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A way to get priors for a problem can be to re-use some knowledge from other similar issues.

This kind of "transfer learning" is applied in this paper:


The good point of bayes rule is that some posteriors can become prior for another problem. Placing this feature on a time line (ie the life of the learning agent), we can imagine the agent starting with poor prior and refining, generalizing it after experience.
It reminds me the field of "developmental robotics" were agents have a strategy to choose new problems on wich they can learn a lot.


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computers and humans who is very powerful obviously human being because human
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Well...the best thing that every human has and the machine not is only Laziness :) nothing do to do!

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The things that computer and animals differentiate one from another is that we can adopt different way to learn something that in the go (adaptation).

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