Genetic_Programming_Theory_and_Practice_XIII

(C. Jardin) #1

148 W.P. Worzel


Following Holland, this suggests that at a minimum, such a system could evolve
new and unexpected behaviors as it interacts with the environment by prescribing
actions in the real world (e.g., when to plant or harvest a field or what route a car
should take). Taken to the limit, one could speculate that a complex system could
“come alive”—providing, of course, that there is a consensus of what being alive
means! In the realm of pure speculation then, as the IoT grows, and our capacity
to distribute processing of information across devices, one may begin to approach
the question of whether, and how, the Singularity could occur. Perhaps the best way
to approach this is to point out that we have one example of evolution reaching
intelligence in the natural world on earth. If the singularity is reachable in-silico
(a proposition about which the author is skeptical), then our best plan of would
be by mimicking the mechanism of evolution while using inputs from the natural
world.
At this point we reach the end of the known world and the author will end by
repeating an excerpt of a poem by Alice Fulton used in Holland ( 1995 ):


...Its point of view? One
with the twister in vista glide,
and the cricket in the ditch,
with riverrain, and turbines’ trace.
Inside the flux of
flesh and trunk and cloudy come,
within the latent
marrow of the egg, the amber
traveling waves is where
its vantage lies.
Entering the tornado’s core,
entering the cricket waltzed by storm–
to confiscate the shifting give
and represent the with-
out which.

References


Ackley D, Littman M (1991) Interactions between learning and evolution. In: Langton C, Taylor C,
Farmer C, Rasmussen S (eds) Artificial life II. SFI studies in the science of complexity, vol X.
http://www2.hawaii.edu/nreed/ics606/papers/Ackley91learningEvolution.pdf
AllJoyn (2012) Documentation. Tech. rep., AllSeen Alliancex, https://allseenalliance.org/
developers/learn
Almal AA, Mitra AP, Datar RH, Lenehan PH, Fry DW, Cote RJ, Worzel WP (2006) Using genetic
programming to classify node positive patients in bladder cancer. In: Keijzer M, Cattolico M,
Arnold D, Babovic V, Blum C, Bosman P, Butz VB, Coello C, Dasgupta D, Ficici SG, Foster
J, Hernandez-Aguirre A, Hornby G, Lipson H, McMinn P, Moore J, Raidl G, Rothlauf F, Ryan
C, Thierens D (eds.) GECCO 2006: Proceedings of the 8th annual conference on genetic and
evolutionary computation. ACM, New York, pp 239–246

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