Addressing this question from a computational point of view, research into machine learning has shed a lot of light on the questions concerning the prerequisites of learning. In particular, the *No Free Lunch Theorem* is the result of an attempt to quantify the amount of prior knowledge required for extracting information from data. This theorem is described as follows:

> "The No Free Lunch Theorem Of Optimization (NHI) is an impossibility
> theorem telling us that a general-purpose universal optimization
> strategy is impossible, and the only way one strategy can outperform
> another is if it is specialized to the structure of the specific
> problem under consideration." Yu-Chi Ho, *Simple explanation of the no
> free lunch theorem of optimization*

This might be formalized in logical notation as follows:

- Sxz = x is a strategy for a problem of type z
- P(x) = the performance of x
- Cxz = x is specialized to problems of type z

> ∀xyz[(Sxz & Syz & P(x) > P(y)) → Cxz]

From this, it's fairly easy to conclude the following:

> ∀xz[(~Cxz & Sxz & P(x) > 0) → ~Ǝy[Syz & P(y) = 0]]

**Translation:**  Without any prior knowledge of a given type of problem, if a strategy for that type can be expected to be successful, it must be assumed that there exists no strategy of the same type that consistently fails.

What's interesting about this is that the strategy in question is pitted against others of the same type that is not distinguished from it in any way. In other words, such strategies are generic applications, and the *No Free Lunch Theorem* teaches us that the expectation of success depends on the assumption that the particular type of problem in question must be solvable with equal probability by any generic application. Therefore, from a purely empirical perspective, it must be assumed that the initial stages of learning are of such a type that any generic strategy will have a greater than random chance of success. Otherwise, empiricism is false.

That's really a huge assumption to make when it's remembered that all machine learning programs have certain assumptions programmed into them. The designers of such systems don't expect the machine to learn, for example, that data contains information or that concepts can be extracted by comparisons and other logical operations. In other words, there is no such thing as a purely empirical AI program because they are always hard coded with some basic a priori knowledge.

Consequently, it's unimaginable what such a generic strategy could possibly mean if it is supposed that it consists of none of the assumptions hard-coded into current machine-learning programs. Unless you're willing to place your faith in impossible odds, empiricism is clearly false.