Here’s a line of reasoning that seems to suggest the possibility of an interesting region of decreasing f(x, x+1). It focuses on human evolution and evolutionary algorithms.
Human intelligence appeared relatively recently through an evolutionary process. There doesn’t seem to be much reason to believe that if the evolutionary process were allowed to continue (instead of being largely pre-empted by memetic and technological evolution) that future hominids wouldn’t be considerably smarter. Suppose that evolutionary algorithms can be used to design a human-equivalent intelligence with minimal supervision/intervention by truly intelligent-design methods. In that case, we would expect with some substantial probability that carrying the evolution forward would lead to more intelligence. Since the evolutionary experiment is largely driven by brute-force computation, any increase in computing power underlying the evolutionary “playing field” would increase the rate of increase of intelligence of the evolving population.
I’m not an expert on or even practitioner of evolutionary design, so please criticize and correct this line of reasoning.
I agree there’s good reason to imagine that, had further selective pressure on increased intelligence been applied in our evolutionary history, we probably would’ve ended up more intelligent on average. What’s substantially less clear is whether we would’ve ended up much outside the present observed range of intelligence variation had this happened. If current human brain architecture happens to be very close to a local maximum of intelligence, then raising the average IQ by 50 points still may not get us to any IQ 200 individuals. So while there likely is a nearby region of decreasing f(x, x+1), it doesn’t seem so obvious that it’s wide enough to terminate in superintelligence. Given the notorious complexity of biological systems, it’s extremely difficult to extrapolate anything about the theoretical limits of evolutionary optimization.
Here’s a line of reasoning that seems to suggest the possibility of an interesting region of decreasing f(x, x+1). It focuses on human evolution and evolutionary algorithms.
Human intelligence appeared relatively recently through an evolutionary process. There doesn’t seem to be much reason to believe that if the evolutionary process were allowed to continue (instead of being largely pre-empted by memetic and technological evolution) that future hominids wouldn’t be considerably smarter. Suppose that evolutionary algorithms can be used to design a human-equivalent intelligence with minimal supervision/intervention by truly intelligent-design methods. In that case, we would expect with some substantial probability that carrying the evolution forward would lead to more intelligence. Since the evolutionary experiment is largely driven by brute-force computation, any increase in computing power underlying the evolutionary “playing field” would increase the rate of increase of intelligence of the evolving population.
I’m not an expert on or even practitioner of evolutionary design, so please criticize and correct this line of reasoning.
I agree there’s good reason to imagine that, had further selective pressure on increased intelligence been applied in our evolutionary history, we probably would’ve ended up more intelligent on average. What’s substantially less clear is whether we would’ve ended up much outside the present observed range of intelligence variation had this happened. If current human brain architecture happens to be very close to a local maximum of intelligence, then raising the average IQ by 50 points still may not get us to any IQ 200 individuals. So while there likely is a nearby region of decreasing f(x, x+1), it doesn’t seem so obvious that it’s wide enough to terminate in superintelligence. Given the notorious complexity of biological systems, it’s extremely difficult to extrapolate anything about the theoretical limits of evolutionary optimization.