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When you first started out, did you
plan to have several platforms for your YouTube,
Twitter, Facebook, everything?
Or did you stick with one?
Or did it just develop?
I spent 15 years throwing everything I could
at every platform that would have me.
I did not know what would take off and what wouldn't.
And I had those one-hit wonders on various platforms
for a while.
But yeah, it took 10, 15 years before,
luckily, extremely luckily, the one that worked
was YouTube science communication.
I got lucky that it was the one that is able to pay my rent
and also about truth.
There is a version of me that got lucky
with something else a year earlier
and has now spent five years honing his craft
as a stand-up comedian.
I did try to do that once.
Turns out, not for me.
See?
Great.
Another question, please.
I'm worried about deep fakes.
So am I.
And you talked about the gatekeepers.
Is it not true that those deep fakes could become
the gatekeepers of the future?
Oh, I do hope not.
For those who don't know, deep fakes
are, again, artificial intelligence machine learning
systems that can essentially swap and generate faces.
So if you've ever seen the video of--
there's one from a few years ago of Barack Obama
just swearing and doing some comedy lines.
Because they got someone to impersonate his voice and then
just put his face on it.
I wish I knew how that's going to turn out.
It is going to make some incredibly convincing
fake news.
But given that people on Facebook
can currently be fooled with a headline and a domain name
that no one's heard of, I don't know.
I think the problem will be one that
convinces someone at one of the gatekeepers that it's honest.
One of the scandals, I think was during the Bush administration,
when--
I'm going to slander one American news organisation.
I can't remember which three letters it was.
Took a memo that had blatantly been printed
in Microsoft Word as a proper typewritten memo
from many years earlier about either Bush or Kerry's war
record, something like that.
That was a deep fake, and it was done
with Microsoft Word and no technical skill whatsoever.
And it convinced a news agency that wanted to believe it.
I think it will be possibly easier to convince people
of that, but I don't know which point that becomes
a significant threat.
I hope that answers the question.
OK, next question, please.
With a lot of these recent recommendator systems
and Google, all that, unfortunately, a lot
of these platforms, unfortunately,
will have to make the choice of what's popular
and yet what's to be shown.
And if someone wants to search for, I don't know,
let's say cats, they'll have to decide what kind of cat
to show people.
Yeah.
What are some of your thoughts on the things
that these algorithms should optimise
or ways that we can check on these algorithms, given
the fact that you've said that existing algorithms can
be where people will cheat them or, alternatively, that--
and these are also tied to, for example,
companies' bottom lines.
Yeah.
In an ideal world, then we would optimise for truth.
In an ideal world, we would be able to look at something
about homoeopathic vaccines and go, well, yeah,
they shouldn't pass that on.
But how you do that without also giving
that power to people with very different views, I don't know.
Yesterday-- actually, I didn't have time
for this in the presentation.
Yesterday, The Guardian leaked the moderation guidelines
of a company called TikTok, which
is short video clips that are generally not used
by anyone over the age of 25.
It baffles me.
According to the article in The Guardian,
the moderation rules for Turkey include blocking--
and by that, I mean they're just not amplifying.
You can put it out.
They just won't show it to anyone else.
Anything to do with the rainbow flag or anything
to do with LGBT stuff.
That is not illegal in Turkey.
But the Chinese company that runs TikTok
has decided to do that anyway.
So I am really wary of giving that recommendation.
It's why I preface that with "in an ideal world."
Because if you give that power to people,
it can be used by the--
I hate saying it, because it's that polarised,
and it's more complicated than that,
but it can be used by the other side.
I'm also wary of saying, well, it
should be truly neutral, because algorithmic bias is there.
If you can somehow remove algorithmic bias,
yeah, absolutely, it should be open and truthful.
Do you have a follow-up?
In some senses, the bias in these algorithms
is actually what makes them useful.
Because if you want to search for cats,
it is probably a good idea that cats--
the algorithm knows what kind of cats you like to show you
that cat, which is what makes-- you know.
Otherwise, there will be a plain database.
You would just look up cats, and these are everything else.
One thing that I didn't go into, because I'm
talking about more mass algorithmic stuff,
is that every one of these recommendations
is going to be personalised for you.
The amount of data tracking that's involved in that
is astonishing, and it's kind of outside the scope
of what I'm doing.
But yeah.
The trouble is how much you try to convert people to your side.
I certainly do think that the big tech
companies have a moral imperative
to not let the Nazis in.
But where you draw that line, I don't know.
So we take a question from the balcony, please.
Hello up there, by the way.
I was mostly looking down there.
Sorry, you folks.
It's absolutely blinding if I look up there.
This is somewhat of a follow-up to the previous question.
I work in tech, and something that we struggle with
is that we don't let our biases affect
the platforms that we build.
And the thing that I'm kind of worried about
is the echo chamber effect.
So as people in tech, mainly coming from the Bay Area
and building these platforms, it will
become more homogenised to what people in tech want to do.
So I'm kind of wondering what your thoughts on that.
And also, as a follow-up, where government intervention
should come in for what should and shouldn't
be regulated here.
So this is a question that should
be answered by a large and diverse group of people.
Because that's what you need for something like that.
If it is a homogeneous Bay Area group, then yeah, of course
their biases are going to infect the platform.
I think that's probably the right word to use.
But equally, TikTok is an app coming out of China.
Their biases are going to infect the platform.
The solution is, as I think you implied there,
having a diverse hiring process, making
sure you've got people from all sorts of social and economic
and every background in there to try and make the product work.
But god knows that's a talk in itself,
an hour-long dissertation on how you make that work.
How you do government intervention on that,
you could require certain percentages
of certain sex, ethnicity, gender, ethnicity, anything
like that.
I got those the wrong way around twice in this presentation.
It sucks.
Sorry.
You could require that.
I do not have the data to know whether that's a good idea.
I think the data's probably out there.
I'm not familiar with it.
Great.
Take the next question from here, please.
Hi.
I thought the talk was fantastic, by the way.
Thank you.
What do you think is more likely to lead to radicalization--
an echo chamber or a place where there's
no regulations on free speech?
So an echo chamber.
If you've got that and the people are in it,
you will find that radicalization.
When talking about the example of Reddit,
the whole place was set up as anyone can say anything.
But within the communities there, they
could freely choose to turn themselves into echo chambers.
So I think it's more likely to happen in one of that,
when someone gets there.
But I also think that if you have this big, open site
like YouTube with a recommendation engine that
can loosely connect communities all over the place, that that's
more likely to create an attractor that leads people
into that.
That is a horribly fudged and half-arsed answer.
I apologise for that.
I think if you're asking me if you've got a site where
anyone can say anything and you've got a site where it is
just this small group, I would worry about the small group
more.
But I would worry about the big site turning into that.
Thank you.
Question, James.
Thank you.
Hi.
I noticed you did not talk about coordinated inauthentic
behaviour.
And I'm curious and there may not be an algorithm for truth.
Are you perhaps more optimistic about our battle against bots?
And can we get to a point, through biometrics or something
like that, in which we can actually verify whether or not
this content is at least coming from a real person
and, thus, take a big step in the war for protecting truth?
Yeah.
It's a very, very difficult choice, that.
So coordinated inauthentic behaviour is colloquially known
as the Russian bot farm.
It is large groups of people working together
to push a political point to shout people down.
Twitter is actually doing some reasonably good work
tracking that.
They have a transparency thing that lets you see how many
people they have banned for coordinated inauthentic
behaviour.
But that's, of course, only the ones that they've spotted.
The solution to that is essentially
the opposite of everything that the web was built for.
The web was built to be something
that interprets censorship as damage and routed around it.
And it was built to be that you could be anonymous,
that you could be whoever you wanted to be.
Without completely restructuring the web,
I don't think that's a good thing.
Because we've seen what happens on Facebook.
For a while, Facebook required people to use their real name.
I think they still do.
They stopped doing this quite as much now.
But you could report people for having an inauthentic name,
and you would be required to send
in ID with that name on it, which was obviously used
to attack all sorts of groups.
It was used to attack trans people, who
don't have their ID in the same name as the one they use.
If you require everything to be posted by a real person, yes,
that will silence coordinated inauthentic behaviour,
but it will also silence people who are afraid of speaking out.
I think there was a news article-- and God knows,
don't quote me on this-- that said there was--
again, for anyone watching this on the internet
it's September 2019--
that there was a Republican majority in the Senate
for impeaching Donald Trump in an anonymous vote.
But all their votes are recorded.
And so they have that problem on a small scale.
This is a long, long way of saying, again, I don't know.
That's two extremes, and you can't find that wonderful place
in between them.
Because as with anything that works on that scale,
you can't have both and have both of them work.
How optimistic am I?
Not very.
Because you know, it's been a long time
since I've been on the other end of this,
but 8, 10 years ago, I was setting up Twitter bots.
Not for malicious purposes, but just for projects.
And it's not difficult. It's designed not to be difficult.
Because the more roadblocks you put in the way of someone
joining the site, the less users sign up.
I can put faith in some machine learning systems
to detect them, but I think there's
more likely to be an arms race of detection and evolution
and detection, evolution.
We'll take the last question from the gentleman
two rows in front of the previous one along-- please,
James.
This gentleman here, in the blue.
In the Superman hoodie, Superman T-shirt.
Hi.
Thank you.
This course was quite interesting.
When we talk about the echo chamber, the Nazi bar, and all
the rest of it, one thing probably, for some reason,
you covered or did not need to cover is about profiling.
So the likes of the dissolved company Cambridge Analytica,
they had narrowed it down to a science.
I don't remember, but 82, 86 profiles.
Now, when we talk of something like that,
that ends up promoting the whole idea of echo chambers, which
then creates a bubble, which is essentially segmentation, which
goes away from the personal recommendation
and creates groups of individuals
who are programmed to do certain things a certain way.
What's your view about that?
I think there's a large and important distinction
between the advertising targeting there,
which is very much like--
the segmentation that they have there and the segmentation
that any advertiser has is enormous.
But I do think there's a distinction
between that segmentation, which is creepy
and the sort of automated recommendation engine
which is profiling you based on the more anonymous things
like what you watch.
I think there's a difference between the two.
They're certainly creepy in very different ways.
And I'm trying to figure out what
the difference between them is.
I think it's the human element in there.
Cambridge Analytica was using AI to track people and sort
of target that sort of thing, but there was someone going,
this is what we want these people to believe.
We're going to target people who are interested in animals
and live near the coast, and we're
going to tell them that Brexit could affect animal rights
or ocean rights.
We're going to target people who live in the cities
and are on low incomes and tell them
that Brexit is going to help their jobs
and help their income.
I think it's very different for a human to make those decisions
and say, these are the beliefs we're going to try and force
people down people's throats.
I think that's very different to an algorithm
accidentally creating these rabbit holes.
That's a horrible point to end on.
Oh, my god, that's awful.
I see a different view.
So, Tom, what you have shown us this evening is
the depth of thought addressing the question of there
is no algorithm for truth.
And I put a question mark on the end of that,
because I was questioning it.
But you have shared with us this evening
real insights as to, A, how important this topic is and, B,
just actually how complicated.
And you are here at the Royal Institution, Tom,
because, actually, you provide not only entertainment
and inspiration but, frankly, a depth of
thought that I personally really admire
and I found extremely engaging.
And I trust the audience has, as well.
And therefore, I would invite you all
to thank Tom in the most usual way.
Thank you.
Thank you very much, everybody.
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