I think you have been missing the point of my postings. I am quite aware that the number of 'instructions' in a computer is of little relevance to what it can do. A greater number of instructions just makes programs run faster and makes programming easier.
A computer that only has two instructions is still allowed to process an arbitrarily large amount of information. Just because the control function of the computer is extremely tiny does not mean that you can't build incredibly large and expressive systems. I think you misunderstood what having a small instruction set means.
No I did not, in fact you are practically repeating what I said in the post you are responding to (#486) while claiming I do not understand the question. I do understand the problem quite well. The problem is not how many instructions there are in a process but how much code is needed to accomplish a task. By the term 'code' I mean either code within the computer or within the program since as we both agree they are interchangeable to a great extent.
My point has been all along that you need a lot more than just a few lines of code to accomplish the task of specifying the vast variety of life we see. You need a lot more than the 5-6 rules which Wolfram claims is all that is necessary to pre-scribe the complexity of living things (and which started our discussion).
Turing machines have a halting problem, living things do not. -me-
From this statement, I'm not sure that you actually grok the Halting Problem. I would also state as a relevant point that there exists novel Turing Machine (i.e. universal computer) models that effectively "cheat" the halting problem
I do understand it. I understand that a Turing machine does not understand what a human immediately perceives. Yes, you can 'cheat' to stop the halting problem, but then that is not what I was talking about is it? It is no longer a classical turing machine. You have been forced to intelligently design a way around the problem.
It is a Turing Machine; you can both trivially implement any Turing Machine on it and it can be implemented on any Turing machine. Perhaps it isn't "classical" in the sense of being utterly conventional, but that doesn't have any merit anyway. Welcome to the bleeding edge computer science.
The reason that there are so few fundamental behaviors is that while the instructions it needs are extremely simple, the effects are powerful and pervasive. It doesn't operate on a "register" or "datum", but on finite "patterns" in memory, sort of like a Turing Machine with a continuously variable number of tapes. (ObNote: All multi-tape TMs are mathematically equivalent to single-tape TMs.) Furthermore, all the operations are mathematically purely stochastic in the abstract but functionally deterministic (in fact, there isn't a single floating point value or operation in the entire thing, being purely integer); this confuses people but it is a consequence of there not being any functional concept of absolute values, only relative ones.
My point being that there are a lot of things that qualify as Turing Machines that fall way outside most people's conception of what a Turing Machine is. Some of these, like the model I just mentioned, are much more potent and powerful TMs than the "standard" one that everyone is familiar with anyway, hence why they can't reasonably be ignored.