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Accepted Paper:

Knowing Nature  
Alexandru Balasescu (Royal Roads University, Victoria, Canada)

Paper short abstract:

We want to believe that AI can somehow give us mastery over nature by simply measuring and managing it. But how did we come to think that nature is separated from us, measurable, and manageable? This paper will explore the implications of relating to the environment as a quantifiable category.

Paper long abstract:

We want to believe that AI can somehow give us mastery over nature by simply measuring and managing it. But how did we come to think that nature is separated from us, measurable, and manageable? And how is this conviction reflected in the way we generate knowledge about, and understand nature today? What are we consciously or unconsciously leaving aside so we can build our predictable models that regularly fail?

This way of knowing, that seems natural throughout modernity, operates a fundamental break in the flow of things, so to speak. Mostly, in order to measure anything, first we recreate it according to our interest, and separate it from the rest of the system. In other words, this type of thinking extracts a fragment from a phenomenon, renders it measurable, and then re-presents it as being the truth about that phenomenon.

What happens when we relate to nature as a measurable entity in a quasi digitized world?

This paper will explore the questions above and propose a possible alternative to the overarching "knowing by measurement" paradigm.

Panel P22
Artificial? Naturally! Climate Change, AI, and the Quantification of Nature
  Session 1 Monday 6 June, 2022, -