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For that matter, you can look at a neural net and say "it's because this number times this number plus this number ... > 0". Which is a very low level explanati
by tlb 8y ago
For that matter, you can look at a neural net and say "it's because this number times this number plus this number ... > 0". Which is a very low level explanation, but complete and correct. Humans don't provide complete and correct explanations at any conceptual level.
And there are lots of ways to get higher-level explanations from neural nets. For instance, if you retrain without a subset of samples and it gives a different answer, you can say that those excluded samples were relevant.
- AstralStorm 8y agoAn explanation needs to have explanatory power, which is a statistical concept. Also specificity. The "sum of numbers" explanation is not specific - it can be used to justify anything. Additionally it has low power as minor changes will result in a different outcome. A Markov model can give you some of that information, unlike a neural net, but it is still hard to get them to be specific. An expert system can be specific and use an approach with good statistical power, but it's at a mercy of anyone putting the data in. (unless somehow extended to statistically evaluate the inputs too somehow)