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Our next presentation comes from a
man who is from Great Britain
but now lives in Greece.
Now, I don't know if you just try
to escape the bad weather in Britain or
the Brexit, but we're sure
a happy he made all the way
here to Hamburg today.
Now, the latest elections in the US
and the referendum on
the Brexit have shown us that
with all the collected data
and technology that we use
to predict the outcome of
such elections,
we can do it. We cannot
really know everything or predict
everything.
And you dark age as an explanation
of what we cannot no longer
know about this world and what we can do.
About this hour, our speaker, James,
here is an artist,
a publisher and a writer.
And his writings have appeared in
magazines and newspapers all around the
world, such as Wired,
The Observer and The Guardian.
He's also a very experienced
lecturer in
conferences, in universities
and at different events.
So we're very happy he's here today.
So please welcome him and give him a warm
round of applause.
James Freital.
Hello, uh, thank you very much for that
introduction. Uh, thank you very much for
having me.
Thank you very much for coming here.
This is huge and amazing and I'm very
grateful. And I was, um.
Yeah, I just put hello, there are the new
Dark Age because I didn't want it to
sound quite so frightening at the outset.
I don't know, like it's a thing
I'm working on at the moment.
And I was also asked today to talk a bit
broadly about some of the work that I do.
So a lot of this is going to be like a
bit me, me, me, and that I'm going to
show some things
I've made as a way of explaining
the way that I think through technology
and society and various stuff around
that. And hopefully there'll be
connections to that.
And I'll talk quite a lot about
one particular project I did this
year and then
some kind of extrapolations from that
to kind of where I think I
and perhaps we need to be thinking
next.
And I actually my background
is as a computer scientist, but I'm a
seriously lapsed one.
I managed to graduate with a masters
degree without actually learning to
program, which tells you everything you
need to know about sex education in the
UK and has since been trying
to teach myself desperately ever since
and really badly.
But I've also been a literary publisher
and a journalist in a number of other
things.
Um, but it was my kind of background in
publishing and my interest in the Web
that sort of got me slowly moving
towards the place I made now, which
is increasingly in the art world, but
always, always crossing over.
And this is a
good example of the kind of where
I put things together, which is a few
years old now.
But it was when I was working as a
publisher, but wanted to start to talk to
people about how technology
and the Internet in particular changed
the way in which we putting knowledge
together.
And so what I did was I took
the complete history of a single
Wikipedia article.
I took the entire changelog of it, which
was the article on the Iraq war, which at
the time I made this book
was it was about seven years of data
on a single Wikipedia article.
And because I was working at a publisher
at the time, I learned how to make books
properly. So I typeset it all.
It's all in like neat little three
columns, quite small type.
But when I had that all print and bound
that single Wikipedia article, Changelog
took a 12 volume size
so that the scale of an
awful old style encyclopedia
and what I was thinking through this work
is the fact that
the way in which
we build stuff with technology
doesn't really necessarily change
the world so often. But the way we
understand how that technology functions
changes the way in which we can interact
with the world.
And so this is still an old school
encyclopedia.
Wikipedia is still an old style
encyclopedia, but the technology we've
built that underlies it allows
us to put that together in a way that
allows us to see all the changes we made
to it. We can see the fact that
encyclopedias are not final
statements of fact, but processes
of argument over time with many, many,
many voices contributing to them.
It's a machine for historiography rather
than for history.
And that, for me, captures something
quite fundamentally different about the
way we're capable of building stuff
today.
And so my projects go back and forth
between like things that exist as
big, solid objects, which are kind
of quite easy to understand
and more kind of material software
things. And this was
a project that I ran for three and a half
years on various social media
tracking reports of drone strikes.
So I was working with journalists
from places like the Bureau of Investigative
Journalism who were tracking drone
strikes in
undeclared theaters of war.
So these were the covert ones in Pakistan
and Yemen and Somalia.
These reports were being posted and
those who are kind of interested in them
could find out slightly more about them.
But it struck me as deeply strange that
this entire war was going on
with our images, which
is deeply strange in our current times.
I mean, and for a long time, for more
than a century, we've they used to send
illustrators to battlefields, write with
pencils and papers.
In an age of mass media where we're
saturated with images, there was this
whole war going on without them.
And yet at the same time, we spent the
last 10, 20 years photographing
the entire earth from space.
You can pull out your phone and look into
any spot on the Earth's surface.
And I wanted to pull those kind of
infrastructures together to close those
loops. So every time there was one of
these covert drone strikes reported, I
went to a digital mapping service,
use various ones to find as
close as I could the actual landscape
in which that occurred.
It's kind of re-supply some of these
images back to this ongoing
fight.
And I post those to Instagram, to
Twitter, to Tumblr is these long
sequences and trying to create
a kind of visual record of what was
occurring. One of the good things that
happened is that these images started
to then be used in other places.
So media, when discussing drone strikes,
instead of just running like a photo of
a Predator drone supplied to them
by the US military would actually show
images of the places where these things
were occurring, the actual landscapes
that we're kind of talking about here.
And that attempt to make these kind of
hard to see, I don't want to say
invisible because you can always say
thanks and good and I
don't want to say that, but it is hard to
see things kind of more visible.
I take that kind of out into the real
world as well.
So for several years, I've been doing
these drone shadow works,
which are one to one realizations.
And that came of me just wanting to know
more about these big machines, I think,
which I am very unlikely to ever see in
the real world.
But I have, you know, the tools
to find out just the size of it by
just a simple thing like that and go and
put that out in the street where people
can actually see and kind of measure
oneself physically against the thing.
It really changes the way people see
and understand these things.
And I've painted these all over
the world and it's totally open source
that. So there's a guide online to join
your own. And people have then
subsequently taken that and done it both
as art installations and
protests and all these kind of.
And I just get like people sending me
pictures occasionally when these things
have appeared around the world, which is
amazing.
It's very simple to just set out
to do a thing in order to understand it
and then realize a whole bunch of stuff.
By doing that physical action,
what the drone shadows will emphasize is
actually the the intentional
invisibility of these things, the fact
that they're designed not to be seen, not
to be seen physically because they fly
so high, they're invisible, but not to be
seen politically as well, because
because they automate systems that would
otherwise lead
to body bags and all the reasons, you
know, the military is like drones
and.
And that got me into the habit of doing
other things in the street as well.
This is a rainbow I drew
in Ukraine a couple of years ago.
And if you spend half as much
time as I have on Google Maps, you
might have seen the rainbow planes.
That kind of I find them deeply
beautiful. I find them if
you're scouting around airports or
whatever, you might spot these things in
Google Maps and what they are.
They're artifacts of satellite mapping.
So the satellite is passing very fast
overhead, taking images of the ground.
The planes are flying very far below
them.
And the thing is that satellites don't
see like humanzee.
And this is the point at which I become
fascinated because I'm interested in
seeing how technology sees the
world in fundamentally different ways
than we do.
And satellites see using a variety of
sensors, both visible spectrum,
red, green, blue and
in a high definition, black and white in
this case and deep into
the infrared and the ultraviolet.
So they see things that we're not capable
of seeing. And this these little glitches
are very interesting because they allow
us to glimpse inside that system
and see the way in which the machine
is seeing the world and attempt to kind
of reflect that back by
making them visible and
addressable and kind of pointing these
things out. In this way, it makes it
possible to to think about them
from in slightly different ways.
And a couple of years ago,
I did this project in South London
and.
This was called the right to flight, and
this is on top of a multistory car park
in south London where
we built these silos, one of which is a
hangar for this blimp.
And the blimp is kind of amazing.
It's the thing called a helicopter, which
is a hybrid of a balloon and a kite
invented by slightly mad British guy who
I had to spend several days in the pub
with. And the helicopters
are amazing because it's better than both
balloon and it has helium to
lift and but also a kite to
fight the wind so it doesn't get blown
down like a normal balloon, but has much
more left than a regular kite.
And and I was flying it
off this rooftop to kind of investigate
what it was like to be able to have
that view from above, that kind of
classic surveillant God's eye view
of the world. How would it feel for me to
be able to make a kind of aerial images
like this to to become
to to access this airspace
above London that was is otherwise
kind of heavily restricted.
And and it was a beautiful project and
it ran for several months.
But it also waited me out quite a large
extent because obviously
the first thing I did was work a camera
on this thing because aerial photos are
beautiful and I want to see all this
stuff from above.
And very quickly, I started to become
really uncomfortable with that because
I was doing surveillance, essentially,
and I was while I try to inform
people what I was doing and I,
I looked at various ways doing, I kind of
released the footage openly so I would
see the products of what I was doing in
this kind of stuff.
No one actually cared why
this is in part Londoners not really
giving a shit about anything, but also
whether it's just a
lack of awareness of the thing or whether
it's just the fact that London is so
subject to surveillance all the time
anyway. And this is kind of off
people's minds. But I was their
responsibility was entirely on me
to stop doing this, essentially
that I suddenly got to a point where I
couldn't make any more images like this.
And I realized that in many ways it's
quite hard to make artworks about
surveillance that don't essentially do
more surveillance.
And that's a really bad pattern to get
into because I don't think it helps.
I think it just kind of reinforces the
logics of surveillance and normalizes
it for people.
And so I stopped myself.
I took the cameras off and I end up
putting like a kind of pirate box on
there so people could share that felt
like a more useful
kind of relationship to have with it.
And so I start to look at more kind of
pedagogical ways to talk about these
things, like this project,
which is called Citizen X, which
is a browser extension.
Very simply, anyone
can download it tracks your browsing, but
entirely privately data doesn't leave
it. I do not share it with anyone,
including myself.
But as you're browsing the web, you can
hit the button and it will pull down or
show you very simply stuff.
That's very simple for most people in
this room, I'm sure where the Internet
thinks you are and where
the Internet thinks the website you're
visiting is.
So it shows you this relationship of the
the physical infrastructure, the
Internet, and like many artists, have
been kind of investigating the physical
infrastructure of the Internet for a
while and thinking it's important to sort
of point at this stuff and get this
magic cloud place you hear about isn't
some faraway, strange place.
It's like it's a real thing.
It's here. It's large buildings filled
with computers in legal jurisdictions
subject to certain laws.
Um, but but there's there's
weird stuff going on under that as well.
So with this project, I didn't just want
to highlight that physical Internet.
I wanted to highlight what that physical
Internet does to your rights, which
is in this case based on NSA documents
showing how the NSA assigns citizenship.
And what the NSA does is it
when it's pulling in all that data, it
looks at your browsing habits and it
says, well, based on where this person
is going on the Internet and this sort of
behavior they have, there's this
percentage likelihood they're American
because that's an important determinant
for the NSA because they're supposed
to not track Americans.
So they have to have some mechanism for
making this distinction.
So moment by moment online,
your citizenship, which used to be this
really solid thing that used to be a
thing determined by your passport, by
your place of birth, that you could prove
those of us who are lucky to hold stable
citizenship. So you could prove with a
document that is no longer the
case, your right to privacy is being
determined algorithmically all the time
by tracking of your behavior.
And so what's this next does is it looks
at your and your
very simple behaviors online,
and it builds you one of these
algorithmic citizenships to say this is
what actually your web browsing behavior
would look like if someone
more nefarious was looking at it and how
that might kind of come to affect your
rights.
And I was looking
at the sky a while back because I was
looking for these planes, which are Kovar
Metropolitan Police surveillance
planes, and hang out in fields quite
a lot looking for weird stuff like this.
And I'm.
What I found instead was, was these
flights, this
is an example of where I go into fields
and stare at the sky and find myself
stood next to a bunch of kind of weird,
interesting people, because you end up
in the fields around airports with the
plane spotters, the controllers
and activists working on various subjects
and working with a bunch of activists on
tracking deportations from the U.K.
This is the system the British government
uses to deport people,
which is basically where they hire
private planes because it got
embarrassing to do it on on a general
general usual aircraft.
And in the middle of the night, they put
people onto these planes and they fly
them out of the country. And this is a
very hard system to see because
it happens in the middle of the night at
private terminals, airports.
It happens within closed courtrooms
and within privatized
detention centers.
And so working with
some friends of mine who are architects
and architectural modelers, we used
planning permission, went to the council.
We got the files on these kind of places.
We did these kind of classic
investigative journalism practices
of finding eyewitness accounts,
finding council documents and
visiting and sketching places where we
could where we weren't allowed to
photograph in order to visualize them.
So this is a courtroom in the center of
London. In the UK, it's illegal to
photograph courtrooms.
So there's no photographs exist of this
space.
But there are very, very shiny
architectural visualizations that we made
of it. And this is an interesting
courtroom because it's a courtroom in
which special laws apply.
The UK passed a special law for
immigrants appeals commissions
where there's suspicion of terrorist
activity or some kind of security risk,
where they can present secret evidence,
which is evidence that the defendant
and their legal team are not allowed to
know about. It makes a complete mockery
of the idea of a fair trial.
And what's interesting is the way that
the way in which that appears as
architecture in spaces like this, because
you can actually point to it.
The divider on the left here is
what separates the public viewing gallery
from the spooks viewing gallery.
And the closed witness box over there
is so that members of the security
personnel can Schizo's can
appear without giving away their
identities.
And this culminated in a
eight minute film you can watch online
called Seamless Transitions, which
documents these
these various spaces.
The courtroom, which you just saw, a
detention center near Heathrow Airport
and the final airport to which people
are deported, which is Stansted, if
anyone goes to the UK on holiday.
And and
what what again, I was trying to do in
this these this film
was not necessarily
paint like just
what you would traditionally do with
photojournalism.
So this was commissioned by the
photographers gallery in London.
And and it was
when it first went on show that it was
alongside a huge,
amazing deep dives to the archive
of something called the Black Star
Archive, which is an archive of human
rights photography from the 20th century.
It's extraordinary, but it's very much
what you think of when you think of 20th
century photojournalism, which is the
kind of sharp black and white photo
a face in a crowd, this frozen moment,
this moment from which we can supposedly
draw like this particular story,
this one signal thing which will explain
everything else.
And it's and it's a fantastic way of
working. But in this, I kind of realized
I was doing the opposite, which I was
using the architecture of these spaces
and also the way in which work
like this is put together, which is
usually based on research assemblage,
co-operating with other artists
visualizes and also cooperating
with the software and architectural
software that makes this is the same
software that generates a huge amount
of the built environment around us.
And yet we still kind
of assume there's one master architect
at the heart of one of these things,
rather than several thousand people
working on Code AutoCAD or whoever it is
who now have a huge stake in our built
environment. So all of these different
pressures come into play on trying to
depict not a particular place,
but the entire legal and
infrastructural system that goes into
creating the situation that
results, which is horrific deportation
system.
So that's a very brief
and swift run-Through of some
of the sort of things I do.
I want to talk a little bit more about
about this project, the cloud index,
which requires a little bit of history,
which I hope will be interesting.
This is this is one of my favorite
people, Lewis Richardson
in nineteen twelve thirteen.
Richardson was a meteorologist
who was working the media observatory
in Scotland, doing a very, very early
kind of weather calculation, mostly just
recording the weather and trying to like
think about what it might mean to start
to have this kind of huge.
Body of weather data, and
then in 1914, he was called up for the
First World War and he was a Quaker,
so he was a pacifist.
So he joined the ambulance division and
spent the war running
around with stretchers.
The other thing he spent the war doing
was that he'd taken a bunch of data, a
bunch of readings from the observatory
and from other observatories around
Europe that had been gathering for the
war. And over the course of two
to three years during the war,
while in trenches while under fire, he
performed the very first mathematical
calculation of a 24 hour weather forecast
just over the area of northern Europe,
just over the area that you see here.
But he figured out what the matter was
to take a bunch of historical weather
data and advance that
24 hours to to
push that prediction forward into
the future, which is an incredible
achievement. Let's say it took him
several years to do this with pencils and
paper.
And when he analyzed the
results, because obviously the day he was
doing was by that stage, almost a decade
in the past, he turned out to be broadly
correct.
Some things were a bit more exaggerated,
whatever. But but his his kind of
his thesis worked.
And because, of course, this was a time
before what we now think of as computers.
He didn't he thought this was a kind of
achievement, but thought realizing its
full potential was a very long way off.
And he
in the in this book, the one I showed
before, he goes on this kind of beautiful
little fantasy where he describes
what it would take for his weather
forecasting to actually
achieve some kind of industrial scale and
be economically useful.
This is a painting of that fantasy
by Stephen Conolan from 1986,
where he
Richardson described this vast globe
on the inside of which would be painted a
huge grid covering the entire earth,
which weather forecasters could kind of
point to read data off,
calculate it, have it replaced.
You can see there'll be teams of
mathematicians then still called
computers work in the middle.
And then it's kind of amazing
infrastructure of information, telegraphs
and all the kind of communications
infrastructure, the age pouring
information into this place and pouring
it back out again.
And so this was he knew it was possible,
but he thought, you know, mankind would
never actually get to the point
that it was possible.
And mankind did get to that point
in in 1950,
when the very first
mechanical calculation of a 24
hour weather forecast was performed,
which was done on the ENIAC, which is a
computer that some of you have probably
heard of, which is my personal
favorite computer.
The first stood first
mechanical stored program computer built
in the 1940s, the University of
Pennsylvania.
And this is the process for doing
it.
And they put together a team of
meteorologists in 1948
onwards. And in 1950, they ran
this very, very first calculation or the
very first successful calculation of a
single day's weather forecasting over the
northern United States.
And when
they did it, it took about three weeks to
run. Right.
Which doesn't sound so great.
But you have to remember that the ENIAC
broke down a lot of the time.
And what they realized
was that actually, if they subtracted all
the time when the machine hadn't been
running from the process, they got it
down to 23 hours and 30 minutes.
So they had very for the very first
successfully time, calculated the
weather faster than the weather itself
was happening.
Right. This is an incredibly
significant moment.
And John von Neumann, who worked on this
project, wrote in the project log that
we have achieved the dreams of Lewis
Richardson. Computation now advances
faster than the weather itself.
And I like the fact that it happened on
the ENIAC, which, as I say, is my
favorite computer for those who aren't
familiar with it. And here it is.
And it was
an early mainframe, so occupied
two huge rooms, first at the University
of Pennsylvania and later at the
Plymouth Proving Grounds, Aberdeen
Proving Grounds, where it
was moved after the war.
And it was you know, it required a lot of
fiddling and plugging in.
And if you read
Turing's Cathedral, which is a great book
about the kind of history of the early
kind of computational programs, there's a
beautiful quote in that from a guy called
Harry Reid, who was an engineer
who worked on this computer
in which he says, I always
thought of the ENIAC as a very personal
computer. Now we think of a personal
computer as something that we kind of
carry around with us.
But the ENIAC was a computer that you
lived inside.
It completely surrounded you.
And actually for me like that, that
division of personal computer is
obviously and I think increasingly not
true because we all live inside
this kind of shell of computation.
Now, we know that these are mostly
terminals to other other
connections, whoever owns them, whether.
We build these things outwards or not,
whether we're talking to satellites or
not, the ENIAC has not contracted into
these things, is actually expanded out
into kind of vast shell around us.
And the other thing that I really like
about the NSA is, you know, it's one of
these computers, this generation
of computers were the last ones that
were kind of truly physically legible.
So computation now is legible to
those of us, probably not me, who are
really smart enough to kind of follow
logically step by step,
what kind of process is happening with
the machines, with the ENIAC.
You could do that physically with your
eyes by looking at the blinking lights so
that you could watch a computation kind
of unfold on you across the walls.
You could actually see that as it went
from different parts of the machines to
the to the architecture.
You could kind of follow that.
And that kind of legibility also seems to
me something quite crucial to kind of go
back to and reflect on.
As I mentioned, one of the people who
worked on that was John von Neumann,
who I'm sure you're most familiar with,
is the kind of inventor the von Neumann
architecture worked on a lot of these
early projects and obviously was working
on both
the weather project but also on the
Manhattan Project, because this is here
with with Oppenheimer, who
ran the Manhattan Project and the
same computer, the ENIAC was used for
both the weather forecasting programs
and for the development of nuclear
bombs. These are the kind of two grand
computational arms races that really
kind of got computing going
in the 1940s and 50s.
And von Neumann had this line
which which he did, which he wrote first,
and a commentary on
on the atomic on the weather project,
but later recycled talk about the
Manhattan Project, which was kind of his
view of what computation could do
and particularly computational
prediction. And he said that all stable
processes we should predict and or
unstable processes we should control.
And that was his view of what you could
do with computation.
You could you could model and control the
world sufficient degree that you could
predict the outcomes.
So
I wanted to to take some of those ideas
of the weather and our computational
predictive abilities and bring them
somewhat up to date, and I was I got
particularly fascinated with what's
currently happening in neural networks
because I studied statistics
a while back.
I studied II in
the early noughties.
But basically the idea of the 90s that
was dying, so we were taught
neural networks is a kind of historical
anomaly, like a nice idea, some
interesting theory, but basically a dead
end. And the field kind of went quiet
for 10 years and it's just starting to go
kind of massive again.
The last five years, neural networks have
become and
starting to kind of become inside
everything essentially and rather
interesting ways. And so I thought it was
important to kind of reevaluate them and
explore them again.
And this is from a
paper from
the beginning of this year.
Things move fast in this world
of Facebook research paper
about decisions, deep conversation,
deep convolutional generational
adversarial networks, which is something
that they've been they've been pushing
and using.
And this is from a new network
generation from that paper where they
took thousands and
thousands, thousands of bedrooms, uh,
pictures of bedrooms.
And they asked this network to simulate
new bedrooms.
So each of the images you see here is not
a bedroom that exists in the world.
It's a bedroom that has been dreamed up
by a new network that's seen many,
many pictures of bedrooms.
And it's fascinating to
see what it's like to try these things on
and the more public
image of neural networks, which
is kind of everyone's a lot of people
have seen is is the deep dream
stuff from Google.
And so what deep dream is,
is an attempt to see back through
these networks and try and understand how
they think about and make images.
So it's kind of instead of asking
the the network
to look at an image and tell you what it
sees, it's asking it basically
gives it
blank or fixed images
and it's kind of reverse them back
through that network, kind of imagine and
visualize what it can see in the world.
And it turns out those things are
terrifying.
And I think I think this should be
acknowledged more.
And when
the guys on the machine learning team at
Google first came up with Deep Dream,
apparently they showed it to Google and
we're like, look what we made.
And I was like, never release that.
And in fact, they weren't allowed to.
The dream leaked, first
of all. And I don't know how, but it's
interesting.
And then they had to come out and say,
OK, this is what this thing is.
And everyone went, whoa, man, computer
tripping balls and didn't
and didn't. You know, there hasn't been a
huge amount.
And I don't think,
you know, the people behind that
are quite annoyed that there hasn't
actually been a bit more thinking about
what this kind of stuff entails, because
it's an.
What new networks are doing in the world
is kind of interesting and
you can do weird stuff with new networks
and Visioneering actions and decisions,
in particular this from the same paper.
So you can do addition and subtraction.
You can take a bunch of images associated
with certain concepts, and you
can then produce new images that combine
elements of those things.
So this is a network that has never seen
a picture of a smiling man, but has seen
the these other distinct
stages.
And you can do to check on that to
produce new kinds of image outcomes.
And so I thought, well, if a computer can
dream of bedrooms and it can dream of
smiling man, it can dream of all things
and it can possibly also dream of the
weather.
And so I took.
You might have noticed from the other
work, some quite obsessed with satellite
imagery, so I took eight years
of satellite data
of weather over the U.K.
This is from the
EU Space Programs Mattio site,
which sits up there well above NUL
Island, watching the Earth every day,
several images a day of weather systems
over the Western Hemisphere and
chop that down into images over the UK,
which came to about 16000 images, which I
stress is really not enough for doing
this kind of work. But I only had eight
years of data on the other input,
which was polling data from the UK
on their voting intentions in the EU
referendum.
And I'm sorry about Brexit.
I'm not going to speak of that again.
I said I am because this is what this
project is about.
And this is this is a short
snapshot. I actually had eight years of
polling data. It's a bit sparse at the
end, but it was enough to extrapolate
eight years of information.
And so I built one of these
networks with the assistance of Jean
Kogan, who might be around here somewhere
and who's brilliant and
fed. It is of
whether images from space, eight
years of images of cloud formations and
eight years of this
polling data about the EU referendum and
my new network dreams new weather
formations dream new kind of
cloud patterns.
And once this started to make these kind
of associations, it was
then possible to say, well, you've seen
what the weather looks like on these
certain occasions when the voting is
trending in these particular directions.
And so just as we did the addition with
the smiling man cetera, let's do some
additions on the voting and say, well,
what should the weather look like if
the entire country was to vote in one
particular direction or in the other
direction to generate weather
potential wethers that we
could simulate if we wish
the democratic outcomes to be different
and and the ones at
the extremes are what they are.
I actually kind of enjoyed more
the outcomes that played around with
undecided voters.
This seemed to me actually something that
really required more attention
in the aftermath of
the very divisive elections of the kind
of various kinds.
We've seen this year to try and put a
little bit more emphasis on not on
hard Brexit or
Trump or whatever it is, but on the huge
space within that of of indecision
and unknowingness that actually guides
and hugely influences a lot
of our elections.
And the reason that I thought this would
be a worthwhile thing to do is because
we can change the weather now
if we if we want to.
This is Vincent Chafer in his laboratory
at General Electric in
the mid 1950s.
Vincent Schaefers, the man who invented
cloud seeding, which is the
realization that you can place into
into early form in clouds, crystals
of silver iodide that will cause those
clouds to precipitate.
You can you can you can raise
smoke levels. You can burn off fog.
You can enact quite large scale changes
to the atmosphere.
When Schaefers work started to appear
in the 1950s, there was a huge public
debate in the US and elsewhere
about what the possibilities
of weather modification might be.
There was a huge belief there's a huge
military belief that the weather would be
the weapon of the future.
And the US used weather modification
extensively in Vietnam and had a two
year program of seeding
with silver iodide clouds over
Vietnam.
And in order to basically obliterate
the Ho Chi Minh trail, they reckon they
extended the monsoon soon period
by an average of six weeks each year,
making it rain more and more and more.
And when that leaked
again leaking,
they stopped it immediately.
And there's been little
willingness in the West to continue
cloud seeding experiments ever since
then, but it doesn't stop other
people. These are Chinese, whether
making rockets.
The Chinese government has a
weather department of some 50000
employees whose job it is to make the
weather.
They active very much in agricultural
locations, either make it rain on the
crops or preventing it haling on crops.
But they also do a lot
of work clearing the air for
official parades, for the opening of the
Olympics, for these kinds of events.
So the
weather is is a tool that we could
be deploying, should we wish to, and
one that we might increasingly want to
think about deploying
in the in the not too distant future
when we have to ask ourselves quite
serious questions about what we want to
do with the environment, how we're
willing to use the things that we know,
how we wish to use those things, whether
we have the strength of
our beliefs in our ability
to act in the world
and and what do we do
this? And and it is
because I'm no longer convinced,
actually, that it's.
All to understand the world
just to processing kind
of vast amounts of information
that I don't think this is any longer
a productive, useful way of dealing with
the world. And but I realize that I kind
of wanted to retain the cloud
not as a kind of bad metaphor of kind of
loss, the engineering or corporate
control, and not as that kind of
materialist look.
I found the data cable kind of
like materialization of the world.
But as a way of thinking, the world
is recognizing that the cloud is
cloudy, that what it reveals
to us is often actually a kind of
difference and differentiation around the
world. And that is not a purely
kind of fixed channel of information
that's there to tell us how the world is,
but asks us to question it and thinking,
think about it more.
And this is increasingly necessary
because this informational
view of the world, this view that by
gathering information, we kind of make
more knowledge and more sense is
increasingly at risk.
And I'm sure, again, Moore's
Law is familiar to most of people in this
room, the world, that
the number of transistors doubles every
couple of years and some space to get
more and more power, more and more processing
power.
This is its inverse.
This is something that has been named a
room slower by people who work in the
pharmacological sciences.
This is the graph of
money spent on medical research
against drug discovery.
And it's going the other way.
The more money that's put into this,
the less and less we're learning.
And those various proposals
that have been put forward to
reverse analyzes of this, which is a long
running and long standing problem in
pharmacology, and I'm one of the
most prevalent theories is is essentially
too much information.
If you know how drug discovery is done
now, it's not basically people in
white coats in the lab and it basically
looks like a server farm.
It looks like large machines, it looks
like robots. And it looks like very, very
large datasets of information being
automatically run through these
high throughput screening machines
in order to test new drug reactions
against one another.
And this this problem seems
to be, uh, uh,
continuing. And one of the ways
in which science labs are dealing with it
is increasingly putting people back into
the process, not as some kind of, like,
airy fairy, uh,
you know, computers, bad people, a
magical kind of way, but simply
that there seems to be some other way of
thinking, the world that doesn't rely on
vast, vast datasets, but on plotting
slightly distinct tanon machine equations
through them.
The example I like of that is,
is the story of advanced chess.
Um, so this is the famous
game in 1997
between Kasparov and Deep Blue when
IBM spent a decade building a computer
to beat this one poor guy at
the only game he'd ever loved.
And
it was, you know, is this was this insane
moment because we basically like our
idea of intelligence is always like the
one thing we're still holding out.
And you'll notice in any discussion of AI
that what constitutes intelligence has
kind of receded for decades as
the machines have got better and we've
got know not that best.
You can continually move the barriers,
but at this point, chess was the thing.
Right. And it was a really shocking
moment when possibly the
greatest chess player at a time of all
time was beaten by this machine.
Um, and,
you know, Deep Blue was,
uh, was a brute force machine.
That's what it did. It basically was
capable of doing such
vast search through the kind of
development process space of
the game has evolved that it could
outthink any human by
a pure kind of brute force approach of
thinking ahead. And that that ultimately
seems to be right. We finally built
enough processors to beat a human.
Um, and what was interesting is that
while lots of people kind of like, oh,
God, that it the machines take over.
Kasparov came back just one year later
with a thing he called advanced chess,
which is a new kind of chess, which was
not humans versus
machines, but humans and machines versus
humans and machines, because it turns
out that today and,
you know, and this has become very
successful and
a you know, not
even a supercomputer today, advanced
computer will wipe the floor with any
human alive, but a human using
quite a relatively weak computer assisted
by it will wipe the floor with even the
biggest supercomputer.
There's a some kind of interesting
complementary ness to the way in
which this thought happens.
That seems to me one model of how we can
think about our relationship technology,
not as this kind of, uh,
completely oppositional thing, but as a
form of cooperation and kind of thinking
through problems together.
Um, but
at the same time, there's something also
that that's. Coming out of that at the
same time, because this is where we next
put intelligence, right, this is
into this is the Avago game
from early this year when Google's deep
mind, again, just a massive spoilsport
machine played
Lisa, all one of the greatest
go players in the world.
And despite a heroic fourth game,
third game, fourth time,
deep mind one and so go is now
again one of these things that we
held up as being such an important bit of
intelligence and is
now kind of has again been taken by
the machines.
And while I really look forward to
advance go and all the kind of
interesting things that will come out of
new ways to play it, there's something
fundamentally different between deep mind
and
deep blue.
And that is we don't understand how deep
blue, how four go.
So it does what it does.
We don't understand how the new network
actually beat these old test theories
and those kind of ways of watching the
game and picking them apart.
But essentially, this was a machine that
was trained first by a team of humans and
then trained against instances of itself
and its learning ability.
Well, it played against other instances,
so it was kind of exponential and
and it thought processes because it's in
a neural network in the way it's in train
that your network is unintelligible to us
at some kind of deep fundamental level,
we can maybe produce the kind of weird,
deep dream mindstate images
for for go, but they won't tell us what
the the process is that
allows this this to happen.
Everyone's heard this is the example of
of the strangeness of neural networks.
Uh, I've checked this story and it
doesn't appear to be true, but it's the
classical story of how strange
neural networks are and why they
do things that we can't quite understand,
which is that the US Army
commissioned a
neural network to recognize tanks.
They have a team of researchers, a tank,
a bunch of soldiers, and they said go and
hide in the woods, take loads of photos,
feed them into a neural network.
And we want a new network that can see
tanks in the trees, that can see
camouflaged artillery and so on, so
forth. So they went out.
They took these photos, they were
backslap.
They built a
neural network. They run all the images.
And, uh,
and it worked perfectly on
this kind of X thousand images that they
had the the new and every time
classified. Yes. A tank in this image.
Now, there's not even when the tank was
like hidden deep in the trees and
invisible as they went back to the army
to get new network and like here, go with
know and the Army put its new pictures
in and it completely failed.
It didn't work at all.
And when they went back, because they
were working with a relatively small
dataset, they looked at the images
again and they realized that the tank had
only been there in the morning.
And so the images of the tank had been
shot in the morning and all the images
without tanks have been shot in the
afternoon. And the new network is really,
really good work out whether it was
morning or afternoon and but
it completely and utterly failed to see
tanks. And
and these are the kind of stringencies
that occur when using technologies where
we don't quite see the way the machine
sees the world and we're building
new networks into
all of our things.
Now, this is Nvidia's program
to, um, to train
cars, to drive or they basically just put
the neural network in the car connected
to the computer and driven around for a
while. So it learns to recognize the
world around it without any external
cues. It doesn't know the rules of the
world, rules of the road.
It doesn't know what anything else looks
like. It's just watched humans drive for
so long, it figures it out for itself.
Um, these things are becoming
inside the things around us all the time.
And and we're actively building
the tools of our own mystification.
This I find particularly strange.
This is from a Google brain experiment
just a couple of months old, where they
are trained
to neural networks to hide their
communications from a third party.
So they and Alice
and Bob in this arrangement evolve
their own cryptographic system
to protect their communications
from Eve. And I myself think it's
a really great idea that we should
train the machines to be able to talk in
secret behind our backs.
I think this is
struggling to see and
see why we would want to do this.
Um.
There's this idea, though, of course, of
being able to gather all the more data
about the world, make it
to collect everything in
order to make better decisions, is, of
course, one that we see across,
across and
across domains.
This is the this is the approach
of surveillance agencies.
This belief that by gathering all of the
information there will be able to make
better decisions. They will be able to
pull out and they will build a model of
the world that is so complete that
they'll have a full understanding of
it on which they will be able to act.
And it strikes me that
this this is
not actually all too different from the
way in which we also oppose this belief
that actually the belief in
information itself being somehow
sufficient to change opinions
to
to sort of enact some greater good,
naturally on its own behalf is also
the the approach of
transparency of
kind of full information release
that the the
NSA and WikiLeaks essentially believe
the same thing, which there is some
smoking gun information at the heart of
the world, that if we only bring it to
light, everything will kind of magically
be made better.
And that that that seems to me to be
an increasingly insufficient way of
viewing the world.
This is, as a small
example, Facebook's analysis of hot
topics for November 2016 with
WikiLeaks right up there in the old men
quadrant of most discussed topics
from the time of the election.
And I'm a big fan of Julian
Assange's original
network analysis of secrecy that posed
the kind of leaking as this kind of like
gripped in authoritarian regimes
that would kind of make them grind to a
halt. But it does appear to me at the
moment that right now it's kind
of merely oiling a machine that none
of us want to keep running, that we're
locked in a kind of deadlock
of an information
war, that we're both playing
against each other in ways that
are not going to break us out of this
this opacity, transparency,
dialectic any time soon.
And all around us, this
it seems to me that
it's becoming harder for these predictive
mathematical information BAE Systems to
work, and we need to ask very specific
questions about what that's doing
to us and to our societies.
There's a huge fuss around
polling
and the poor
performance of polls, both in the
referendum and Trump and everything else.
But, um, but most of that seem
to focus on the fact that, like, the
mathematical models were wrong and they
have to be revised and we kind of build
better ones rather than saying these
things are things that actually poison
the discourse as part of the process,
that when you are running polls,
running metrics, running faulty models
like this all of the time alongside
everything, you're actually shaping the
outcomes themselves.
So when polls are done during a campaign,
they don't just say what people are going
to do at the end. They change people's
behavior during the campaign.
And they also change what is politically
possible because people run polls and
they say, oh, people don't like that.
We're not even going to put this out into
the public discourse.
Modeling and predicting in this way
restricts the kind of actual
possibility of discourse around us.
The debate around fake news
is a kind of startlingly
interesting one, but one that is not
also historically new or novel or
should not be and also needs to be, I
think, very specifically, historically
situated as something that as an idea
of suddenly that we can't know
this stuff or that it doesn't emerge
from quite specific ways
of seeing the world. I was particularly
struck by
and some of the reporting in recent
weeks that went on and on about
the fact a lot of this fake news stuff
emerging from the former Yugoslav
Republic of Macedonia
that cited it, particularly in this
place, without any particular context
around the political situation
in that place, is possibly being related.
So the story, if you don't know it, is
that lots of kids in Macedonia and
particularly in this one tiny village set
up those these websites, which are
getting more hits on Facebook, The New
York Times or any kind of educational
media by completely making up stories
and are getting tons and tons of ad
revenue.
And it strikes me that this is a place in
which to have a government
in Macedonia, which for the last 10 years
has pursued a policy of fake
news as government policy.
This is part of Skopje 2014,
part of the Macedonian government
project to build an entirely
fake history for Macedonia
by erecting statues of Alexander the
Great and Philip of
Macedon and classicists buildings
with kind of Hellenic columns and this
kind of stuff in a completely kind
of a historical move that's been opposed
by lots of people in the country.
But as a result of a kind of huge,
resurgent nationalism and nationalism,
that actually depends on feeding a kind
of fake stories.
And we seem to be optimizing a lot of
other stuff for this as well.
And we seem to kind of desire this
inability to fully and
to see and understand the world.
These are images that
our researcher Robert Smith discovered
in his own photo stream when
he he posted originally
the two pictures on the left.
And Google's I think it's
called like automatical algorithm or
something, produced the image on
the right, which is an image that
combines them both to have both people
smiling. But it is an image of a moment
that didn't happen.
Right now, it's
a it's a memory of something that
never actually occurred.
And likewise, Adobe is
pushing a thing you might think would
Vercoe, which is a way
of editing
sound as you would edit text.
So a basically a tool for
a kind of public tool, a kind of
Photoshop for audio essentially
that allows anyone to kind of create
these kind of things. Our entire,
uh, a lot of our technological advances
seem to be in order to kind of create and
change and mold the fabric of reality in
ways that are not always visible and
transparent to show the visitor.
And the outcome of that is a world in
which people do not believe anything
when when complete authority of
any kind of trust in institutions.
No, I'm No.
One for blind trust in institutions
either.
But the fact that when in the UK you had
a referendum and
something like more than twenty five
percent of views of
participants believe that was going to be
rigged by the security services
is a very worrying situation to be in.
And that is a result of
a kind of a crisis of literacy,
an inability to deal with these
increasingly complex narratives
that are supplied by our technologies.
More information, as we saw in the
pharmacological research we've seen, the
neural networks doesn't necessarily lead
to better decisions and in fact, often
kind of hardens people against them.
The sociologists, Fredric Jameson
in his work Cognitive Mapping describe
this is the scope conspiracy
theory in particular as this attempt
cognitively to map
landscapes of information which are
simply too vast for people to grasp
that no one person can hold this
entire system of information inside
their head. It's the same problem as big
data. It's also the same problem we
encounter every day on the
Internet because we are continually
faced with stuff that doesn't map into
our model of the world.
And that is existentially terrifying
because we're quite weak, you know,
lizard brain creatures, and
that we have not equipped ourselves with
literacy for dealing with complicated
and often paradoxical information.
And as a child of the kind of hippie
Internet. Right, this is deeply worrying
because for me, the Internet has always
been a place of incredible emancipatory
possibility, a place in which
you have access to all of this
information and it makes the
world more comprehensible to you.
It gives you greater agency,
and that allows you to make
more social better or whatever.
You seem very naive.
You, it turns out, but
a really important one to get across
because we're living in a world where
information has been increasingly
available for a long time
now and is in.
Recently riven by fundamentalisms and
deeply entrenched differing points of
view, so we need to think very carefully
about what our
what our responses
are, what are the new literacies that we
need to build, not to understand
raw information, but to understand these
these differences between information.
And there was a I
took the title of this talk from a piece
in The New York Times from
a few weeks ago by the director of
the Global Weather Forecast, Global
Weather Foundation, a big weather
forecasting agency, huge
computational weather forecasting data,
the full deal.
And what he was saying was that
alongside this, uh,
uh, growth
of information available to us is also
huge and cataclysmic change.
Climate change is now progressing to
such an extent.
The atmosphere is warming to such an
extent that
the models that we've built based
on the 100 plus years of weather
data we've been gathering, no longer
apply.
The weather models are actually getting
worse because the underlying
models that generate them no
longer work so well in
a rapidly changing climate.
And we're actually moving towards a place
where we're going to know less about
the world than we did previously.
We're going to be able to predict less,
and that's going to affect our ability to
prepare for it.
This.
No, not that yes,
I'm running out of time.
I want to take a couple of questions, one
who's got them? And here's what I
think.
I do not think the Internet
and so much of the stuff that we've built
on top of it is a tool for
understanding the world as some kind of
abstract entity that we can
regard as a static and
stable. Rather, it is a tool for
understanding our own ability to
understand and interact with the world.
And it's the most extraordinary complex
and advanced such tool we've built as
a species. For me, it's a it's a it's a
cultural tool. It's something that
emerges like language and writing
emerged.
And and what's extraordinary about
it is that no person or no
single set of intentions
created this thing or imagined what we
might do with it for so its uses.
It's an unconsciously generated tool for
unconscious generation.
And it's this model of complexity.
It's kind of cloudy cloud.
And if we are to take our kind of
technological understanding of the world
seriously and I feel that we
must recognize that it's teaching us to
expect and understand differences
in the world, to understand cloudiness
and complexity and not conformity
and similarity.
Thank you very much.
Thank you so much, James.
We now have a couple more minutes to take
questions.
Please use the microphone to show up at
the microphone so I can see you
over there afterwards.
Anyone?
And right, it's not all Bellator
is also good.
OK.
Are you sure you don't have any
questions?
This is your chance.
I think we're good at that.
Thank you very much.
Since Friday.