>KW: It seems what we are dealing with here is the systematization of the possible: the target is something unknown that, by way of systematization, becomes a c

>KW: It seems what we are dealing with here is the systematization of the possible: the target is something unknown that, by way of systematization, becomes a circumscribed and definite possibility – or rather an array of definite possibilities. The deployment of algorithms is thus ‘mobile’ and ‘flexible’ but, because it is oriented towards definite possibilities, not as much as it may seem; in fact there is only a definite and therefore limited spectrum of possibilities that algorithmic modelling can address. Does this situation change in the face of self-learning algorithms, given their inductive capacities to create semi-autonomous associations in an ever-growing range of possibilities? When I read this, I get the strong sense that machine learning is being misconstrued, and most likely profoundly overestimated in its capabilities.