@Rj Brown
This certainly smacks of gross over-training to me.
That's another laughable concept that should get people ejected from the field of Artificial Intelligence. If the system gets worse when it is supplied with more data, it is fundamentally learning the wrong things. Honest people that understand how the current fad of machine learning works know this. If the system depends on the humans building it to precisely "know when to stop", it isn't AI and shouldn't be seen as reliable enough to trust with anyone's life.
@Mike D.
I was wanting to see the data in my particular case.
Yeah, that's the human reaction to such an outcome. People somehow think that if they see what the system was seeing they can figure out an answer to an implicit question of "What was it thinking?" But that's the problem; it's not actually thinking about anything meaningful, so you won't get a satisfactory answer. It's just correlating pixels in the images it gets fed. It doesn't know what a sign is or what letters are or what numbers represent. From a security/safety perspective, the more useful thing to do with that kind of data is generate an adversarial network (as is done by these researchers and others Bruce highlights) that allows us to better understand why these algorithms inherently cannot be absolutely trusted.
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