As a software engineer I spend my time authoring in deferent languages for different tasks; each programming language has strong points and weak points, however, all in all when it comes down to it they all do the exact same thing; although some do it more efficiently than others; some are better at working with GUI, others are better suited to working with low level functions like hardware drivers, others are better for working with databases or text parsing; in the end, we are left with line after line of processor specific assembly instructions that manipulate electrons within the CPU and relate peripherals.

Over the course of many years I have experimented with various languages that where suppose to be the hottest thing with respect to AI, like Prolog or Lisp; once again, when it comes right down to it, none of these languages brought us any closer to achieving any thing resembling AI, they merely gave us another way at looking at certain problems and different was of linking data structures.

It is my opinion; the unique problem of  eAI†will not be solved by some new super spiffy programming language; that‛TM]s putting the cart before the horse, as we have absolutely no understanding of what makes up intelligence in the first place. The secretes of AI will most likely be discovered by someone working in the field of genetics or behavioral science that learned to program in VB or what ever the  enon- programmers†programming language is at the time thus enabling said scientist to test and verify the resulting theoretical algorithms.

This will most likely be a core set of functions derived from these newly discovered algorithms that rarely or never change along with some type of polymorphic script generation that is capable of authoring new code or altering existing code and data structures. If this holds true, then it really doesn‛TM]t matter what programming language we used, consequently the commercially available AI applications will be authored in C or whatever the popular programming language stuffed down the gullet of the aspiring software engineers by academia is at the time.

This is my keyboard, there are many like it, but this one is mine!

I doubt that reasoning in humans or animals has much to do with these sorts of formal logic languages. In the article it's claimed that extensions to the language A can represent non-deterministic outcomes, but on closer inspection this doesn't seem to be true. To represent probablistic causal relations I think you need more than just a "may affect" function. When you're dealing with real world events things are rarely known with absolute certainty, and even things like object recognition are based upon probabilities.

Why does everyone get so caught up with "silicon" based advances in AI? The vast majority of human advancements have not come from brilliant quantum leaps in thinking, like Einstein's relativity, but by noticing what nature does and duplicating it.

We already have successful experiments hooking up live biological neural tissue to gold-plated electrodes that connect to machines. Wouldn't the next huge advancement come when we learn to hook up a small mammalian brain to a robot and simply allow it (the brain) to perform the needed AI tasks, whether on-board or remotely?

No logic system of rules can encapsulate human reasoning, if we must trust Godel's theorem.

It seems that A uses classical logic. That is, a representation system where the orthogonality operator is trivial (like classical physics) - i.e. every state is orthogonal to every other - hence all states are distinguishable.

That is simply a poor choice for cognitive representations (for the same reason that classical physics cannot represent quantum mechanical states). Only state spaces that have a projection-like orthogonality operator can describe quantum mechanics and I'd wager that only spaces with a non-trivial orthogonality operator of some kind will be adequate for representations necessary for cognition of any significance.

Classical logic assumes that P(a|b) = P(a&b)/P(b) [Bayes' axiom] - which leads to a contradiction in all cases except where orthogonality is trivial (i.e. classical systems).

Since cognition involves prediction (i.e. it involves representing probabilities), why assume such an approximation?