computer vision and categorization
decision making relies on categorization.
and this categorization, confining an otherwise abstract thing to a name, is instinctual.
a human acts, makes a decision, forms a response, based on the environmental state or conditions they measure, and their preexisting biases or prior state.
translated to code, it’s
if x do y
if y is programmed it’s preordained.
drive towards increased efficiency inevitably leads to the automation of deciding and acting, and as such, categorization through models capable of that instinctual identification.
and because decisions carry weight,
because action evokes consequence,
because of the speed at which automated decisions take effect,
because of the potential scale of that effect,
because of the abstraction of direct culpability,
exploring the motivations that drive the automation of certain decisions,
[ and of the creation of certain models and their intended uses ]
becomes a fun exercise in identifying how age old human desires find an advantageous channel / medium of expression in the tools of our current technological systems.
from keeping tabs / storing identity
[ a known system is predictable and can be toyed with ]
to feeding that data back
[ distract, control ]
to every elusive optimization objective
[ towards what end ]
observing how structures and systems react to and use computer vision, AI, and automation is entertaining af.
observing how the self reacts and responds to the resulting benefits and impositions is also entertaining af.