“Prior Wisdom” is a reference to Bayesian Inference.
Bayes Theorem states that how well we can make inferences from any event (the posterior probability) depends on what we already know about the world (the prior probability) and what we can say about how our hypotheses affect the evidence (the likelihood). The more accurately our priors depict the real world, the better we can make sense of the problem at hand. From the Wikipedia page on Prior Knowledge For Pattern Recognition:
Prior knowledge refers to all information about the problem available in addition to the training data. However, in this most general form, determining a model from a finite set of samples without prior knowledge is an ill-posed problem, in the sense that a unique model may not exist. […]
The importance of prior knowledge in machine learning is suggested by its role in search and optimization. Loosely, the no free lunch theorem states that all search algorithms have the same average performance over all problems, and thus implies that to gain in performance on a certain application one must use a specialized algorithm that includes some prior knowledge about the problem.
My take is that the all of human wisdom can be encoded in the prior. All learning effort is directed towards “knowing all there is to know” through modelling a prior that accurately depicts the nature of the world.
I see this pattern in all spheres of human knowledge (maybe its just a cognitive bias!), including both the natural and human sciences. Hmm, need to think about how can I use this prior wisdom. 🙂