This was actually part of a conversation I was having with this colleague regarding whether or not evolution can be viewed as an optimization process. Here are some follow-up comments to what she wrote above related to the evolution angle:
We could define the natural selection system as:
All configurations = all arrangements of matter on a planet (both arrangements that are living and those that are non-living)
Basis of attraction = all arrangements of matter on a planet that meet the definition of a living thing
Target configuration set = all arrangements of living things where the type and number of living things remains approximately stable.
I think that this system meets the definition of an optimizing system given in the Ground for Optimization essay. For example, predator and prey co-evolve to be about “equal” in survival ability. If a predator become so much better than its prey that it eats them all, the predator will die out along with its prey; the remaining animals will be in balance. I think this works for climate perturbations, etc. too.
HOWEVER, it should be clear that there are numerous ways in which this can happen – like the ball on bumpy surface with a lot of convex “valleys” (local minima), there is not just one way that living things can be in balance. So, to say that “natural selection optimized for intelligence” is quite not right – it just fell into a “valley” where intelligence happened. FURTHER, it’s not clear that we have reached the local minimum! Humans may be that predator that is going to fall “prey” to its own success. If that happened (and any intelligent animals remain at all), I guess we could say that natural selection optimized for less-than-human intelligence!
Further, this definition of optimization has no connotation of “best” or even better – just equal to a defined set. The word “optimize” is loaded. And its use in connection with natural selection has led to a lot of trouble in terms of human races, and humans v. animal rights.
Finally, in the essay’s definition, there is no imperative that the target set be reached. As long as the set of living things is “tending” toward intelligence, then the system is optimizing. So even if natural selection was optimizing for intelligence there is no guarantee that it will be achieved (in its highest manifestation). Like a billiards system where the table is slick (but not frictionless) and the collisions are close to elastic, the balls may come to rest with some of the balls outside the pockets. The reason I think this is important for AI research, especially AGI and ASI, is perhaps we should be looking for those perturbations to prevent us from ever reaching what we may think of as the target configuration, despite our best efforts.
This was actually part of a conversation I was having with this colleague regarding whether or not evolution can be viewed as an optimization process. Here are some follow-up comments to what she wrote above related to the evolution angle:
We could define the natural selection system as:
All configurations = all arrangements of matter on a planet (both arrangements that are living and those that are non-living)
Basis of attraction = all arrangements of matter on a planet that meet the definition of a living thing
Target configuration set = all arrangements of living things where the type and number of living things remains approximately stable.
I think that this system meets the definition of an optimizing system given in the Ground for Optimization essay. For example, predator and prey co-evolve to be about “equal” in survival ability. If a predator become so much better than its prey that it eats them all, the predator will die out along with its prey; the remaining animals will be in balance. I think this works for climate perturbations, etc. too.
HOWEVER, it should be clear that there are numerous ways in which this can happen – like the ball on bumpy surface with a lot of convex “valleys” (local minima), there is not just one way that living things can be in balance. So, to say that “natural selection optimized for intelligence” is quite not right – it just fell into a “valley” where intelligence happened. FURTHER, it’s not clear that we have reached the local minimum! Humans may be that predator that is going to fall “prey” to its own success. If that happened (and any intelligent animals remain at all), I guess we could say that natural selection optimized for less-than-human intelligence!
Further, this definition of optimization has no connotation of “best” or even better – just equal to a defined set. The word “optimize” is loaded. And its use in connection with natural selection has led to a lot of trouble in terms of human races, and humans v. animal rights.
Finally, in the essay’s definition, there is no imperative that the target set be reached. As long as the set of living things is “tending” toward intelligence, then the system is optimizing. So even if natural selection was optimizing for intelligence there is no guarantee that it will be achieved (in its highest manifestation). Like a billiards system where the table is slick (but not frictionless) and the collisions are close to elastic, the balls may come to rest with some of the balls outside the pockets. The reason I think this is important for AI research, especially AGI and ASI, is perhaps we should be looking for those perturbations to prevent us from ever reaching what we may think of as the target configuration, despite our best efforts.