
00:45:53,000 --> 00:45:56,239
Speaker 1: want your nest at home to learn so you don't

799
00:45:56,239 --> 00:45:59,440
Speaker 1: have to keep adjusting the thermostat that counts. That's the

800
00:45:59,640 --> 00:46:05,120
Speaker 1: that's sheen learning to me. The big argument against having

801
00:46:05,400 --> 00:46:10,120
Speaker 1: humanoid robots, and the quagmire that seems to begin, is sociability.

802
00:46:10,719 --> 00:46:13,239
Speaker 1: That seems to be the whole reason anybody wants a

803
00:46:13,320 --> 00:46:15,960
Speaker 1: humanoid robot, because you can make you know, you have

804
00:46:16,000 --> 00:46:20,160
Speaker 1: a ruma vacuums. You can make a driverless car um

805
00:46:20,200 --> 00:46:23,799
Speaker 1: as Olivia Solan wrote and wired a couple of years back. Um,

806
00:46:25,040 --> 00:46:26,520
Speaker 1: you know, why why do you have to make a

807
00:46:26,600 --> 00:46:28,839
Speaker 1: robot butler to park the car? Just make a car

808
00:46:28,920 --> 00:46:32,160
Speaker 1: that parks itself. And it seems like that's where we're

809
00:46:32,200 --> 00:46:35,800
Speaker 1: going right now. Um. So when you add this extra

810
00:46:35,880 --> 00:46:41,080
Speaker 1: layer of humanoid, you add all of this additional problems

811
00:46:41,160 --> 00:46:44,600
Speaker 1: and troubles and redundancies, like like, for example, if you're

812
00:46:44,600 --> 00:46:48,279
Speaker 1: gonna make a humanoid robot that throws a ball, this

813
00:46:48,440 --> 00:46:52,759
Speaker 1: this humanoid robot to appear real needs to have a

814
00:46:52,760 --> 00:46:56,040
Speaker 1: little bit of follow through. But as far as the

815
00:46:56,120 --> 00:46:58,279
Speaker 1: robot the machine is concerned, it can throw the ball

816
00:46:58,360 --> 00:47:01,600
Speaker 1: and just stop where the releases. It doesn't need to

817
00:47:01,600 --> 00:47:04,560
Speaker 1: go anymore. But it's gonna look weird and robotic if

818
00:47:04,560 --> 00:47:06,960
Speaker 1: you want to get past that uncanny valley, which is

819
00:47:07,000 --> 00:47:10,080
Speaker 1: another problem. Um, the thing has to have followed through.

820
00:47:10,400 --> 00:47:13,799
Speaker 1: That's totally unnecessary. You can make a robot that can

821
00:47:13,840 --> 00:47:16,800
Speaker 1: throw a ball and the goal is to throw the ball.

822
00:47:17,520 --> 00:47:19,719
Speaker 1: You don't have to add the follow through, but you

823
00:47:19,840 --> 00:47:23,840
Speaker 1: do when you're making it a sociable humanoid robots. So

824
00:47:23,880 --> 00:47:27,280
Speaker 1: it seems like that's the path that will lead everyone

825
00:47:27,320 --> 00:47:30,160
Speaker 1: is straight that I don't get. Yeah, I I like

826
00:47:30,320 --> 00:47:34,359
Speaker 1: the idea of designing robots for specific tasks because you

827
00:47:34,400 --> 00:47:37,239
Speaker 1: can really focus on getting the task done. So there

828
00:47:37,320 --> 00:47:39,960
Speaker 1: I see this as two separate branches. I see the

829
00:47:40,000 --> 00:47:43,799
Speaker 1: branch of developing the humanoid robot as pushing forward a

830
00:47:43,880 --> 00:47:47,320
Speaker 1: lot of different areas of thought that could be applied

831
00:47:47,360 --> 00:47:51,319
Speaker 1: in multiple disciplines, so that will benefit from that. I

832
00:47:51,400 --> 00:47:55,600
Speaker 1: see the development of robots as unitaskers as being important

833
00:47:55,600 --> 00:47:58,799
Speaker 1: to actually handle the jobs that are the three D S.

834
00:47:58,920 --> 00:48:02,319
Speaker 1: That's dirty, different called dangerous, all right, So those are

835
00:48:03,760 --> 00:48:07,120
Speaker 1: the jobs that maybe they revolve a lot of repetition,

836
00:48:07,560 --> 00:48:10,799
Speaker 1: which can cause injury over time, or it can lead

837
00:48:10,840 --> 00:48:13,759
Speaker 1: to mistakes because you've done the same task so many

838
00:48:13,760 --> 00:48:16,240
Speaker 1: times that you start to kind of zone out. Robots

839
00:48:16,280 --> 00:48:19,800
Speaker 1: will never zone out. Um, if it's a dirty job

840
00:48:19,880 --> 00:48:23,120
Speaker 1: where it's something that's undesirable by people, robots don't care,

841
00:48:23,360 --> 00:48:27,000
Speaker 1: they'll do that. Or if it's dangerous, if it's bomb disposal,

842
00:48:27,280 --> 00:48:31,399
Speaker 1: or if it's something like the Mars Curiosity rover. These

843
00:48:31,400 --> 00:48:34,759
Speaker 1: are dangerous jobs that you wouldn't necessarily want to put

844
00:48:34,760 --> 00:48:39,120
Speaker 1: a human into if you had the alternative. So all

845
00:48:39,160 --> 00:48:42,120
Speaker 1: of these things, those that's where robots really makes sense

846
00:48:42,120 --> 00:48:44,600
Speaker 1: to me um to to go to the places that

847
00:48:44,680 --> 00:48:46,880
Speaker 1: are difficult for us to go to. Maybe that's you know,

848
00:48:47,160 --> 00:48:51,080
Speaker 1: deep sea exploration, space exploration, that kind of thing, or

849
00:48:51,280 --> 00:48:53,840
Speaker 1: to do jobs that might be dangerous having a first

850
00:48:53,880 --> 00:48:56,799
Speaker 1: respond to robot to survey a scene to make sure

851
00:48:56,880 --> 00:49:01,000
Speaker 1: that a structure is remaining solid while maybe there was

852
00:49:01,040 --> 00:49:02,920
Speaker 1: a fire and it has to make sure that the

853
00:49:03,880 --> 00:49:08,080
Speaker 1: it's not going to collapse in on rescue mission that

854
00:49:08,160 --> 00:49:10,800
Speaker 1: kind of stuff. Um, But do you need those things

855
00:49:10,840 --> 00:49:13,000
Speaker 1: to be to come out and be able to tell

856
00:49:13,040 --> 00:49:16,640
Speaker 1: a joke or something. And most of them don't need

857
00:49:16,719 --> 00:49:20,239
Speaker 1: to be any sort of humanoid form factor either, which

858
00:49:20,880 --> 00:49:24,480
Speaker 1: greatly simplifies the actual development of the robot and thus

859
00:49:24,520 --> 00:49:26,920
Speaker 1: cuts down on the cost, so you can you can

860
00:49:26,920 --> 00:49:30,439
Speaker 1: achieve the task you're trying to achieve for less money

861
00:49:30,520 --> 00:49:32,440
Speaker 1: than if you are trying to build this this general

862
00:49:32,520 --> 00:49:36,480
Speaker 1: purpose machine. So then we come to this ultimate question,

863
00:49:37,440 --> 00:49:43,000
Speaker 1: why what's the purpose of humanoid robot? Well, I think

864
00:49:43,000 --> 00:49:46,080
Speaker 1: the I think the purposes too fold. One is again

865
00:49:46,200 --> 00:49:51,200
Speaker 1: to have that specific goal in mind that allows you

866
00:49:51,239 --> 00:49:56,359
Speaker 1: to define where your indpoint is. I believe that when

867
00:49:56,360 --> 00:49:59,600
Speaker 1: you have that defined in goal, it makes it easier

868
00:49:59,640 --> 00:50:01,520
Speaker 1: for you to build on the things you need to

869
00:50:01,560 --> 00:50:04,200
Speaker 1: achieve it, as opposed to having an open goal where

870
00:50:04,200 --> 00:50:07,680
Speaker 1: it's just I want to approve improve a I that's

871
00:50:07,719 --> 00:50:09,920
Speaker 1: so open that it's hard to get direction from it.

872
00:50:09,960 --> 00:50:12,600
Speaker 1: But if you think I need to have an artificial

873
00:50:12,640 --> 00:50:16,400
Speaker 1: intelligence that will allow a robot to uh, here's a

874
00:50:16,400 --> 00:50:18,680
Speaker 1: great example. Let's say that the challenge is to have

875
00:50:18,719 --> 00:50:23,000
Speaker 1: a robot leave a room, go down a flight of stairs,

876
00:50:23,560 --> 00:50:27,040
Speaker 1: leave a building, get into a vehicle, drive the vehicle

877
00:50:27,080 --> 00:50:30,440
Speaker 1: to a different location, get out of the vehicle, go

878
00:50:30,480 --> 00:50:33,919
Speaker 1: into another building, break through a wall and put out

879
00:50:33,920 --> 00:50:39,280
Speaker 1: a fire. That's a real, actual robotics challenge challenge. Yeah.

880
00:50:39,360 --> 00:50:42,120
Speaker 1: Dr Henrik Kristensen told me about this. Is I mean,

881
00:50:42,160 --> 00:50:44,600
Speaker 1: it really is a challenge. It's not just a real challenge.

882
00:50:44,640 --> 00:50:48,839
Speaker 1: It's a real challenge. It's like a DARPA challenge. So

883
00:50:49,000 --> 00:50:51,600
Speaker 1: it's a uh he was telling me about this, and

884
00:50:51,680 --> 00:50:53,560
Speaker 1: you start to think about all the things that have

885
00:50:53,640 --> 00:50:54,920
Speaker 1: to fall in line for you to be able to

886
00:50:54,920 --> 00:50:57,200
Speaker 1: achieve the skull That is a valuable thing. But I

887
00:50:57,239 --> 00:50:59,319
Speaker 1: think the other thing is the social aspect. I think

888
00:50:59,360 --> 00:51:02,720
Speaker 1: that there are people who would benefit from a robot

889
00:51:02,840 --> 00:51:06,560
Speaker 1: that is able to give some form of social comfort.

890
00:51:06,640 --> 00:51:11,080
Speaker 1: Let's say for the elderly who need to have some

891
00:51:11,120 --> 00:51:14,960
Speaker 1: form of interaction. Um. You know, that could actually be

892
00:51:15,080 --> 00:51:17,520
Speaker 1: a really valuable tool. And in fact, there's a lot

893
00:51:17,520 --> 00:51:20,919
Speaker 1: of work that's going into robotics to help people like

894
00:51:21,040 --> 00:51:27,760
Speaker 1: the elderly who may have real emotional and psychological problems, um,

895
00:51:27,840 --> 00:51:31,000
Speaker 1: due to loneliness. Do you think that robots are the

896
00:51:31,000 --> 00:51:34,520
Speaker 1: answer to that. I think that robots can help. I

897
00:51:34,560 --> 00:51:36,480
Speaker 1: don't know, I would never go so far as to

898
00:51:36,520 --> 00:51:39,759
Speaker 1: say answer, but couldn't you also make the argument that

899
00:51:40,280 --> 00:51:46,720
Speaker 1: if you created robots that displaced human jobs and also

900
00:51:46,800 --> 00:51:53,640
Speaker 1: simultaneously said, hey, this, this nursing home sector is about

901
00:51:53,680 --> 00:51:56,120
Speaker 1: to explode because we've got a bunch of baby boomers,

902
00:51:56,120 --> 00:51:59,920
Speaker 1: and ways the society of now decided that our elderly

903
00:52:00,040 --> 00:52:05,600
Speaker 1: need human interaction more than we've more than we've carried

904
00:52:05,600 --> 00:52:08,920
Speaker 1: it out before. So let's create this whole other industry.

905
00:52:09,000 --> 00:52:13,400
Speaker 1: Or let's expand this industry of elderly caretakers and fill

906
00:52:13,760 --> 00:52:17,800
Speaker 1: those jobs with people who have been displaced by worker robots.

907
00:52:18,239 --> 00:52:22,720
Speaker 1: Wouldn't that be better? That might be, or you could again,

908
00:52:22,960 --> 00:52:25,640
Speaker 1: looking at the way a lot of roboticists framed this,

909
00:52:26,320 --> 00:52:28,920
Speaker 1: they say, all right, well, it is a reality that

910
00:52:29,080 --> 00:52:34,600
Speaker 1: robots are taking over actual jobs. But the hope is

911
00:52:34,640 --> 00:52:38,239
Speaker 1: that it also ends up creating new jobs that are

912
00:52:38,280 --> 00:52:43,640
Speaker 1: better paying jobs, less dangerous jobs friendly, more old books friendly.

913
00:52:44,480 --> 00:52:47,680
Speaker 1: But yeah, like um, the idea being that that it

914
00:52:47,800 --> 00:52:52,080
Speaker 1: frees up people and encourages the pursuit of jobs and

915
00:52:52,200 --> 00:52:57,560
Speaker 1: engineering in computer science. Now, we live in the real world,

916
00:52:57,640 --> 00:53:00,759
Speaker 1: and we understand that it's a lot more complex than

917
00:53:00,840 --> 00:53:03,720
Speaker 1: telling someone who's been working on a manufacturing line. Hey,

918
00:53:03,960 --> 00:53:06,239
Speaker 1: I'm sorry your job's gone because there's a robot here.

919
00:53:06,239 --> 00:53:08,799
Speaker 1: But guess what, we have an opening and engineering. So

920
00:53:08,840 --> 00:53:11,560
Speaker 1: if you just go and pursue a four year degree

921
00:53:11,600 --> 00:53:14,200
Speaker 1: and then some post graduate work, you'll be right back

922
00:53:14,239 --> 00:53:17,799
Speaker 1: to work. That's that's obviously not uh something that's going

923
00:53:17,840 --> 00:53:21,120
Speaker 1: to be easy, especially in the short term. But the

924
00:53:21,200 --> 00:53:24,000
Speaker 1: long term hope is that more and more of these

925
00:53:24,080 --> 00:53:27,719
Speaker 1: jobs that are are dangerous for people, are less desirable

926
00:53:27,760 --> 00:53:31,480
Speaker 1: for people, will be taken up by robots, and then

927
00:53:31,680 --> 00:53:35,280
Speaker 1: the will there will be the creation of better jobs

928
00:53:35,320 --> 00:53:37,000
Speaker 1: that are higher up on the food chain. And I

929
00:53:37,040 --> 00:53:39,759
Speaker 1: think that makes sense to me. It's just the once

930
00:53:39,840 --> 00:53:44,120
Speaker 1: you enter the sociability, yeah, because without sociability, you there's

931
00:53:44,160 --> 00:53:47,279
Speaker 1: no reason to create a humanoid robot. Everything else can

932
00:53:47,320 --> 00:53:51,239
Speaker 1: look like a robot. Yeah. Um, so it's it's when

933
00:53:51,239 --> 00:53:53,920
Speaker 1: you enter sociability that you lose me. Not only does

934
00:53:53,920 --> 00:53:55,839
Speaker 1: it can it look like a robot, but we can

935
00:53:55,880 --> 00:53:58,239
Speaker 1: still socialize with it even if it doesn't look like

936
00:53:58,280 --> 00:54:01,440
Speaker 1: a human people. There. You know, there's the story Rumba

937
00:54:01,520 --> 00:54:05,000
Speaker 1: owners named their rumba. Yeah, so you we we end

938
00:54:05,080 --> 00:54:09,360
Speaker 1: up having these kind of emotional attachments and investments in

939
00:54:10,000 --> 00:54:12,560
Speaker 1: things that don't look Not only do they not look human,

940
00:54:12,640 --> 00:54:15,680
Speaker 1: they don't look like any other animal that we would

941
00:54:15,680 --> 00:54:20,160
Speaker 1: interact with on a like owner or and pet or whatever.

942
00:54:20,200 --> 00:54:23,440
Speaker 1: I mean, they they're They're just a robot. So I

943
00:54:23,440 --> 00:54:25,160
Speaker 1: think at the end of the day, Josh, I think

944
00:54:25,200 --> 00:54:27,279
Speaker 1: we're on the same page. We think humanoid robots are

945
00:54:27,280 --> 00:54:30,640
Speaker 1: an interesting idea, but not necessarily the end goal. There's

946
00:54:30,719 --> 00:54:34,120
Speaker 1: there's not a whole lot of of incentive to go

947
00:54:34,239 --> 00:54:36,880
Speaker 1: after it for its own purposes. We can see the

948
00:54:36,920 --> 00:54:39,319
Speaker 1: benefits of going after it in the sense of the

949
00:54:39,360 --> 00:54:42,239
Speaker 1: developments that are made in that pursuit help us in

950
00:54:42,280 --> 00:54:45,600
Speaker 1: other ways. And that wraps up this classic episode of

951
00:54:45,640 --> 00:54:47,680
Speaker 1: tech Stuff. I hope you enjoyed it. If you have

952
00:54:47,800 --> 00:54:50,680
Speaker 1: suggestions for topics I should cover in future episodes, please

953
00:54:50,719 --> 00:54:52,880
Speaker 1: reach out to me on Twitter. The handle for the

954
00:54:52,880 --> 00:54:55,960
Speaker 1: show is tech Stuff h SW and I'll tell to

955
00:54:56,000 --> 00:55:04,640
Speaker 1: you again really soon. Text Stuff is an I Heart

956
00:55:04,760 --> 00:55:08,479
Speaker 1: Radio production. For more podcasts from I Heart Radio, visit

957
00:55:08,520 --> 00:55:11,600
Speaker 1: the I Heart Radio app, Apple Podcasts, or wherever you

958
00:55:11,680 --> 00:55:13,000
Speaker 1: listen to your favorite shows.