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there are also a nearly infinite number of ways of modeling a brain in a computer, and only a finite (or possibly nonexistent) fraction of that space will yield a conscious copy of the original meat-brain. Science fiction writers usually hand-wave this step: in Heinlein\'s \"Man Who Sold the Moon,\" the gimmick is that once the computer becomes complex enough, with enough \"random numbers,\" it just wakes up.

Computer programmers are a little more skeptical. Computers have never been known for their skill at programming themselves -- they tend to be no smarter than the people who write their software.

But there are techniques for getting computers to program themselves, based on evolution and natural selection. A programmer creates a system that spits out lots -- thousands or even millions -- of randomly generated programs. Each one is given the opportunity to perform a computational task (say, sorting a list of numbers from greatest to least) and the ones that solve the problem best are kept aside while the others are erased. Now the survivors are used as the basis for a new generation of randomly mutated descendants, each based on elements of the code that preceded them. By running many instances of a randomly varied program at once, and by culling the least successful and regenerating the population from the winners very quickly, it is possible to evolve effective software that performs as well or better than the code written by human authors.

Indeed, evolutionary computing is a promising and exciting field that\'s realizing real returns through cool offshoots like \"ant colony optimization\" and similar approaches that are showing good results in fields as diverse as piloting military UAVs and efficiently provisioning car-painting robots at automotive plants.

So if you buy Kurzweil\'s premise that computation is getting cheaper and more plentiful than ever, then why not just use evolutionary algorithms to evolve the best way to model a scanned-in human brain such that it \"wakes up\" like Heinlein\'s Mike computer?

Indeed, this is the crux of Kurzweil\'s argument in Spiritual Machines: if we have computation to spare and a detailed model of a human brain, we need only combine them and out will pop the mechanism whereby we may upload our consciousness to digital storage media and transcend our weak and bothersome meat forever.Indeed, this is the crux of Kurzweil\'s argument in Spiritual Machines: if we have computation to spare and a detailed model of a human brain, we need only combine them and out will pop the mechanism whereby we may upload our consciousness to digital storage media and transcend our weak and bothersome meat forever.

But it\'s a cheat. Evolutionary algorithms depend on the same mechanisms as real-world evolution: heritable variation of candidates and a system that culls the least-suitable candidates. This latter -- the fitness-factor that determines which individuals in a cohort breed and which vanish -- is the key to a successful evolutionary system. Without it, there\'s no pressure for the system to achieve the desired goal: merely mutation and more mutation.

But how can a machine evaluate which of a trillion models of a human brain is \"most like\" a conscious mind? Or better still: which one is most like the individual whose brain is being modeled?

\"It is a sleight of hand in Spiritual Machines,\" Kurzweil admits. \"But in The Singularity Is Near, I have an in-depth discussion about what we know about the brain and how to model it. Our tools for understanding the brain are subject to the Law of Accelerating Returns, and we\'ve made more progress in reverse-engineering the human brain than most people realize.\" This is a tasty Kurzweilism that observes that improvements in technology yield tools for improving technology, round and round, so that the thing that progress begets more than anything is more and yet faster progress.

\"Scanning resolution of
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