Or, ML algorithms will, without anyone understanding what's going on, weed out people prone to depression or women more likely to become pregnant in the next two years. It's ML, not just bureacratic rules codified into some code.
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Why is an institution implementing an ML system without understanding how it could be wrong any different to an institution implementing a database without having that understanding?
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Cannot even reverse/engineer debug. I think a better grouping is that ML is opaque like humans, but as humans we have some insight into human foibles. Traditional databases (or programs) are like bureaucratic rules: they can be a maze, but you can potentially figure them out. 1/2
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We have a handle on why and how the NYT news side behaves, as well as the op-ed page. They even write editorials explaining their reasoning (which you can further analyze), and we have fields of study on why and how institutitional power operates. Not at all there for ML.
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Gentlemen, the first commercial global database, the 1953/1960 Sabre airline reservation system generated quite a bit of fear by the general public. There were news reports and protests about “a big computer brain”. Fear subsided as reservations time dropped from 90 mins to 1 min
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Replying to @BrianRoemmele @stevesi and
Since the dawn of the computer age we have moved from data to knowledge (only recently). Current non-cybernetics based ML/AI will move us closer to insight, but not nearly to true wisdom and understanding. It is these last steps where all the big stuff takes place.pic.twitter.com/re2UB3HgNn
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Steven, indeed sir. I hear ya. History always repeats this fear of a contrived unknown. From the edge creatures of the flat earth to our fears of our age. Only rational discussions like presented on this thread casts a light to the dark shadows. Hope there is more like this.
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