Lengthy response follows: Ignore if not interested. John M wrote: (Some criticisms which I am struggling with genuine effort to understand, independent of attacks on style, which I may have started. Sorry
A few factors: 1. BRAINS AND BRAIN SOFTWARE ARE HIGHLY SPECIALIZED AND OPTIMIZED One thing I didn't state clearly. While I'm proposing a model of mind as software, brain as hardware, this just tells us what a mind is capable of computing, in principle; that is, computable functions. Every half- decent computer, and every different high-level programming language, is Turing equivalent, but each design yields different things that can be computed conveniently or quickly. Turing-equivalence says nothing about performance, and performance, on certain types of computations, is key to how a brain of an animal works. So a brain is a computer optimized for certain types of computations, and the software of a brain would be pretty specialized in its detailed form to do computations efficiently on that type of hardware. Also, if you are uncomfortable thinking of software as having to be loaded onto a brain to get it to function, then think of the software as being already in the brain, in analogy to the software being implemented as ROM firmware or processor microcode, or some other more comfortable analogy. Just because it's already "built-in" doesn't mean it isn't essentially software. I would define the essence of software as being "information processing procedures". 2. THE HUMAN MYSTIQUE - LET'S GET OVER OURSELVES, PEOPLE One of the biggest bugbears that would-be AI researchers face is "the human mystique" attitude. The attitude that we are so fricking amazing that no one could possibly understand us using our puny science. Well, I think AI researchers would agree that humans (and other animals) are pretty amazing indeed, but that doesn't stop an attempt to make inroads into understanding how human minds work (or how a generalization or interestingly similar variation of our minds work). The AI approach is to try to tease out general insights about cognition, knowledge, intelligence by putting theories of same to the test of implementation on a computer. That is, if you could create an alternative implementation of a process that seemed to be perceiving and thinking and acting (say, conversing about many domains and new domains) with similar effect to a human perceiving and thinking and acting, then you have learned at least something about the perception and thinking processes in general. Maybe you have just learned more and more about what is NOT ESSENTIAL to those processes, but if that's the case, at least you will have eliminated a lot of the cloudiness of considering it all to just be unfathomable levels of complexity. 3. THE QUALIA OF CONSCIOUSNESS IS NOT EXPLAINED BY AI, BUT LOTS IS I didn't claim that AI yet gives us any adequate insight into the "qualia of consciousness." But at least AI research can eliminate as mysteries a number of behaviours closely tied to the qualia of consciousness. To wit, it proposes that reflective cognition (a well understood process) on the relation of a self-symbol to environment symbols may have something to do with some of the behaviours that we associate with consciousness. And it proposes that the shifts of "primary attention" that seem to be a notable aspect of "the qualia of consciousness" can be explained as emergence to the fore of the cognitions of some few of the many different cognitive agent processes supervising cognition effort at the highest level, in response to different primary- drive-related priorities at various times, and also in response to how well those cognitive agents have succeeded at coming up with a relevant answer to something. I would say that if we are to be able to theorize cogently about how the qualia of consciousness come about, at the least we have to be able to eliminate the above factors from consideration, and claim that "there is still something else, separate from all that, and it is THIS." <-- still a mystery (to me anyway). 4. GIVE ME HINTS OF A BETTER THEORY OF MIND THAN INFORMATION PROCESSING If you don't like a theory of brain as hardware, mind as software, it is your responsibility, in the scientific tradition, to come up with a better theory of mind. I am truly interested (no sarcasm). 5. DON'T JUST SAY "IT'S VERY COMPLEX". BE MORE SPECIFIC Appealing to "complexity" or "the unfathomable complexity of the whole" (paraphrased) seems to me to be a mystic's cop-out, similar to the religious arguments of yore. Maybe that's not what you're doing. I'm not reading carefully enough. 6. REDUCTIONISM ISN'T EVERYTHING, BUT IT IS DAMNED USEFUL It seems to me that it is the whole reductionist approach that you are attacking. I would counter that while reductionism is certainly never the whole answer, it does at least produce some simpler questions (about subsets of reality) which it is manageable to try to find real answers to. If you don't like reductionism at all, please stop using all those nasty products of it, like any technology more advanced than a rock to throw. I hope you're not just proposing that we give up the entire scientific project and sit and say "ommmm". Don't get me wrong, I respect people who do that and would like to be able to myself, but I find that the scientific method (which requires reductionism as one of its techniques) is also useful. 7. EMERGENCE OF COMPLEX SYSTEMS IS WAY COOL. AI ALREADY USES THAT IDEA There is nothing incompatible between AI (i.e. intelligence-as- information-processing) research and the realization that there is profundity in the emergence of complex systems with emergent structure and behaviour. The two ideas go hand in hand. 8. DON'T USE QUANTUM MECHANICS AS A CRUTCH Don't use QM as a crutch to obtain the needed air of mystery surrounding human cognition. QM may very well be involved somehow, but a lot can be explained without resorting to it. For example, a convincing illusion of free will could be generated simply by the operation of an extremely complex, layered, classical information processing machine whose behaviour is determined in a fixed but maximally complex way by its information inputs and its algorithms, which themselves are altered in complex ways over time by the inputs.