Learn From Machine Learning
David Weinberger – an academic, editor for MIT press and part-time writer in residence at Google – observes that while the world we live in is one of extreme specificity, its predictability doesn’t necessarily make it understandable. He says that machine learning may have revealed that the everyday is more accidental than rule-governed, as we’ve previously thought.
On good days, the world seems like a well-run railway: things happen according to principles, laws, rules and generalisations that we humans understand and can apply to particulars. We forgive the occasional late trains as exceptions that prove the rule. But other times we experience the world as a multi-car pile-up on a highway. The same laws of physics and of governments apply, but there are so many moving parts that we can’t predict the next pile-up and we can’t explain the details of this one – ‘details’ that can let one car escape with a bent fender while another erupts in a fireball.
What’s true of a car pile-up is also true of an uneventful autumn walk down a path arrayed with just exactly those leaves and no others. They are both accidents in which interdependencies among uncountable particulars overwhelm the explanatory power of the rules that determine them. One outcome we curse. The other in a quiet moment we may marvel at.
Now our latest paradigmatic technology, machine learning, may be revealing the everyday world as more accidental than rule-governed. If so, it will be because machine learning gains its epistemological power from its freedom from the sort of generalisations that we humans can understand or apply. The opacity of machine learning systems raises serious concerns about their trustworthiness and their tendency towards bias. But the brute fact that they work could be bringing us to a new understanding and experience of what the world is and our role in it.
Via Aeon, 15 November 2021. Read the full article here.
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