Will a robot take my job? | The Age of A.I.

6.15M views5804 WordsCopy TextShare

YouTube Originals

The fear of losing jobs to computers is a common one among millions of people, and one that many hav...

Video Transcript:

This is kinda wild. "Will a robot take my job? " is one of the most googled questions.

It kinda makes sense. You know, when phones were first invented, there was probably horse messengers freakin' out all over the joint. Vinyl's making a comeback, but fidelity aside, why bother, when you can have a curated playlist with thousands of songs right on your phone?

Come to think of it, I can't tell you the last time I shot a film. . .

on film. See, change makes people panic, but things evolve regardless, so we accept it, we move on. And now that we're entering the so-called fourth industrial revolution, yeah, automation's gonna replace some jobs, but it will also create new ones, and probably give rise to industries that never existed.

That's the topic some of these next folks are exploring, how machine learning is making things more productive, more efficient, safer, and cleaner. [director] And cut! That felt okay.

You wanna go again? -No, we're set. -Okay.

[director] Maybe just step back on that mark. Here? -Yeah.

-Okay. [director] That's good. [sighs wearily] Right, yeah, I think I get it.

[horn honks] [Downey] The trucking industry is at a crossroads. E-commerce is driving demand for shipping higher than ever, but the harsh realities of long-haul trucking are sending professional drivers looking for other work, leaving a 50,000-driver shortage in the U. S.

alone. [Dr Ayanna Howard] There are of course some industries that will be impacted by A. I.

in terms of labor workforce being diminished, but there are some industries that don't have the workforce to sustain it, and so that disconnect, that's where A. I. can fit in very nicely.

[Fitzgerald] My name is Maureen Fitzgerald. I've been driving trucks for 32 years, and I was ready to make a change. Three, two, one.

-[device beeps] -[man] Engaged. [automated voice] Autonomous driving started. [Downey] Approaching retirement, Maureen saw automation coming, and assumed it would replace her.

She was partly right. Automation is coming, but her job won't be lost. It will evolve.

I've been in this career a long time, I've seen how everything has been changing. The monotony of coast to coast, and the pay being low, and, you know, you're sleeping in a sleeper, you're sleeping at a truck stop. It's hard.

It's a hard life. I saw an ad for a test driver, and I said, "That has to be the perfect job. " [alerts beep] [brakes screech] [Downey] Okay, but this isn't just about Maureen.

We've all heard about self-driving cars, but self-driving trucks, why are they setting the pace of the autonomous driving industry? [Pedro Domingos] We already have robotic airplanes. Most airliners fly themselves, but self-driving cars are a harder problem because the roads have a lot more things going on than the air does, and driving on the freeway is much easier than driving in the city.

So we will see fleets of automated trucks, I think, long before we see self-driving cars in the city. The company of TuSimple starts with a dream. .

. a dream that we wanted to build the A. I.

technology for autonomous trucks. [engine starts] [Arda Kurt] Can you give me one full rotation to the right? The truck was originally not made to be automated.

Okay, can you give me 50% throttle? We add our intelligence, the servers, and all the sensors to make this work. We're in the sleeper of a TuSimple tractor.

We have removed what normally would be beds and refrigerators, and we've added our computer, which is the brains of the A. I. Drving the car is what is called an "A.

I. complete problem. " If you solve it, you solve every other problem in A.

I. However, to solve it, you need to solve every problem in A. I.

So it requires vision, it requires robotic control, so it requires motion and navigation, but it also requires social interaction with the other drivers, so at the end of the day, in order to drive a car well in the city, you need everything. It requires an enormous amount of common sense. [Downey] Common sense.

That's what Maureen is doing here. . .

overseeing the A. I. truck as it learns to navigate real-world conditions, ready to take over if anything goes wrong.

Today, they're taking a ten-mile loop to test scenarios that a human driver might encounter. Can this 10-ton vehicle safely navigate a busy freeway? Can it merge and change lanes?

[man] All right, ready. Three, two, one. Engaged.

[automated voice] Autonomous driving started. My name is David Ruggiero, and I'm a test engineer with TuSimple. My job is to let her know what's happening, let her be absolutely comfortable the truck is doing what it's supposed to do.

Gear change. . .

There we are. When the vehicle is engaged, she is fully aware of what's going on around the truck, but she doesn't have to touch anything. [Ruggiero] All right, clear on the right.

[Fitzgerald] Not clear on the left. [Ruggiero] I see the left approaching. Never is one drive or one day ever the same.

[Fitzgerald] The left is now clear. Is it gonna go? -We're good!

-Come on. [Fitzgerald] The truck was cautious. .

. So I'm still seeing full braking. [Fitzgerald] It was probably overly cautious.

Just go manual? Yeah, we can do that. [automated voice] Autonomous driving off.

[Ruggiero] There we go. [Fitzgerald] All righty. -[device beeps] -Engaged.

[automated voice] Autonomous driving started. Going 45. Looks good.

Center lane. [Fitzgerald] I feel like I have a relationship with the truck. Easy, easy.

. . I talk to the truck.

I tell it when it does good. I've never been a test driver before, and it's never been a truck driving by itself before. We're both actually learning together.

Still green. [Ruggiero] Still green. .

. We're off throttle, slight brake. Okay, red light.

[automated voice] Left turn. [Fitzgerald] Turn signal is on. [Ruggiero] I see the lead van.

Human drivers cause 30,000 or more deaths per year. [tires squealing] Long-haul trucking can be dangerous, it can be boring, and therefore, it's ripe for automation. [Fitzgerald] On the right.

[Ruggiero] Okay, I see the other car moving, I do see green. Still green. .

. Still green. [Ruggiero] I see no oncoming.

We are making the turn. [Fitzgerald] That'll work. All right, the new merge ramp.

[Downey] Merging onto a highway is difficult and dangerous. It requires mastering a complicated set of physical and mental skills, having keen sensory and spatial awareness, preparing for unpredictability and human fallibility. .

. [Ruggiero] And we're in it. [Downey] .

. . and doing it all fast and fresh each time, otherwise you risk crashing, or worse.

Turn signal is on. [Ruggiero] Okay, I'm showing a left intention. [Fitzgerald] We are gonna have a truck next to us, but he's moving over.

Okay. Confirmed. [Fitzgerald] Looks like we are clear to get over, the pickup is moving.

[Ruggiero] This truck is measuring what's coming up behind it, how fast they're coming up behind. . .

Looks a little congested up there. Yep. [Ruggiero] It has a full 360 view, all the time.

[Price] The A. I. uses the images that are coming from the cameras with other sensors, LiDAR, and radar.

LiDAR is a laser range finder that is measuring the distance to objects 360 degrees around. It gives us a three-dimensional picture of the world. In the virtual world, there is a direct correspondence of the map and the location where the vehicle is, and the behaviors of other vehicles, their locations, their speed, their future intentions, and once all these things are re-created, it's basically asking the computer to play a game.

Okay, tire front, continuing at 57. Good job. [Ruggiero] When I'm looking at the data that the truck is feeding me, not only am I seeing the current environment, but I'm seeing ahead into the future.

And we have a slow vehicle cutting us off. I see the cut in, at a 51. [Fitzgerald] That was a good one.

[Ruggiero] Okay, I saw the cut in. If we need to make a lane change, the truck is going to know before I do. Okay, I currently don't have a-- There we go, I have an intention.

We're going right now. [automated voice] Left change. [Fitzgerald] That's fine.

We're good. All right, let's go. [Downey] Okay, so this is where things get interesting.

This truck's tricked out with sophisticated cameras and software, but it still doesn't have one critical element. . .

the thing that A. I. may never have.

The central problem in A. I. is that human beings have common sense and computers do not.

We take common sense for granted. We know how the world works, and everything that we do in our daily life involves common sense. .

. Pretty steady stream now on our left. [Ruggiero] I see it.

. . .

and people have been trying to imbue A. I. with common sense since the beginning, and what is extraordinary is, at the end of the day, how little progress we've made.

[Downey] The endgame is that, one day, the AI truck will not need Maureen, David, or any person to ride with it. Each one of these test runs is a building block toward that goal, designed to help it learn how humans operate and make decisions, even when you get thrown a curveball. [Fitzgerald] This truck is messing with us.

[Ruggiero] Just catching the vehicle. He's all over the. .

. -He's coming close to the line. -Mmm-hmm.

[Ruggiero] I have a left intention. Uh, lane is blocked. We were having a little bit of challenges from one of your camera trucks.

[Ruggiero] All I need is a gap. Just looking for the gap. Come on, you can pass him, it's okay.

[Fitzgerald] The truck was cautious, because it couldn't predict what that camera truck was doing. Why is he going so slow? [Downey] The film crew's vehicle was shooting the truck for the whole afternoon, and, yeah, it wasn't driving normally.

The camera truck was what we would consider a non-compliant vehicle. It was doing things that a normal driver, you wouldn't expect them to do. [Fitzgerald] Doesn't trust it.

[Ruggiero] Okay, I see the cut in. Sixty and slowing. Fifty-two and slowing.

Still seeing a left intention. [Fitzgerald] Let's see. Okay, we are no longer clear.

No, it's full. [automated voice] Pre-left change. We're not going.

He's all over the place, that guy. [Downey] The truck wants to pass the camera crew, but the constantly changing speeds and unusual behavior confuses the AI. .

. so it refuses to take the risk. [automated voice] Canceled.

There are behaviors that the truck has to learn to become human, in some sense, because not everybody else is operating at the same level. All right. Turns out that getting the first basic systems was a big milestone, but then there's this long tail of difficult situations that are much harder to solve.

[horn blaring] When you're merging, do you have to give way, or does it make sense to go forward? That requires a level of understanding that we humans barely have, but machines aren't there yet. [Ruggiero] That's definitely gotta be the most interaction I've seen with one vehicle.

[Fitzgerald chuckles] [Ruggiero] The big takeaway on today's run. . .

there is no such thing as a perfect run. Eventually, we hope that the trucks react to a change in environment as quickly as we can, regardless of what we throw at it. [Fitzgerald] Okay, slow down for the gate, and looks like they're going to let us turn.

[Ruggiero] Beautiful. [Fitzgerald] Thank you, people. We still have a lot of work to do to validate the system, assure that everything is perfected, and this will take substantial driving experience, simulation experience.

[Fitzgerald] Watching something develop over time, you never thought it would learn like this, and now it is learning, and you feel like a proud parent. It was making some awesome decisions that I've never seen it make before. I get asked all the time by people, you know, "Oh, you're taking truck drivers' jobs away by having autonomous trucks," and I don't believe that's true, because it's not taking away jobs, it's taking the place of a job that a driver doesn't want to do.

[automated voice] Thanks for riding with TuSimple. Ta-da! High five.

[automated voice] Autonomous driving off. [Fitzgerald chuckling] [Downey] What Maureen thought was the twilight of her working life actually became the dawn of something new, and it's an age-old phenomenon. Tractors didn't make farming obsolete, and video didn't kill the radio star.

It just provided new opportunities. New technology is changing how we think about work. [man] Hey, Garrett, that east on 424 should be good.

10-4. [Howard] I think the biggest changes that we face when we think about A. I.

and the future of A. I. is what do we do when A.

I. changes what the job functions are, and what the workforce looks like, which it's going to do. So this is a fear, because change, people don't like, and change that you don't understand.

. . is terrifying for a lot of people.

[Anthony Otto] Port of Long Beach is the second-busiest port in North America. . .

and the first fully-automated container terminal in the United States. [Downey] To most people, shipping and logistics aren't that sexy, but to an A. I.

, it's a dream. With over 14,000 people moving more than eight million giant containers around the world, this port handles about $200 billion worth of cargo a year. Automating this industry will make it more efficient, for sure, but also safer.

[Otto] As you can see this fence here, from this point on, all the way for about an eighth of a mile that way is a fenced-off no-man zone, running entirely off of software. In the old design, you had the vessel's activity mixing with the truck activity, competing for space, congestion, safety issues. .

. . .

. but the vessel sizes just continued to grow, seven times larger than the ships that we ever had in the beginning, so there was a need for a new model, which is what LBCT is. [Josh Johnson] When a ship pulls alongside, we're using different algorithms to figure out traffic, scheduling, dispatching, and planning.

We're doing this with 10 different cranes at the same time, 50 different vehicles at the same time, and we have 30,000 different places to put that container in the yard. When we offload a container from a ship, the first thing it's gonna do is get picked up by a ship-to-shore crane that does have an operator inside. -Hey, Lily, it's Boyle.

-[Lily] Yeah, I copy. [Boyle] Okay, Lily, which side you wanna start on? [Johnson] Ship-to-shore cranes will set the containers on the platform.

On that platform, there are over 20 cameras that use Optical Character Recognition, or OCR. Another crane will then pick it up, put it onto a fully-automated vehicle. That vehicle will drive through the yard, and it'll get to the right spot, planned by the system.

What looks like a little wad of bubble gum, these are transponders buried in the ground. They're in a grid pattern, there's more than 10,000 out on the production field. There's an antenna on the front, and the rear, and they read them as they drive, and then transmit that to the system to let it know where it is.

Once it reaches the yard, an automated stacking crane will pick it up and deposit it into a yard block. From the block, it'll eventually get deposited onto a truck or onto a bomb cart, where it can make its way onto a train. I don't have my key.

[man] On my way. That's why we work in teams. -[man] What'd you say?

-[laughs] [Downey] They say that we can eventually learn to adapt to an environment that mixes robotic and human elements, but it's more difficult for machines to adapt to us, so here, they need to be separated, because of safety. Because humans are unpredictable, automation can be physically dangerous to people, because if I'm a 200-ton machine and I don't see you? I might just run you over.

[winches clanking] [man] This is the high-danger area. When we go out in the field, we call for a restriction, and they block a certain area of the production field, and no AGVs know to go in that area, and we can safely work. You want these two right here?

-[man] Yeah. . .

-Okay, cool. This facility, it is fully automated, but it's not as if someone comes in, pushes a button, and puts their feet up on their desk. It is still employing hundreds of individuals on any given day.

. . How does everything look on that side, Eric?

[Eric] Got a broken rail over here, Julio. We're gonna have to weld it up real quick. Okay.

Grab my hood from inside the truck, please? Here we go, guys. Watch your eyes.

Eyeballs! You know, this is an asset-intensive facility. The number of mechanics necessary to maintain this facility has certainly grown.

[Julio] My background is automation. That's why I decided to come here, because it was high-tech. [Johnson] Instead of a yard crane operator in a cab alone all day, now we're working remotely in the operations room.

About 80% of the time, most of the faults are resettable from here. [woman] Okay, easy fix. Easy fix.

I'm Garrett Garedo, crane mechanic here at LBCT. This is the central operations system. A bypass key will allow the operator to bypass a function.

[Downey] Automation can make the workplace safer, and this is a good example. . .

taking someone out of the yard, and putting them into the control room. Right here, I'm gonna need to move this container over, because it's not sitting too good. You ready?

-Yep, go ahead. [Howard] A. I.

, like any technology that comes into our society, it changes the workforce. There's this misconception that there will be fewer of these new jobs. That's actually not the case.

Let me move it back. [Howard] There are new jobs, there are different types of jobs. [man] I was a crane operator.

Back in the day, we'd have to climb up five stories into the cab. It was a little hard, especially on the back, and then when automation started, they just shifted me over, and now it's a lot easier. [laughs] If you don't embrace it, you're just gonna fight it, and it's not gonna.

. . You gotta use it as a tool.

Right on the money. [Otto] You know, our obligation was to re-train those same individuals into the new jobs that that technology has created. We are the progress that was needed for the goods movement industry.

For the foreseeable future, there will continue to be a lot of things that only humans can really do. So I've dispatched a mechanic out to STS-1. Okay.

[Domingos] And so the future of work is not humans being replaced by machines. It's humans figuring out how to do their job better with the help of machines. [Downey] Structured environments are good for automation.

Smooth surfaces, right angles, less chance for chaos. But what about more complex, human environments? Can we learn to work with robots hand-in-hand?

[man] Oh, there we go. Wait a second. Stop.

Let's see if that works. [Downey] RoboHub is a premiere robotics incubator at the University of Waterloo in Ontario, Canada. Gripper's not working on the left.

Maybe if I turn a little bit. . .

[Downey] One thing they're doing is developing A. I. and robotics to use in environments that are unstructured, more human.

Like at home. Calm down, calm down. [Brandon DeHart] TALOS is one of the most advanced humanoids on the planet.

It can walk, it can talk, it can see you in 3D. . .

but it can't do most of those things out of the box, so you really have to take it as a tool and teach it how to do a lot of these things. On TALOS, we want to explore two different aspects of A. I.

First, to perceive objects in the world. . .

[DeHart] So it has cameras in its eyes, and it also has a depth camera where it can actually see how far away things are within its vision, much like we do with our depth perception, and as soon as TALOS has that ability to see, "Oh, there's a human here, there's an object back here," then it can start building a map of its world, so that it knows in 3D what is all around it. [Howard] Computer vision is a way to mimic how we see the world. I can say, "Well, that's a TV," or "That's a cat," or "That's a dog," so what is it that I'm looking at when I'm looking at an image?

Computer vision is trying to figure out what are the objects that are in this image that represents what we would understand. And now I can teach the robot, basically by grabbing it, and I will just gently move the robot around, and guide it towards the bottle. .

. and you just hit the right trigger button to execute the same motion. Should I close it?

No, no, it will do that automatically this time, because I already kind of integrated this. [Werner] Also we want to explore application of A. I.

in understanding locomotion in terms of balancing. No, it's going to crash. [laughs] Played too much.

The biggest misconception about robots is that they are more capable than they are, that they're more generalized than they are. . .

[clattering] . . .

and they're not quite there yet. [Werner] Okay, let's give it one more shot. [DeHart] So you may have seen humanoid robots that can do parkour, do backflips.

The only ones that are really comparable are the Valkyrie that was designed by NASA, of which there's only two or three in the world. . .

and the Atlas from Boston Dynamics, but most of them have very few sensors, and a lot of the time, they will be remote-controlled, or given a very clean, pre-scripted path, and in a situation like that, what they're doing is pushing the limits of the mechanical systems. That's very similar to what we're doing here, except on our case, we're pushing the software side. [Brynjolfsson] The next step going forward is to start replacing all of our dumb, blind, dangerous machines with machines that have sensors and vision built into them, that can work side by side with humans to be more productive and safer at the same time.

[Werner] Maybe we can carry a table with the robot together? So if we put the table in its hands. .

. [DeHart] So for a task where the robot might be carrying something with a human, it's going to need to be able to feel how hard the human is pushing, pulling, much like if you're being guided in a dance. Can we also tighten the grippers, Alex?

Yeah, yeah. So as humans, we have a sense of touch, and we have a sense of how much force we're applying, and is being applied to us. With TALOS, it doesn't have a skin, it's hard plastic everywhere, so it can only really sense what's in its motors.

. . [Werner] This will take a second.

We have to teach it how to translate that into sort of a sense of touch. Try to keep it somewhat level, but if you kind of twist the table as well? [Werner] Let me show you how much is actually possible.

The one who writes the controller is always braver than the rest of us. [woman laughs] [Werner] And you just start pulling a bit. .

. and then the robot will walk with you. TALOS is using compliant control in the upper body, which basically makes him soft.

The robot automatically senses the forces being applied to the table. The human is still fully in control. This is very important to the safety of humans surrounding the robot.

Within robotics, the areas where you're really gonna see the important advances are those environments that are relatively controlled and predictable, so a good example is an Amazon warehouse. In those environments, you already see lots of people, and also lots of robots. .

. and they're working together. [Brynjolfsson] Once you have a process, and you've reduced it to an algorithm, you can replicate it so that if a robot can learn a whole new skill, you can copy that knowledge through the cloud to all the other robots, and now they all have that same skill.

It's a whole new world, a whole new kind of economics, and we're just beginning to understand its implications. [horn honking] [Downey] A whole new automated world still hard to imagine? Maybe it's more about what we're gaining than what we're losing.

Will automation make things faster? More efficient? More productive?

Tastier? [man] I've been making pizza for 15 years. In my life, I've probably made two million pizzas.

. . so coming to Zume was awesome, because it was like, wow, this is different than any other pizzeria.

[Alex Garden] So I got the idea for Zume about seven years ago when I realized that Big Pizza was built on one central conceit. . .

which was that there wasn't a better way to do it. I thought, "I wonder if there's a better way to do it? " [Downey] Big Pizza.

Doesn't have the same ring to it as Big Data, or Big Pharma, but it's not just about pizza. It's also about big waste. Last year in the U.

S. , about $200 billion worth of food was wasted. $200 billion.

That's a lot down the drain, and also up in the sky. Waste is a huge contributor to greenhouse gas emissions. [thunderclap] Perhaps half of all food that's produced in the world is wasted.

Artificial intelligence can help us change the supply chains so that they are much more efficient. If we can then have an impact there, that already has huge, huge implications. One of the classic tensions in every food business is producing too much, and having waste, or not producing enough food, and having stock outs.

It takes years and years of practice for a human to forecast that, but that's actually a data problem that machines, and A. I. in particular, are really, really good at getting right.

So at its core, Zume is a fundamentally new logistics model. We predict what we're going to sell before you even order it, and that whole predictive layer of the business is all driven by A. I.

[Chris Satchell] When Zume first called me, I obviously thought, "Why on Earth does a pizza company want a Chief Technology Officer? But then when I heard the vision, the fact that we were going to be able to change an industry by focusing on this amazing end-to-end platform, which just happens to also produce amazing pizza, then it made sense. [Downey] To disrupt Domino's, Zume uses machine learning to try and forecast how much pizza people want, what kind, and when.

It crunches dozens of different variables. . .

location, day of week, weather, what's on TV, past ordering trends, and then predicts how many Sgt. Pepperonis San Francisco will want on a Tuesday, for example. Our supply chains are incredibly inefficient.

Our food processing systems waste a lot of food. If we could improve predictions, we could eliminate most of that waste. We'd know what the demand was going to be, where the products were going to be, and ultimately, that would make us all better off.

Absolute perfection would be no pizza gets wasted, and anytime a customer looks, there's always one of the type of pizzas they would like available. So it's an optimization problem. How close can you get?

[Downey] Optimization, right, but what else? "End to end" is not just about prediction. It also involves automation, or.

. . cooking.

Pizza and robots, very exciting concept. Pepe and Giorgio, they dispense the sauce. We have Marta here, who spreads the sauce on the pizzas.

. . So we're still on Veggie Zupremes, guys.

These are looking good. [man] . .

. and down here at the end, we have Bruno, who helps deliver the pizza from the assembly line to the ovens, or around the bypass, and in the back, Vincenzo, who helps load the pizzas onto the cartridges. [Downey] Based on its algorithm, the A.

I. predicts how many pizzas, and what kind, to load onto the mobile kitchens. When orders come in, it also decides which mobile kitchen will bake it, and exactly when to put each one in the oven.

Every single pizza has its own cooking profile and recipe. So each one of these ovens actually is a robot. They're connected to cloud services that're always monitored, making it possible for one person working in here to cook up to 120 pizzas an hour.

[Zaida Ybarra] It's humans and technology working together. It's pretty awesome. That and the pizza.

I love pizza. Pizza is one of my favorite foods. We use A.

I. in simulation around how do we get the right delivery estimated time of arrival? The A.

I. can suggest here's the best place to put a truck so that we get the hottest, freshest product to our customers. So we have a BBQ Chicken.

BBQ. . .

[Ybarra] When we get the orders, our system controls the ovens, which controls the cook time, and how long the pizzas need to cook. [Satchell] The A. I.

can change the workflow on one of our vehicles to make sure the pizza's cooked at the last moment. . .

. and then the system chooses which driver to send it to. Here's your order.

. . A BBQ Chicken ranch.

It's algorithms plus, it's A. I. plus humans, not A.

I. instead of humans. Now we have a pineapple that is ready to go.

[Ybarra] Their predictions for the amount of pizzas that we carry on the truck are pretty spot on. Last Zupreme of the night. Sometimes we'll have a bunch of pizzas left over by the end of the night, and we'll think the prediction was probably wrong for today, and then boom, an order comes in, and the next thing you know, another order comes in, like, eight pizzas at once.

All of that data gets fed back into the learning algorithm, so every week we're trying to evolve our learning algorithms to do a better job than the week before. Part of what we're optimizing for is reducing waste. Maybe if you can use prediction to go back into the supply chain and predict more accurately what you need to grow, and where you need to get that product, I think you could fundamentally change things.

We're in the early stages of a massive change in technology that's allowing machines to do a lot of tasks that previously only humans could do. [Downey] The future of work is evolving. Automation's making the workplace safer, greener, more efficient, for sure.

If we can figure out how to integrate A. I. technologies, not to replace humans, but to augment their abilities, to make life more fun, to make us more productive and more creative.

. . I think that's where the power of the machine will come.

[Downey] Thousands of years ago, we were hunter-gatherers. Eventually, we became farmers, and now that we're maybe entering this fourth industrial revolution, one that connects the physical, biological, and digital, it's not just jobs that are being changed by A. I.

, but us. We are the progress. So why focus on the rear-view when we can look to the road ahead to see what we're becoming?

[woman sighs] [Ford] It's a real challenge in any case to build robots with the kind of mobility and dexterity that begins to approach what human beings have. Kind of the cliche that we all think of is that we'd like to have a robot that can go to the refrigerator and grab a beer for us. I mean, if you think about what's involved with that, that's still an enormous challenge.

A robot that's able to do that has to be heavy enough to go to a refrigerator. It has to be able to open the door, and it has to have the visual perception and the dexterity to reach in and grab the beer, and already that implies a fairly heavy machine, otherwise it would just tip over. Building a robot to do that is not just difficult, but it's gonna be fairly expensive, so I think that it's gonna be quite a while before we really see the kinds of robots that we imagine from the science-fiction world operating in our daily lives.

[robot] Get ready for a picture. Smile, please.