Looking at a half circle and guessing that the “missing part” is a full circle is as much of a blind guess as you can get. You have exactly zero evidence that there is another half circle present. The missing part could be anything, from nothing to any shape that incorporates a half circle. And you would be guessing without any evidence whatsoever as to which of those things it is. That’s bling guessing.
Comment on Court Bans Use of 'AI-Enhanced' Video Evidence Because That's Not How AI Works
ricdeh@lemmy.world 2 months agomaking a blind guess at what could be there, based on an existing data set.
Here’s your error. You yourself are contradicting the first part of your sentence with the last. The guess is not “blind” because the prediction is based on an existing data set . Looking at a half occluded circle with a model then reconstructing the other half is not a “blind” guess, it is a highly probable extrapolation that can be very useful, because in most situations, it will be the second half of the circle. With a certain probability, you have created new valuable data for further analysis.
CapeWearingAeroplane@sopuli.xyz 2 months ago
UnpluggedFridge@lemmy.world 2 months ago
But you are not reporting the underlying probability, just the guess. There is no way, then, to distinguish a bad guess from a good guess. Let’s take your example and place a fully occluded shape. Now the most probable guess could still be a full circle, but with a very low probability of being correct. Yet that guess is reported with the same confidence as your example. When you carry out this exercise for all extrapolations with full transparency of the underlying probabilities, you find yourself right back in the position the original commenter has taken. If the original data does not provide you with confidence in a particular result, the added extrapolations will not either.
CheeseNoodle@lemmy.world 2 months ago
And then circles get convictions so even if the model did somehow start of completely unbiassed people are going to start feeding it data that weighs towards finding more circles since a prosecution will be used as a ‘success’ to feed back into the model and ‘improve’ it.