 This is gonna sound like a scam, but I found a way to make extremely small amounts of money online. So the one that caught my eye here is Pinterest, which we all know and love Pinterest. Determine the topical relatedness between pieces of text. 40 cents. Wait, there's one more. So this was find info from an email for three cents. They're just gonna email me. And then I like find info from it, which seems cool. It turns out Amazon runs an online marketplace that farms out basic tasks computer programs have a hard time with. It's called Mechanical Turk. Named after a robot from the 1700s. But people just call it M Turk. So what I want to do now is Leonard Monteiro will pay me three cents to write the prices shown in an image. I'm not totally sure why this is like worth doing money, but okay, let's do it. It looks like a parking meter. I hope that this is for a good cause somehow that this is like this app is helping people rather than just sending bills to people. So I feel like on the other end of this task, there's some sort of automated parking meter app, which is weird, but it turns out stuff like this happens all the time. Nearly every successful AI product has human beings behind it. You just don't see them until you look at the big picture. So what's going on here? Is there actually an app that claims to read parking meter fines, but it's actually humans doing it? Or am I helping train their AI and these meters are just hypothetical examples? To figure that out, I asked our resident AI expert, James Vincent. So I did a little Googling here and yeah, sorry Russell, this is not a parking meter at all. This is actually a little gadget you put in supermarkets and you scan barcodes to check the prices. Now the bigger question is why does someone want you to write down all these prices? And I have two answers for you. In the first case, creating training data. Say you want to make a machine vision system that automatically does what you're doing. How does it actually know where to look in the picture? How does it know what the barcode scanner looks like? To teach you that information, you need to feed it labeled data. You need to get a human to do that labeling. In this case, Russell, he labels the data, it goes into the system and the system learns what these things look like. Oh no, there's so much more. That is how you train an AI system, but sometimes these systems, they don't work, right? So use case number two, what's that? Well, that might be where the AI system actually can't do what it says it can do. It might be that there's too much glare in the picture and it can't read the numbers on the screen very well. In those cases, you need to throw the data to someone who has the intelligence to work out what's going on. That's not a machine, that's a human like Russell, and they will label the data for the machine and return it back to the end user. Sometimes companies are upfront about this and sometimes they lie about it too. Sometimes they will say, yes, we've got a whizzy AI system that's doing all this automatically and actually they don't. Turns out that that AI is a lot of low paid workers on a system like Mechanical Turk, like Russell, providing this data in the background. If you've ever filled out a CAPTCHA, you've probably done some of that work yourself. In theory, those tests are meant to verify that you're human, but Google has started using them to collect data for other products too. Typing out this blurry word could help the character recognition algorithm in Google Books. These skewed numbers are probably helping confirm an address in Google Street View. The most recent CAPTCHAs ask you to identify all the squares of a picture that have a car in it, at the same time that Google's Waymo branch is trying to help train self driving cars. Even a simple task like setting a timer with Google Assistant can require an army of contractors manually annotating the data, as a recent Guardian investigation showed. Sometimes users do the labeling themselves. Facebook has some of the best facial recognition data in the world because they already have dozens of pictures of your face. You added them yourself. Multiply that across billions of users and it's all the data you need to build a facial recognition system, which can then start automatically tagging your friends in the next set of pictures you upload. Suddenly, Facebook has one of the most advanced facial recognition systems in the world, and they didn't have to pay a dime for it. When researchers at Google were trying to build a depth sensing camera, they went even further. What they really needed were a bunch of videos where mobile cameras explored static space from different angles. But where would they find that? Google downloaded 2000 mannequin challenge videos, fed them into an algorithm, and a new kind of depth sensing software was born. Think about it. Every minute, 500 new hours of content are added to YouTube. If you're training an AI, that's a lot of video to draw on. And there are no copyright restrictions on what you can use for training data. The same goes for websites, images, Wikipedia pages. It's all just there for the taking. This has been a huge driving force for the AI boom. These systems need lots of examples to recognize even the most basic patterns. That used to mean months of data entry, but now you can scrape everything you need from the internet in a matter of hours. And the people who made the mannequin challenge videos, they didn't think they were encoding depth information. If the researchers hadn't talked about their training system, it would feel like they'd done it all on their own. The remarkable thing about AI systems is that even though they are built on a foundation of human intelligence, they regularly transcend that and do something that surprises us or goes beyond what we thought was possible. One fantastic example of this is the AlphaGo program, which was designed by DeepMind, which is Google's AI lab here in London. And in 2016 and 2017, it played and beat the human champions of the ancient board game, Go. There's one particularly famous moment. It's now known simply as Move 37. It was a move that was so unusual, so counter to human expectations, that the match's commentators thought it was a mistake. But it wasn't. It was a beautiful play that completely undermined Lee's match and led to AlphaGo winning the game. And it was something that humans couldn't teach. It was something that the machine had learned by itself. Yes, it started from a foundation of human intelligence, but it went beyond that. This, I think, is why people get so excited by AI. We're a long, long way away from building computers that are as flexibly intelligent and sort of sophisticated as humans. But we can still build algorithms and systems that exceed human intelligence, even in very specific domains. But that's AI at its best. The flip side is when an app needs a description of what's in a photo, and the photo recognizing algorithm just doesn't work. So you get a human being to fill it in, usually through a post on Mechanical Turk. That's a very old trick, going all the way back to the machine that gave the site its name. The original Mechanical Turk was this guy, a master chess playing robot, hundreds of years before there was anything we would think of as a computer. The Turk could beat most chess players, playing so well that people thought it was a technological marvel. But really, it was just a trick. There's a human being inside, hiding under the table and directing the moves from below. It was a human being dressed up as a machine, a trick no one had thought of until then. And as Amazon can tell you, the trick still works. Thanks for watching. Hope you liked the video. If you want to know more about AI, we did a whole video about sort of what these changes look like at a social scale, whether AI is destroying jobs or going to make everything free. So you can check that out here or like and subscribe.