Hello in this presentation, I will present findings from a research on practices and
politics of Artificial Intelligence in Visual Arts.
My name is Baptiste Caramiaux, I'm a CNRS research at ISIR, Sorbonne Université.
And this work results from a collaboration with Sarah Fdili Alaoui, associate professor
at Université Paris Saclay.
In recent years, contemporary art has seen the emergence of a new movement involving
Artificial Intelligence (or AI).
Artists from this movement have used the latest technological advances of, AI and machine
learning to create visual arts pieces.
So for instance, inceptionism creates abstract representations and shapes from trained neural networks
Generative models allow to navigate databases, datasets, or speculate on artificial or natural
history, create imaginary landscape and portraits.
However, the way the media conveys the use of AI in Arts is often using a narrative that
tends to introduce AI as an autonomous technique, with which anyone could paint like Monet
or compose music like Bach.
In this research, we wanted to discuss with the artists themselves and investigate How
do artists, who evolve the AI-Art field, with a long term experience of using AI in their
creative process, think, perceive and use Artificial Intelligence in their art-making?
To address this question, we had first to frame what we call AI in this research.
On the one hand, AI is understood as set of computational techniques that aim at providing
the machine with the capacity to achieve high-level cognitive or sensorimotor tasks, such as speech
recognition or learning how to play games.
In particular, in this paper, we call AI, machine learning algorithms able to learn
from data a program that can generate new data that look like the data used to train
the system, or to be able to extrapolate from the data .
Typically, such algorithms are based on generative deep learning techniques.
On the other hand, AI is understood as a cultural object.
AI is a cultural object that has been the subject of many fictions in the Western Culture.
Nowadays, AI algorithms are imbued by the representations, values and belief of whoever
developed them.
In the specific case of data-driven machine learning, data themselves, used to train are
culturally-dependent and convey values and belief from whoever curated and annotated
them.
So having framed what we call AI in this work, let me introduce our research method.
We conducted semi-structured interviews with 5 world renowned artists, who have long-term
experience with machine learning and have exhibited their work widely.
These artists are Memo Akten, Jake Elwes, Marion Klingemann, Kyle McDonald and Anna
Ridler.
With them, we discussed several topics discussed were : the notion of AI, the esthetics conveyed
by the technology, the workflow adopted by the artists, the control and interpretability,
the evaluation of their work and the notion of authorship and governance.
From these interviews, we have conducted thematic analysis, performed by the two authors.
Let me present now some of our findings.
Our analysis gave rise to two main themes that we titled : Practice of AI-Art and Cultural
Politics of AI-Art
From these two themes, we have extracted a certain number of sub-themes that constitue
the core findings of our work.
In this 8-minute presentation, I only report part of the results, so I invite those who
want to know more to read the article.
First, we found that crafting is a central notion and comes back in the interviews, in
the way artists work with data and models.
Taking a quote from Anna Ridler.
I actually think there are a lot of parallels between craft and art and between data and
models.
I think there are a couple of things, like the relationship between craft, which is anonymous
and less well regarded, its repetitive nature, and art.
And when you see the datasets, the datasets are anonymous, and nobody talks about them,
they're out there and people are using them.
In her work, the artist is comparing the creation of dataset as a crafting process.
And actually her work is dual as she exhibits the dataset itself as well as the output of
the model.
We also found that working with AI is working with surprises, where serendipity is key,
but linked to experiential skill of the technology.
Taking a quote from Mario Klingemann.
It reminds me of Francis Bacon.
Iin fact now I feel closer to him.
There is this work with oil paint, he looks for accidents.
That's exactly the point, when you work with oil paint, it also has this certain behaviour
where everything mixes, so you can't deliberately push it in a certain direction - that's what
he says.
In working with it, there comes a time when a certain constellation is right.
Then you stop, and you also have to decide, is this the best solution?
Should I work on it?
That's what I find in my work, keep training and you may destroy it entirely.
Here the artist mentions the importance of accident and mistake which involves the existence
of a ‘good’ behavior.
This existence of a good behaviour stems from the engineering culture of AI, within which
performance metrics are standardized.
Last example, we found that working with AI involves the artists’ liability in the way
works are presented or the terminology is used.
Taking a quote from Kyle McDonald.
There’s the understanding of what artificial is, a kind of artificial as opposed to natural.
I don’t find that to be a super helpful distinction personally because of the fact
that I see intelligence as very collective and hybrid itself.
What do you call a crowd of people working together?
Is that a natural intelligence or is it kind of artificially created out of their relationships?
I’m not sure.
This idea distinguishing between the natural and artificial, for me, it connects to other
kinds of classifications that have been used to reinforce power.
And that’s disturbing to me.
I think we have to be careful about using those kinds of terms.
The artist points out that AI has been widely used in the media as well as in academic world
in order to refer to a wide variety of techniques, systems, and values.
These definitions and scope delimitations have an inherent ethical agenda.
Finally the discuss few points.
From our findgins, uncertainty and unpredictability are creative design materials, which need
more formalisation from the community.
Second, tensions arise from accepting the opacity of the algorithms and resisting it.
The less control they have over the system, the more standard may be the response.
And that questions the authorship.
Artists often take also a political stance and this politicl stance may be more prevalent
in AI-Art than other movement in Art and Technology, which emphasis the understanding of AI beyond
a purely technical object.
Thank you very much for your attention.