It’s a process of very few inputs, yielding small outputs, via a simple formula that people love.
To try and make conclusions, scientists often look to isolate variables and make simple connections to inform wider theories. Modularization fits that instinct perfectly, following the notion that as tasks become more complicated, we simply connect another coping mechanism to mitigate the challenge.Įven in science, this is a familiar concept. People’s instincts are to crave simplicity. The issue has as much to do with human instinct as technical understanding. It is this idea of complexity that companies are struggling with. ML works off the past, so time ordering becomes another critical element to take into account - all when that database is getting larger and the implications of accurate forecasting become more severe. The resultant decision arrives following innumerable overlapping inputs - a tower of Lego blocks - where every factor depends on other factors.Įven more complicated is the time causality element.
From an AI standpoint, these dimensions culminate in millions of predictions being conducted, in real time, every day.
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