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Imagine if you go to your work at the bank tomorrow and instead of a well documented, maintainable and formatted code see a gibberish. And your neural coworker
by codedokode 3y ago
Imagine if you go to your work at the bank tomorrow and instead of a well documented, maintainable and formatted code see a gibberish. And your neural coworker tells you that it is just a problem with your capabilities if you cannot understand it. He just refactored it to improve performance. That's the situation with machine learning today.
- Jack000 3y agoThe thing is, it's not gibberish. A sufficiently small language model can be understood by humans: https://twitter.com/karpathy/status/1645115622517542913 https://twitter.com/karpathy/status/1645115622517542913 The explanation is perfectly sensical, just too complex for humans to understand as the model scales up. The thing you're looking for - a reductive explanation of the weights of a ANN that's easy to fit in your head, does not exist. If it were simple enough to satisfy your demands, it wouldn't work at all.
- zelphirkalt 3y agoYet, when a master player makes a decision what move to play, they often have concrete reasons for it, that they discuss in after game analysis. They evaluate some advantage or chances higher than others or some risks greater than others and calculate specific sequences ahead to be sure to solve a subproblem correctly and base their decision on that.
- pixl97 3y agoBanks don't typically attempt to solve P=NP problems. Meanwhile things like stock markets attempt to with things like partial future prediction, which means all possible outcomes are not calculable in finite time, hence they use things like ML/AI.