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TED2018

James Bridle: The nightmare videos of children's YouTube -- and what's wrong with the internet today

Filmed:
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Writer and artist James Bridle uncovers a dark, strange corner of the internet, where unknown people or groups on YouTube hack the brains of young children in return for advertising revenue. From "surprise egg" reveals and the "Finger Family Song" to algorithmically created mashups of familiar cartoon characters in violent situations, these videos exploit and terrify young minds -- and they tell us something about where our increasingly data-driven world is headed. "We need to stop thinking about technology as a solution to all of our problems, but think of it as a guide to what those problems actually are, so we can start thinking about them properly and start to address them," Bridle says.

- Artist, writer
Working across technologies and disciplines, James Bridle examines technology, knowledge and the end of the future. Full bio

I'm James.
00:12
I'm a writer and artist,
00:13
and I make work about technology.
00:15
I do things like draw life-size outlines
of military drones
00:18
in city streets around the world,
00:22
so that people can start to think
and get their heads around
00:24
these really quite hard-to-see
and hard-to-think-about technologies.
00:27
I make things like neural networks
that predict the results of elections
00:31
based on weather reports,
00:35
because I'm intrigued about
00:37
what the actual possibilities
of these weird new technologies are.
00:38
Last year, I built
my own self-driving car.
00:43
But because I don't
really trust technology,
00:45
I also designed a trap for it.
00:48
(Laughter)
00:50
And I do these things mostly because
I find them completely fascinating,
00:51
but also because I think
when we talk about technology,
00:56
we're largely talking about ourselves
00:58
and the way that we understand the world.
01:01
So here's a story about technology.
01:03
This is a "surprise egg" video.
01:07
It's basically a video of someone
opening up loads of chocolate eggs
01:10
and showing the toys inside to the viewer.
01:13
That's it. That's all it does
for seven long minutes.
01:16
And I want you to notice
two things about this.
01:19
First of all, this video
has 30 million views.
01:22
(Laughter)
01:26
And the other thing is,
01:28
it comes from a channel
that has 6.3 million subscribers,
01:29
that has a total of eight billion views,
01:33
and it's all just more videos like this --
01:36
30 million people watching a guy
opening up these eggs.
01:40
It sounds pretty weird, but if you search
for "surprise eggs" on YouTube,
01:44
it'll tell you there's
10 million of these videos,
01:48
and I think that's an undercount.
01:52
I think there's way, way more of these.
01:53
If you keep searching, they're endless.
01:55
There's millions and millions
of these videos
01:58
in increasingly baroque combinations
of brands and materials,
02:00
and there's more and more of them
being uploaded every single day.
02:03
Like, this is a strange world. Right?
02:07
But the thing is, it's not adults
who are watching these videos.
02:11
It's kids, small children.
02:14
These videos are
like crack for little kids.
02:17
There's something about the repetition,
02:19
the constant little
dopamine hit of the reveal,
02:21
that completely hooks them in.
02:24
And little kids watch these videos
over and over and over again,
02:26
and they do it for hours
and hours and hours.
02:31
And if you try and take
the screen away from them,
02:33
they'll scream and scream and scream.
02:35
If you don't believe me --
02:37
and I've already seen people
in the audience nodding --
02:38
if you don't believe me, find someone
with small children and ask them,
02:41
and they'll know about
the surprise egg videos.
02:44
So this is where we start.
02:47
It's 2018, and someone, or lots of people,
02:49
are using the same mechanism that, like,
Facebook and Instagram are using
02:53
to get you to keep checking that app,
02:56
and they're using it on YouTube
to hack the brains of very small children
02:58
in return for advertising revenue.
03:02
At least, I hope
that's what they're doing.
03:06
I hope that's what they're doing it for,
03:08
because there's easier ways
of making ad revenue on YouTube.
03:10
You can just make stuff up or steal stuff.
03:15
So if you search for really
popular kids' cartoons
03:18
like "Peppa Pig" or "Paw Patrol,"
03:20
you'll find there's millions and millions
of these online as well.
03:22
Of course, most of them aren't posted
by the original content creators.
03:25
They come from loads and loads
of different random accounts,
03:28
and it's impossible to know
who's posting them
03:31
or what their motives might be.
03:34
Does that sound kind of familiar?
03:36
Because it's exactly the same mechanism
03:38
that's happening across most
of our digital services,
03:40
where it's impossible to know
where this information is coming from.
03:43
It's basically fake news for kids,
03:46
and we're training them from birth
03:48
to click on the very first link
that comes along,
03:50
regardless of what the source is.
03:52
That's doesn't seem like
a terribly good idea.
03:54
Here's another thing
that's really big on kids' YouTube.
03:58
This is called the "Finger Family Song."
04:01
I just heard someone groan
in the audience.
04:03
This is the "Finger Family Song."
04:05
This is the very first one I could find.
04:06
It's from 2007, and it only has
200,000 views,
04:08
which is, like, nothing in this game.
04:11
But it has this insanely earwormy tune,
04:13
which I'm not going to play to you,
04:16
because it will sear itself
into your brain
04:18
in the same way that
it seared itself into mine,
04:20
and I'm not going to do that to you.
04:22
But like the surprise eggs,
04:24
it's got inside kids' heads
04:25
and addicted them to it.
04:27
So within a few years,
these finger family videos
04:29
start appearing everywhere,
04:32
and you get versions
in different languages
04:33
with popular kids' cartoons using food
04:35
or, frankly, using whatever kind
of animation elements
04:37
you seem to have lying around.
04:40
And once again, there are millions
and millions and millions of these videos
04:43
available online in all of these
kind of insane combinations.
04:48
And the more time
you start to spend with them,
04:51
the crazier and crazier
you start to feel that you might be.
04:53
And that's where I
kind of launched into this,
04:57
that feeling of deep strangeness
and deep lack of understanding
05:01
of how this thing was constructed
that seems to be presented around me.
05:04
Because it's impossible to know
where these things are coming from.
05:08
Like, who is making them?
05:12
Some of them appear to be made
of teams of professional animators.
05:13
Some of them are just randomly
assembled by software.
05:16
Some of them are quite wholesome-looking
young kids' entertainers.
05:19
And some of them are from people
05:23
who really clearly
shouldn't be around children at all.
05:25
(Laughter)
05:28
And once again, this impossibility
of figuring out who's making this stuff --
05:30
like, this is a bot?
05:35
Is this a person? Is this a troll?
05:36
What does it mean
that we can't tell the difference
05:39
between these things anymore?
05:41
And again, doesn't that uncertainty
feel kind of familiar right now?
05:43
So the main way people get views
on their videos --
05:50
and remember, views mean money --
05:52
is that they stuff the titles
of these videos with these popular terms.
05:54
So you take, like, "surprise eggs"
05:59
and then you add
"Paw Patrol," "Easter egg,"
06:00
or whatever these things are,
06:03
all of these words from other
popular videos into your title,
06:04
until you end up with this kind of
meaningless mash of language
06:07
that doesn't make sense to humans at all.
06:10
Because of course it's only really
tiny kids who are watching your video,
06:12
and what the hell do they know?
06:16
Your real audience
for this stuff is software.
06:18
It's the algorithms.
06:21
It's the software that YouTube uses
06:22
to select which videos
are like other videos,
06:24
to make them popular,
to make them recommended.
06:26
And that's why you end up with this
kind of completely meaningless mash,
06:29
both of title and of content.
06:32
But the thing is, you have to remember,
06:35
there really are still people within
this algorithmically optimized system,
06:37
people who are kind
of increasingly forced to act out
06:42
these increasingly bizarre
combinations of words,
06:45
like a desperate improvisation artist
responding to the combined screams
06:48
of a million toddlers at once.
06:53
There are real people
trapped within these systems,
06:57
and that's the other deeply strange thing
about this algorithmically driven culture,
06:59
because even if you're human,
07:03
you have to end up behaving like a machine
07:05
just to survive.
07:07
And also, on the other side of the screen,
07:09
there still are these little kids
watching this stuff,
07:11
stuck, their full attention grabbed
by these weird mechanisms.
07:14
And most of these kids are too small
to even use a website.
07:18
They're just kind of hammering
on the screen with their little hands.
07:21
And so there's autoplay,
07:24
where it just keeps playing these videos
over and over and over in a loop,
07:26
endlessly for hours and hours at a time.
07:29
And there's so much weirdness
in the system now
07:31
that autoplay takes you
to some pretty strange places.
07:34
This is how, within a dozen steps,
07:37
you can go from a cute video
of a counting train
07:40
to masturbating Mickey Mouse.
07:43
Yeah. I'm sorry about that.
07:46
This does get worse.
07:48
This is what happens
07:50
when all of these different keywords,
07:51
all these different pieces of attention,
07:54
this desperate generation of content,
07:57
all comes together into a single place.
08:00
This is where all those deeply weird
keywords come home to roost.
08:03
You cross-breed the finger family video
08:08
with some live-action superhero stuff,
08:10
you add in some weird,
trollish in-jokes or something,
08:12
and suddenly, you come
to a very weird place indeed.
08:16
The stuff that tends to upset parents
08:19
is the stuff that has kind of violent
or sexual content, right?
08:21
Children's cartoons getting assaulted,
08:25
getting killed,
08:27
weird pranks that actually
genuinely terrify children.
08:29
What you have is software pulling in
all of these different influences
08:33
to automatically generate
kids' worst nightmares.
08:37
And this stuff really, really
does affect small children.
08:39
Parents report their children
being traumatized,
08:42
becoming afraid of the dark,
08:45
becoming afraid of their favorite
cartoon characters.
08:47
If you take one thing away from this,
it's that if you have small children,
08:50
keep them the hell away from YouTube.
08:54
(Applause)
08:56
But the other thing, the thing
that really gets to me about this,
09:02
is that I'm not sure we even really
understand how we got to this point.
09:05
We've taken all of this influence,
all of these things,
09:10
and munged them together in a way
that no one really intended.
09:13
And yet, this is also the way
that we're building the entire world.
09:16
We're taking all of this data,
09:20
a lot of it bad data,
09:21
a lot of historical data
full of prejudice,
09:23
full of all of our worst
impulses of history,
09:26
and we're building that
into huge data sets
09:29
and then we're automating it.
09:31
And we're munging it together
into things like credit reports,
09:32
into insurance premiums,
09:36
into things like predictive
policing systems,
09:37
into sentencing guidelines.
09:40
This is the way we're actually
constructing the world today
09:42
out of this data.
09:45
And I don't know what's worse,
09:46
that we built a system
that seems to be entirely optimized
09:48
for the absolute worst aspects
of human behavior,
09:51
or that we seem
to have done it by accident,
09:54
without even realizing
that we were doing it,
09:56
because we didn't really understand
the systems that we were building,
09:58
and we didn't really understand
how to do anything differently with it.
10:02
There's a couple of things I think
that really seem to be driving this
10:06
most fully on YouTube,
10:10
and the first of those is advertising,
10:11
which is the monetization of attention
10:13
without any real other variables at work,
10:16
any care for the people who are
actually developing this content,
10:19
the centralization of the power,
the separation of those things.
10:23
And I think however you feel
about the use of advertising
10:26
to kind of support stuff,
10:29
the sight of grown men in diapers
rolling around in the sand
10:31
in the hope that an algorithm
that they don't really understand
10:34
will give them money for it
10:37
suggests that this
probably isn't the thing
10:38
that we should be basing
our society and culture upon,
10:40
and the way in which
we should be funding it.
10:43
And the other thing that's kind of
the major driver of this is automation,
10:45
which is the deployment
of all of this technology
10:49
as soon as it arrives,
without any kind of oversight,
10:51
and then once it's out there,
10:53
kind of throwing up our hands and going,
"Hey, it's not us, it's the technology."
10:55
Like, "We're not involved in it."
10:59
That's not really good enough,
11:00
because this stuff isn't
just algorithmically governed,
11:02
it's also algorithmically policed.
11:05
When YouTube first started
to pay attention to this,
11:07
the first thing they said
they'd do about it
11:10
was that they'd deploy
better machine learning algorithms
11:12
to moderate the content.
11:15
Well, machine learning,
as any expert in it will tell you,
11:17
is basically what we've started to call
11:20
software that we don't really
understand how it works.
11:22
And I think we have
enough of that already.
11:25
We shouldn't be leaving
this stuff up to AI to decide
11:29
what's appropriate or not,
11:32
because we know what happens.
11:33
It'll start censoring other things.
11:34
It'll start censoring queer content.
11:36
It'll start censoring
legitimate public speech.
11:38
What's allowed in these discourses,
11:40
it shouldn't be something
that's left up to unaccountable systems.
11:42
It's part of a discussion
all of us should be having.
11:45
But I'd leave a reminder
11:48
that the alternative isn't
very pleasant, either.
11:50
YouTube also announced recently
11:52
that they're going to release
a version of their kids' app
11:54
that would be entirely
moderated by humans.
11:57
Facebook -- Zuckerberg said
much the same thing at Congress,
12:00
when pressed about how they
were going to moderate their stuff.
12:03
He said they'd have humans doing it.
12:06
And what that really means is,
12:08
instead of having toddlers being
the first person to see this stuff,
12:10
you're going to have underpaid,
precarious contract workers
12:13
without proper mental health support
12:16
being damaged by it as well.
12:17
(Laughter)
12:19
And I think we can all do
quite a lot better than that.
12:20
(Applause)
12:22
The thought, I think, that brings those
two things together, really, for me,
12:26
is agency.
12:30
It's like, how much do we really
understand -- by agency, I mean:
12:32
how we know how to act
in our own best interests.
12:35
Which -- it's almost impossible to do
12:39
in these systems that we don't
really fully understand.
12:41
Inequality of power
always leads to violence.
12:45
And we can see inside these systems
12:48
that inequality of understanding
does the same thing.
12:49
If there's one thing that we can do
to start to improve these systems,
12:52
it's to make them more legible
to the people who use them,
12:56
so that all of us have
a common understanding
12:59
of what's actually going on here.
13:01
The thing, though, I think
most about these systems
13:03
is that this isn't, as I hope
I've explained, really about YouTube.
13:06
It's about everything.
13:10
These issues of accountability and agency,
13:12
of opacity and complexity,
13:14
of the violence and exploitation
that inherently results
13:16
from the concentration
of power in a few hands --
13:20
these are much, much larger issues.
13:22
And they're issues not just of YouTube
and not just of technology in general,
13:26
and they're not even new.
13:30
They've been with us for ages.
13:31
But we finally built this system,
this global system, the internet,
13:32
that's actually showing them to us
in this extraordinary way,
13:37
making them undeniable.
13:40
Technology has this extraordinary capacity
13:41
to both instantiate and continue
13:44
all of our most extraordinary,
often hidden desires and biases
13:48
and encoding them into the world,
13:53
but it also writes them down
so that we can see them,
13:54
so that we can't pretend
they don't exist anymore.
13:58
We need to stop thinking about technology
as a solution to all of our problems,
14:01
but think of it as a guide
to what those problems actually are,
14:06
so we can start thinking
about them properly
14:09
and start to address them.
14:12
Thank you very much.
14:13
(Applause)
14:15
Thank you.
14:21
(Applause)
14:22
Helen Walters: James, thank you
for coming and giving us that talk.
14:28
So it's interesting:
14:32
when you think about the films where
the robotic overlords take over,
14:33
it's all a bit more glamorous
than what you're describing.
14:36
But I wonder -- in those films,
you have the resistance mounting.
14:40
Is there a resistance mounting
towards this stuff?
14:43
Do you see any positive signs,
green shoots of resistance?
14:47
James Bridle: I don't know
about direct resistance,
14:52
because I think this stuff
is super long-term.
14:54
I think it's baked into culture
in really deep ways.
14:57
A friend of mine,
Eleanor Saitta, always says
14:59
that any technological problems
of sufficient scale and scope
15:01
are political problems first of all.
15:05
So all of these things we're working
to address within this
15:07
are not going to be addressed
just by building the technology better,
15:10
but actually by changing the society
that's producing these technologies.
15:13
So no, right now, I think we've got
a hell of a long way to go.
15:17
But as I said, I think by unpacking them,
15:20
by explaining them, by talking
about them super honestly,
15:22
we can actually start
to at least begin that process.
15:25
HW: And so when you talk about
legibility and digital literacy,
15:27
I find it difficult to imagine
15:31
that we need to place the burden
of digital literacy on users themselves.
15:32
But whose responsibility
is education in this new world?
15:36
JB: Again, I think this responsibility
is kind of up to all of us,
15:41
that everything we do,
everything we build, everything we make,
15:44
needs to be made
in a consensual discussion
15:47
with everyone who's avoiding it;
15:51
that we're not building systems
intended to trick and surprise people
15:53
into doing the right thing,
15:57
but that they're actually involved
in every step in educating them,
16:00
because each of these systems
is educational.
16:03
That's what I'm hopeful about,
about even this really grim stuff,
16:05
that if you can take it
and look at it properly,
16:08
it's actually in itself
a piece of education
16:11
that allows you to start seeing
how complex systems come together and work
16:13
and maybe be able to apply
that knowledge elsewhere in the world.
16:17
HW: James, it's such
an important discussion,
16:20
and I know many people here
are really open and prepared to have it,
16:22
so thanks for starting off our morning.
16:26
JB: Thanks very much. Cheers.
16:27
(Applause)
16:29

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About the speaker:

James Bridle - Artist, writer
Working across technologies and disciplines, James Bridle examines technology, knowledge and the end of the future.

Why you should listen

James Bridle is an artist and writer working across technologies and disciplines. His artworks and installations have been exhibited in Europe, North and South America, Asia and Australia, and have been viewed by hundreds of thousands of visitors online. He has been commissioned by organizations including the Victoria & Albert Museum, the Barbican, Artangel, the Oslo Architecture Triennale and the Istanbul Design Biennial, and he has been honored by Ars Electronica, the Japan Media Arts Festival and the Design Museum, London. His writing on literature, culture and networks has appeared in magazines and newspapers including Frieze, Wired, Domus, Cabinet, The Atlantic, the New Statesman and many others, and he has written a regular column for The Observer.

New Dark Age, Bridle's book about technology, knowledge and the end of the future is forthcoming from Verso (UK & US) in 2018. He lectures regularly on radio, at conferences, universities and other events including SXSW, Lift, the Global Art Forum and Re:Publica. He has been a resident at Lighthouse, Brighton, the White Building, London and Eyebeam, New York, and an adjunct professor on the interactive telecommunications program at New York University.

More profile about the speaker
James Bridle | Speaker | TED.com