Hmm, learning to fly without replicating a specific bird is analogous to the problem of general AI. This discussion thread started with a claimed analogy between chip simulation and mind uploading, which is more the problem of replicating a specific bird. If I claimed to be able to upload your mind, then proceeded to scan or mince your brain, and then showed your relatives a general AI, they would be unimpressed.
EdS
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Indeed. More interesting perhaps, is that destructive scanning would become viable long before non-disruptive scanning. Also note: a slow-running simulation which turns out to be in agony doesn’t have to suffer for much subjective time. Presuming the ‘owners’ care about that.
Indeed, important, but not a difference in kind: you build a model which is as accurate as it needs to be.
Yes, late, and yes, slow. But it’s what you have to do when you don’t understand the thing you wish to duplicate. Making a brain is one thing, making a specific brain is another.
There is an analogy here: the visual6502 simulator just simulates transistors, with an adequate but imprecise model. It loads a description of a chip—presently the 6502 - and then acts out the behaviour of that chip. Other 6502 models out there were written by understanding how the CPU works—we only had to understand how transistors work. Michael Steil’s presentation at 27C3 includes a graph claiming orders of magnitude less work for the same fidelity.
To upload a mind into a computer without having to understand how minds and brains work, one might similarly model at the neuron level and then upload a description of the neuron characteristics and connectivity.
Peter Monta has done this over the last few weeks: see his tools on github—we mention it on the visual6502 wiki. There are lots of interesting possibilities ahead...
Anders Sandberg gives a good lecture (Google TechTalk) called “Whole Brain Emulation: The Logical Endpoint of Neuroinformatics?” which responds to some of the points raised here. See youtube