- 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 one's
"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 1700's.
But, people just call it MTurk.
So what I wanna 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 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.
(upbeat music)
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 a 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 it 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.
- (laughing) 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, you 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 wizzy 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 CAPTCHA's 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 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?
(atmospheric music)
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 was 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
is now known simply as move 37.
It was a move that was so unusual,
so counter to human expectations,
that the matches 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 learnt by itself.
Yes, it started from a foundation of human intelligence,
but it went beyond that.
This, I think is where people get so excited by AI,
we're a long long way away from building computers
that are as flexibly intelligent and 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 was 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, I hope you liked the video.
If you wanna know more about AI
we did a whole video about what these changes look like
at a social scale, whether AI's destroying jobs,
or gonna make everything free.
So you can check that out here,
or like and subscribe.