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This is why I want any ML device to be able to explain itself. It could train on your before-and-after examples and come up with a list of what it thinks you wa
by function_seven 9y ago
This is why I want any ML device to be able to explain itself. It could train on your before-and-after examples and come up with a list of what it thinks you want it to do.
For your example, it could list:
“Remove interior spaces from each item”
or it could say:
“Remove the middle character from any 7-character strings to make them 6 characters in length”
You would be able to do something with that.
- ChristianBundy 9y agoIIRC you _can_ get this, but it's a huge algorithm that doesn't do things in a way that would probably make sense to a human. It would be amazing to be able to transform code into human language.
- function_seven 9y agoI was sloppy in my own examples. I’d be perfectly satisfied with an AST, or regex, or other non-English explanation. But something that I can audit is what I’m after. Otherwise this tool would never be trustworthy enough to let lose on billions of rows of data, with silent errors occurring throughout. (Well, I guess it depends on the nature and importance of the data. Cat photos, meh. Drug prescriptions? Ahh!)
- jononor 9y agoMaybe also synthesize and suggest property based tests, by one specify also some invalid examples. Then these checks could be ran for each transformation. For instance: - 123 456 = 123456 valid - 1234567 = 123567 not valid (dropped 4) Properties: - output may not contain whitespace - no number characters may be dropped - characters may not be reordered
- paulddraper 9y agoNeural nets are infamous for not doing this. Learning algorithms that produce decision trees are usually used in this situation.
- allenz 9y agoIt's theoretically possible for a neural net to do this; the network just needs to have the explanation as an output. I agree that decision trees would be more reliable and easier to train, but I'm not sure if hardcoding every feature is scalable.
- function_seven 9y agoHow do you know that the explanation jives with the other outputs, though? It seems like a turtles-all-the-way-down situation, because now I want to see how it was properly introspective of its own decision making. Also seems like it’s another magnitude of complexity in the neural net to have it not only train and learn on your inputs, but also train and learn on its own training and learning.
- snaky 9y ago>How do you know that the explanation jives with the other outputs, though? The third neural net would do the checking, obviously.
- allenz 9y agoNeural networks don't do anything as sophisticated as self-referential introspection. They just fit the outputs you train them with. The training data you provide would have to include the desired explanations. Consistency is enforced by the dataset, and also by the model. Both outputs would read from the same hidden layer--the one that encodes the desired transformation.
- randomsearch 9y ago> the network just needs to have the explanation as an output And how would you evaluate whether the explanation was correct or not?
- Houshalter 9y agoYou give it explanations as training data and it tries to predict them.
- zitterbewegung 9y agoSome ML systems (like decision trees) can give you a comprehenable way that they made the decision (would give you a list of if conditionals). Unfortunately many can't do this (random forests) . Having an AI that can explain itself in every situation why it does it also has to do with the underlying techniques. For instance random forests generate a random sample of the features and creates decision trees. So the explanation wouldn't be much useful to you.
- nerdponx 9y agoNot true. Look up "partial dependence plots".
- jnpatel 9y agoDataWrangler [0] (now productionized as Trifacta Wrangler [1]) does pretty much that. It gives you suggested lists of transformations such as "Cut from position 18-25 as the Year column", that you can chain together as your data cleaning pipeline. [0]: http://vis.stanford.edu/wrangler/ http://vis.stanford.edu/wrangler/ [1]: https://www.trifacta.com/products/wrangler/ https://www.trifacta.com/products/wrangler/
- pishpash 9y agoThis add-on already does that. (Did nobody try it??) It shows in the pane a list of candidate transforms, seemingly ranked in some descending plausibility order. They have semi-readable names. You get to choose one to apply.
- randomsearch 9y ago> This is why I want any ML device to be able to explain itself. This is the problem of lacking explanatory mechanisms in ML. Note that some techniques that are very out of vogue at the moment, such as Genetic Programming, are much better than neural nets in this regard.
- OscarTheGrinch 9y agoYes, this system should do have an intermediate step of spiting out a checklist of clear rules and the user can select the best fit, saving the human the time it would take to search from a bloated dropdown of all possible rules.
- sundarurfriend 9y agoThis is the stated goal of the Explainable AI initiative, (spearheaded afaik by DARPA, though Google tells me corporates have also began work on it). I hope it works out well because there's going to be a lot of AI code in the near future, and the thought of them all being inscrutable black boxes is pretty scary.
- teddyh 9y agoBut, you know, if you saw something like, all your visible examples were like the strings 123 456 234567 345 678 and the program replies with something like what you wrote: “Remove the middle character from any 7-character strings to make them 6 characters in length”, it would actually take a programmer’s mind to be able to envision why this might in some cases be wrong. Most people who are not programmers would, I think, see this as equivalent to “Remove interior spaces from each item”. I suspect that the skill required to choose an algorithm correctly is the exact same skill required to actually being a programmer. All this then buys you is that you don’t have to remember the function names.