"I have started a massive empirical effort to catalogue mental models that are ambient in our field, to formalize them, and to then validate them with experiments. [...] I think it’s the first step to developing a layered mental model of deep learning" http://www.argmin.net/2018/01/25/optics/ …
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Replying to @o_guest
This goes beyond my knowledge, just a question: would having specific models at various layers of abstraction lead to a specific output at each level?
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If so DL would stop being a black box
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Replying to @twitemp1
Mostly agree. I think understanding (i.e., shining a light into a black box) is linked to predicting but not so straightforwardly.
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Replying to @o_guest
Probably not. I would guess that formal analysis is much needed
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Replying to @twitemp1
Yeah, exactly. You can not understand something (ergo still a black box) and yet still memorise and even duplicate all the input output mappings.
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This is why I take a stand when people say cog sci is reverse engineering the brain or mind. It's so much more than that!
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A while ago somebody on twitter insisted that I need experience in reverse engineering because it's so important to cog sci and science in general. Was painful.
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Also just in case this blows the up again. Just because I said reverse engineering doesn't open up a black box by definition doesn't mean that a human reverse engineering something won't end up opening up the black box. It's just not inherently true.
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You can reverse engineer a chip and create an equivalent chip that does the same stuff but in a different way. Or you can end up fully understanding the original chip. But to reverse engineer doesn't 100% imply you understand anything more than the input output mappings.
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