You really do not want to think about this much computing power:
Yampolskiy then asks, could an immensely powerful computer succeed where all the others have failed? One way of approaching the question, he suggests, is by asking, what is the computational power of biological evolution? Some truly impressive figures result:
“If all the DNA in the biosphere was being transcribed at these reported rates, taking an estimated transcription rate of 30 bases per second, then the potential computational power of the biosphere would be approximately 1015 yottaNOPS (yotta = 1024), about 1022 times more processing power than the Tianhe-2 supercomputer . . ., which has a processing power on the order of 105 teraFLOPS (tera = 1012)…
“If we were to simulate 1025 neurons over a billion years of evolution (longer than the existence of nervous systems as we know them) in a year’s run time these figures would give us a range of 1031-1044 FLOPS.39 –Yampolskiy Rv. Why We Do Not Evolve Software? Analysis of Evolutionary Algorithms. Evolutionary Bioinformatics. January 2018. Doi:10.1177/1176934318815906
These are not small numbers, even in the computing world. But even if these numbers were available, we need not assume that anything like human intelligence would necessarily result, via purely Darwinian means, if we ran only one simulation
Denyse O’Leary and Roman Yampolskiy, “Can computers evolve to program themselves without programmers?” at Mind Matters News
But read the fine print: We would need to run many trials of planets in parallel in order to simulate the real conditions in the universe. Yampolskiy concludes, ‘In fact, depending on some assumptions we make regarding multiverse, quantum aspects of biology, and probabilistic nature of Darwinian algorithm such compute may never be available.’”
That raises some questions about Darwinism in nature but we’ve talked about that before…
Further reading:
Can AI really evolve into superintelligence all by itself? We can’t just turn a big computer over to evolution and go away and hope for great things. Perpetual Innovation Machines tend to wind down because there is no universally good search. Computers are powerful because they have limitations.
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He is correct that the Darwinian algorithm itself is wrong.
And while Yampolskiy is correct in his assumption that the Darwinian algorithm is wrong, Yampolskiy is incorrect in his belief that a ‘alternative algorithm’ will be able to the mimic the ‘human level’ intelligence that is necessary to create computer algorithms in the first place. The primary reason for this failure of algorithms to ever be able to mimic human level intelligence is obvious. Humans possess free will, algorithms do not. And it takes free will to be truly creative.
The basic question of intention comes in strongly here. Computers, like abacuses, are tools to fulfill human purposes. Each new REAL development, as opposed to ACADEMIC development, came in response to a human purpose. Most of the purposes were military or commercial or industrial. Improve the accuracy and speed of a cannon, improve the accuracy and speed of bookkeeping, improve the accuracy and speed of lathes.
The self-evolving computer would need to have a purpose beyond simply proving a point, otherwise it wouldn’t know which way to evolve.
When human organizations outgrow their original purpose, they turn to crime. Parkinson’s most important law. We’re seeing it right now with the Public Health Officers who were no longer needed to improve sanitation, so they switched to committing a holocaust.
In short, the self-evolving computer is guaranteed to do evil.
Regarding the linked article in which researchers try to evolve an algorithm, I notice one difference between these attempts and what must have been the situation on the ground if Darwinism were true. In the simulation, survival to propagate the next generation is based on relative fitness, and not absolute fitness. For this reason the results of simulations like this have much less to say about the real world.
After all, it is entirely possible, and has at times been the case, that none of the population survives an event, or that just about everyone survives.