to generate its output, and in general with size n input it may be able to take more than 2^2^n steps.
But what if one allows Turing machines with more complicated rules? With 4-state 2-color rules it turns out to be possible to generate the same output as examples (c) and (d) in just a fixed number of steps. But for none of the other 3-state 2-color Turing machines shown do 4-state rules offer any speedup.
Nevertheless, if one looks carefully at examples (a) through (h) each of them shows large regions of either repetitive or nested behavior. And it seems likely that this reflects computational reducibility that should make it possible for sufficiently complicated programs to generate the same output in fewer than exponentially many steps.
But looking at 4-state 2-color Turing machines examples (i) through (l) again appear to exhibit roughly exponential growth. Yet now—much as for the 4-state Turing machines in Chapter 3—the actual behavior seen does not show any obvious computational reducibility.
So this suggests that even though they may be specified by very simple rules there are indeed Turing machine computations that cannot actually be carried out except by spending an amount of computational effort that can increase exponentially with the length of input.
And certainly if one allows no more than 4-state 2-color Turing machines I have been able to establish by explicitly searching all 4 billion or so possible rules that there is absolutely no way to speed up the computations in pictures (i) through (l).
But what about with other kinds of systems?
Once one has a system that is universal it can in principle be made to do any computation. But the question is at what rate. And without special optimization a universal Turing machine will for example typically just operate at some fixed fraction of the speed of any specific Turing machine that it is set up to emulate.
And if one looks at different computers and computer languages practical experience tends to suggest that at least at the level of issues like exponential growth the rate at which a given computation can be done is ultimately rather similar in almost every such system.