Mama Bots Having Baby Bots

to actual human beings. Like if it's a prerecorded type message and I navigate through a menu using buttons, and I don't have to talk to a person, that's awesome. They're certainly less offensively smelly. Well that's just because we haven't mastered the artificial intelligence sector of creating really lifelike bo Yeah, well we have, we have scientists working on that. Yeah, yeah, no,

I was kidding. But there are, of course situations where it's much better to be a robot than to be an organic life form, and we've talked about plenty of them on the podcast before. Maybe in space exploration where you're at risk, or in or in search and rescue scenarios where again a human would be at risk or you know, any number of hundreds of others. Really, Yeah, just just performing the same physical job. Anything that would make a human board or or repeated stress injury, that

kind of stuff. I mean things that where you do it once, it doesn't hurt, but you do it, you know, once every minute for eight hours a day, and it's and eventually, yeah, the button pushing muscles start to wear down in the bones and etcetera. Yeah, robots don't get don't get physically injured, they don't get emotionally injured. It's real hard to traumatize a robot. Yeah, I know. Now,

we we've talked about the advantages. Of course, the life forms have over robots and limited in each individual case. So a person like a worker is a lot easier to train for a new task than a robot is. Well, depending on if if the robot is mechanically suited to the new task, you can reprogram it s well. But

but a worker can learn many new tasks. Yeah, hypothetically, most robots are pretty good at doing one thing right, right, Um, Yeah, robots, as it turns out, they pretty much can do what you built them to do, and they're pretty bad at doing anything outside of that. In general, right, Robots don't really evolve. Yeah, so yeah, so humans are more adaptable on the individual scale, but they're also more adaptable as

a type of thing. Sure, over time, if the air pressures from the environment on a living organism, it will evolve, it will adapt, or it might go extinct. But assuming it doesn't go extinct, it will adapt and evolve to to fit the environment that it needs to make its living in. Robots don't do that, no, they remain. Yeah, so that's what we're going to talk about today exactly. So let's talk about like, like general robots right now.

They they are too pretty distinct pathways for robot design in general, and of course there are a lot of branches within these, but we can go one path where we design a robot for a specific task and that's all it is supposed to do. So rumba is a great example. What a rumba is supposed to do is clean the floor. It's not supposed to do anything else other than it doesn't clean the cat box. In my opinion, this is this is probably like of robots and all

of the good ones. Yeah, the most efficient and the most effective robots, I would argue, are of this type because we only have to concern ourselves with the design elements that will enable the robot to complete its task, and we eliminate everything else. Right, you don't need to include anything that doesn't involve cleaning the floor, or navigation or returning to like a docking station to recharge. When you're designing a robot like the Rumba, you just need

those basic elements in it. Anything else is superfluous. Uh. Doesn't stop us from doing things like having crazy Rumba fights where we put a balloon and something sharp on on a couple of them and have them joused each other. But it doesn't mean we don't put our cats on top of them and just watch what happen. Maybe put our cat in a shark costume first and then put the cat on top of a roomba. Yeah, that sort

of thing. But in general, they just do what they were supposed to do, and if you wanted them to do anything else, you're kind of out of luck. Or we tried to build general purpose robots that are capable of doing lots of different tasks hopefully yeah, to varying degrees of mediocrity because it's hard. It's not because you know, No, yeah,

it's not because the people working on these aren't smart. Yes, because the job is a thousand times more difficult, right, because you have to anticipate lots of different things, a lot of different changing conditions. This would be the example of the darker robots we talked about previously, where UH challenge robots exactly. Yes, the DARPA Challenge robots, the ones that had to replicate a uh kind of a first

responder situation, search and rescue kind of. Yeah. They to do like drive a car to a building, open a door, go inside, twist to lever, and cut a hole in the wall. They had to plug in a cable in one case. Yeah, they had to walk across rubble. They had to go up some stairs and then fall over. Uh didn't have to fall over, but a lot of them sure did. At any rate, we saw how difficult it is to design a robot that can do these things. And keep in mind, these were all tasks that the

various teams knew about beforehand they were designing these. Yeah, yeah, I mean it was this was all stuff that they knew they were going to have, the robot was going to have to be able to do. Uh. So, building a robot that could end up anticipating all sorts of stuff, whether it's a single robot or a you know, a robot that can then design the next generation of robots. That's something that we really haven't perfected yet, but there

are some really good reasons why we would want to. Clearly, being able to have a robot that could either adapt itself or adapt the next generation of robots would lead to much more efficient machines over time, and so this is something that we would really like to see in technology. It's not it's not just in robotics. We're also seeing it in computers. So there's they call it, you know, evolutionary computation or evolutionary computers or evolutionary robotics because that's

essentially what we what we mean. We're talking about a machine that looks at the ability of another, like a next generation of machines to do a particular task, evaluates them, and then makes decisions on how to alter that generation to produce an even more effective generation after it. Uh the goal always being to come up with the most ideal design for whatever purpose you have in mind. And this isn't a super super new idea, no. Now, there have been people who have been working on this for

a while. Back in two thousand eleven, researchers with NASA published a paper on evolutionary computation that was used to design new efficient antenna's way back in two thousand six. So the paper was published in two thousand and eleven, but the actual project was yeah in two thousand and six. And what they what they were pointing out was that

designing an antenna is really challenging. It requires that the builder to have a very detailed knowledge of how antenna's work, which already limits your pool of various builders, and even then it's just painstaking to create an actual efficient, working antenna.

So what they did was they created evolutionary algorithms for a computer to design an antenna for spacecraft auto matically that the computer system went through various designs of antenna and essentially simulated tests of them to see which one would be the most effective. They then took the designs that were predicted to be the highest performing antennas by this computer program, and they took it to the same manufacturer who was already building the antenna intended for the spacecraft.

Then they tested all three of the antenna, the two that were the two best performer ones in the computer program, and the one that people had designed for the spacecraft, and found that the two that the computer had designed were more effective. Yeah, so it was one of those examples of this approach actually working better than what we humans could do. Now, maybe you don't know the answer

to this, but if you do, I'd be interested. In this case, we're the what what we might call the mutations introduced into the models in in the algorithm that that tested out antenna's where the mutations direct did where they programmed in? Or were they random mutations? Like? Was it really more like evolution where a random things thing happens and then the system tests is this any better? Well, as we've talked about in previous episodes, random with machines

is really hard to do. It was more like they were the computer was given an enormous number of variables and started to test them in various configurations without going through every possible one. Because the goal of evolutionary computer computation or evolutionary robotics is to make sure that you come to the most ideal form of whatever it is

you're going for without having to test every possible variation. Because, of course, thus things get more complex, those variations increase in number until it would take you till the end of time to test all the different ones to come up version one one seven eight nine seven, seven seven four three is the best. You would take forever to

go through all of those. So what these are designed to do is to test ones that are a best guests already of being effective, measuring those against other best guesses, then combining ones. Like if you were to find a generation that works particularly well and another generation that works particularly well, you might say, well, what happens if I combine the best design elements of both of these into a new design. Does that increase the effect of the

efficiency and effectiveness or does it decrease it? Because it doesn't, you know, adding to awesome things together does not guarantee you to get a third, even more awesome thing. Oh yeah, yeah. I was wondering because I'd read about the use of evolutionary algorithms before in the design of new planetary rovers like unmanned planetary rovers, and testing different variations on the models in a in a computer simulation that would naturally

select the highest performing ones. Yeah. Yeah, it's very similar to that. And uh, the thing about the difference, you know, we're going to talk about a robot that works along these principles. The biggest difference between a robot and a computer, obviously is that a robot is working with actual physical matter, not just simulations. And so there's some practical limitations that

you encounter in that case. Right, you have to work with real physical matter that has weight, it has mass, it occupies space, there's a limit to how heavy it can be. Uh, you know, you can't just magically increase the size of whatever it is in a simulation and then just see how it works. You have to physically put this thing together. And so it's a really interesting approach.

And we have seen a couple of examples of people working with true physical matter, but uh, it's mostly been in things like working with a program that builds stuff out of lego. So there is an actual example of this. There were some evolutionary computational experts that design systems that would allow a machine to build other objects out of legos, but you had to build limitations into the computer program so that the machine would follow the rules of physics.

In other words, you couldn't have to lego pieces occupy the same physical space at the same time, right, Sure, And some of these computations aren't going to I mean, it's hard as a programmer to build all of those limitations into something that has to work in reality, because the computer isn't going to understand I mean basic stuff, and so so it might when you take it out of the lab, out of the out of the computer simulation stage, it might operate very differently than you were

expecting it. Then the computer was expecting it to. An analogy I would make and has nothing to do with computers or robotics, but an analogy I would make is if you were running a role playing game. You're you're the game master of a role play game, and you think you have anticipated everything your players are going to do, and you have made up a masterpiece of a module, and your players are going to have an amazing time, and three minutes in the players decided to do something

you could not have possibly anticipated. It makes perfect sense within the context of the game. And then you and you, yeah, you gotta throw away all the stuff you worked on and say, well, well, I guess like it's sort of like if you imagine a horror movie where the characters all walk up to the spooky house, take one look and say no, and then walk away like, well, there

goes the horror movie. Same sort of thing. Well, we wanted to specifically talk about some researchers from the University of Cambridge who were working with a robotic system that used evolutionary robotics. So imagine, essentially, you've got a robot that builds other robots and tests them to see which designs work the best, and then either UH either has a design go forward or eliminates designs and starts tweaking things to try and find the best physical design of

a robot to complete a specific task. Now that sounds super cool. I need to explain some stuff first so we can manage our expectations. Yeah, yeah, okay, so so so performance of these of these baby bots, as we might call them UH in this case was how fast and far it could move, like kind of scuttle across the surface exactly. So it's not like it was performing open heart surgery. No, No, they decided to hold that for the next experiment. Perhaps, Now these robots all we

were supposed to do. We're create a locomotion power that could move it from one point to another point, and uh they would. The experiment measured how long it took these little devices to move across this particular expanse they changed up. That didn't make the cut. They didn't they didn't continue on. Uh that they were taken apart. They were taking up bodily by the mamabot. Actually, to be fair, I think all of them were taken apart, but yes,

uh uh yeah they were. They'd be scuttled, they'd be scuttled and then they'd be scuttled and and and harvested for their organs essentially. So that was the that was the criteria that the robot used. Um and here's how it gets here. Let's break this down because that's that's like an overview of what happened, but it's really interesting when you get to the specifics. So what they wanted to do is they wanted to design a system that

could design, test, and change robotic designs relatively rapidly. And it needed to be a a system that could work within the lab, but wasn't designed to be a practical system that could go through every single possible arrangement of the various pieces. For yeah, um, the pieces are essentially two different types of cubes that will get to in

a second. So they wanted their approach to contain this is a quote from their paper paper, a limited number of evolutionary iterations, and that was, you know, the the idea of let's let evolution lead us to what should be the best version of these robotic designs, as opposed to just doing trial and error, measuring everything and then

going with the top performer. Now, they did say in their paper that while this is really interesting and could in fact be a breakthrough in science and technology, you have to admit that there are limitations to this technology cannot necessarily be applied to mass production. Mass production relies

heavily on automation. Automation is different from mass customization. So they said, you know, there there are certain practical applications for this kind of approach, but it's not gonna be like we're gonna design these robots and they are going to magically make all of our factories work at efficiency. Uh. It's uh. They said that there's a challenge to developing automas design of quote a large morphological diversity end quote

morphological obviously meaning that the actual form of these things. Yeah. So the basic design of the experiment was to use a robotic arm that was the Mama butt, so it's a robotic arm, and had to gripper fingers, so just very simple robot um. One of the gripper fingers actually had a a little nozzle through which it could squirt glue,

so they could glue pieces together. Uh, and it was connected to a computer that was running the evolutionary software to build the robots out of these little cube modules. And they had two types of modules. They had passive modules, which really just small wooden blocks that were painted black. The reason they were painted black was so that the cameras that the robot was using essentially as eyes, could easily pick them up. Let's see where the little passive

ones were. So these couldn't do anything, right, they were just connectors. And then you had the active modules. These were a larger cube and one face of the cube was attached to a small motor that could rotate at a certain amplitude in frequency, so it could rotate the face of the cube, which is kind of the basis of the locomotion of the piece. Right, So, so imagine a Rubik's cube where only one side can rotate around, but it does so on a motor. That's essentially what

we're talking about. And so different cubes would have faces that rotated at different speeds, and the all of the information was contained within the cube so that the robot would be aware of that. It wasn't like the robot was randomly picking blocks and some could turn at a certain speed and others would turn at half speed or

twice speed or whatever. The robot was actually quote unquote aware of which cubes could do what and uh and by by fitting them together in different combinations, uh yeah, you could you could get the box to kind of kind of wiggle, to kind of hop and wiggle across the table. It reminds me um of the motion of some of the the less mobile toys and toy story um kind of just sort of like scuttling along it ones that didn't have legs. Yeah. Yeah, it's flipping adorable,

you guys. I am going to see if we can find video that's share able to share with you because it is so cute. It makes me think of a lot of like, you know, wind up toys that that move because they're doing this repetitive motion. Uh, and that's enough to propel them across a surface. Uh, not with anything. They can't necessarily steer or anything, but they can move

the same sort of thing. Like the idea was that, all right, well, if we pair this passive module with these two active modules in this configuration, will that create movement that's faster and more effective than this other design. So the computer they used was a regular desktop PC, nothing particularly special about it, but it was running a controller program that was using the matt Lab language m A T L A B and that's used for interactive

environments of developing algorithms, among lots of other stuff. The robotic arm could easily grip and rotate any module it shows, and could stick two of them together using what they called a hot melt adhesive or h m A. Yeah, essentially like hot glue, like glue gun glue is more or less what that was, And it could build robots

by combining multiple modules together. Each module had what they called a gene, which essentially described the type of module it was and the motor control of that module, and also included the construction parameters. Of the module construction parameters. Essentially, it was things like basic rules for the robot to understand so that it could effectively build a robot. In other words, if you want to build a robot, you're probably gonna need to put a larger piece down first

before you put a smaller piece on top. It was building them vertically, so it needed to know, Hey, if you try and build a robot this other way, this thing's gonna fall over. So you need to be able to build them in you know, these are the general rules you need to follow, essentially the rules of physics, so that your robot will be built the way you intend to build it. Uh. So a robot was made up of these modules, and it was said to have

a genome. It was the collection of these genes and uh these genes would either work or not work together. And robots could have between one to five genes modules. Yeah, now they if it's having five. The max was three active too passive. You could not have more than three active simply because it would make the robot too heavy for the gripp or to grip. Right. Yeah, that's that's one of those limiting factors in reality there. Yeah, so if it were more than fifty grams of mass. The

robotic arm, the gripper just couldn't maintain a grip. It would drop it and it would break. And so practically you could have a maximum of three active and two passive pieces connected altogether. They didn't necessarily have to have that many. Some of them were only three or two uh two blocks large at large two blocks in total, but at any rate, the construction parameters were these very

simple directions and it allowed a lot of flexibility. So the robot arm essentially could decide which modules to use and how to connect the two together or three or four however many together, and then test it so that the what would happen after it builds one of these, after it squirts the glue and everything. Um, you could actually send it a genome essentially a recipe saying here are the modules I want you to use and the configuration I want you to put them in, and this

is going to represent the first generation. You could do that, or you could make it uh more of a random approach. Well. Once built, the mother robot would lift the finish modular robot, move it to the testing area, which is just a flat surface for the robot to crawl across, also known as the arena. In some cases it was a hard surface. In other cases they used a carpeted service, and at

least in one they used a foam surface. Yeah. They they slowly moved towards the foam surface as they realized throughout the experiment that the robots were having undo trouble. I was sad to learn that the foam they were talking about was like, you know, kind of like mattress foam as opposed to foam party. It's a little bit robots just getting down and raving. I come from the specific time, y'all. Anyway, Wow, Joe to a foam party has been no pope, you like pink II. Here we go,

all right. So the modular robots would then be activated wirelessly. They had inside of them essentially a receiver for Bluetooth or WiFi, and that would activate them to go into uh, you know, their basic motorized action. Yeah, and then there would be a couple of cameras, including an overhead camera that would measure their progress across the surface, and then they would essentially, uh take the the distance they traveled

and within a certain amount of time. Originally they went with eight seconds, so after eight seconds, we see how far they've gone. Uh. They had to switch that to four seconds because later tests the modules were moving quote unquote so quickly. Yea, it was working, so they needed to have They needed to shorten the amount of time because otherwise the robots would just travel out of the view of the camera. So they had to shorten the amount of time so that the robot arm could make

determinations of which ones were the most effective. Uh So, after testing, the modular robots were disassembled by hand, so the so the mother did not have to kill her babies. Uh, the unfeeling scientists got to do that. Uh. So then they had to also remove all the h a the hot melted agent material, and then they were replaced onto the work area. The work area as you would imagine. Uh, each module had its specific place in the work area, so that way the robot quote unquote knew where to

go to pick specific modules. Because these machines are not that smart, right, you have to put the things in the right orientation and the right um location for the robot to be able to grab them. We've talked about this with other robots too that are combining objects if you have the objects in a specific order. I think we talked about this with the cooking robots. Specifically you have to have them in a specific place, to specific order, or else everything is just going to come out mixed

up and awful. It couldn't recognize what was what. Yeah, yeah, it's just following like it understands quote unquote understands the uh qualities of each item, but only if the right item is in the right place. So if you put all the active modules where the past of one should be and vice versa, it would it's not gonna be able to tell the difference exactly. So at that point, the evolutionary system would begin to tweak the genomes. It could swap out genes in a genome, or it could

combine different genomes. So essentially it would be like breeding two robots, saying, if if robot from this generation and another robot from that generation were to combine, here are the qualities that would emerge from that, keeping in mind essentially the robots choosing which qualities would emerge. Because you're still limited. You can't have more than five genes, so it's not like it would just be additive. It would have to be selective in which genes from which two

got selected to be combined into a new one. And then the theory was that, or at least the hope was that it could design another generation of better performing robots, and some of them might not perform well. It may turn out that the genes from robot one and the genes from robots seven are not as compatible and they actually perform worse than either one or seven did individually in the generation before. And that happened a couple of times, Yes, it did. So they held a total of five experiments,

and between these experiments they changed up the surface. They made some tweaks to various rules. They actually numbered them one A through one D and then two. Yeah, they did five five different runs, but four of them fit under the category of experiment one. So each experiment started with ten agents other in other words, ten basic robot designs UH that represented ten different genomes, and then each experiment ran through ten generations, so you've got a hundred

different designs total per experiment and UH. The first four experiments started with some randomly generated designs consisting of one to three motorized elements, and they put all the construction constraints they had designed in play for those initial experiments.

The testing environment was changed a couple of times. That's when they started with a hard ground, then they moved to the carpeted service and then the foam h and humans started helping out the mom about eventually to hold the component's study during the build phase, uh so that she would not drop them basically. Uh And they also began manually inspecting the baby bots to be sure that none of the pieces were going to collide with each other during the test, because that would damage the pieces

and make things less fun for everyone involved. Yeah, especially since that would affect any future Yeah, because if you if the cube gets damaged, then you it's hard to determine if the robot would have performed better had it worked with a brand new cube. And uh So they

were being very careful at that point. A little human intervention wasn't necessary because it was you know, this is like a proof of concept type of approach anyway, So the fifth experiment, the initial generation of agents was not generated randomly. Instead, they picked some of the best performing designs that came out of the previous experiments, So generation one was actually made up of robots that had already been built in the first four experiments, and just said,

all right, let's made me think of you FC. Let's take the champions of all these different fighting disciplines and put them together and see what happens. Except instead of fighting, they're supposed to make babies, so totally different UFC. I guess in that sense, I would suppose. So yeah. I mean, I don't know a whole lot about the UFC, but I know a great article written by a certain Jonathan Strickland you should read. Uh. Interestingly, the test could that

be found on how stuff works dot com. Could. In fact, if you go to how stuff works dot com and look how the UFC works, you will find an article I wrote ages ago. It was awesome. Um. So interestingly, the test that produced the most improvement from first generation to the last generation was the fourth test. They actually plotted out each generation's performance on a kind of a line graph chart, and UH, they average the ten robots fitness,

that's what they called the performance of moving across the surface. UH, and they the fourth test saw steady improvement with one exception, uh, where at general ration five there was a slight dip in performance and generation ten saw very slight decline in performance from generation nine, so generation nine did the best out of the fourth test. The fifth test saw the longest declining trends. So this was the This was the one where they took the champions from the previous tests

and started with those. So this is the DEVO group. Yeah, yes, and exactly. They had a devolution uh in generation over generation from three to seven, so their their performance actually declined, not steadily, but in each generation there was a decrease in fitness. But then it all turned around. Yeah, they started to see improvements again and all the way through

to generation ten. So it was one of those things where by the end, I think in every single case, the generation ten robots outperformed the initial generation of robots in all the tests. There were some cases where generation ten didn't outperform, maybe generation in seven or eight, but in all of them they outperform generation one. Uh. It

was really interesting. The fourth test, top performing agents in the first generation average two point eight centimeters per second, and by the tenth generation it had increased to six point seven centimeters per second. Yeah, so more than twice and twice yeah, yeah, so that's pretty exciting. That they were able to take this approach and increase the speed of these mobile agents by a factor more than a

factor of two, which yeah, yeah, the researchers did. Uh. Did note that the disadvantage and having the mom about manually test each generation is that it takes time, and so there's a little bit of a payoff balance between running simulations first versus going straight into that real world testing. And they were talking about how they hoped to streamline the process in the future by using simulations to select the most likely successful models and then begin testing with

those instead of kind of starting from scratch. Yeah. Uh. And in fact, they had talked in their paper or about how, yeah, this is this is a balance, right, because when you go with the pure simulation mode, it may turn out that when you when you transfer the simulation to reality, things don't behave exactly as you had anticipated. Perhaps the simulation was unable to take all the different factors into account, or it just maybe that, you know, it's just in the real world stuff behaves a little

differently than the idealized virtual world. But a combination of the two is probably the best approach, because, like Laurence says, if you do everything physically. Then you need to have the luxury of time on your side, just because it will take this time to physically build these things, and plenty of blocks and glue, yeah, and lots of humans to d glue the blocks, and and these robots are just blocks, right, I mean this this is the about

as unsophisticated a robot as you can get. So if we were talking about a robot designing a future robot capable of doing something really sophisticated, it would obviously take even more time. Yeah, I don't know. I've met some pretty ansophisticated robots. Well, I mean they really like cheese. It's they don't tip. Well, um, they prefer they prefer light beer to I p a s. Yeah. Yeah, that's that's why I was giggling a second second ago. That's sad,

Please please continue. Well, no, no, The cool thing to think about is that imagine a future where we have machines capable of designing a new generation of machines that are better adept at doing whatever they need to do than the previous ones, and then can even learn from that and build even better ones in the future, perhaps even building a better computer to design the next generations and eventually you arrive at or deep thought, because that's

exactly what deep thought was in Hitchecker's Guide to the Galaxy. Was that well, deep thought said it could give the answer to the question of life, the universe and everything, but could not give the question and said, in order to get to the question, it could design a computer that would be even more advanced and be able to answer that question after a really long time. But that's all.

That's the best it could do, is that it could design a better machine than it to be able to answer or to come up with what the question was. Of course, we all remember the answers forty two question turned out to be was six times eight. Just shows you that something's wrong with the universe. But now this is really kind of I'll get you a computer. Um. Yeah,

obviously it's a joke in hitchecker Sketch the Galaxy. But this is this is the neat ideas, This this approach where we can set certain types of machinery on a pathway to reach this possibly increasingly efficient means of evolution

to create better tools. Um. This is also obviously one of the principles that underlies certain versions of the singularity, right, like this idea that we get to a point where evolution is so constant that there is no meaningful way to describe the present anymore because it's everything is changing all the time. And uh, and this is sort of the kind of stuff that would be necessary for that

particular version of the singularity to come to pass, will it. Well, let's just say that based upon what's going on right now, it's gonna take some time. We got some real cute wiggly blocks in in the meanwhile, though, So yeah, I am still very skeptical that we will see anything close to the singularity on a time scale that kurtz Wild has predicted. Do you mean twenty to forty years? Yeah, it's so this was kind of fun. I mean, if you, if you get a chance, you can read the paper.

The paper is actually quite easy to read. It's um it's not an inaccessible paper, and it is available. It is very accessible. It's it's available for free on the internet. Yep. So you can read all about the experiments. They go into detail. They really I didn't go into a lot of detail about the differences between the five different runs they did, just because it would have gotten super technical still understandable, but just bogged down in a lot of

technical details. But it's all there in their their paper. We'll try to remember to link it on various forms of social media if you want to google it for yourself. The full name of the paper is Morphological Evolution of Physical Robots through model free phenotype development blin Yes. And if you want to get in touch with us and and ask us to cover a specific topic. Maybe there's something about the future you have always been curious about and would like to hear our take on it, let

us know. So does an email the addresses f W thinking at how Stuff Works dot com, or drop us a line on Twitter or Google Plus. We are f W thinking at both of those, or search f W thinking and Facebook. We'll pop right up. You can leave us a message and we'll talk to you again really soon for more on this topic and the future of technology. This is forward Sinking dot Com, brought to you by Toyota. Let's go places,