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Short abstract:

Machine learning applications operate downstream of instruments, and are themselves instruments: technical apparatuses which serve to sense and present. This paper explores ML as concatenations of instrumentation: cascades of sensing, presenting, and then sensing and presenting again.

Long abstract:

Machine learning is itself an instrument - a technical apparatus which can serve to sense and present, and make available to interaction that which could not be engaged before. But machine learning applications themselves operate downstream of instruments, reliant on the long pathways of sensors that generate data, and which in turn configure ML and feed its algorithmic analytics. This paper thus explores ML as concatenations of instrumentation, or recurrent cascades of sensing, presenting, and then sensing and presenting again.

Instruments (and not only algorithms and data), deserve our attention. Instruments differentiate and materialize the world according to their design, though not so much as to be ‘determined’. By definition an instrument must be capable of surprise, of revealing something other than what was expected or even hoped, though not so much so as to produce incoherence, a finding that cannot be placed and understood.

We explore the matter through the case of energy generating windmills. These windmills consist of an aggregation of components (eg. gearboxes, transmission, turbines), each of which are themselves replete with data generating sensors, and in turn these data are fed into machine learning models (eg. for predicting maintenance needs or energy output). In sum, sensors layered on sensors, which at each stage are themselves agentic, that is, consequential in how they intermediate phenomena, presentation and interaction.