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Human cognition itself is a black box model. We routinely make snap decisions first and then justify them after the fact. Anything that requires serious conside
by m0zg 7y ago
Human cognition itself is a black box model. We routinely make snap decisions first and then justify them after the fact. Anything that requires serious consideration is darn near insurmountable for a human mind in the presence of incomplete information, emotions, and conflicting motivations.
So while I'd like neural nets to be more interpretable, to me it'd take a distant second place. The first would be to get models that actually work better than humans for practical tasks, even under limited circumstances.
- Rapzid 7y agoI share this sentiment. It occurs to me that human decisions making can be either explained a very high level, or a very low level(neurons firing). But the magic in between is too complicated to draw direct lines between stimuli and decisions. AI seems to be the same way. We have statistical models that explain how most everything works at a low level. That a simple math formula can approximate just about anything. How we arrive at weights that separate data sets in different dimensions and etc. For convolution networks we also understand how layer "decisions" flow through to other layers. But, it's too complicated to look at an image and explain exactly how the input pixels will result in a classification output. I'm not sure how much I should care about that.. Seems like a problem for mathematicians/statisticians. Having the doctor explain himself gives me no more true insight into how his brain works than having somebody explain at a high level how the robot was trained and reacts in different situations.