On Jun 1, 2008, at 12:39 PM, Mark Waser wrote:
What do you mean by computationally simple?
Meaning there is a trivial set of functions and/or computational model that captures the utility. No need to accommodate patterns below the very high noise floor of wetware or which do not have a material computational purpose (e.g. side effects of biological maintenance).
Explain to me how *you* construct a neural network that takes all of this into account.
It depends on what you mean by "takes all this into account". Unless you are a biologist of some type, physical fidelity is a complete waste of time but you seem to leaning that way. The seconds hand of a mechanical clock may be driven by a complex dynamical system but that does not make it not equivalent in every important way to an utterly trivial solid-state counter.
So in short, I would not construct a "neural network that takes all of this into account". I would construct a functionally equivalent computational model that coincidentally converges on an approximation of the structure and behavior of a biological neural network. I'm interested in AGI, not physiology.
Obsessing over biological fidelity is the hallmark of cargo cult AI, the fervent hope that with sufficiently elaborate neural network theater the gods will deliver a mind.
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