 
please take your seats and our first
speaker is David Durer who's a cheering
fellow and also from the University of
Oxford David say close to the mics
thanks for coming along
yeah I'm David rhew I'm a musicologist
gosh I'm recognizing several people in
the audience too you know me already but
lovely to meet everyone else as well I'm
going to talk about sort of computers of
music but I've got a particular sub
narrative journey here which is doing
the title I'm gonna talk about using
computers humans using computers to do
creative things but then computers doing
things that require creativity in humans
so I'll kind of end up there that's the
that's the music in a life piece my
story all starts around one particular
point in history because I want to take
us back to the time of Ada Lovelace and
Charles Babbage which is the early 1800s
so another title for this talk would be
creative algorithmic ins interventions
and the imagination of late Ada Lovelace
my imagination made it lovely so I mean
her imagination which we will talk about
but also how we imagine her because she
has been if you like appropriated in
different ways and I hope that the audio
is working I'm hearing different effects
up here I know I'll explain something
about the different ways Ada Lovelace
has been portrayed or even ventriloquist
I don't know if everyone knows about her
let me just say a few words so she's
born in the early eighteen hundreds she
was born into quite a privileged
background and her father was infamously
Lord Byron she's held up today as the
first programmer first female programmer
as an icon of women in computing and
that's really really important but that
isn't what she did and I hope I will
present something about that in
particular what she has done around
music she collaborated with Charles
Babbage famously the inventor of the
difference engine you can find one of
those in the Science Museum and also the
design of the analytical engine which
hasn't been built yet this is a artist's
impression and Sidney Petros impression
of down in this of engine you can see
it's a huge steam-powered computer you
can see the steam engine there and all
the different bits of that computer
although they have strange names like
the mill are actually pretty much it's
surprisingly what we use to today when
we were working with modern processors
so this is you know early eighteen
hundreds went ahead of its time a little
bit of a story about the two of them so
Lovelace and Babbage knew each other
they they work together they devised as
a good word devised programs together
she understood the design of this thing
the analytical engine and one day
Babbage was giving a talk about their
name is Glenn Ginn and this was in in
Italy and it was transcribed
interestingly in French by luigi
menabrea who went on to become the
Italian Prime Minister some years later
back in London Charles Wheatstone is
another character from the early 1800s
suggested to Babbage that Lovelace could
translate the the French description at
the inner engine into English she did
that it's published in this volume
called scientific memoirs and in doing
that she extended the article by about
three times with her own notes and it's
those notes that she published there
which is the legacy that we talk about
today when we discussed the work of Ada
Lovelace and for example and there'll be
another quote later this one is quite
important in the computers and music
community
where she talks about whether the
analytical engine could generate music
in fact she was talking more generally
about doing things other than with
calculations and numbers and that's
important because at the time that was
the focus for these these machines
likely an engine it was about
calculation but she could see that you
could use that for other things and I I
would claim that interdisciplinarity and
that sort of vision of the computers
being used in that art context as well
is actually what makes her stand out I
will come back to that so on music she
says suppose II for instance that the
fundamental relations of pitch sounds in
the science of harmony and musical
composition the set set rule is such
expression and adaptations that's the
operations of the analytical engine the
engine might compose elaborate and
scientific pieces of music of any degree
of complexity or extent unfortunately
she didn't live to do this and the NF
engine was never built unfortunately
there's a long story about Babbage
trying to get funding to do this but
that's another story so we kind of did a
thought experiment and we said well what
if Ada Lovelace had live longer what if
Charles Babbage had built the ennovation
what music would they have produced so
this is kind of a creative response so
this this historical story so in 2015 we
had a symposium in Oxford celebrating
the 200th anniversary of the birth of
Ada Lovelace organized by my colleague
Ursula Martin I'm on stage there in this
picture you can see Emily Howard who is
a composer and Emily Harris is probably
in another stage as we speak because my
collaborators in this work are
presenting on the creative stage at the
same time so Emily is a mathematician if
you decide who's now become a composer
and she composed a trilogy through Ada
Lovelace and that's why we had her the
events and in particular she's very good
at explaining how she uses mathematics
in the generation of the museum and the
composition of the music and that's what
we picked up on and so so in order to
work out what Ada Lovelace might have
done hypothetically counterfactually
we took an emulator or a simulator of
the end
the physical thing doesn't exist but you
can run one in software and we took some
mathematics from the early eighteen
hundreds we talked to mathematicians in
order to understand what yeah
the mess that was around aliens around
now but lots of messing around now
wasn't around there so we had to
understand the maths and we did some
simple actually
but remarkably effective algorithmic
composition and for the purposes of that
events and some of the events we've done
since then we simply use the Fibonacci
number sequence and one of the
properties of the fibonacci sequence is
that if you reduce it my clock
arithmetic modulo something 12 or a
clock then it generates a repeating
sequence and we've done all sorts of
experiments for that where we played the
secrets to an audience we asked them
what bits of music we're hearing that we
pull those pieces of music out and we
use it in compositions so a couple we
had at the time this is my favorite fib
35 which we have a jazz version of and
this is not one where we sort of cut
things vertically in all the numbers
that were generated to produce a piece
of music that sounds a bit like nineteen
seventies children's TV music anyway
more recently last october in every road
and an event we produce a piece of music
that really is clock arithmetic because
this is Fibonacci modulo 12 like a clock
and by taking that sequence of numbers
we never changed the numbers we don't we
don't touch the maths but we can change
the mapping of the numbers into into the
music we play that sequence against
itself at different times different
speeds back was inverted much in the way
people might have played around with
trying to experiment with an idea that
that love is talked about sort of
scientific music so it's trying to
capture that idea and we had a human
performer on a heart because the heart
is the instrument Ada Lovelace played
and we did that kind of can the audience
tell which bits created by human and
which prime machine I'll come back to
that point actually you could tell
pretty easily you can often tell him
those experiments by looking at the face
of the performer
- completely Doctor Who type story and
that imagine we've gone in the TARDIS
back to the eighteen hundreds in had
never Doctor Who where we met Lovelace
and Babbage and we prayed as a music
imagining now put Lovelace in the TARDIS
and bring her back to today what would
she do now what would she make of
computing today so taking the same code
that we use there we've programmed that
upon a number of arduino x' and these
are bigger ones we have smaller ones as
well
so each of these is like an analytical
engine and then we've done some
experiments with having multiple things
I didn't know if they would have ever
thought about having more than one
engine so we've done those experiments
as well you can see some numbers here
because it's all about numbers and notes
so that's a kind of a creative response
so something that happens historically
it's an exploration counterfactual in a
past future and is that useful academic
research where we actually make the
point that it is because what we're
doing is understanding the process of
the time rather than just studying if
you like the product so that there's a
sort of method within humanities
research where you do close reading you
look very hard at the literature and the
evidence that is that is still around
from the time through accidents of
preservation and discovery and we work
with the the Lovelace letters archive in
the body and library and you can you can
you can read those very careful knots of
biographers debate at Lovelace has done
exactly that and there's many books
Armada love this but what we were trying
to understand is much more what was
going on in her head so cluster which
one to have sent her the person rather
than the icon which won't understand
what's going on in her head and and by
actually doing it by repeating it we
believe we get more insight into that so
in a sense the digital process I think
that we're doing here is a kind of close
reading it's kind of new humanities
method to understand what was going on
at the time and that prototype we're
building is kind of a theory so that
counterfactual story I've told it's kind
of a theory about what might have
happened the other picture here
incidentally he
is a bit of Babbage's design notation
he's an extraordinary engineer and this
this is a notation that's used to
describe the mechanics of the intensive
engine and another bit of experimental
humanities is that a colleague at Royal
Holloway I'm agent Johnson is building
bits of analytical engine from a
mechanical engineering viewpoint and
trying to understand those designs and
video how beverage intended that
notation and how well those machines
work and it isn't having just how they
work when they work it's how they work
when they when they they fail there's
what happens when things go wrong
because that's all designed in and you
wouldn't know that unless you actually
built the things to find out so that's
our academic perspective on this in
terms of the my sort of issue about the
appropriation of Lovelace is really
important that she's an icon for stem
incidentally I say Lovelace not ADA
because we talk about Babbage we took we
used surnames because unreason but they
didn't Lovelace she just gets called ADA
so I'm being consistent to building a
Lovelace not that that was always her
name and and in the January issue the
BBC music magazine I had an article that
sort of challenged that I said actually
okay we can claim she's the first female
programmer but you had really to be much
more nuanced and accurate about that she
devised programs in collaboration with
Charles Babbage and was the first to
publish those okay Maps what actually
happened what she was definitely first
on in an undisputed way is thinking
about using computers for things other
than calculation and that isn't what the
others were doing at the time so I hold
her up not as Nikon a stem but an icon
of steam something art in there as well
that article goes on to explain how
important music was she wrote in her
letters that music was at least as
important as mathematics to her and that
isn't something that the Lovelace
program is often talked about how does
this fit in with the cheering Institute
well I'm working there as part of a
special interest group called data
science and digital humanities one of
the things we have going on in that
group that group incidentally is a
project called living with machines led
by Ruth Arnott
marry and this is the reexamining that
period of time we've just been talking
about we're looking at the Industrial
Revolution but applying data science
techniques looking at the record records
of the time looking at newspapers and so
on in order to do a data science led
re-examination the Industrial Revolution
and that's something obviously that's
been widely studied but not in this way
before so that's a really good example
of a digital humanities project going on
in the sharing which is very much data
science and oriented so have a look on
the web to find out more about the
projects only recently started I'm sure
you would hear a lot more about it in
the future while I have a some weaving
on the screen a quote from Lovelace to
make my point about not just numbers we
may miss out we may say most actually
that the analyst vision weaves algebraic
or patterns just as the jacquard loom
whose flowers and leaves just could it
evidence that she wasn't just thinking
about calculations so this is not a
group of the cheering one of the areas
of data science and music is using
computers to analyze music and that's
really the digital musicology piece is
sometimes called computational
musicology music information retrieval
it's an area of place of science that's
been around for quite a while it's a big
vibrant community it's probably been
around because it's before things were
called data science so there's
definitely a lot of data science we can
do in the analysis of music for example
we recently had an event in the cheering
and these with the series of talks so
this gives you a flavor about not just
how we use data science to analyze music
but the applications of having done that
so these are real applied pieces of data
science these come out of the project
called fast fusing audio semantic
technologies that's incidentally
coincidentally also live from queen mary
by sandler now another quote as I go my
narrative journey to computational
creativity
another quote from Lovelace the only
Clinton has no pretensions to originate
anything it can do whatever we know how
to order it to perform
it can follow analysis but it has no
power of anticipating any analytical
relations or truths its provinces to
assist us in making available what we
were already acquainted with so this is
sort of Lovelace on machines and
creativity and in the same way that
music quotation from her notes has been
widely cited since especially in last
two or three decades so is this one to
do with computers and creativity and
it's led to quite a discussion over the
years so Alan cheering let's mention the
man himself and in his famous sort of
Turing test paper Computing Machinery
intelligence in 1950 so just over 100
years after that vase her paper was 1843
talked about lady Lovelace's objection
so the idea that the computers could do
things that would require creativity in
humans and margot Bowden the Sussex in
her book the creative mind myths and
mechanisms teased apart these issues
about creativity to four separate
questions which you can address in this
area so it's been a very provocative
quotation from the basis work that
debate still goes on so you can you all
decide yourselves but where you fall on
this one where this has taken us is from
using computers do creative things like
the algorithmic composition to using
computers to do things that require
creativity in humans and this is where
we're using AI to generate music is
exactly what's being presented on
another stage as we speak and what we've
done to engage people with this work
rather than like the half example I gave
earlier is these experiments we should
clinical bark or BOTS so we have some
music that's being composed of like
opposed I mean put together literally by
PhD Steve laid low where he's generated
some music from an AI in the style of
bark the author of the code is also
around somewhere Christine pain and then
he's taking those fragments that have
been generated by AI and his
interspersed there with bill bits of
bark we kind of had this game where you
get someone to human to perform
the work and the audience have to say
which bits human and which mr. AI so we
have people holding up his a card red or
blue showing which it is we did this at
the Barbican in 9th of March did it
again and lots for last week we're doing
it again later this week but the Royal
Northern College of Music and it's
really interesting and different
audiences a good at spotting the
difference between the two but it
doesn't convey our real intention
because it suggests that what we're
trying to do is replace humans with
computers and we're not I'm more in the
Lovelace rotation mode here what we're
trying to do is assist humans in being
creative so got laid low is an
orchestral proposal he's using AI to
assist him in orchestral composition so
that's where we've come in on this
spectrum about I think the leaders be
creative I'll be finishing her and just
if anyone wants to follow up something
to the count 4 is in November at the
Barbican a second of November we had
kind of made a lot of a stay at the
Barbican it's not quite the right date
they did a lot of a statement and and
all the people I've mentioned about laid
low market so tiny
oops and Emily Howard will all be there
and we're putting on a day about exactly
this with this revisiting of the Ada
Lovelace story thank you very much
who is another fellow of the alan turing
institute McGraw all right so a good
afternoon is a pleasure for me to be
here
but so that it is a pleasure for for me
to be here and to be talking about some
of our recent work on artificial
intelligence for part investigation so
my my background is on mathematics of
data I'm an electrical and computer
engineer but it's been I've developed an
interest in in this area in in the last
few years last couple of years and it's
been a privilege to work in this area
because we are about the opportunity to
work with people at the other end of the
spectrum so art historians curators
heritage scientists people that have a
completely different perspective but
equally important to me is that it also
flows back in our direction because this
field in particular it presents us with
some very concrete challenges that
motivates us for example to develop new
algorithms that you know do not occur
for example in other challenges
occurring in other domains so let me
just give you an idea about the type of
challenges that we are here and this has
been some of you may have seen these it
has appeared on the news about 18 months
back and it's a a portrayal of Sir John
Maitland the Lord Chancellor of Scotland
and this painting has been examine it
under x-rays and what people are
discovered is that beneath this painting
there's a portrait of Mary Queen of
Scots and this has generated quite a bit
of excitement because portraits of the
Queen are are very rare and therefore it
was a very interesting discovery and the
conjecture is that well at the time it
was probably not a very good idea to
have portraits of the Queen at the time
over execution so these are these may
have motivated no people to Commission
to paint something on top of these other
painting and examples like these abound
in in this field so for example
van gock van gock was a very poor
painter and therefore it did not have
the means to buy for example canvas so
people estimate that about 30% or so of
obvious paintings they have been overt
buy and other painting so there's
concealed concealed designs so one
problem that arises for example in this
field is I mean is there a wife for
example to reveal some of these conceal
designs that that are present in many of
the paintings that we now observe for
example in galleries so this is one of
the challenge but in this field there is
you know far and many more challenges
and questions so you know beyond for
example try to reveal conceal designs
one might also be interested for example
in visualizing in the drawings so the
drawings that painters for example do as
they conceive their artwork there is the
need also to understand for example the
materials that are present in a painting
for conservation purposes for example
and part of the challenge has to do with
the fact that some of these objects are
extremely complex and here we got an
example for example of a cross-section
of a painting and of course you know
there's there the support the canvas for
example where a painter eventually
produces the artwork but on top of the
painting there's what we call there's a
ground layer then eventually there's
these preparatory sketches and then many
many paint layers that eventually gave
in a rise to this design so for us for
example to you know understand the
painting and eventually for example
understand materials and the different
designs present in a painting so there's
a need to understand all these you know
complex complex structure now one way is
to try for example to take samples of
the painting micro samples I know people
do that but of course this is
destructive it is invasive and there's
so much we can do so the other
alternative is to recur for example to
imaging type optic techniques and here
for example one may for example resort
to electromagnetic radiation in order to
see through for example the various
layers that are present
in the painting so what we see so is is
for example the response of a painting
to a visible light but if we were for
example to use say other wavelength is
or other zones of the spectrum then
there is the open that we can probe
through all these various layers in the
painting to try to understand its
complex complex structure and in fact so
this is what people have been doing in
museums and inherited science for a same
time in fact upon the introduction of of
x-rays so so initially no x-rays were
tested on on a painting and x-rays for
example or x-radiography they they have
been used routinely in museums since the
30s or so in addition to x-rays people
have been using many other imaging type
of techniques for example people have
been using ultraviolet induced visible
fluorescence people have been using
infrared reflectography now is used
routinely in museums and and beyond that
so people have been using more recently
all kinds of sophisticated type of
techniques for example multispectral
imaging hyperspectral imaging x-ray
fluorescence some of these instruments
in fact now they are becoming you know
portable so that they can be used for
example in in museums so for example if
we were to take this painting at Dona
Isabel Purcell so it's a painting the
National Gallery by Francisco de Goya
so if we were to observe its eye under
different different electromagnetic
areas of the spectrum so in x-rays so
this is what it looks like and their
infrared this is what it looks like and
some of the more recent techniques
multispectral imaging hyperspectral
imaging and x-ray fluorescence so what
they produce essentially is what we call
data cubes and essentially these are
images of the painting at different
wavelengths is or images of the painting
at different energies in the case of
x-ray fluorescence for each pixel so
what they do is they produce a spectrum
or we can also slice the cube in the X Y
dimension to get for example
an image now the availability of all
these data so in fact what it does it it
opens up the possibility for us to use
digital technology machine learning in
particular to try to understand
paintings and address some of the
challenges that I've mentioned before
and in fact this is not entirely new so
for example the Bangkok project which
was introduced I believe around 2008
2009 so this was initiated by Rick
Johnson from Cornell University in
partnership I believe with van Gogh
Museum and the idea was to try to use
images of paintings to understand for
example van gock and since then you know
people have been using all kinds of
techniques signal processing image
processing machine learning to try to
understand paintings paintings better
man but many of the techniques that
people have been using or the data that
people have been using to understand
paintings were very simple data sets
based on for example RGB images of
paintings x-ray images or for example
infrared images and what we are trying
to do so this is in fact this is being
funded now by EPSRC
there's also elements that are being
funded by Royal Society so what we are
doing in this project which is a
partnership between my University
Imperial College the National Gallery
and there's also other partners on board
so we are trying to leverage all these
huge high dimensional and
multi-dimensional data sets that are
being produced by a in museums on
artwork in particular hyper spectral
images and x-ray fluorescence type of
images to try to understand it to try to
understand paintings so for example you
know some of the questions that we are
addressing or in the in in this project
we like number one so we want to collect
and prepare new data sets acquired on
paintings primarily paintings from the
National Gallery but we are also
engaging with other galleries the
National Gallery of Art in Washington
for example and then on top of these
data sets that we are developing we also
want and
to develop all kinds of data analysis
algorithms to make sense of the
paintings so for example to understand
the different materials that are present
in a painting which of course is
extremely relevant for conservation type
of purposes beyond that so we are also
addressing a number of case studies so
beyond material characterization we want
for example to reveal say concealed
designs in a painting we want to reveal
pentimenti we want for example also to
reveal the in the drawings present in a
painting now we are complementing these
research activities which involve the
development of new algorithms with a
member of impact activities such as for
example the development of open software
tools that we want to make available to
a museum so that they can interpret a
paintings but also ranked organized many
other activities such as workshops
conferences and also launched within the
UK a network on artificial intelligence
for art investigation which is also
something that we are trying to pursue
in conjunction with deterring now I
would like to terminate with with with
the case that is something that we have
done very recently that showcase is what
is what AI promises for at this
particular this particular field and
what I'm showing here is is the Ghent
Altarpiece which is a very famous
painting by Van Dyck Brothers so it's an
altarpiece so essentially there's an
open and there's a closed position so
the idea is that on festive days these
other pieces open and on non first tip
dies the other piece is is closed so
there's some panels that here in this
case are painted on both sides and then
there's others that are painted on a
single side only and the problem that we
are being a tasket Widow the challenge
for us was the following so if we for
example examine some of the outer panels
so here we've got say one other panel
related for example to the image of if
so on on on the rear panel so what we
are we say an Angels so so and if we are
to X right for example these paintings
but as I mention
before there is an interest or in
observing a painting and they're
different
radiations because these reveals
interesting properties about the
painting so in this particular case if
we were to x-ray say this particular
panel so what we are going to get is a
superposition of x-rays where each one
is going to contain features of the
images on both sides of the panel so in
the x-ray that we are showing here for
example so we've got featured relating
to if and we also are features related
to the angel on the other side of the
panel now it's difficult to interpret
these x-ray because it contains the
superposition of two images so our
challenge was to try to disentangle
these two x-rays so producing one that
we might say to the image of Eve only
and producing another one that relates
only to the angel and so what we're
using this particular case in fact this
is a problem that we have pursued for a
couple of years now so we have published
it on on on this problem using all types
of techniques not entirely satisfactory
and more recently so we are used an eye
type of approach which we showcase here
and our approach essentially involves
using a deep learning type of algorithm
so what we do here is we've got this
deep learning algorithm that is trying
to convert say an image so both images
of one side of the panel and the other
image from the other side of the panel
into the corresponding x-ray and so it's
the image to image translation Network
let's put it that way but in addition we
want to do that in such a way that the
sum of these two x-ray synthetically
generated x-rays they are consisting
with the mixed x-ray that we have access
to so I mean this is the general idea so
there's a deep network or two deep
networks behind this idea so then you
know what we need to do is to try to
train these networks but what I'd like
to emphasize here and again you know
related to some of my previous remarks
that the nature of some of the problems
that arise in this domain are very
different from those that arise in other
domains is that this is entirely and
super
so we don't have examples here upon
which we can learn how to separate
x-rays so instead this is a complete
itself supervisor algorithm there is no
notion of training set there is no
notion of a testing set I mean the
training set and the testing set is
exactly the same is entirely as self
supervised type of algorithm and these
are some of the results that we can
actually produce so for example in this
case so we've got the image on one side
of the panel so this relates to Adam
we've got the image on the other side of
the panel again so there's I think in
this case there's a profit on the other
side and I'm also showcasing the mix
text right so this is the data that we
have access to and upon training and
applying our algorithm so what we get is
we get presumably so the image of or the
x-ray associated with Adam and we also
have the x-ray associated with the other
side of the panel okay
now just to give you an idea or now this
for example might compare to the state
of the art so these are some of the
results that we are previously and we
can see that you know previous
algorithms they do not quite get to
separate the x-rays and using any itok
approaches we are able to achieve a far
much better separation so we have tested
these on the oven panel we have also
tested these on nips panel and again so
very very similar very similar results
right
so to summarize so so we are convinced
that artificial intelligence technology
as potential to address outstanding
problems arising in art art
investigation
with implications for example for our
presentation for art art conservation
there's many other outstanding problems
each with specific challenges and in my
view so I think that it is an excellent
opportunity because not only are we
solving very interesting problems
arising in this particular domain but
also this domain is is offering us the
opportunity to develop enter
new algorithms and supervised approaches
semi-supervised approaches developing
understanding that for example we are
not yet yet achieved so this is the team
behind his work so while the champions
of his work are my student Zahra so she
she was one of the driving forces beyond
his work she's about to graduate as well
as barracks over at Duke University but
we have also had the privilege to work
with applied mathematicians and members
of the National Gallery so this is an
area that we are developing further we
are also developing these in the Turing
so we are trying to launch initiatives
in the Turing associated with this area
so if you are interested you know please
do get in touch
thank you well we have a third very
interesting perspective now from Luba
Elliot who is an artist a researcher and
an AI curator selgeh tu Luba we've had
two very interesting talks both on music
and using a eye for art investigation
and my talk will look at how artists are
using and thinking about AI and in
contemporary art so I'll give you an
overview of what's been happening over
the past few years starting with this
point and how many of you are familiar
with with this type of aesthetic with
deep dream okay lots of hands are going
up this is very good I think the media
did its job well even if now this
project is maybe for like four or five
years old now but I think this deep deep
deep dream project which came out of one
of the Google offices and July I think
it was 2015 also it's it basically works
like this so you have face and then this
algorithm gets excited by various random
features in the face and it starts to
emphasize them and find random colors
and creatures and you can think about I
guess why this type of aesthetic would
be interesting artistically right
because it's kind of very bright it's
very kind of particular
yeah I think the mainstream loved it and
lots of artists from the technical
community started experimenting with it
and yes I include this project by Daniel
embrace II who is normally a
computational photographer and he likes
to combine those techniques with with
deep dream and I don't know how well I
can actually see the screen but there is
there are some trees and it's like a
landscape scene and deep dream is used
to really kind of shape some of the
features and textures in the image but
it's not completely overpowering and I
always like to show that as a good
example of how you can use one of these
AI techniques for yet to really kind of
experiment and play around with
aesthetics without making the work
explicitly just about that technique and
then came stare transfer which is kind
of in September of the same year in 2015
and yeah but technique basically works
by if you have a photograph this
technique can change it into the style
of mourning or Picasso or another artist
and if you don't know much about
contemporary art or how the current art
world works then you're likely to be
Anna quite excited by it because you can
change your portraits into the style of
your favorite impressionist artist and I
remember a couple of summers ago there
was an app called Prisma that let you do
that very quickly but whenever I show
something like this to the art community
particularly those who work in the
contemporary art field that always
shocked that people and the tech
community would consider that a valuable
technique for artistic
work currently because of course you
could look at this as a way of simply
replicating the styles of artists pasts
and not really innovating or thinking
how you can render a scene in a very
kind of different way so yeah it has I
guess controversial to an extent and
yeah here are some examples of Jean
Kagan and kind of playing around with
with style transfer and Mona Lisa and
then also expanding the definition of
what style transfer can be and I think
this is one of the examples of how this
technique can be applied in a more
interesting way so if you kind of
broadened the definition of what style
can be to Google Maps calligraphy or
astronomy or sometimes I see if you just
combine different artistic styles to
come up with something really unique
that could also work
and recently Sofia Crespo has been an
artist he's been working with the style
transfer in some way I think she's been
minimizing the content which is one of
the two components and they're in the
system and she's been coming up with
these crazy new underwater creatures
which I think are quite beautiful and
they kind of show how some of these
algorithms can be creative and come up
with new kind of visuals and creatures
that didn't exist before but could have
maybe and then came something called the
gam and this was already also like a
couple of years ago and I feel the art
world is still very much
excited with using baganz of
degenerative adverse ion networks to
generate images and this is because
partly when they first came out they
produced a lot of images that had eyes
kind of limbs misplaced so they were
quite realistic but something was a
little bit off with them and a lot of
people in the our community found that
quite exciting and that you can sort of
see that in the work by Myra Clingerman
and I think he is one of the most
exciting AI artists in terms of really
knowing how to combine all the different
techniques surrender visuals of I guess
the human face or the human form and
this is some of his work from two or
three years ago and sometimes people
from the art world say that it reminds
him of Francis Bacon because I guess
also the face is a little bit mashed up
and yeah the lens are sometimes odds but
yeah initially this is kind of the way
the algorithm tried to yeah construct
the human body and yeah this is sort of
mario Clingmans work from last year and
as you can see it's it's much more
realistic and yeah merry Klingon is very
prolific and if you're interested in
seeing more work like that I recommend
that you find him on Twitter he has an
account at quasi mundo and there's lots
of different kind of facial images that
he's done and nowadays of course a lot
of these AI techniques enable us to
generate some very realistic images of
politicians and celebrities and and
other people and artists are also
starting to experiment with with some of
these techniques and one of these
artists is the new bikini and she tried
to do this kind of deep fake artwork
called euro revision which combines to
two politician centuries of may and
angle America who this doesn't have
found but if it did then they would be
reciting Dada poetry kind of based on
the political speeches and yet also can
singing along for I'm Eurovision and now
I'll move on to some artistic projects
which unlike the first group I showed
which focus more on the technique like
this group of artists focused more on
the data set which is of course kind of
very very important in terms of
determining what kind of image or
artwork you will end up with in the end
and I like to start with this work by
Raman Lipsky who is a landscape artist
based in Berlin and he's been painting
lots of kind of landscapes in his career
he was never particularly interested by
the digital world and so at some point
he decided to work with a team of
technologists to see how he could
augment or change his practice and so
this is a black-and-white photograph of
a night scene in LA and Raman Lipsky
proceeded to make nine different
paintings based on this night scene and
this is kind of his typical style of the
time so normally it's just black and
then one type of color in each event and
then he gave these nine paintings to his
technical collaborators who proceeded to
train a neural network to generate
images kind of in his style based on
those nine paintings and this is what
they got which is quite different
because the composition is a bit free
and there are multiple colors in each
image and Raman Lipsky proceeded to
respond to that and make some paintings
based on kind of the images that were
generated by the machine and this is
what he has done so yet he has tried
sometimes to include different colors
yeah the composition sometimes becomes a
bit more abstract and then again this
set went into the machine to generate
those images which are kind of abstract
and sometimes have some very odd color
combinations and again the Roman Lipsky
painted a response to that and yeah this
is very much a abstract and let's see if
I have yeah so as you can see I think
this shows how you can use some of these
your machine learning techniques to help
you perhaps come up with ideas also
based on kind of your own paintings and
how you can challenge your practice and
kind of your ways of working without
necessarily just kind of sticking to
working in the digital form because
ultimately his final words for still
paintings which I find refreshing
sometimes in the digital world and next
artist I like to mention is Anna Riddler
who is also very much concerned about
the datasets she uses to make her own
work and in in a piece she did called
Fall of the House of Usher which is
based on this like a white film I think
it was from 1929 and it's based on an
Edgar Allan Poe's short story what she
did was she made 200 ink drawings like
like this image and here are some more
images that she can have painted herself
from watching this short film and then
she proceeded to train picks two picks
so one of these neural network
algorithms to generate
images in her style and compiled them
into an animation which you can see here
and I work quite closely with this
artist and for me it's always been quite
interesting to hear about her kind of
perspective and what she has noticed
about the changes I guess what the
machine has picked up from her style so
here as you can see sometimes the
eyebrows and their eyes would be kind of
generated by the machine in a very
similar fashion and that's because in
her artistic practice and a Riddler
might draw these like facial features
very similarly and so the machine would
also get confused or sometimes there
would be a background object that
appears the reappears because to an
artist if it's a background object it
might not be very important and it might
be left off occasionally and of course
the mission would also kind of pick up
on that and it would basically amplify a
reinforce all the kind of stylistic and
artistic decisions made by the artist
and similarly to Raman Lipsky she also
did in this process where she gave some
of yes yes she generated the first video
based on an original set of 200 ink
drawings she made and then once she got
the generated video she made another set
of ink drawings which then and which
then went into the machine to generate
the second video and then from the
images of the second video should
generate more as she drew more images
that can give her this fashion see a
little bit complicated but you can also
kind of see how the generates image
changes that it becomes a bit more kind
of abstract and blurry and every time
and yeah I guess that's partly due to
the kind of the new versions of the ink
drawings that Anna makes that match the
style of the previous films and yet
these are some of the ink drawings and
then just to give you an idea of also
how some of these techniques can be
applied to very different types of
datasets then this is some work by jäger
crafts looking at the sculptures from
antiquity and nowadays if you go to all
the museums you will notice that a lot
of the sculptures they have maybe the
noses or parts of the head missing
because they've been around for like
2,000 years or longer and what this
artist tried to do is he tried to get to
training an eye on these images of
antique sculptures and get them to
generate various designs as you can see
here and some of them are perhaps not as
realistic as others and a bit more
fantastical but in the end what he did
was he created an exhibition where he 3d
printed some of the kind of components
generated by by his designs and and
complemented some of the existing
sculptures with that so these are some
examples and now I will move on to a
series of works that look at this a ie
feet a little bit more critically
perhaps and I'll start with this by
Scott Kelly and Van Pelt poking horn who
are two artists from New Zealand and
they were interested in going to
national parks and putting up these
billboards which gave you
recommendations of where else you could
go to do so other national parks because
clearly what you want to see in a
national park is recommendations of what
else you can do and reminders of kind of
Technology and capitalism and all this
other stuff and this is some more of
their work and then also this and I
particularly like this one because it
blocks the slides so you can't actually
use the playground anymore really if you
want to fly down safely but yeah I think
it's a kind of it's a great comment on
how we are excessively influenced by
technology and by these recommender
systems in our lives and to show how
kind of AEI or some of these BOTS
generated images of sculptures can
impact a community I I like to show this
work and by gillian de swerve and much
plummer Fernandez and who were or I
guess they like this website called
Thingiverse
which has lots of different designs of I
guess 3d models that you can print and
then maybe use for something and think
these are examples of some of the models
that you can kind of find on the website
and they created a bot that made a
mash-up of all these models and these
are some examples the titles were also I
think machine generated so this is an
open overlord nozzle and that's a
plastic action car and I guess if you
were looking for a useful design
or useful a practical design on the
platform that might not be quite it but
yeah anyway this platform became
overflowing with with these designs
because the bot was creating mashups and
it was pasting all the time on the
newsfeed and it was really interesting
to see what the feedback was from the
community because some people were
wondering whether that was actually spam
or if it was just like a human way of
kind of communicating and then somebody
was just very annoyed that this bot was
hogging all the attention because it
kept dominating the news feeds and then
somebody else at the bottom they were
just really happy that the model became
part of this kind of artwork so I think
it's it's an interesting to see the
diversity of the reactions and can make
people think as to what is kind of human
generated artwork what isn't and
ultimately it became an exhibition where
all these kind of mashups became printed
and exhibited and in the Netherlands and
visitors could vote whether it was art
or spam and I think art got a lot of
votes but you go to an art gallery maybe
you're expecting art so maybe there is
some bias there and yeah there's there's
also lots of artists who are very much
preoccupied with the influence these AI
techniques for having and our society
particularly in terms of facial
recognition it seems to be yeah quite a
popular topic and this is the work by
constant alert called dull dream so if
you remember I showed you the
multicolored deep dream at the beginning
so this is kind of the opposite so this
algorithm works by reducing kind of the
features in an image so it becomes more
blurry and yeah this is one example this
is another and yeah you probably can't
recognize Trump so well on this image on
the right
and there's also a website called dull
dreamed XYZ
where you can go and upload your own
picture and then it would also be kind
of changed a bit so it's not really
recognized by by others and by computer
systems as you so it's I guess an
attempt to hide away from the facial
recognition algorithms and civilian
systems and so on this is similar to
what Adam Harvey has been doing in his
project so CV dazzle which has been
going on I think for about four years or
so and he he works out that a couple of
years back if you added one new if you
added two triangles to your face then it
would not be detected as a face but if
you only had one yes the face would be
recognized
I'll see if you had a wacky hairstyle
sometimes your face would not be
detected but I think nowadays I'm not
sure if all these kind of tricks would
pass so he is he has a new project
called hyper face which I think works in
a different fashion by trying to blend
your face into the texture of the
background behind it but yeah it's still
kind of very much looking at how you can
avoid facial recognition and then I
think yeah I mentioned one or two
projects because I'm clearing out of
time and this one by shang tsung back
kim jung-han who who are a duo of
artists based in south korea and they
still work with this kind of a facial
recognition technique but in a more of a
fine art context and they works together
with various painters who were given the
task to paint a portrait together with
the facial recognition algorithm which
was kind of looking at the what they
were painting and if it detected a face
then the artists had to kind of do
something to the painting so that it
would not be detected as a
and these are the portraits it's some of
the some of the artists came out with
and I think some of these you can
probably recognize as well it could be a
portrait but I always think that this
one on the left
I wouldn't immediately think it's like a
portrait it seems to be quite quite
different from what I would imagine as a
portrait and I think I'm pretty much out
of time
so I will skip through all of these
remaining ones but I'll get to the slide
with my email in case you do have any
questions you can always write to me or
I think there might be some time for
questions now
you
