> From: Benjamin Goertzel [mailto:[EMAIL PROTECTED] > > State of the art is: > > -- Just barely, researchers have recently gotten automated > program learning to synthesize an nlogn sorting algorithm based on the > goal > of sorting a large set of lists as rapidly as possible... > > -- OTOH, automatic synthesis of logic circuits automatically carrying > out > various tasks is now a fairly refined science, see e.g. Koza's GP III > book > > All in all we are nowhere near having AI software that can automatically > synthesize large, complex software programs.
There has to be a lot of room for improvement over the state of the art, as usual. > Automated program learning is part of the Novamente system but the > architecture > is designed so that only small programs need to be learned, carrying > out particular > internal or external tasks/functions. Still, this is the most > resource-intensive part of > the Novamente system (the part that's most likely to require > supercomputers to > achieve human-level AI). Why is it the most resource intensive, is it the evolutionary computational cost? Is this where MOSES is used? John ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=8660244&id_secret=69106938-3905b5
