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So welcome again to everyone
in the audience, welcome also to everyone
watching via livestream from around
the world.
We have the pleasure to welcome Christoph
Engelmann. Now, Kristof
is an author, a teacher and a researcher.
He researches at the Institute
for Advanced Studies on Media
Culture of Computer Simulation
that is at the Low Fana University
in Luna Park.
In his talk today, he will focus
on the enabling dimensions
of drone warfare.
That's a little spin by especially
looking at data gathering and
the mapping of social graphs.
We will have a chance for Q&A
in the end.
So please take notes.
If you have questions in the end, you can
ask them.
We have plenty of time.
But for now, with further ado, please
help me welcome Christoph Engelmann
to his talk on Graff's drones and
phones, the role of social graphs
for drones in the war on terror and
stuff like.
Thank you.
It's exciting to be back.
It's my third talk here, and as most of
you know, I've previously
talked about history
and present day developments of
identity, media, authentic authentication
technology.
Um, my last talk I gave
in a pretty hungover state, which
you will see if you watch the video.
I slept better this night and had some
coffee needmore.
And you know, what I'm going to do today
is pretty much ask a very
similar question.
Then the questions I have asked before
in this context here is how do
you identify people?
But here in the context of drone warfare,
because whenever to talk
about the debate came to drones and
surveillance in the past decade,
I always wondered how do they actually
identify the individuals they
see in the video feeds?
Because that's an important part of of
the whole war on terror, discriminate
individuals and decide
which ones you want to kill or capture
and which ones not.
And many may argue that drones
kill quite indiscriminately.
And that is definitely true.
But at the same time, one can see,
especially since the publication
of the drone papers by The Intercept in
October, there is a fairly elaborate
process, a bureaucratic and
administrative process
being put in place, at least in the
American context,
a process that that, um,
is meant to positively identify
the individuals you're tracking and
eventually taking out
many, many people involved, many, many
hours of of surveillance.
There's this idea of
an unblinking eye of
looking at people for weeks and
weeks and weeks and start
the whole observation cycle anew
once the eye blinks, once you lose
visual tracking.
But of course, this visual
tracking somehow is suspicious.
First of all, the quality might not be
too good. Secondly,
I couldn't fathom that they use biometric
technology in the context of this video
feeds because that
would be too tall of an order.
So they must have been something else.
And that is what I'm trying.
Part of this puzzle I think I
can offer some answers to
that might be more that we don't know
about as far as all of this research.
It's difficult to do because there's
little publicly available.
You have to puzzle together from the
Snowden files, from from other leaks
and also from doctrinal papers or
like PMA and PhD works that
people in the military do, which is an
interesting resource. If you start to
look at those, um, you will find
tons and tons of interesting papers
tackling exactly these problems.
So one little clue came in 2008
or 2009 and this Wired article
about tagging technology that's used in
the war on terror.
Basically, it's kind of RFID
or, um,
added tags or technologies.
Does that work in some way in the
optical spectrum by
tagging people with paint or any kind of
other medium that
then will show up in certain
spectrum? Oh, the CAT scan.
That's a pity.
But that, again,
seems to be fairly difficult to do
because you need access to the
individuals or, you know, things
they own, equipment they have in order
to do that.
And also the question is how discriminate
can can this get?
So, um, there must
be, as I said, something else.
And, uh, in order to further my
argument that I want to make today,
which to outline it once
more, focus less on the drones and on the
infrastructure, I have to take you on a
little detour through history, more
precisely the history of graph theory
and those of you are trained computer
science or maybe some sociologist,
probably no way more than I do about
graph theory. And I'm happy to learn from
you in the Q&A or afterwards.
The foundations of graph theory
is Prager, and this is the River Prager.
We are late in the eighteenth century by
the Swiss mathematician Leonid Oilor,
and he was employed at the Academy
of Sciences in St. Petersburg,
which had opposed the so-called
Koenigsberg. A little problÃ¨me.
Um, and
the question was, can one cross the seven
bridges of Koenigsberg crossing the
river Plager here and never
cross the same bridge twice?
And Euler's solution was basically to
abstract this geographical ensemble
into a set of points and the connections
which can be depicted like this
or depicted like that.
And both
graphs, these are graphs,
isomorphic, they are the same.
Even though they look different, they
show the same relationship, the same
topology.
OK, and what we are seeing is four points
called vertices or not
in graph parlance, as well as seven
connections which represent the bridges
in this problem,
which are called edges.
So the knots of artists are the
landmasses.
And the edges
are the bridges and
oilor could show that all four landmines
and Koenigsberg would have a have an
uneven number of bridges and that in such
a set up, it is impossible to find
a way that crosses the bridge only once.
And Gravas typically notate it like
this, a graph is averted and an
edge.
And as I
as I said, um, the
number of edges between two, what is.
I usually called weights and the more
edges you have, the more weights you have
in a given graph and despite this
graphical representation I just showed
here.
It is important to remember that this
is less a visual tool.
It is also a visual tool, but less
so.
But for foremost,
a mathematical description
where you can do calculations
on and graphs have found
applications in a wide range of fields
from chemistry, whether relations between
atoms and molecules can be represented
as such in linguistics or in
neuroscience, where the connections
between neurons are described
via graphs.
And some of you might have seen this
presentation is actually by the NSA.
Um, they have
two slide sets publicly available.
They have a nice little journal
for public consumption on their website.
And in the 2014,
um, volume on Big Data,
they basically explain their interest in
graphs, um,
and this this latest thing from there.
So this is what
in neuroscience parlance, you would call
a connectome. It's the individual
graph of the connections between
the synapses of a given brain,
which is a pretty big data
problem, as you can see.
Um.
And let's not least, of course, you have
graphs and computer science where they
provide one of the most important data
structures, useful file since the Linux
file system, which is a tree, which is a
special form of graphs for dependancy
matters, for control, for
representations, compilers and so forth.
But there's
another important branch of science with
graph theory has made an impact in which,
moreover, is important to the war on
terror discussion.
And the argument I want to make here, and
this is sociology.
And let me just give you
a very short insight about the
weird way this debate
has taken in sociology.
Um.
So they didn't enter
sociology, straightforward
as usual, with new paradigms.
They came basically from the sidelines in
this case in the guise of Jacob Moreno,
a flamboyant figure most
widely known for the innovation of
psychodrama,
which is a sort of improvised
theater. We had a workshop on that or a
talk on that at a conference
here and
who also was a social reformer
engaged with convicts, with orphans,
single mothers and so forth in the 1930s
and 40s of the last century,
and alongside using psychodrama
to educate and empower his clients.
Moreno also developed a technique which
which he called psychological Geographe
or social Mitry.
And basically Moreno depicted
individuals as points interrelations as
airlines.
And here you can see one
of his psychological geography's
from his book, Um, Who shall Survive
in Britain in nineteen thirty
four? And that's a school class
in their first year, the boys on
the left and the girls on the right.
And as you can see, there is quite
a lot of connections going back and forth
between the individuals
in this class and between the genders.
And basically Moreno
mapped out these connections
using, um,
you know, questioning the people who you
do you talk to and how often how often do
you spend time with such and such
and so forth.
And after two years, the same
psychological biography looks like this.
Boys and girls are basically
strictly separated, except for two
individuals which basically cross the
gender line here.
And this already pretty much looks like a
graph by
oilor, but it needed a couple
of trained mathematicians to make that
connection.
And cutting a long story short, if you
want to read up on this, there's a nice
book available for free online by Linton
Friedman on social network theory,
where you can find basically the history
of these developments.
So this mathematician was accessible.
Villars, who worked together
with, quote, Levine and other famous
psychologists of the first half of the
20th century
at the MIT in the Research Center
for Group Dynamics, and together
with two PhD students,
Richard Lewis and Albert Perry,
they provided the first formal
definition or proof
of such, what we would call
social graphs and
more of a, um, invented
more or less a class of measures which we
now call centrality measures
where you can mathematically,
um, measure,
uh, the degree of connectedness and
the importance of a given
word is in a graph.
So the context they did this research
in was basically
companies, um,
which had an interest in knowing
the difference between the formal and
informal hierarchies within
their institutions.
So this is at the heart of Fordism, of
Taylor Ristic, um, uh,
work organization methods where you have
very, very strict hierarchies, the boss
on the top and the worker on the lowest
level and many middle managers in
there. And what the research showed is
that despite his formal organization,
you have very important informal
networks and early
social network research was
concerned, um, with
with the relation between those two
networks and with finding out who is
actually central in the information flow,
uh, in these organizations.
Another, um,
important field of research in this
context was, uh, pharmaceutical
companies who wanted to know
how to push, um,
um.
Uh,
the medications into the market and
for this, they needed to know which
doctors influence opinion,
so they mapped out basically
the informal networks between doctors in
order to find out which doctors
do you need to give basically or do you
need to target for your advertisement?
This is in the 50s and 60s.
And this is the context where central
measures first develop.
They then move over into problematic
research, basically finding
out which academic side to.
Which is another example for more or less
informal networks,
so.
Let me skip a little bit here,
OK, so far for the historical outline,
um, which served to highlight the
context of the emergence of sensuality
measures from organizational psychology
and sociology.
It is the study of oral and written
communication that is important,
uh, in groups where these centrality
measures emerged in Western
morality, became the index for the
stability or instability of an
organization and the tools of means
or what we today call social networks.
Um, so many mathematical
ideas on concepts emerged,
um, in these discourses
and thus way before computers came
into the pictures.
So jumping forward to today or basically.
Let's let's take that back.
Jumping forward to 2000, so 15 years
back, we have seen
the rise of Grauwe industrial complex.
First of all, of course, Google proof
that graphs and graphs and morality
measures and page rank is basically
a graph strategy measure that came
out of leel cuts centrally to measure
idea in which he invented in the
early 50s, also in the context I just
described.
So Google
proved.
That
that these measures are instrumental in
managing the seemingly unmanageable
dynamics of the Internet
and showed that you
could graph out billions
of Web websites and advertisers as
back to back and to back links as
edges, and
that you can provide a tool for navigate
navigation in this context.
And since 2005, we have
seen a number of new actors that
emerged, um,
which managed not only to capture the
relations of websites, but of individuals
into actions and graphs.
Facebook, LinkedIn, Friendster,
Instagram, Tumblr and so forth.
All rely on graphing out their respective
populations.
And in 2011 12,
the consultancy Gartner offered
the following take
on this development.
Um, they basically say
there are five essential strategic graphs
in today's economy.
This is the paper which is paid
content. If somebody can provide me with
the full paper, I should be thankful I
only have part of it.
And so this is
this is from from this report.
So the five, uh,
strategic graphs, the social graph,
Intergraph consumption graph, entrance
graph, a mobile graph.
Um, search.
Would, you know, is probably
intent or interest, that's a little bit
unclear, right, but that's basically what
Google owns.
The consumption
graph, more or less, is owned currently
by Amazon.
Um,
the mobile graph is split across
a couple of telcos.
And the social graph, of course, is
more or less owned by Facebook
and Twitter, at least in the context of
of Western industrialized
nations.
So now, you know, most of you will
probably made the connection.
Is it just the economy that understood
the important or strategic relevance
of graphs?
And certainly not.
So after the Snowden revelations,
the German Bundestag sort
of parliament implemented an
investigative committee
to study the the extent of NSA
and five E surveillance,
especially in Germany.
And part of the proceedings was the
invitation of the NSA whistleblower
William Binney, who also has
been here at the Chaos
Communication Congress, I think, two
years ago, and asked
by the German members of parliament what
they actually did at the NSA.
Vinny answered the following.
Um, we built a relationship
in what we call a graph, a social network
of the world. So
the transcripts of of of
the sitting of the committee are
available via WikiLeaks.
It's 187 pages
paper. It's really interesting to read
because Binney basically explains
back and forth how they graphed out the
world and how important that was.
Um, he mentions
graphs and social graphs, I think 15
times or 16 times.
He never once gets asked actually by
any member of the committee what it
actually is.
They either know and have a quite good
understanding of this concept or
they just ignored what they interested
in. If you read this papers.
Uh, it's interesting to see.
Hmm.
That's a lot of questions about career
pathways within the NSA on the one
hand. So how do you become somebody,
you know, like him, a technical director
at the NSA, how much money he makes?
Um, he says
20 percent less than a U.S.
senator. That's the highest pay grade.
And NSA, um,
and then, of course, they're very
interested in how far
Germany was targeted in this context.
And the other interesting tidbit is
that the NSA,
at least he says, uh, gave
the, uh, Woodenness and the German
equivalent, the source code to this,
um, project, uh, in
the early 2000s.
So Binnie's
project was called Thin Thread,
which basically we
don't know exactly what it is, but
basically seems to be a huge
graph database.
And
the difference to what came later
and why Behney became an NSA
whistleblower
was that it basically did two things.
First of all, it wasn't a full take
approach like the NSA and five eyes
do now. So they didn't take
all of the Internet traffic.
And secondly, it would encrypt
the data of individuals with U.S.
citizenship.
Right.
Um, if
one is to believe Drake and Binney,
Thomas Drake, the other, um,
whistleblower, um,
the successor to Synthroid
was a project called Trailblazer,
which was implemented by private
contractors and basically did away
with these two restrictions that they
had since RedHat.
So that's that's their basic problem,
right? I mean.
It's
it's astonishing
how strongly they oppose
basically Trailblazer, whereas Swinford
already, if you think about it,
was basically graphing all the world and
that's what he says.
And so there's
another interesting element in here,
and this is the timing of this.
Behney mentions in the NSA
report that they started in the early
90s with this project and had
a working prototype in the second half of
the 90s to anyone
familiar with the
infrastructural necessities of graph
processing and to developments in the
commercial field in the past decade.
So what you know, these companies
I just showed from the Gartner report
basically did so key value
stores, mass produce and other means of
distributed computing.
You will agree that it's quite
astonishing that they already, in the
second half of the 90s were basically
able to do this.
So the important part here is
that it's not only businesses, but also
governments that have understood the
value of graft and acquired the means of
generating and exploiting them.
And I believe what has happened in the
past 15 years, basically
between 2000 and today is
an ongoing race to graft the world.
And not only a industrial complex,
but a military academic industrial
complex has basically emerged.
On the commercial side, we have what
Bruce Sterling called suspects I just
talked about on Facebook, Google, Amazon
and so forth.
And if you look beyond basically the
Western world,
you will see that China has own
Tencent, Renren, Baijiu, Sina Weibo
and the Russians have V.K.
Contactor, A Momir as
Facebook equivalents.
And basically these are all in
all invitations for you to become
graphed. Right.
Veeco contact you get MP three for free
and they're happy to have you as a node
in their system.
On the government side, we have the NSA
and five eyes which are in the
business of graphing the world.
And the papers from the Snowden archives,
if you look at them so far,
only provide indirect
material because they
concentrate more on the means of
access and collection of data.
So PRISM Buruma, Fairview
and all these acronyms basically
describe how to get the data.
And we know relatively little,
at least on the basis of those papers
on the aggregation and analytical tools.
William Binney, to come back to him
last year in 2014,
explained the threat and the Snowden
files to select Orlean's at a dinner in
Washington and pointed out that Stello
went mainly in Marena, are the graphing
tools used for discovery
and development of targets by the NSA.
And this again
slides which you will find on the net.
It's really interesting to read through
because it's basically a Q and A where
Binney explains how this
is supposed to work.
Um, we don't really know if
he actually tells the truth, but
what he says is basically you have you
graph as
much as you can and then you use
centrality measures in order to
create what he calls sort of suspect
is the, um,
in the green
bubble here.
And these individuals are the ones which
you follow up further on and profile out.
So but what do you do once you have
something like these sort of suspects,
a graph of a terror network, if
you want to call it that?
And this is a question of much debate in
both doctorial doctrinal literature
as well in the academic research
I mentioned in the beginning.
So the military academic research
around so-called dark networks.
So this would be a dark network in the in
the bubble here, the bad guys
and a striking example of the thinking.
So there's there's tons of papers I just
pulled out to to show you a little bit
how they conceptualize that
striking example is the U.S.
Counterinsurgency Field Manual published
in 2006 to much public fanfare.
I don't know how many Americans are here.
Maybe you remember, because even The New
York Times reported about this.
The U.S. military I mean, this is in the
depth of of Iraq and Afghanistan wars.
They had no counter insurgency,
insurgency doctrine.
The last doctrine was was written in the
70s in the context of the Vietnam War.
And then after, you know, the
way that war went, nobody wanted to do
Quoin, as they call it,
counterinsurgency.
Um, but
three or four years into
these wars, they figured out we probably
need a doctrine.
So they had
this guy, right. One, David Petraeus,
who later became the CIA director until
he was basically removed from that
position in 2012.
This is another interesting book which
you really should read, even
though it's an allergy.
And, you know, Fred Kaplan is
not a good author, but it's if only
if only. Well, he you know, he's one of
those military
experts.
But if only half of the stories
about the Iraq war, he
tells, are true, it's pretty amazing what
happened in the political
system in the U.S. at that time.
And he also, Petraeus writes, does this
counterinsurgency manual and of
course, there's an appendix of sort on
social network analysis and exploitation
in there, which is really, really short.
Just a couple of pictures.
And here's an example
of how they envision this.
So basically, what you see
is a graph of some kind
of insurgent terrorists
or whatever you call it, subgroup,
which they map out who
and now the important doctrinal, um,
uh, notion is shaping.
Right. So what you want to do in a
counter-insurgency or war on terror
context is shaping the graph of of
the enemy.
So you have
the network on the left side
and you implement some measure like that
in the surge.
Then you grab out a couple of people to
start a food distribution and during
this time basically map out
the communications,
graph out the insurgents.
And the idea is to shape
the graph in a way that it reveals
information about who is important in
this context.
If you can use, of course, the centrality
measures I
mentioned in the beginning.
So, again, just the shaping and there's
much debate about how to do
this, actually.
And another interesting
example for what I just described
is this paper that came out of West
Point, the Network Science Center
there are shaping operations
to attack robust terror networks.
And the use of publicly available
data set the so-called Tanzania
data set, which is a graph of al-Qaida
1998 in the context of the
bombing of the U.S.
embassy.
And what they offer
is basically an algorithm that
promises to automate the
decisions which nodes
to attack in order to shape your
graph.
And the idea is to render
the graph more
fragile
by generating more sensuality,
by making it less distributed, more
central.
So basically, the algorithm
tells you which nodes do you need to
attack in order to have a star shaped
network in the end and then know
who is actually the most important person
in here?
Because, I mean, the quality is bad here
and it's really tiny in the paper.
But basically in a they don't know who
who bin Laden is and they want
to know who that is.
And this algorithm basically offers
attack. This notice is not this node.
And eventually the communications
will lead to bin Laden, which is actually
this, uh, individuals down
here. So they call this fragility,
which is basically the inverse, you know,
network the inversion, but tied
to sensuality and
which seeks to find a set of nodes whose
removal maximize the networks wide
sensuality.
And we also include the problem of no
strike list.
So basically, the organization will take
those people out, which you which are not
supposed to attack.
And this is because we were talking
of terror and social networks of, uh,
often includes restrictions against
certain individuals and so forth.
They also prove even prove that this is
an entry NP complete
problem.
So read this paper
is really interesting and there's many,
many more. I think this is one of the
more important ones.
So this is basically an algorithm that
helps decision making in in shaping
grafs. Right.
So but how do you actually
get those graphs when people are not
online all the time like we are and have
those little cell phones in them and are
on Facebook and so forth?
And that was the problem basically that
you had in a country like Afghanistan
and what the US military did, what
basically was basically to hire
anthropologists, uh,
they sent anthropologists in field and
asked people those questions.
You, uh, what are so
the, you know, people in the villages and
so forth, um,
what five people here have, you know, the
longest, etc.
and you enter that in a tool like
this where
you basically generate a graph
of the given population
in your area and hence can
shape it.
This is the map map human
terrain. They called Courtice
the human terrain system, which was
closed down a year ago.
It ran from 2006 to 2014,
very controversial.
The American Anthropological Association
actually came out to oppose
the system and
protested strongly against it.
There's an interesting movie about it
which I recommend to watch.
It's actually not a um.
I'm not sure if they actually use the
software.
This is just taking.
From a handbook of the map
human terrain system,
but what they definitely used and what
you will find in many, many papers is
IBM's analyst notebook, which is
basically a software
tool which offers you to map all social
graphs and you get the centrality
measures and can use
that in order to track
and profile individuals.
And of course, what they use is
here, which I think owns the market
for for graphing technologies in
this context.
Um,
anybody who actually has worked on a
plant workstation.
I'm still waiting to find somebody.
I strongly encourage you to watch
the YouTube, the YouTube video
on counter-terrorism that volunteers
up, it's really interesting.
So Pawlenty is basically appetitive,
founded START, a startup
found in 2006
which has seen a meteoric rise and
which started out as
with a software product allowing graphic
graphing for government entities
and now has branched out in all kinds of
fields, mostly fraud detection
in banks. But they also do philanthropic
engineering. So if you want to map out
your clients
as a philanthropist, philanthropist,
Pawlenty has a solution for you.
OK, so this
is the analytical level.
But of course, you know, what about
the drones and
what I think we have to think about,
uh, drones as is
less tools of visual collection,
but more tools of collection of
communication, metadata, basically
cell phone data.
And if you look in all those
presentations which float around on the
Web, you will stumble upon
all those plots that the drones carry.
Of course, they always have a camera and
they have a visual tracking tool
on board, but they also have the
communications and networking.
This is the Argos pod, which is a wide
area.
Surveillance system, they
have the communications and networking
spot, which basically surveillance in
all the communication data in a given
area, and that data
gets aggregated
and graphed out.
So I think we have to think about drones
as crawlers, just as the Googlebot, as a
crawler that indexes Web pages.
And Dilling's what drones
basically do is they crawl an area
in index all the communication that is
going on, allowing you to build
up a graph of
that.
Um, of course, there's also
the other side. They not only collect,
they also target
and nodes of the graphs they have
created.
And this is another strand of the
doctrinal debate that has been going
on in the past 10 years because
the initial arment of drones were the
Hellfire missiles, which were anti-tank
missiles developed in the 80s, which have
quite a strong warhead where you
have the problem of
collateral damage.
And what has basically
happened since 2005 is a race
to find, um, ammunitions
which allow you to target individuals.
So that's called discrete effects.
Right.
This is, um,
so personal targets,
the fact that his personal is plus less
than leave them also you I don't know
what less lethal means if something
like that hits you.
And I mean, this is just one example.
There are many competing systems.
Um, this this call this is called
Viper Strike.
And they all come with this nice little,
uh, circles, which
tell you the blast areas of different
ammunitions. And basically, as you can
see, it's it's relatively
small compared to, uh,
to a Hellfire missile or other
ammunitions.
So this is basically what shaping can be.
There are two ways to shape a graph.
Uh, kinetic and non kinetic.
Non kinetic would be the food
distribution shown in the
counterinsurgency manual or
the snatch and grab and kinetic
shaping is basically the
euphemism for killing people with tools
like that.
And I think what we have seen, if
you think about it in a doctrinal context
and also in the history of aerial
warfare, is that
we have a shift for weapons of mass
destructions to weapons of individual
destruction. This is basically they went
into Iraq to find weapons of
mass destruction. They came out of it
with weapons of individual destruction.
This is, I think, what has happened.
Right.
So to come to an end.
Thank you,
I think and you know,
this is ongoing research and basically
presenting in my toolbox and the ideas
I'm developing right now, and this, of
course, needs to be fleshed out.
But what has happened since
2000 may be a little bit earlier,
which at least is what
Binnie's what
knew would indicate. What many said would
indicate is that Graff's have emerged
as strategic or geostrategic assets
and that there is a race by governmental
but also business entities to
basically gref all the world.
Who owns the biggest gref.
That's important, right? Facebook has
one point two or one point four billion
people graphed out.
Also, there's this problem that they blur
the distinction between maps and
territory because the graph is not just a
map, right?
It's not just a visual representation.
It's actually a dynamic representation
of the activity, of the
communication, of the relations of the
individuals,
which you, of course, already shape
in the moment you observe it,
but which provides a different
level of actionable intelligence than
a map would do.
Right.
And last not least, is really difficult
to obfuscate graphs.
It's really difficult to get away from
that. You can encrypt your communication,
but you still would be visible as nodes
who communicate.
And would Binney and other people also
Snowden always highlight and that's
basically the metadata aspect, that
aspect. That's that's what graphs do.
They ingest metadata and
give you an idea who is connected
to whom in
in what form of relatedness, how close,
how far and so forth.
So basically, it's very difficult
to to get
to stay out of Graff's or
to use to develop tools which
will actually
create false leads or any kind of other,
uh, uh, wrong impression of you
in a graph.
So to wrap this up, I think
we need to think about, um,
who owns graphs and
what does that mean for us and how can we
deal with that? Thank you.
We have 20 minutes for questions,
so if you have questions, please go
to one of the four microphones
in the aisles.
We're going to start with a question from
the Internet where everyone else is
getting ready for the microphones, the
Internet, please.
We got only one question here.
Could you tell us the name of the YouTube
movie about human talent graphics
software?
OK, so what is the
volunteer demo
on counter-insurgency?
And I actually
forgot the name of the Human Terrain
movie, just Google Human Terrain and
movie. You'll find it at Solli IMDB.
I don't know if the whole movie is
available on YouTube.
Thank you.
OK, then in the front
on the right, what is
that left side.
First of all, thank you for your talk.
And I have a question regarding
the
the creation of sea grass,
because if you create those kind of
crafts, you have certainly have some kind
of measurement error because it's very
hard to to, uh, to
create these graphs.
And most contrived measures
are very sensitive to those kind of
measurement errors. So my question is,
do you know about any kind of research
that has focused on the question
of, um, how noisy
these grass are and what kind of
implications of there?
Because if you make some kind of
coexistence based on those graphs, maybe
you killed the wrong guy.
Yeah. Yeah. Well, I'm,
uh, I'm I'm not familiar
with that with with the academic
literature on that. But that's that's
actually questions that, for example,
BINYA gets asked and doesn't really
answer. Right. If you look in the NSA,
doesn't
he? He remains
fairly unclear in
that context.
If you look at, for example, the MIT
Network Research Center, you will find
papers that address that question
because, um, one
of the things you will see also in the
in the drawn papers by The Intercept
is that basically they try to
either optimize
the decision making so the responsibility
can be delegated to a machine
or else put it on as many
shoulders as possible.
So there is a review process of a review
process of a review process involved
in those decision making, uh,
decision making on on on who to
target. And that's how I think they
try to deal with that.
But the technical side, of course,
is probably really
complicated. I'm not a technical expert
on on on these questions.
Thank you so much.
OK, then, question over here.
Thank you very much. I just wanted to
make sure that I got this shaping thing
right, because does that mean
that people are arrested or even
killed just because they happen to be
a node in the graph?
Yeah, that makes the graph
more central, but it's removed not
because they did something bad or are
suspected terrorists, but just because of
the information you can gain from
removing that person from the graph.
Is that what's shaping essentially?
Yes, that's so
I don't know.
And it's probably
without a security clearance
and possible to get the information, if
greedy, fragile, which is the algorithm I
showed there is actually deployed
somewhere. Right.
But the doctrinal papers all basically
say you have to identify
persons, you know, that
have a certain influence within the
network as depicted by certain
centrality measures, and either take
them to extract information from them or
take them in order to see what happens in
the graph.
But that's that's exactly what they're
doing. That is what shaping does.
OK, thank you.
Then we have a question on the side, on
the microphone, the front.
I have two questions for you.
First of all, things representing this
talk from American's perspective, it's
pretty fascinating to see how this is
ingested in a European context.
First of all, did you look at the
technologies used on mounted
on these two poles, selectors
off the ground?
And have you looked at the application
of biometrics onto these
onto these technologies?
And then the second question, that's all
one question.
The second question is about prediction.
Pelletiere and other technologies
develop. Other similar platforms have
predictive elements that are built into
their algorithms.
Have you looked at how those are used to
target individuals within those networks
for extraction or whatever?
I didn't get the part of the on the UVs.
Did you study the technologies used to
pull selectors off the ground, DRT boxes
and so forth?
No, I didn't.
So I didn't really look into the
technical details. So to give you.
So in order to give you an idea who you
know what I do, basically, I'm interested
in the media change of statehood.
So how we move from
a from a state that was basically
built on on paper administrations to
digital administrations and how new tools
come into the picture and shape
or
change, how
a nation state, for example, deals
with something like individual identity
with borders or the notion of a
territory.
So I'm looking at that level and
I'm not an expert on the on the
technical, you
know, details here.
But a look
into the Snowden files.
There's there's a couple of catalogs that
will give you some information on the
tools that they used
to acquire the data and, you
know, capture phone signals and so forth.
I don't know anything about biometrics in
the context of these.
I don't think they're deployed there.
To what extent? Because biometrical
technologies just happened to have too
high false positive or false negative
identification rates.
And I think what they still do is
basically have individuals sit there
and use humans
as
tools for identification, like.
OK, then we go back over here to
this microphone, please.
I'm Henrik.
I'm a reporter.
I've written a number of stories based on
classified documents and stuff.
And I can understand your frustration in
not having access to a lot of this
material.
And I was wondering, can you talk a
little bit more about classified
research and which use
which tradeoffs you as a researcher see
in going into that work?
There's stuff going on at Heilbrunn in
the U.K. between the U.S.
HQ and a university.
There's West Point in the US.
But this is stuff that you as someone
publicly facing cannot access.
That's the first question. The second one
is the relationship between the graph
work done in the commercial world and
what your impression is of what's going
on in the secret world.
The strength ratio is one much
further than the other one cetera.
Well, um, as for the
classified research, I mean, that's
basically something that I have to accept
that I, you know, work on the
basis of leaks and
publications by academics
in the military context.
Um, and one way to
to deal with that is to
basically stay in the historical realm.
So, you know, look back in time
and try to puzzle puzzle out what
happened in the 80s and 90s.
You can talk much about present day
developments because you just don't know.
You can, you know, make educated guess
guesses as best as for the relation
between business and government, uh,
graphing. Uh, I don't know what
I think and what is fairly evident.
If you, uh, if you look at the documents,
um, and also academic debates
in the first part of of of, um,
uh, 2000, you
will find there's a lot of going back and
forth. Right.
I think that's just, you know, it's
there's not a conspiracy or anything.
It's just a discussion, a discourse that
emerged where people from academia talk
to people from military, talk to people
in, uh,
um, in the NSA and other
secret services.
And where
this these ideas basically
were floating around and some people made
a business out of it, uh, facing
consumers.
Other made a business out of it, facing
the government and others made a business
out of it, you know, uh,
facing not consumers,
but, um, uh,
businesses. Right.
So I think that's that's that's for sure.
I'm not I have no idea
how good or bad the
graphing graphing capabilities of,
uh, GHQ or
NSA is.
Uh, but it's pretty certain that they use
Pawlenty, for example.
And Pawlenty is something
like a software package that you can
deploy fairly quickly and does
what you need.
So this, uh, um,
the commercial sector seems to be pretty
strong, uh, in this.
And over here in the microphone to the
left.
Hello, thank you for your talk.
I've got one more
of a remarkable question.
You were questioning whether a biometric
identification actually might
be useful, you know?
I've got one idea what
actually I fear might
work, and that is
movement pattern detection,
because there definitely is
a lot of research also
in the public about it.
And it even works
on the basis of people
that only are like five pixels tall.
Yeah, yeah.
So, um, yeah,
what you will find is talk
about pattern of life exploitation
and pattern of life is basically
mapping out who you relate to, but also
where you, where you spend, uh,
time doing a day during night.
So what is your better known place.
Right.
Where do you eat and so forth.
And part of this targeting
process, identification process
is basically developing
a pattern of life signature for an
individual. And that, of course,
is also used for prediction.
For example, where will this individual
be tomorrow at noon time
and so forth?
That definitely is part of this process.
Um, other biometrical
means, like gate detection
or facial recognition and so forth, uh,
I don't think will be used in drone
context. If you if you there's another
field, this ideally, um,
counter ideas or, uh, improvised
explosive devices, they use quite
a lot of biometrics there, but that's
fingerprinting and getting a printing on
the ground, scanning on the ground.
Yeah, I definitely agree that
those things will not
be available for drones.
But I mean, my fear
is that things like
your walking pattern are just things
that you probably have no chance of
escaping to.
Thank you.
Um, quick question in between,
has the Internet any
on the Internet is silent?
Oh,
OK. Then we go back over here to the
site. Please, please speak
directly into the microphone.
Sure.
I have like two quick observations on
this. One of them is that this actually
kind of apparently a little open source
ecosystem forming.
So a few years back and it's a
kind of open source.
One of the databases accumulate and
that's been picked up by a lot of
academia. They've rebranded it, I think,
before, but they're kind of using
the NSA could quite happily.
And then a few weeks back, you
kind of layered on top with their own
open source database thing.
And it's kind of interesting to see that
they're actually open sourcing the stuff.
The other thing is kind of I wonder
whether you've seen this as well.
I think there's a thing about most big
data and it seems to apply to these
national security things as well, where
you talk about it quite a lot.
But then in the end, you come out and
come down in like day to day business to
very simplistic things.
Right. So you talk about all these grauwe
and behavioral patterns, things.
And then if you look at the actual
documentation for something like
XKeyscore, it turns out it's basically
a version of GRAP. Right.
So it's basically like very, very
simplistic metric.
And I wonder whether how much of the kind
of have you seen any indications
of how much of this is actually kind of
your head in the clouds
kind of thinking that then in practice
looks completely different?
Hmm.
To let a question, you know, I don't
know, because you can't really look
inside these projects.
But my guess would be it's fairly
simple. It's just a scale.
Right, which is not simple.
It's the it's accessing
lots of lots of data points,
ingesting the data and being able to
to store it indefinitely and search
it.
Benjamin Britten, who comes out with a
book called The Black Stack,
called this the Big.
So basically in the past decade, they
could haul in all the data of the world
and, um, that, you know,
provides, of course, a strategic resource
which other entities might not
have access to.
As for a Kumalo,
yeah, I found it interesting, too.
I think basically I mean, if you look at
Hecke News and all those websites, you
will find a lot of debates about, um,
you know, uh, the software infrastructure
of the stacks of the of the big five
companies, Apple, Amazon,
Microsoft.
Facebook,
and I wouldn't wonder
if they basically, if there's a lot
of knowledge and also tools going back
and forth, you know, it's it's
probably look, if you would have a chart,
you know, that shows you how
the stack looks and the NSA probably
doesn't look much different from Amazon
and maybe Amazon even runs parts of it.
So and that's what I mean by military,
academic, industrial complex.
It is just knowledge floating around.
It's know how that is available
and that can be applied, you know,
academically, it can be employed in a
business context, can be employed in this
context.
It's just a discourse that
that emerged once so much data
was around. And the necessity to graph
that out turned out to be valuable.
OK, thank you, and then we have
one more question on the left side,
please.
You told us about shaping of social
graphs. The military does
is removal of not the only operation
that was done there or is there also
research on inserting nodes or
taking over nodes for
food distribution is not removing
nodes.
It's giving nodes something and
an opportunity to see
where the food ends up.
Who talks to whom.
What do the people tell you
when when you give them food?
And I mean, basically targeted
ads is shaping, right?
I mean, if Facebook
helps you out some something on your
timeline, you click on it.
It has successfully shaped the graph.
Right, so
that, you know, in a doctor,
in a military doctor, in the context, of
course, there's there is an option
available that that includes
removing nodes. But the knowledge,
um.
It goes far beyond that, right?
The possibilities then just
removing an old.
OK, we have a question
on this side, on the right side, please.
It's not really a question, just some
thoughts regarding the previous question.
So Kumalo is actually a fork of each
base, which is an open source project.
I totally agree that it is
to some degree different.
But in the end, the core is the same
technology. OK, and also recently
I noticed that GCE HQ
has released an open source
graph to, let's say,
distributed graph two.
So it's the same of it.
I have it on my cell phone.
Sorry, but
what I wanted to say is that the industry
actually has tools that are really
similar, or at least we can
think so towards also
the other people are using.
Thank you.
Are there any more questions?
Then please go to the microphone, no
internet is silent, OK?
Thank you so much for the talk.
Thank you.