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Speaker 1: Brought to you by the reinvented two thousand twelve camera.

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Speaker 1: It's ready, Are you didn't touch? With technology? With tech

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Speaker 1: stuff from how stuff flix dot com. Hello, agaan everyone,

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Speaker 1: and welcome to text stuff. My name is Chris Paulette

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Speaker 1: and I'm an editor at how Stuff works dot com.

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Speaker 1: Sitting across from me, as is typical of days like this, Yeah,

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Speaker 1: Friday's That would be senior writer Jonathan Strickland. I looked

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Speaker 1: at my notes and I didn't like them. Hey was

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Speaker 1: that a quote? What were you actually saying that? It's

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Speaker 1: it's both both ay quote and it's true excellent that

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Speaker 1: those are always the best, right, Yes they are. So

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Speaker 1: today we're going to talk a little bit about robots,

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Speaker 1: which we before we started recording, we were I was

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Speaker 1: talking about robots. He was insisting on saying robots, So

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Speaker 1: I didn't think you would actually do it in the episode.

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Speaker 1: Of course I'm going to do it the sod. My

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Speaker 1: goal is to alienate all of our listeners until no

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Speaker 1: one's left. Uh, and I'm doing quite well, I think.

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Speaker 1: Uh No, so robots if you prefer. We we wanted

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Speaker 1: to talk about this mainly because there was kind of

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Speaker 1: a cool story and and really cool video that came

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Speaker 1: out recently recently as the recording of this podcast. This

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Speaker 1: is a March twenty three if you're if you guys

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Speaker 1: at home or wondering, because you know, of course we

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Speaker 1: published these well after we've recorded them. But the story

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Speaker 1: was about a company called Kiva. Yes, Kiva Systems technically,

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Speaker 1: and Kiva Systems makes robots. They make a warehouse robot

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Speaker 1: system that's all about finding and retrieving inventory using potentially

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Speaker 1: more than the thousand robots if you were to have

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Speaker 1: a really complex system. Now, most of the systems they've

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Speaker 1: they've incorporated have been smaller than that, but the idea

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Speaker 1: is really cool. And the reason they're in the news

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Speaker 1: right now is because, I mean, the company has been

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Speaker 1: around since two thousand three, in one form or another.

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Speaker 1: It started off as a with a different name, but anyway,

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Speaker 1: it's been around since two thousand three. The reason why

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Speaker 1: it's kind of hit the news recently is because another

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Speaker 1: company has acquired Keyva Systems. Yes, that would be Amazon

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Speaker 1: dot Com. Yes, for the princely sum of seven dred

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Speaker 1: and seventy five million dollars, so just under a billion um.

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Speaker 1: So yeah, this is a big news and you would think, oh, well,

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Speaker 1: that makes perfect sense that Amazon would go after something

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Speaker 1: like this, like they would at least employ a system

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Speaker 1: like this, if not actually go out and acquire it,

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Speaker 1: because I mean Amazon is all about warehouse management in

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Speaker 1: order to fulfill customer needs. So yeah, So the company

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Speaker 1: actually was started in two thousand three, as we said

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Speaker 1: a moment ago, by a guy named Mick mounts Um,

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Speaker 1: and he knows a lot about fulfillment. He actually kind

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Speaker 1: of learned the hard way by being part of a

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Speaker 1: company known as Webvan. So web Ban, for those who

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Speaker 1: don't remember, was an online service where you could actually

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Speaker 1: order groceries online and this company would go out and

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Speaker 1: fill the orders and deliver them to your door, so

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Speaker 1: that way you didn't have to go out to the

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Speaker 1: grocery store. And this this was one of those companies

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Speaker 1: that was was expanding very quickly just before the dot

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Speaker 1: com bubble burst and there was the big crash. So

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Speaker 1: if you've heard O our podcast about the dot com crash,

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Speaker 1: you probably heard us talk about web Ban. Part of

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Speaker 1: the problem they had was that the cost for their

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Speaker 1: system was greater than they had originally um projected, and

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Speaker 1: so they were not able to bring in enough revenue

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Speaker 1: to cover their costs and as a result that the

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Speaker 1: company itself collapsed. Yeah. Mr Mount seems to be one

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Speaker 1: of those people that learns from experience. Well, that seems

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Speaker 1: like a reasonably good thing to do. Yeah, that he

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Speaker 1: I think it kind of bugged him based on what

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Speaker 1: on the information that I've picked up up on on

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Speaker 1: doing the research and and he started thinking, you know what,

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Speaker 1: there's got to be a better way to handle UH

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Speaker 1: storage and fulfillment. So yeah, he has a very technical background.

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Speaker 1: Um you know, and uh, I believe as a matter

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Speaker 1: of fact, he's an m I T graduate, M I

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Speaker 1: T and Harvard Business School graduates, so he had he's

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Speaker 1: probably reasonably well equipped to handle this kind of technical

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Speaker 1: and the business background. He also partnered with two other people, uh,

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Speaker 1: Raphaelio Dondrea. Thanks there, Hoffey. It's hard for me to say.

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Speaker 1: Who was a engineering professor at Cornell and then at

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Speaker 1: e t H which is the Swiss Federal Institute of Technology. Yeah,

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Speaker 1: he had led Cornell to several UH tournament victories in

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Speaker 1: a in robotics competition. It's like the robo soccer games. Yeah. Yeah,

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Speaker 1: he he had actually helped design robots that were able

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Speaker 1: to uh to to accomplish goals in a very efficient way.

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Speaker 1: And so when Mounts was looking for potential folks too

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Speaker 1: to work with, Dendrea became one of the natural people

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Speaker 1: that he should see because of his his extensive experience

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Speaker 1: with the robotics. And then the third founder is Peter Wherman,

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Speaker 1: who is a multi agent systems expert and a former

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Speaker 1: professor of computer science with North Carolina State University. So, uh,

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Speaker 1: and I think they were friends first, they didn't. It

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Speaker 1: wasn't just one of those like personality. Yeah. So they

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Speaker 1: these these three men were combining their knowledge to try

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Speaker 1: and build out a system of robots that could fill

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Speaker 1: this need of being able to seek out inventory within

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Speaker 1: a warehouse, retrieve it and bring it back to someone

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Speaker 1: for packaging. So when you're filling an order for a customer,

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Speaker 1: you're working in a warehouse and you the order comes in.

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Speaker 1: A customer has asked for a particular kind of DDD player,

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Speaker 1: and that one DVD player is located on one shelf

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Speaker 1: in this massive warehouse. It takes a lot of time

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Speaker 1: for a human to track down which shelf it is,

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Speaker 1: walk to that section of the warehouse, retrieve it from

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Speaker 1: their walk back to the shipping department and ship that out.

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Speaker 1: So the idea here is that you would have a

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Speaker 1: robot system where as soon as an order comes in

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Speaker 1: and uh and a person is ready to ship it,

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Speaker 1: that person pushes a button, the robot zooms off exactly

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Speaker 1: to the point where the the item is lifts up

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Speaker 1: that entire shelf. Because the robots, if you were to

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Speaker 1: look at these things, they look kind of like room

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Speaker 1: buzz that are on steroids. You know. They're these sort

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Speaker 1: of sort of squarish robots that have rounded it rounded

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Speaker 1: corners um and they are they're low to the ground

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Speaker 1: and they run on wheels, and so when you look

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Speaker 1: at them, they don't you know, they're not humanoid or

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Speaker 1: anything like that. They what they do is they scoot

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Speaker 1: around on the floor underneath the shelves and then what

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Speaker 1: they have a little screw drive that comes up. It

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Speaker 1: lifts a platform up that lifts the shelves off the floor.

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Speaker 1: And in fact, the robot rotates in an opposite direction

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Speaker 1: of the screw at the same rate, so that the

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Speaker 1: platform remains from the shelf's perspective stationary, and just it

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Speaker 1: increases in height, but it does not rotate. Yeah. Now

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Speaker 1: if you if you think about it, this is this

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Speaker 1: is important because you have a vertical shelf. These are

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Speaker 1: essentially UM a rectangular prism going straight up. So the

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Speaker 1: base of the shelf is square and and is roughly

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Speaker 1: just larger than this robot. Um. But it goes up

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Speaker 1: and it's got stuff on it. Well, you know, if

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Speaker 1: you have things on the higher shelves but not necessarily

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Speaker 1: on the lower shelves, that's going to change the center

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Speaker 1: of gravity considerably. And if um, this this screw drive

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Speaker 1: didn't have that counter rotation, uh, it could cause the

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Speaker 1: shelf to topple or be unsteady. And the thing is UM.

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Speaker 1: When I first started getting interested in in the Kiva

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Speaker 1: story and was watching the videos, I was and that's

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Speaker 1: so cool watching the little bot outside spin like that.

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Speaker 1: It's really cute. UM. And then I realized that it's

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Speaker 1: being done for a very specific reason. And not only

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Speaker 1: is it cool looking, it's functional. Functional. Yeah. So it

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Speaker 1: lifts the shelf directly off the floor, and then it

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Speaker 1: will maneuver back to the person who has pushed the

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Speaker 1: button to fill this order. It then shines a laser

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Speaker 1: pointer essentially onto the particular item that needs to be

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Speaker 1: scanned and shipped, and so then the worker would pick

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Speaker 1: up that item, scan it, put it in a box,

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Speaker 1: and tape it up, and then it's ready to be

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Speaker 1: shipped out to the customer. UH. And then the robot

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Speaker 1: will take the shelf back to where it needs to be.

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Speaker 1: And meanwhile there's a computer system that's keeping track of

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Speaker 1: all of these movements. That's it's not just the movements

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Speaker 1: of the robots, but where the shelves are currently located,

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Speaker 1: what orders have gone out, and sort of an inventory

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Speaker 1: control system. So you've got you've got multiple layers of

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Speaker 1: software here. You've got a certain layer of software that's

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Speaker 1: in the robot, you have a certain layer of software

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Speaker 1: that's in the workers station where they are filling out orders,

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Speaker 1: and then you have the overall system. You have layers

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Speaker 1: in there as well, and there's a lot of overlaps

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Speaker 1: so that robots don't need to know necessarily everything every

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Speaker 1: other robots doing, but the system does. And the reason

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Speaker 1: for that is if you've got hundreds of robots all

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Speaker 1: going through this warehouse filling out orders, then there's a

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Speaker 1: lot of potential for collisions, which would be a bad thing.

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Speaker 1: You don't want like, oh, there's this one robot carrying

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Speaker 1: a shelf full of snow globes, and as other robot

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Speaker 1: carrying a shelf full of DVDs, and as other robot

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Speaker 1: carrying a shelf full of paint guns, and the three

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Speaker 1: of them converge, and then you have a combo that

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Speaker 1: just doesn't work even in a Reese's Peanut Buttercup kind

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Speaker 1: of universe. So it's sort of like a robot in

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Speaker 1: a china shop. Yeah, it really is. So the system

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Speaker 1: has to keep track of where all the robots are,

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Speaker 1: and the robots have various ways of the hecting what

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Speaker 1: other robots are nearby, so that way they can all

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Speaker 1: move through and reach their goals without colliding with one another.

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Speaker 1: And it's actually, if you watch the videos, it's it's

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Speaker 1: captivating to see how these robots will move within fifteen

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Speaker 1: centimeters of one another, get so close to each other,

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Speaker 1: and yet they'll stop in enough time allow one to

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Speaker 1: go through and the next one will pass on, and

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Speaker 1: and you multiply that by a hundred, and it's just

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Speaker 1: phenomenal to see this kind of it's almost like it's

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Speaker 1: almost like a BF ballet. Well, um, One of the

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Speaker 1: things too, that the computer system is doing, um to

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Speaker 1: handle this is it's not just inventory control, it's traffic

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Speaker 1: control as well. But the bots also have a part

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Speaker 1: in this. Now. Um, if you haven't seen this yet

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Speaker 1: and you're going to go check out a video, you

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Speaker 1: might have this picture in your head of a lot

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Speaker 1: of robotic carried shelves running around willy nilly. Well, that's

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Speaker 1: not exactly the way it works. If you look at

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Speaker 1: the videos closely, or even if you don't, you could

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Speaker 1: see their patterns on the floor and you might say,

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Speaker 1: you know, well, you know, they kind of look like tracks,

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Speaker 1: but they're not tracks. Um, not in the sense of

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Speaker 1: a railroad sort of system or tracks that are embedded

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Speaker 1: in the floor. The robots must follow, right, which is

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Speaker 1: which is kind of interesting too, because you know, something

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Speaker 1: like that would dramatically increase the cost of installing a

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Speaker 1: system like this. Instead, Um, it's an optical system. It

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Speaker 1: basically has barcodes, if you will, in the different pathways

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Speaker 1: that are are you know, painted onto the floor or

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Speaker 1: however they are attached. I'm not sure exactly how they

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Speaker 1: are are imprinted on the floor. Um. Anyway, the the

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Speaker 1: robots have scanning system built into the bottoms of them,

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Speaker 1: so they know, uh, the the UM control system can

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Speaker 1: tell them what path to follow, you know, get on

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Speaker 1: track number two because there's another robot coming back this

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Speaker 1: way on track number one, and it tells it how

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Speaker 1: far to go over and exactly where to go. UM.

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Speaker 1: And it's also very cool to watch the human employees

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Speaker 1: engage with this system because UM unlike past inventory systems

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Speaker 1: where a worker would have to walk down rows of

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Speaker 1: shelves to retrieve a particular item or two if something

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Speaker 1: comes back to put the item back where it belongs,

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Speaker 1: the shelves come to them. So they are standing at

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Speaker 1: their workstation UM ready to package things up. And uh,

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Speaker 1: it's it's very actually, I don't know if you've watched

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Speaker 1: any of these videos, these particular videos, not just the

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Speaker 1: traffic stuff. UM. It's actually kind of creepy because the

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Speaker 1: the employee will take off a box and we'll scan

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Speaker 1: it with a little handheld laser scanner and put it

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Speaker 1: down on their station and the shelf will just drive

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Speaker 1: off and the next one will move over to the

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Speaker 1: shelf space at their station and wait for them to

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Speaker 1: scan the item that that belongs. UH. In the in

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Speaker 1: the package. So you know, the person will put in

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Speaker 1: the package and tape it up and put the label

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Speaker 1: on it and send it down that the conveyor to

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Speaker 1: shipping um and then you know, this next box comes out,

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Speaker 1: they'll scan it and the shelf drives off on its

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Speaker 1: own um. And the same thing too for returns. The

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Speaker 1: shelf drives out, they'll put they'll scan an item, put

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Speaker 1: it on the shelf, and the shelf drives off. It's

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Speaker 1: really kind of weird, but the employees that I saw

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Speaker 1: interviewed said it's much less taxing and they can get

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Speaker 1: far more work done because they're retrieving more items or

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Speaker 1: or putting more items back on the shelf, and they

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Speaker 1: don't have to do nearly as much work to achieve

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Speaker 1: that because the shelves themselves are doing the work. Yeah,

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Speaker 1: and currently the robots UM the regular production model can

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Speaker 1: carry up to four which is a thousand pounds. That's

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Speaker 1: a lot of weight. It's a lot of weight, but

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Speaker 1: it's still they're working. They have other prototype models that

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Speaker 1: can carry much more than that because depending on what

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Speaker 1: you're shipping around maybe like for example, a warehouse that

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Speaker 1: has lots of masonry in it, it's going to be

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Speaker 1: really lots of heavy palettes and shelves and stuff. You

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Speaker 1: would have to have a robot capable caring much more

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Speaker 1: than a thousand pounds in that case. These these robots

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Speaker 1: do increase efficiency. I saw some projections that suggested that

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Speaker 1: a factory that uses conveyor belts and and you know,

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Speaker 1: more conventional systems to get materials to the people who

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Speaker 1: need to ship them out, that it might take for

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Speaker 1: a typical warehouse something like seventy five workers to be

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Speaker 1: able to um to to carry out the orders a

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Speaker 1: certain number of orders in a certain amount of time,

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Speaker 1: but in using this system would be more like twenty five.

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Speaker 1: So you've just reduced the need of your workforce significantly

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Speaker 1: and by fifty people, which means savings for the company.

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Speaker 1: This also brings us to another discussion that always comes

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Speaker 1: up when we talk about industrialization and autumns, Asian and

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Speaker 1: robots in particular, which is the concern that these systems

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Speaker 1: will replace human beings and reduce the need for human workers,

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Speaker 1: thus in a way contributing to the unemployment rate, which

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Speaker 1: is which is a legitimate concern. I mean there is

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Speaker 1: it is there is a legitimate concern that there are

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Speaker 1: certain systems involved that will reduce the need for people

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Speaker 1: to work and therefore more people will be out of work. Now,

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Speaker 1: this is a story that has been with us since

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Speaker 1: the dawn of the industrialization age. You've heard Chris talk

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Speaker 1: about the sabo that wooden shoes being thrown into a

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Speaker 1: loom in order to sabotage the loom, because these these

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Speaker 1: looms were somewhat you know, not automated in the sense

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Speaker 1: that we think about today, but they were following a program,

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Speaker 1: and we're reducing the needs of a skilled weaver so

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Speaker 1: that you know, you could have a semi unskilled worker

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Speaker 1: run this machine and they could produced the same sort

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Speaker 1: of quality of work that a skilled weaver could. That

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Speaker 1: raised concerns back in pre industrial Europe. Well that we've

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Speaker 1: seen that whole story unfold throughout the eras of of

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Speaker 1: industry and um there's been some people who say, sure,

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Speaker 1: it's going to take over those those manufacturing jobs, but

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Speaker 1: then they're gonna be more jobs opened up because you're

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Speaker 1: gonna have people who need to make the robots and

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Speaker 1: repair the robots, which is true, but it's not a

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Speaker 1: one to one. First of all, the skill set you

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Speaker 1: need to work on a factory floor, and the skill

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Speaker 1: set you need to maintain or build a robot are

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Speaker 1: dramatically different. Uh. It's not to say that someone can't

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Speaker 1: master both sets of skills, but it's just it's not

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Speaker 1: like it's automatically transferable. And secondly, you don't need as

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Speaker 1: many people building out or maintaining robots as you would

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Speaker 1: to maintain the number of people you need to maintain

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Speaker 1: or build the robots for a warehouse of robots, there's

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Speaker 1: fewer than the number of workers you would have if

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Speaker 1: there are no robots at all, right, for that same warehouse.

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Speaker 1: So no matter what, you still have people who are

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Speaker 1: not going to have work, at least not in that warehouse.

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Speaker 1: So what this really brings to light is that there

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Speaker 1: needs to be a focus in the education phase to

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Speaker 1: make sure that whatever education you are getting is going

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Speaker 1: to be uh something that is usable in the workforce,

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Speaker 1: because the skill sets that the workforce is looking for

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Speaker 1: may be dramatically different than what are being offered within

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Speaker 1: the education system. So the burden of responsibility falls on

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Speaker 1: not just the students, but the education system itself because

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Speaker 1: we have these education systems out there that are not

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Speaker 1: necessarily offering the same sort of courses that would be

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Speaker 1: useful to a person once they graduate to get a job.

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Speaker 1: And part of that is because the education system is

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Speaker 1: not terribly nimble. It's a it's an institution, and institutions

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Speaker 1: have a lot of of inertia gas and yet it's

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Speaker 1: really hard to get them to to respond quickly to

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Speaker 1: changing situations because that's just not in their nature. So

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Speaker 1: we do have this issue of robots potentially taking away

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Speaker 1: jobs and even jobs like not not just with the

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Speaker 1: KEYVA systems. I mean, that's one element, right, but this

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Speaker 1: goes across all all kinds of industries, not just warehouses.

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Speaker 1: We've seen other industries also affected by this UM. So

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Speaker 1: you've got that concern. Well, hopefully the ideal UM outcome

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Speaker 1: is that people will be able to start focusing on

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Speaker 1: the types of skills that robots really aren't designed to handle,

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Speaker 1: and that there will still be plenty of work to

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Speaker 1: go around. It's just that there has to be a

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Speaker 1: new focus on the type of work that you can

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Speaker 1: pursue to. It really starts to hit people who may

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Speaker 1: not have a lot of formal education. Uh, they are

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Speaker 1: at the greatest disadvantage in that their jobs, depending on

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Speaker 1: what industry there and their jobs maybe one of the

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Speaker 1: jobs that are easily converted to robotics. Uh. And they

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Speaker 1: are not. They don't have a background in education where

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Speaker 1: they can easily switch gears. Uh. So we're probably gonna

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Speaker 1: see continued bouts of of uncertainty and and and doubt.

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Speaker 1: Might as well throw fear in there to get the

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Speaker 1: whole fund in there. And uh, there's probably gonna be

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Speaker 1: some some rocky, some rocky travels between now and when

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Speaker 1: we reach our idyllic civilization where the robots cater to

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Speaker 1: our every need and we don't even have money anymore. Okay. Well, also,

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Speaker 1: you know, if you if you remember in the the

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Speaker 1: on the documentary Rocky three, Uh, they do have a robot.

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Speaker 1: They buy a robot Rocky I forgot about. And the

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Speaker 1: uncle gets to play with a robot. It always brings

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Speaker 1: him a frosty beverage. Yes, anyway, I mean, yeah, they're

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Speaker 1: they're Kiva's um by far not the only company to

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Speaker 1: be building uh equipment like this, that's true. And one

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Speaker 1: of the uh, you know, I've seen several I'm fascinated

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Speaker 1: by industrial machinery type programs in there. Many on our

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Speaker 1: parent companies networks where they make stuff in store stuff,

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Speaker 1: and I just find it fascinating to see. But one

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Speaker 1: of the advantages of the Kiva system UH is that

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Speaker 1: it is able to store things in a far more

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Speaker 1: dense fashion UH, therefore making the most of warehouse space. UM.

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Speaker 1: And it does that on a on a horizontal plane.

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Speaker 1: Because really these shelves are built UH and they can

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Speaker 1: be configured in a number of it s, you know,

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Speaker 1: depending on what the customer needs. UM. But they the

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Speaker 1: shelves are all the same more or less for for

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Speaker 1: these robots UM. However, I have seen warehouses that are

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Speaker 1: very very tall and they have rigid shelving UM and

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Speaker 1: their robotic systems that can travel horizontally and then vertically

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Speaker 1: up to a bin that could be stories high UM

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Speaker 1: and lifts it off with a forklift. You know. They

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Speaker 1: have optical readers very much the same as that Cuba

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Speaker 1: systems do, where there are codes on the bins and

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Speaker 1: it can scan the code and say, okay, well the

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Speaker 1: beIN in a fourteen level six is the one that

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Speaker 1: I need. And it goes up and with the little

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Speaker 1: forklift it you know, pulls out the bin and moves

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Speaker 1: the parts down to the assembly line. UM, and I

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Speaker 1: find that completely fascinating, but really that that maximizes the

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Speaker 1: use of the human workers that are there. UM. So

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Speaker 1: I think there is a trade off to be made, UM,

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Speaker 1: because these systems do and UH make the work a

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Speaker 1: lot less taxing for for those people, and it does

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Speaker 1: give them UH an opportunity to be more productive at

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Speaker 1: their jobs, which I think, at least for some people,

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Speaker 1: would be more more fulfilling. Also, the robots, especially in

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Speaker 1: the again in the Kiva systems, because you know, I've

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Speaker 1: done primarily that research for this UH for this podcast,

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Speaker 1: that the systems are more quiet than the conveyor belt systems.

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Speaker 1: Some of the employees that they interviewed for one of

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Speaker 1: the news reports UM said that whereas before in the

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Speaker 1: conveyor areas they used to have to shout to make

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Speaker 1: themselves heard UM when they needed to, you know, talk

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Speaker 1: to one another, in the robotics room, they basically can

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Speaker 1: talk in a normal voice. UM. There are fewer injuries,

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Speaker 1: which also seems like it would be a boon for employees,

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Speaker 1: but that, of course only applies so long as the

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Speaker 1: robots don't develop sentients and decided to kill all humans

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Speaker 1: and they follow the laws of robotics, we should be

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Speaker 1: pretty much all right, all right, um, and there's a um,

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Speaker 1: you know, better climate control to which is a cost savings.

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Speaker 1: Hopefully they will pass the savings on to the workers. Well, here,

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Speaker 1: the thing is the efficiencies and the you know, as

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Speaker 1: you improve efficiencies, then ideally that means you can either

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Speaker 1: do one of two things. You keep prices the same

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Speaker 1: and your profit margins go up, or you reduce prices

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Speaker 1: and you pass the savings on to everyone else. But yeah,

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Speaker 1: it all depends on how the company works. But yeah,

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Speaker 1: I mean it's these systems are in general, I think

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Speaker 1: they're a good thing. In the short term, they're definitely

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Speaker 1: going to be something that could potentially cause a lot

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Speaker 1: of upheaval, but in the long term, I think that

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Speaker 1: it's you know, it's definitely a benefit. Yeah, yeah, I

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Speaker 1: think so. Um. By the way, in case you're undering,

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Speaker 1: although Amazon did did make this purchase, and it's unclear

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Speaker 1: whether they were going to continue to allow other competitors

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Speaker 1: to buy these systems at this point because it is

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Speaker 1: a pretty new deal, I would imagine they would continue

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Speaker 1: just because of be another stream of revenue. But yeah, well,

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Speaker 1: you know, they're never to be smart company, but who knows,

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Speaker 1: they might say this is well when which case, it

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Speaker 1: gives an opportunity to a competitor to come out with

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Speaker 1: their own system and um people like Toys r US

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Speaker 1: and Timberland UM. Several Amazon subsidiaries have been using these

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Speaker 1: devices for for quite some time, like Quidsy, which owns

420
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Speaker 1: a wag dot com which is a pet food uh company,

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Speaker 1: and diapers dot com and soap dot com uh, plus

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Speaker 1: Zappos UM and they also I think Walgreens was using

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Speaker 1: it as well. Walgreens uses it crighton barrel uh, stap Laze, oh, Staples. Oh.

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Speaker 1: I always wondered about that. Yes, the gap sacks fifth.

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Speaker 1: You have to mind the gap, especially if you're in

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Speaker 1: the UK. UM. But yeah, so they already had a

427
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Speaker 1: pretty good client base. UM. But really that that princely

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Speaker 1: some when you think about it is of seven seventy

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Speaker 1: five million that what that was the second highest Amazon

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Speaker 1: acquisition UM after Zappos, which it picked up for eighty

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Speaker 1: seven million dollars. But Amazon has said that they're going

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Speaker 1: to uh bring their total of warehouses up to sixty nine,

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00:25:27,440 --> 00:25:31,760
Speaker 1: opening seventeen new warehouses. So this acquisition over the long haul,

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00:25:31,880 --> 00:25:34,000
Speaker 1: is going to save the company quite a bit of money,

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Speaker 1: especially if they can build out their warehouses with this

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Speaker 1: technology instead of retrofitting. O ye, if they building it

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00:25:41,359 --> 00:25:44,280
Speaker 1: out with it in mind so that they maximize that

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Speaker 1: efficiency and uh, then you know they'll they'll definitely benefit

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Speaker 1: from that. By the way, do you know what company

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Speaker 1: built the very first industrial robot? The very first industrial robot?

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00:25:58,920 --> 00:26:03,640
Speaker 1: Was it Honda Unimation Nation in nineteen fifty six. They

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00:26:03,640 --> 00:26:06,280
Speaker 1: were developed from there was a fellow named George da

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00:26:06,320 --> 00:26:12,320
Speaker 1: Vole who created the patents for a a automated robotics

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00:26:12,400 --> 00:26:16,480
Speaker 1: system and these were using hydraulics that would pass very

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Speaker 1: heavy objects from one point to another that we're twelve

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00:26:20,359 --> 00:26:24,240
Speaker 1: ft apart from each other. So kind of the great

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00:26:24,280 --> 00:26:27,480
Speaker 1: great granddaddy of the Cuba Systems robots. Yeah, it also

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00:26:27,560 --> 00:26:30,959
Speaker 1: bring heavy things to you. Well, um you know, like

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Speaker 1: I said, I love watching this this equipment and especially

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Speaker 1: the uh well, a lot of the manufacturing robots um,

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00:26:38,359 --> 00:26:40,760
Speaker 1: which is a little different from these logistics machines, but

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Speaker 1: the ones that used the suction cups and they'll pick

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Speaker 1: up say a very heavy car part or a piece

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Speaker 1: of a prefab house or something like that. They'll just

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00:26:51,080 --> 00:26:54,440
Speaker 1: lift it and move it around like it's nothing. Um,

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00:26:54,480 --> 00:26:56,399
Speaker 1: which is why when they turn on us, we have

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Speaker 1: no hope. So on that done, on that time, out

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00:27:00,480 --> 00:27:02,880
Speaker 1: of here. Not a happy note, Yeah, I'm not happy. Note.

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Speaker 1: We're going to uhas, Chris, We're gonna wrap this up.

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Speaker 1: So guys, if you have any discussions, you'd have something

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00:27:10,000 --> 00:27:13,000
Speaker 1: that you think we should cover, let us know. Let's

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00:27:13,000 --> 00:27:15,480
Speaker 1: know on Facebook or Twitter handle there is Tech Stuff

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00:27:15,640 --> 00:27:18,600
Speaker 1: h s W or you can always send us an email.

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00:27:19,040 --> 00:27:23,040
Speaker 1: Our email addresses tech Stuff at Discovery dot com and

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00:27:23,119 --> 00:27:25,680
Speaker 1: Chris and I will talk to you again really soon.

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00:27:27,920 --> 00:27:30,480
Speaker 1: Be sure to check out our new video podcast, Stuff

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00:27:30,520 --> 00:27:33,159
Speaker 1: from the Future. Join how Stuff Work staff as we

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00:27:33,200 --> 00:27:38,040
Speaker 1: explore the most promising and perplexing possibilities of tomorrow. The

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