Foucault, Perec, and Drabinski are all concerned with how a priori classifications not only delineate and form the basis and limits of thinking (as per the way refined description of snow in Eskimo language allow people to think and perceive differently about snow), but can also produce new realities (not just representing it) from the tension between their framing (containing world in categories) and their inevitable overflows. These are very productive frameworks for thinking about classification and knowledge.
When it comes to artificial intelligence/neuro-network, Kate Crawford explains how, depends on how the AI system is “trained,” different patterns of bias may emerge as a result, which is contradictory to how AI evangelists like to sell AI as a post-human “objective” technology. This is, of course, a very relevant and important issue, but I do wonder if the more existential question revolves around the fact that, underlying how humans understand the classifications and biases of AI systems, the neuro-network is more fundamentally like the “culture entirely devoted to the ordering of space, but one that does not distribute the multiplicity of existing things into any of the categories that make it possible for us to name, speak, and think.”
In other words, even though the outcomes of neuro-network calculations are made legible to humans, the precise logic appears to be enigmatic even to the developers of the AI. AI seems to produce confounding results all the time, and so far AI scientists seem to explain why except resorting to always calling for more data. The fact that humans have created a machine whose logic is foreign to even the developers who made it seems like a major philosophical as well as a practical problem.
Thank you, Leila! I appreciate how you’ve connected Foucault’s thought / space dichotomy — the conceptual tabula in which we might conceptually order things, and the physical table on which we might order objects in real-space — to the human-unintelligibility of AI’s ordering logic. I, appreciate, too, your acknowledgment that classifications needn’t merely represent realities; they can create new ones. I wonder what new ontologies AI is conjuring up!