Does this not illustrate what is the fatal flaw in image recognition based approaches with neural networks, that their failure modes are inscrutable?
80% or 95% of the time they do well but the corner cases where they do poorly they fail in ways that are entirely unlike the was our brains' systems fail. Unpredictably. So they can be useful for non critical applications but not critical applications. Like self driving cars....
Stages of grief here... I was looking forward to my car with a cocktail cabinet that would drive me to parties and home again... I believed the hype five years ago. Is this why progress has stalled?
One overview can be found here: https://distill.pub/2018/building-blocks/
So I think others in the space have the same frustrations about the lack of insight into these models, and we're working on ways to get better answers out of these black boxes.
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