Surprising examples of non-human optimization
I am very much interested in examples of non-human optimization processes producing working, but surprising solutions. What is most fascinating is how they show human approach is often not the only one and much more alien solutions can be found, which humans are just not capable of conceiving. It is very probable, that more and more such solutions will arise and will slowly make big part of technology ununderstandable by humans.
I present following examples and ask for linking more in comments:
1. Nick Bostrom describes efforts in evolving circuits that would produce oscilloscope and frequency discriminator, that yielded very unorthodox designs:
http://www.damninteresting.com/on-the-origin-of-circuits/
http://homepage.ntlworld.com/r.stow1/jb/publications/Bird_CEC2002.pdf (IV. B. Oscillator Experiments; also C. and D. in that section)
2. Algorithms learns to play NES games with some eerie strategies:
https://youtu.be/qXXZLoq2zFc?t=361 (description by Vsause)
http://hackaday.com/2013/04/14/teaching-a-computer-to-play-mario-seemingly-through-voodoo/ (more info)
3. Eurisko finding unexpected way of winning Traveller TCS stratedy game:
http://aliciapatterson.org/stories/eurisko-computer-mind-its-own
http://www.therpgsite.com/showthread.php?t=14095
Slime mold can be used to map subway routes.
Edit: Markets can also be seen as a non-human optimizing actor, even if the smallest parts are human.
I am more interested in optimizations, where an agent finds a solution vastly different from what humans would come up with, somehow “cheating” or “hacking” the problem.
Slime mold and soap bubbles produce results quite similar to those of human planners. Anyhow, it would be hard to strongly outperform humans (that is find surprising solution) at problems of the type of minimal trees—our visual cortexes are quite specialized in this kind of task.
I’m not sure smile mold counts—or in general where the border is to pysical processes like soap bubble optimization.
Note that (2) and (3) are formal tasks where the optimizer has access to the full set of rules. My understanding is that a lot of chips designed by optimizing a simulator have been pretty lousy in the real world, either being complete failures that only worked in the simulator because of bugs, or being real solutions, but not being usefully robust.
Actually for (2) the optimizer didn’t know the set of rules, it played the game as if it were normal player, controlling only keyboard. It has in fact started exploiting “bugs” of which its creator were unaware. (Eg. in Supermario, Mario can stomp enemies in mid air, from below, as long as in the moment of collision it is already falling)
It knows the rules in the sense that the game is built into the optimizer. There’s a reason “time travel” is in the title of the paper.
Where is user Thomas? He should have some nice stuff to show I presume
Well, yes. I (and not me alone) have evolved bunch of things and keep evolving them.
What shocked even me, is the possibility of evolving 3D crosswords, 7 or 8 characters wide. I mean, there are about 10^400 combinations of English words in such a cube. And maybe 10^100 consistent solutions. I am not aware of any intelligent program which is able to construct one.
Yet, it’s possible to artificially evolve one such a crossword per minute on a PC. Digital evolution is an underestimated way for doing things. Why? I don’t know and don’t even care. I know I don’t underestimate it. Never have.
https://protokol2020.wordpress.com/2014/08/02/6x6x6-word-cube/
I like this. I’m interested in almost the opposite, amusingly: what types of situations are there where “planners” (natural or artificial or human) can impose a top down solution that will outperform bottom up processes like evolution?