The nightmare videos of childrens' YouTube -- and what's wrong with the internet today
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0:01 - 0:02I'm James.
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0:02 - 0:04I'm a writer and artist,
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0:04 - 0:06and I make work about technology.
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0:06 - 0:10I do things like draw life-size outlines
of military drones -
0:10 - 0:12in city streets around the world,
-
0:12 - 0:15so that people can start to think
and get their heads around -
0:15 - 0:19these really quite hard-to-see
and hard-to-think-about technologies. -
0:19 - 0:23I make things like neural networks
that predict the results of elections -
0:23 - 0:25based on weather reports,
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0:25 - 0:26because I'm intrigued about
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0:26 - 0:30what the actual possibilities
of these weird new technologies are. -
0:31 - 0:34Last year, I built
my own self-driving car. -
0:34 - 0:36But because I don't
really trust technology, -
0:36 - 0:38I also designed a trap for it.
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0:39 - 0:40(Laughter)
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0:40 - 0:44And I do these things mostly because
I find them completely fascinating, -
0:44 - 0:47but also because I think
when we talk about technology, -
0:47 - 0:49we're largely talking about ourselves
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0:49 - 0:52and the way that we understand the world.
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0:52 - 0:54So here's a story about technology.
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0:56 - 0:58This is a "surprise egg" video.
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0:58 - 1:02It's basically a video of someone
opening up loads of chocolate eggs -
1:02 - 1:04and showing the toys inside to the viewer.
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1:04 - 1:07That's it. That's all it does
for seven long minutes. -
1:07 - 1:10And I want you to notice
two things about this. -
1:11 - 1:15First of all, this video
has 30 million views. -
1:15 - 1:16(Laughter)
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1:16 - 1:18And the other thing is,
-
1:18 - 1:21it comes from a channel
that has 6.3 million subscribers, -
1:21 - 1:24that has a total of eight billion views,
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1:24 - 1:27and it's all just more videos like this --
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1:28 - 1:3230 million people watching a guy
opening up these eggs. -
1:32 - 1:37It sounds pretty weird, but if you search
for "surprise eggs" on YouTube, -
1:37 - 1:40it'll tell you there's
10 million of these videos, -
1:40 - 1:42and I think that's an undercount.
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1:42 - 1:44I think there's way, way more of these.
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1:44 - 1:46If you keep searching, they're endless.
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1:46 - 1:48There's millions and millions
of these videos -
1:48 - 1:52in increasingly baroque combinations
of brands and materials, -
1:52 - 1:56and there's more and more of them
being uploaded every single day. -
1:56 - 1:59Like, this is a strange world. Right?
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1:59 - 2:03But the thing is, it's not adults
who are watching these videos. -
2:03 - 2:06It's kids, small children.
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2:06 - 2:08These videos are
like crack for little kids. -
2:08 - 2:10There's something about the repetition,
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2:10 - 2:12the constant little
dopamine hit of the reveal, -
2:12 - 2:14that completely hooks them in.
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2:14 - 2:19And little kids watch these videos
over and over and over again, -
2:19 - 2:21and they do it for hours
and hours and hours. -
2:21 - 2:24And if you try and take
the screen away from them, -
2:24 - 2:26they'll scream and scream and scream.
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2:26 - 2:27If you don't believe me --
-
2:27 - 2:29and I've already seen people
in the audience nodding -- -
2:29 - 2:33if you don't believe me, find someone
with small children and ask them, -
2:33 - 2:35and they'll know about
the surprise egg videos. -
2:35 - 2:37So this is where we start.
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2:37 - 2:41It's 2018, and someone, or lots of people,
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2:41 - 2:45are using the same mechanism that, like,
Facebook and Instagram are using -
2:45 - 2:47to get you to keep checking that app,
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2:47 - 2:51and they're using it on YouTube
to hack the brains of very small children -
2:51 - 2:53in return for advertising revenue.
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2:54 - 2:56At least, I hope
that's what they're doing. -
2:56 - 2:58I hope that's what they're doing it for,
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2:58 - 3:04because there's easier ways
of making ad revenue on YouTube. -
3:04 - 3:06You can just make stuff up or steal stuff.
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3:06 - 3:09So if you search for really
popular kids' cartoons -
3:09 - 3:10like "Pepper Pig" or "Paw Patrol,"
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3:10 - 3:14you'll find there's millions and millions
of these online as well. -
3:14 - 3:17Of course, most of them aren't posted
by the original content creators. -
3:17 - 3:20They come from loads and loads
of different random accounts, -
3:20 - 3:22and it's impossible to know
who's posting them -
3:22 - 3:24or what their motives might be.
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3:24 - 3:26Does that sound kind of familiar?
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3:26 - 3:28Because it's exactly the same mechanism
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3:28 - 3:31that's happening across most
of our digital services, -
3:31 - 3:34where it's impossible to know
where this information is coming from. -
3:34 - 3:36It's basically fake news for kids,
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3:36 - 3:38and we're training them from birth
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3:38 - 3:41to click on the very first link
that comes along, -
3:41 - 3:43regardless of what the source is.
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3:43 - 3:45That's doesn't seem like
a terribly good idea. -
3:46 - 3:49Here's another thing
that's really big on kids' YouTube. -
3:49 - 3:51This is called the "Finger Family Song."
-
3:51 - 3:53I just heard someone groan
in the audience. -
3:53 - 3:55This is the "Finger Family Song."
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3:55 - 3:57This is the very first one I could find.
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3:57 - 4:00It's from 2007, and it only has
200,000 views, -
4:00 - 4:02which is, like, nothing in this game.
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4:02 - 4:04But it has this insanely earwormy tune,
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4:04 - 4:06which I'm not going to play to you,
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4:06 - 4:08because it will sear itself
into your brain -
4:08 - 4:11in the same way that
it seared itself into mine, -
4:11 - 4:12and I'm not going to do that to you.
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4:12 - 4:14But like the surprise eggs,
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4:14 - 4:16it's got inside kids' heads
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4:16 - 4:18and addicted them to it.
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4:18 - 4:20So within a few years,
these finger family videos -
4:20 - 4:21start appearing everywhere,
-
4:21 - 4:24and you get versions
in different languages -
4:24 - 4:26with popular kids' cartoons using food
-
4:26 - 4:28or frankly, using whatever kind
of animation elements -
4:28 - 4:31you seem to have lying around.
-
4:31 - 4:36And once again, there are millions
and millions and millions of these videos -
4:36 - 4:40available online in all of these
kind of insane combinations. -
4:40 - 4:42And the more time
you start to spend with them, -
4:42 - 4:46the crazier and crazier
you start to feel that you might be. -
4:46 - 4:49And that's where I
kind of launched into this, -
4:49 - 4:53that feeling of deep strangeness
and deep lack of understanding -
4:53 - 4:57of how this thing was constructed
that seems to be presented around me. -
4:57 - 5:00Because it's impossible to know
where these things are coming from. -
5:00 - 5:01Like, who is making them?
-
5:01 - 5:04Some of them appear to made
of teams of professional animators. -
5:05 - 5:07Some of them are just randomly
assembled by software. -
5:07 - 5:12Some of them are quite wholesome-looking
young kids' entertainers, -
5:12 - 5:13And some of them are from people
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5:13 - 5:16who really clearly
shouldn't be around children at all. -
5:16 - 5:18(Laughter)
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5:19 - 5:24And once again, this impossibility
of figuring out who's making this stuff -- -
5:24 - 5:25like, this is a bot?
-
5:25 - 5:27Is this a person? Is this a troll?
-
5:28 - 5:30What does it mean
that we can't tell the difference -
5:30 - 5:31between these things anymore?
-
5:32 - 5:36And again, doesn't that uncertainty
feel kind of familiar right now? -
5:38 - 5:41So the main way people get views
on their videos -- -
5:41 - 5:42and remember, views mean money --
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5:42 - 5:47is that they stuff the titles
of these videos with these popular terms. -
5:47 - 5:49So you take, like, "surprise eggs"
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5:49 - 5:51and then you add
"Paw Patrol," "Easter egg," -
5:51 - 5:52or whatever these things are,
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5:52 - 5:55all of these words from other
popular videos into your title, -
5:55 - 5:58until you end up with this kind of
meaningless mash of language -
5:58 - 6:01that doesn't make sense to humans at all.
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6:01 - 6:04Because of course it's only really
tiny kids who are watching your video, -
6:04 - 6:06and what the hell do they know?
-
6:06 - 6:09Your real audience
for this stuff is software. -
6:09 - 6:11It's the algorithms.
-
6:11 - 6:12It's the software that YouTube uses
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6:12 - 6:15to select which videos
are like other videos, -
6:15 - 6:17to make them popular,
to make them recommended. -
6:17 - 6:21And that's why you end up with this
kind of completely meaningless mash, -
6:21 - 6:23both of title and of content.
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6:24 - 6:26But the thing is, you have to remember,
-
6:26 - 6:30there really are still people within
this algorithmically optimized system, -
6:30 - 6:33people who are kind
of increasingly forced to act out -
6:33 - 6:36these increasingly bizarre
combinations of words, -
6:36 - 6:41like a desperate improvisation artist
responding to the combined screams -
6:41 - 6:44of a million toddlers at once.
-
6:45 - 6:48There are real people
trapped within these systems, -
6:48 - 6:52and that's the other deeply strange thing
about this algorithmically driven culture, -
6:52 - 6:53because even if you're human,
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6:53 - 6:55you have to end up behaving like a machine
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6:55 - 6:57just to survive.
-
6:57 - 6:59And also, on the other side of the screen,
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6:59 - 7:02there still are these little kids
watching this stuff, -
7:02 - 7:06stuck, their full attention grabbed
by these weird mechanisms. -
7:07 - 7:10And most of these kids are too small
to even use a website. -
7:10 - 7:13They're just kind of hammering
on the screen with their little hands. -
7:13 - 7:14And so there's autoplay,
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7:14 - 7:18where it just keeps playing these videos
over and over and over in a loop, -
7:18 - 7:20endlessly for hours and hours at a time.
-
7:20 - 7:23And there's so much weirdness
in the system now -
7:23 - 7:26that autoplay takes you
to some pretty strange places. -
7:26 - 7:28This is how, within a dozen steps,
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7:28 - 7:31you can go from a cute video
of a counting train -
7:31 - 7:34to masturbating Mickey Mouse.
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7:35 - 7:37Yeah. I'm sorry about that.
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7:37 - 7:39This does get worse.
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7:39 - 7:40This is what happens
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7:40 - 7:43when all of these different keywords,
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7:43 - 7:45all these different pieces of attention,
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7:45 - 7:48this desperate generation of content,
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7:48 - 7:51all comes together into a single place.
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7:52 - 7:56This is where all those deeply weird
keywords come home to roost. -
7:56 - 7:59You cross-breed the finger family video
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7:59 - 8:01with some live-action superhero stuff,
-
8:01 - 8:04you add in some weird,
trollish in-jokes or something, -
8:04 - 8:08and suddenly, you come
to a very weird place indeed. -
8:08 - 8:10The stuff that tends to upset parents
-
8:10 - 8:13is the stuff that has kind of violent
or sexual content, right? -
8:13 - 8:16Children's cartoons getting assaulted,
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8:16 - 8:18getting killed,
-
8:18 - 8:21weird pranks that actually
genuinely terrify children. -
8:21 - 8:25What you have is software pulling in
all of these different influences -
8:25 - 8:28to automatically generate
kids' worst nightmares. -
8:28 - 8:31And this stuff really, really
does affect small children. -
8:31 - 8:34Parents report their children
being traumatized, -
8:34 - 8:35becoming afraid of the dark,
-
8:35 - 8:38becoming afraid of their favorite
cartoon characters. -
8:39 - 8:42If you take one thing away from this,
it's that if you have small children, -
8:42 - 8:44keep them the hell away from YouTube.
-
8:45 - 8:49(Applause)
-
8:51 - 8:54But the other thing, the thing
that really gets to me about this, -
8:54 - 8:58is that I'm not sure we even really
understand how we got to this point. -
8:59 - 9:02We've taken all of this influence,
all of these things, -
9:02 - 9:05and munged them together in a way
that no one really intended. -
9:05 - 9:08And yet, this is also the way
that we're building the entire world. -
9:08 - 9:10We're taking all of this data,
-
9:10 - 9:11a lot of it bad data,
-
9:11 - 9:14a lot of historical data
full of prejudice, -
9:14 - 9:17full of all of our worst
impulses of history, -
9:17 - 9:19and we're building that
into huge data sets -
9:19 - 9:21and then we're automating it.
-
9:21 - 9:24And we're munging it together
into things like credit reports, -
9:24 - 9:26into insurance premiums,
-
9:26 - 9:29into things like predictive
policing systems, -
9:29 - 9:30into sentencing guidelines.
-
9:30 - 9:33This is the way we're actually
constructing the world today -
9:33 - 9:34out of this data.
-
9:34 - 9:36And I don't know what's worse,
-
9:36 - 9:39that we built a system
that seems to be entirely optimized -
9:39 - 9:42for the absolute worst aspects
of human behavior, -
9:42 - 9:45or that we seem
to have done it by accident, -
9:45 - 9:47without even realizing
that we were doing it, -
9:47 - 9:50because we didn't really understand
the systems that we were building, -
9:50 - 9:54and we didn't really understand
how to do anything differently with it. -
9:55 - 9:58There's a couple of things I think
that really seem to be driving this -
9:58 - 9:59most fully on YouTube,
-
9:59 - 10:01and the first of those is advertising,
-
10:01 - 10:04which is the monetization of attention
-
10:04 - 10:07without any real other variables at work,
-
10:07 - 10:11any care for the people who are
actually developing this content, -
10:11 - 10:15the centralization of the power,
the separation of those things. -
10:15 - 10:18And I think however you feel
about the use of advertising -
10:18 - 10:19to kind of support stuff,
-
10:19 - 10:22the sight of grown men in diapers
rolling around the in the sand -
10:22 - 10:25in the hope that an algorithm
that they don't really understand -
10:25 - 10:27will give them money for it
-
10:27 - 10:29suggests that this
probably isn't the thing -
10:29 - 10:31that we should be basing
our society and culture upon, -
10:31 - 10:33and the way in which
we should be funding it. -
10:34 - 10:37And the other thing that's kind of
the major driver of this is automation, -
10:37 - 10:39which is the deployment
of all of this technology -
10:39 - 10:42as soon as it arrives,
without any kind of oversight, -
10:42 - 10:43and then once it's out there,
-
10:43 - 10:47kind of throwing up our hands and going,
"Hey, it's not us, it's the technology." -
10:47 - 10:49Like, "We're not involved in it."
-
10:49 - 10:51That's not really good enough,
-
10:51 - 10:53because this stuff isn't
just algorithmically governed, -
10:53 - 10:56it's also algorithmically policed.
-
10:56 - 10:59When YouTube first started
to pay attention to this, -
10:59 - 11:01the first thing they said
they'd do about it -
11:01 - 11:04was that they'd deploy
better machine learning algorithms -
11:04 - 11:05to moderate the content.
-
11:05 - 11:09Well, machine learning,
as any expert in it will tell you, -
11:09 - 11:10is basically what we've started to call
-
11:10 - 11:13software that we don't really
understand how it works. -
11:13 - 11:17And I think we have
enough of that already. -
11:17 - 11:20We shouldn't be leaving
this stuff up to AI to decide -
11:20 - 11:22what's appropriate or not,
-
11:22 - 11:23because we know what happens.
-
11:23 - 11:25It'll start censoring other things.
-
11:25 - 11:26It'll start censoring queer content.
-
11:27 - 11:29It'll start censoring
legitimate public speech. -
11:29 - 11:31What's allowed in these discourses,
-
11:31 - 11:34it shouldn't be something
that's left up to unaccountable systems. -
11:34 - 11:37It's part of a discussion
all of us should be having. -
11:37 - 11:38But I'd leave a reminder
-
11:38 - 11:41that the alternative isn't
very pleasant, either. -
11:41 - 11:42YouTube also announced recently
-
11:42 - 11:45that they're going to release
a version of their kids' app -
11:45 - 11:48that would be entirely
moderated by humans. -
11:48 - 11:52Facebook -- Zuckerberg said
much the same thing at Congress, -
11:52 - 11:55when pressed about how they
were going to moderate their stuff. -
11:55 - 11:57He said they'd have humans doing it.
-
11:57 - 11:58And what that really means is,
-
11:58 - 12:01instead of having toddlers being
the first person to see this stuff, -
12:01 - 12:04you're going to have underpaid,
precarious contract workers -
12:04 - 12:06without proper mental health support
-
12:06 - 12:07being damaged by it as well.
-
12:07 - 12:08(Laughter)
-
12:08 - 12:11And I think we can all do
quite a lot better than that. -
12:11 - 12:13(Applause)
-
12:14 - 12:19The thought, I think, that brings those
two things together, really, for me, -
12:19 - 12:20is agency.
-
12:20 - 12:23It's like, how much do we really
understand -- by agency, I mean: -
12:23 - 12:28how we know how to act
in our own best interests. -
12:28 - 12:30Which -- it's almost impossible to do
-
12:30 - 12:33in these systems that we don't
really fully understand. -
12:33 - 12:36Inequality of power
always leads to violence. -
12:36 - 12:38And we can see inside these systems
-
12:38 - 12:40that inequality of understanding
does the same thing. -
12:41 - 12:44If there's one thing that we can do
to start to improve these systems, -
12:44 - 12:47it's to make them more legible
to the people who use them, -
12:47 - 12:49so that all of us have
a common understanding -
12:49 - 12:51of what's actually going on here.
-
12:52 - 12:55The thing, though, I think
most about these systems -
12:55 - 12:59is that this isn't, as I hope
I've explained, really about YouTube. -
12:59 - 13:00It's about everything.
-
13:00 - 13:03These issues of accountability and agency,
-
13:03 - 13:05of opacity and complexity,
-
13:05 - 13:08of the violence and exploitation
that inherently results -
13:08 - 13:11from the concentration
of power in a few hands -- -
13:11 - 13:13these are much, much larger issues.
-
13:14 - 13:18And they're issues not just of YouTube
and not just of technology in general, -
13:18 - 13:19and they're not even new.
-
13:19 - 13:21They've been with us for ages.
-
13:21 - 13:25But we finally built this system,
this global system, the internet, -
13:25 - 13:28that's actually showing them to us
in this extraordinary way, -
13:28 - 13:30making them undeniable.
-
13:30 - 13:33Technology has this extraordinary capacity
-
13:33 - 13:37to both instantiate and continue
-
13:37 - 13:41all of our most extraordinary,
often hidden desires and biases -
13:41 - 13:43and encoding them into the world,
-
13:43 - 13:46but it also writes them down
so that we can see them, -
13:46 - 13:50so that we can't pretend
they don't exist anymore. -
13:50 - 13:54We need to stop thinking about technology
as a solution to all of our problems, -
13:54 - 13:58but think of it as a guide
to what those problems actually are, -
13:58 - 14:00so we can start thinking
about them properly -
14:00 - 14:02and start to address them.
-
14:02 - 14:03Thank you very much.
-
14:03 - 14:08(Applause)
-
14:10 - 14:11Thank you.
-
14:11 - 14:14(Applause)
-
14:17 - 14:20Helen Walters: James, thank you
for coming and giving us that talk. -
14:20 - 14:21So it's interesting:
-
14:21 - 14:25when you think about the films where
the robotic overlords take over, -
14:25 - 14:28it's all a bit more glamorous
than what you're describing. -
14:28 - 14:32But I wonder -- in those films,
you have the resistance mounting. -
14:32 - 14:35Is there a resistance mounting
towards this stuff? -
14:35 - 14:39Do you see any positive signs,
green shoots of resistance? -
14:41 - 14:43James Bridle: I don't know
about direct resistance, -
14:43 - 14:45because I think this stuff
is super long-term. -
14:45 - 14:48I think it's baked into culture
in really deep ways. -
14:48 - 14:50A friend of mine,
Eleanor Saitta, always says -
14:50 - 14:54that any technological problems
of sufficient scale and scope -
14:54 - 14:56are political problems first of all.
-
14:56 - 14:59So all of these things we're working
to address within this -
14:59 - 15:02are not going to be addressed
just by building the technology better, -
15:02 - 15:05but actually by changing the society
that's producing these technologies. -
15:05 - 15:08So no, right now, I think we've got
a hell of a long way to go. -
15:09 - 15:10But as I said, I think by unpacking them,
-
15:11 - 15:13by explaining them, by talking
about them super honestly, -
15:13 - 15:16we can actually start
to at least begin that process. -
15:16 - 15:19HW: And so when you talk about
legibility and digital literacy, -
15:19 - 15:21I find it difficult to imagine
-
15:21 - 15:25that we need to place the burden
of digital literacy on users themselves. -
15:25 - 15:29But whose responsibility
is education in this new world? -
15:29 - 15:33JB: Again, I think this responsibility
is kind of up to all of us, -
15:33 - 15:36that everything we do,
everything we build, everything we make, -
15:36 - 15:40needs to be made
in a consensual discussion -
15:40 - 15:42with everyone who's avoiding it;
-
15:42 - 15:46that we're not building systems
intended to trick and surprise people -
15:46 - 15:48into doing the right thing,
-
15:48 - 15:52but that they're actually involved
in every step in educating them, -
15:52 - 15:54because each of these systems
is educational. -
15:54 - 15:57That's what I'm hopeful about,
about even this really grim stuff, -
15:57 - 15:59that if you can take it
and look at it properly, -
15:59 - 16:01it's actually in itself
a piece of education -
16:01 - 16:05that allows you to start seeing
how complex systems come together and work -
16:05 - 16:09and maybe be able to apply
that knowledge elsewhere in the world. -
16:09 - 16:11HW: James, it's such
an important discussion, -
16:11 - 16:14and I know many people here
are really open and prepared to have it, -
16:14 - 16:16so thanks for starting off our morning.
-
16:16 - 16:17JB: Thanks very much. Cheers.
-
16:17 - 16:19(Applause)
- Title:
- The nightmare videos of childrens' YouTube -- and what's wrong with the internet today
- Speaker:
- James Bridle
- Description:
-
- Video Language:
- English
- Team:
closed TED
- Project:
- TEDTalks
- Duration:
- 16:32
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