Le 2013-05-07 15:50, Paul A. Houle a écrit :
Statistical methods can deal with black swans, but you've got to get away from normal distributions and also model the risk that your model is wrong.
Do you have recommandations on things I could read on this topic? To me it's seems hard to evaluate probability like "I exist", when probability is something which come well after one own existence in an "existential chain". Of course, let's suppose that I do not exist, but some "existialism demon" fool me with the illusion that I do. Then I don't exist, so "I" can't worry on existence since "I" don't exist in the first place. Now how would you evaluate the chance that "I" exists? 1/2, 1?
Since training sets come from the same place sausage comes from, training sets in machine learning rarely teach the algorithm the correct prior distribution of the class. Punch a new prior into the system and it will perform much better.
Once again, do you have recommandations on things I could read on this topic?
Some kinds of sampling biases can be somewhat overcome. Involvement of multiple people smoothes out individual bias. (Kurzweil's project of stealing a human soul with a neural network is already being scoops by projects that are stealing statistical models of many souls.)
I'm affraid that I would need more references here too.
Language zone Wikipedias are obviously biased towards the viewpoint of people in that language zone. Mostly that's a good thing, because a Chinese knowledge base that reflected an Anglophone bias would seem unnatural to Chinese speakers. And that's the point. Useful systems don't "eliminate bias" but they are given the bias that they need in order to do their job.
While I agree with you on this point, I must admit that I don't feel at ease to say it. I mean, being satisfied with "it does the job" may probably already be a cultural bias.
Most of these are connected to the larger mass through just a few categories that would be hard to express as restriction types. Wikipedia is reasonable to require concepts to have a category because really, if you want to assert something exists and can't find some category that this thing is a member of, I wouldn't be so sure that this thing exists.
The concept exists indenpantly of the ontological status of the object to which it refers. Take "nothingness"[1], in fact the english wikipedia article give not a great definition: "Nothingness is the state of being nothing". Well no, nothingness actualy refers to nothing, and any statement which give existential attribute to nothingness is wrong. The only correct statements on nothingness are totologies of "nothingness doesn't exist". But the concept of nothingness do exist, through the thought which sustains it. The thought exists, but it doesn't mean that what the thought is refering to also exists. And as you can see with the nothing article, you can find categories for the concept, even if no attribute would apply to what the concept denotes.
[1] https://en.wikipedia.org/wiki/Nothing
I'm not sure if there is anything I can't do with the current situation, but bear in mind that I'm going to look at DBpedia, Wikidata and Freebase facts too and be willing to do data cleaning processing and hand cleaning of results that I cannot accept. It's a tricky and somewhat expensive process (though it's cheaper than conventional ontology construction), so cleaner data makes this process cheaper and quicker and available to more end users personalized to their own needs to define the categories they need.
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