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 simulationDenyse 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…
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.