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Impossible to say it can be done now, we barely know something about how mind works, much harder to even emulate with current processing technology, which is, largely, an evolution of the Intel 4004 invented in 1971 (yes, we are currently using a technology developed more than 40 years ago). But later this century, we will certainly change the processing technologies and then, we will be able to make a better prediction.
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Whether a processor is x86 or not is immaterial as long as it's Turing complete - an architecture may be more or less efficient, particularly for specific instruction or operation types, but any one can implement the other at a higher level.
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Which, of course, makes the high level implementation dependent of the knowledge of the system to be emulated, we are not yet there, therefore, in this moment it is impossible to make an accurate prediction, unless one with time constrains. And there's the problem of hardware implementation, which, at any level, is the major constrain to any machine.
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It's implementable even if performance is low - Turing-complete languages are capable of calculating anything calculatable even with only a single conditional branching or loop type and operations to increment or decremen a value at a pointer, that is just excessively complex and inefficient even compared to the opcode level where there operations such as shifts. For that reason, even when something biological does not run on opcodes, even if it needs to be a biochemistry simulation, or indeed, even a simulation of an entire world/universe, it's implementable - just scaled to the point of current implausibility.
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Indeed, it is completely implementable at any scale. but right now, we are constrained in resources, hardware in short. Those constrains, along with the emulation or simulation make a Turing machine just that, a simulation, no matter how complete it is (at least, a priori, it will change in the future). Obviously, the simulation could be much better than the actual system.
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