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jsalvatier comments on Bayes' rule =/= Bayesian inference - Less Wrong
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I suppose the question is, how to calculate the priors so they do make sense. In particular, how can an AI estimate priors. I'm sure there is a lot of existing work on this. The problem with making statements about priors that don't have a formal process for their calculation is that there is no basis for comparing two predictions. In the worst case, by adjusting the prior the resulting probabilities can be adjusted to any value. Making the approach a formal technique which is potentially just hiding the unknowns in the priors. In effect being no more reasonable because the priors are a guess.
In statistics, I think 'weakly informative priors' are becoming more popular. Weakly informative priors are distributions like a t distribution (or normal) with a really wide standard deviation and low degrees of freedom. This allows us to avoid spending all out data on merely narrowing down the correct order of order of magnitude, which can be a problem in many problems using non-informative priors. It's almost never the case that we literally know nothing prior to the data.
Using a normal with a massive variance is also a standard hack for getting a proper "uninformative" prior on the real line.