Michelle Kuo: Many of the things that we interact with daily are powered by AI in some way,
but there’s very little understanding about it.
Paola Antonelli: There are artists that are using AI as a tool and there are artists that
want the people to understand more about AI.
Kuo: We sometimes feel captive to or passive to the technologies that are simply given
to us.
And for a number of artists, they want to intervene in those processes at a high level
and think about existential questions of free will of human will, machine will, but also
questions of perception.
How can we see things that are actually not made for us to see?
One thing that artists have always been very good at is taking a tool that exists in the
world and making it do something it's not supposed to do.
When artists are looking at technology as another kind of tool to experiment with, or
maybe even subvert or divert, they're saying, I'm not just going to reject this technology
wholesale at all.
In fact, I might embrace it, but I might try and make it do something else.
Refik Anadol: This the show at MoMA right now how it looks like how many people there
like that's…wow.
Last year we saw amazing breakthroughs in AI research like OpenAI's DALLE, DALLE-2,
ChatGPT, Stable Diffusion, Midjourney.
Like these are all actually following a very similar pattern, which are extremely supervised,
extremely labeled, multi-model AI algorithms allowing us to interact with them.
Kuo: In quote unquote supervised learning, which is the more conventional mode of machine
learning.
Humans are the ones that are essentially tagging these bits of information from the outset
saying, here's a picture of a pencil, this is a pencil.
AI is quite good at saying, "If you tell me to create a blue bird, I'll, I'll show you
a picture of a bluebird that looks real."
Anadol: But this was to me not very inspiring to be honest.
To me, what was more inspiring is what happens if you don't use a technology as it's imposed
to us, but used a different way.
And in this context unsupervised, the exhibition now at MoMA is actually doing something different.
It's not exactly following the labeling data or try to mimic reality.
It is trying to dream and speculate an imagination of a machine.
Kuo: So unsupervised learning means that actually you are letting the machine learning model
do that tagging based on its own sort of...
Antonelli: Who knows?
Kuo: Black box.
Yeah, it's a, actually it's..a lot of it is a black box, like we don't actually know what's
going on in there.
Anadol: For "Unsupervised," we took the entire metadata of MoMA archives, which is around
138,000
Anadol: images.
Kuo: And Refik used that data to create custom software artwork that would interpret and
transform MoMA's collection data.
He has created a large scale presentation of this realtime software artwork.
It's almost like a performance.
It's always changing.
The machine learning model has built an incredibly complex classification system or map of MoMA's
collection.
It has decided it's going to group a number of data points over here and a number of data
points over here, and you create a kind of galaxy, but in that galaxy there's a lot of
empty space.
So then the machine learning model in concert with Refik's team is sort of navigating through
that empty space and saying, "Nothing exists here, but what could exist?"
And that is where the kind of speculative and hypothetical and even what we might call
a kind of dreaming starts to take place.
Anadol: I should really show you this.
This is like real next level.
So what you see on the left side is completely not real.
These are all AI-made potential AI dreams.
So what we can do here, I'm literally flying in the latent space of MoMA archive and reconstructing
these potential dreams.
Once AI starts to create this new reality, we learned that there is not any borders between
these biased categories that we need as humans to understand things.
That is a truly multi-dimensional imagination.
It is blending past, now, and future.
It is blending multiple materials.
It's just convergence of things that we thought they are independent.
Antonelli: The AI is not using our value system, it's not using our points of reference.
It's just peering into another type of mind.
Kate Crawford: I think we are at a crucial inflection point right now.
I've been calling it the generative turn.
It's a moment where what we previously understood as how everything from illustration to film
directing to publishing works is all about to change very rapidly.
There is this assumption that AI systems are somehow highly scientifically objective that
they are presenting a view on the world that is somehow unmediated.
But of course the opposite is true.
You know, these are systems that are profoundly skewed from the absolute beginning, from the
data that they're trained on.
This is something that I worked with Trevor Paglen on, where we studied the training data
sets that are used to teach AI systems to see the world.
Trevor Paglen: My interest in AI is not at all like, oh, what kind of wizbang kind of
image can you make using it?
That's actually totally uninteresting to me.
What's interesting to me is looking at AI as not only a set of technical systems, but
technical systems that have culture built into them that have politics implicitly built
into them and trying to unpack that.
Kuo: Trevor Paglen's work “Behold these Glorious Times!” is a kind of hypnotic video
that is basically flashing at you many different images that are used to train AI.
Paglen: The age of machine learning is kind of characterized by building AI systems and
computer vision systems where the idea is that you have a lot of images of different
things and then you use the neural network to find patterns across those images.
So "Behold these Glorious Times!"
looks at those kinds of training sets, these much larger databases that are used for things
like object recognition, forms of contemporary face recognition, but other things as well.
For example, gesture recognition or gait recognition.
So the video installation goes back and forth between just looking at the images by themselves
at this very fast speed and then starting to get a glimpse of what the machine learning
system is doing internally to try to distinguish these images from one another.
Kuo: Trevor is an artist who is really laying bare the algorithmic and inherent biases in
many AI systems, but also the ways in which these definitions have real world implications,
many of which are obviously terrifying.
Paglen: There's moments towards the end of "Behold these Glorious Times!"
where you're seeing training sets that are made out of things like family home videos
or extremely personal moments in people's lives, and they've been incorporated into
training sets to understand, "Oh, this is a mother like putting down a baby, so we wanna
understand what a mother with a baby looks like so that we can try to sell them diapers
or whatever we want," right?
And so you're seeing the ways in which certain kinds of content are being ingested into machine
learning systems in order to try to capitalize on learning what can be extracted from those
moments of intimacy.
Crawford: It still strikes me as preposterous that we're assuming that a single image can
be given a label, a single word when we know about the multiplicity and complexity of a
single image, the idea that we can so benignly label something as a chair and then a person
as say a debtor or a kleptomaniac.
These are things that are literally happening today in data sets.
And the risk there is that we're starting to see a very simplified and in some ways
just really bleached version of the world.
Paglen: We have all kinds of allegorical and kind of squishy meanings attached to all of
the things that we look at in our everyday lives.
Those are informed by our own histories, our cultures, our own memories.
And so that question of meaning making is all over the place.
Artists, what we bring to the party is thousands, if not tens of thousands, of years of thinking
about what the hell an image is.
The kind of engineering computer science tradition does not have that.
This is a place where artists are bringing voices to the conversation that I think are
quite urgent.
Antonelli: We're talking about AI today, but in truth, the fascination and the fear that
humans have with machines and with technology has been amplified and examined and explored
by artists and by designers since technology existed.
Kuo: Early in the 20th century, artists were fascinated by industrial production and what
that meant for someone like an artist.
Suddenly the most skilled human technician was actually outpaced by an apparatus like
a camera or some kind of forming machine that used a conveyor belt.
And artists like Marcel Duchamp said, "Well, wait a second, I'm actually gonna radically
redefine what an artist does and what art even is."
And he took a ready-made industrial object, like a bicycle wheel, and stuck it on a stool
and said, "This is art, because I say so."
In one fell swoop, he realized this kind of crisis of the artist in the 20th century,
which is what is art if it's not technical facility or total realism.
These are things that suddenly felt scary but also exciting.
Antonelli: I would like to just move to the beginning of the Museum of Modern Art and
in 1934, there was a show called Machine Art in which pieces of machinery were unveiled
and placed on white pedestals against white walls, and the beauty of the machine revealed
to the world.
And slowly but surely, designers have been trying to really understand how to use machines.
So OCR-A from the mid 1960s was a font that was designed to be understood by machines.
A few decades later, it's instead the machines trying to make concept as readable as possible
by humans.
It's an evolutionary process in which humans and machine kind of grow together.
Paglen: There are definitely a lot of hard problems that AI can absolutely help solving.
Having said that, and going back to the idea that the context in which these tools are
always being deployed is by huge corporations, they worry that there is a huge potential
for a massive consolidation of wealth and political power, and I'm concerned that that
adds up to an increasingly inequitable society.
Even if the problems that we want to solve can be solved, it's always about capitalism,
not technology, right?
Crawford: This system is drawing on all of us.
It's drawing on our labor, on our voices, on the earth, on the meteorological layer,
on energy, on water.
So for anatomy of an AI system, we really wanted to show the full life cycle of an AI
system, and in this case, we chose the Amazon Alexa.
So every time you turn to Alexa and say, "Hey Alexa, what's the weather tomorrow?"
You are bringing into being this incredibly complex network that starts all the way back
in the mines, where the rare earth minerals are being extracted through to the end of
life of these devices to really show that full planetary cost of an AI system.
I'm really interested in quite radical approaches of how people could use these tools in ways
that they've never been designed to use.
How might we upend the expectations that these tools are for work or for efficiency, and
to try and find ways to make them inefficient, to find ways to actually make them work against
themselves?
Anadol: I also believe that AI algorithms may have a different purpose.
It's kind of this finding the language of humanity by using collective memories to create
collective dreams and eventually collective consciousness.
The near feature that is coming right now very much will be questioning creativity,
questioning who will define what is real or not, and I think we have to be ready.