Paper short abstract:

One of the perspectives that can give anthropologists insight into the workings of AI is to treat the coding that creates machine learning algorithms as a linguistic or semiotic process. Coding can be seen as a self-reflexive form of ‘semiotic labor’ performed by both humans and machines.

Paper long abstract:

One of the perspectives that can give anthropologists insight into the workings of AI is to treat the coding that creates machine learning algorithms as a linguistic or semiotic process. Colloquially, computer code is said be ‘written’ in a particular ‘language’ and as such, is said to have ‘syntax’ and levels of ‘semantic encoding’. In the twentieth century, attempts to create artificial intelligence through the modeling of ‘expert systems’ by focusing on this symbolic-referential aspect of language, were largely unsuccessful. Of more relevance to linguistic anthropology are the metapragmatic and indexical functions of language, which require social context for meaning to emerge. Similarly, the current focus in AI on machine learning and neural networks has seen a shift away from symbolic models of AI towards emergent, self-reflexive models where meaning is understood to be contingent on context. The metapragmatic features of language, that is, the ability to speak about language using language, find parallel in the code of machine learning algorithms that have been explicitly written to be able to change their own code. Algorithms ‘learn’ from specific instances of a program running much like human speakers learn from social context what is appropriate, or not, to say. Algorithms then reflexively tweak their own code by adjusting weights or parameters in the code itself, which in turn changes their subsequent performances. In this perspective, coding can be seen as a form of ‘semiotic labor’ performed by both humans and machines through a continuous process of self-correction and adaptation.