You do realize that there are people in this thread who can explain to you in fine grain detail how an ML model actually comes to conclusions, without speculating abstract "weird shortcuts".
not really, no. It's more the case that a NN is an encoding of a large dataset space, and if you go far enough away from the average you may find yourself on an island with an individual outlier. This is why you simply get back the training example, it's regurgitating all that it was fed.
For example, try to train a model using a single image, with a space as wide as a sole point you will find that all you get back is that point.
A human being re-inventing without remembering the source is distinct from remembering the source and returning it when lacking other data.
the word "startup" has been a buzz word since ~2012 from my perspective. Ever since I saw established companies start to describe their approach as becoming more "startup-like".
>The ideal toy dataset should be procedurally generated so that researchers can smoothly vary parameters such as background noise, translation, and resolution.
It's been a minute since I last touched ML, but that seems like a fairly extreme claim. Am I wrong in thinking this?