IX. AI, Mathematics and the Forms of Knowledge
Murielle Mobengo: We’ve been talking about tools that extend perception — meditation, philosophy, conversation, scientific technologies.
So where does artificial intelligence fit into this?
Because AI is obviously a tool, but it’s a peculiar one. It doesn’t simply extend what I can see. It can reflect things back to me.
I’ve been experimenting with that in my own work.
I had this idea for what I call the Impossible Museum — a museum that couldn’t exist physically, where works, objects, ideas and spaces that couldn’t ordinarily coexist could be brought into relation.
AI makes it possible to begin visualizing something like that.
It doesn’t give me the idea. But it allows me to encounter the idea differently.
Loïc Yengo: That’s how I tend to think about AI too: as an extension.
The human brain already has limitations. We use all kinds of external tools to augment what we’re capable of doing — writing, notebooks, computers, databases.
AI can become another one of those tools.
But I don’t think the existence of the tool resolves the question of creativity.
If anything, it makes the question more important.
Because having access to a tool doesn’t tell you what you’re trying to observe, what you’re trying to describe, or why you’re doing it.
Murielle: Exactly.
And perhaps it exposes the weakness of an education based primarily on mastering tools.
If the tool becomes extraordinarily powerful but the person using it hasn’t developed perception, discernment or knowledge, then what exactly are we augmenting?
Loïc: Yes.
A better tool doesn’t necessarily make you a better observer.
That’s true in science as well.
You can have enormous quantities of data and extraordinarily sophisticated technologies and still ask the wrong question.
Murielle: There’s another thing I’ve been thinking about.
We tend to associate scientific knowledge with particular forms of expression — equations, papers, diagrams, technical language.
But does scientific knowledge necessarily have to take those forms?
Loïc: No.
And mathematics gives us very interesting examples.
There are mathematical traditions in which mathematical knowledge was expressed in forms that we would now recognize as poetic.
In Indian mathematical traditions, for instance, mathematical rules and procedures could be transmitted in verse.
Murielle: Which is fascinating because it immediately destroys the assumption that poetry and mathematics belong to completely separate worlds.
Loïc: Exactly.
The poetic form has a practical function there.
If you live in a culture where knowledge is transmitted orally, putting mathematical knowledge into a metrical or poetic structure makes it easier to remember and transmit.
So the form isn’t decorative.
The form is part of the technology of transmission.
Murielle: That’s enormously important.
Because earlier we were asking what poetry might learn from science.
But here we have a historical situation in which mathematical knowledge uses poetry as one of its technologies.
Loïc: Yes.
And perhaps that tells us something about the way disciplines become separated.
Today we encounter mathematics in one form and poetry in another, and because the forms are institutionally separated, we begin to imagine that the kinds of knowledge themselves have always been separate.
But that isn’t necessarily true.
Murielle: Which brings us back to polymathy.
Maybe bringing disciplines into dialogue isn’t simply a matter of taking several modern disciplines and putting them around a table.
Perhaps it’s also a way of questioning why knowledge became divided into those particular forms in the first place.
Loïc: Yes.
And what may have been lost when those divisions occurred.
Murielle: So poetry can compress knowledge.
Loïc: Yes.
And compression is important.
You want to convey something complex in a form that can be remembered.
Mathematics does this all the time with notation.
An equation can contain an enormous amount of information in a very small space.
Murielle: Poetry does exactly the same thing.
Loïc: That’s true.
Murielle: A poem can be incredibly compressed. A few lines can contain a philosophical proposition, an image, an emotional experience, a mythology, a rhythm — several levels of meaning simultaneously.
It’s almost a kind of compression technology.
Loïc: And perhaps that’s another bridge between mathematics and poetry.
Both can create languages that allow us to express things that would otherwise require much more elaborate forms of description.
But you need to learn how to read the language.
Murielle: Which takes us back, once again, to training.
[Laughter.]
Loïc: Everything takes us back to training.
Murielle: Apparently we’re designing a school whether we like it or not.
Murielle: But this also changes how I’m thinking about the curriculum.
If we’re asking what poets should learn, perhaps the question shouldn’t only be: Which subjects should poets study?
Maybe we should ask: What different forms can knowledge take?
Because if poetry can carry mathematics, mythology can carry knowledge, music can carry knowledge, scientific notation can carry knowledge — then perhaps part of becoming a poet is learning to recognize knowledge when it appears in forms other than the ones we’re accustomed to.
Loïc: Yes.
And that’s probably true for scientists too.
Scientists can become trapped inside the forms through which scientific knowledge is conventionally communicated.
There may be things that are difficult to perceive because the vocabulary of the discipline doesn’t yet allow them to be expressed.
Murielle: So the encounter between disciplines could actually expand vocabulary.
Loïc: Exactly.
If the role of science is to observe and describe, then having access to more ways of describing may allow you to see differently.
Murielle: Which means poetry might not simply learn rigor from science.
Science might learn something from poetry about the possibilities of language.
Loïc: Yes.
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