99% of AI projects failed of being AGI by simply being diverted into applications. The problem is that it is MUCH easier to write a program to do X than it is to write a system that can learn to do X without your having told it about X to begin with, for any X.
Assuming we can stay focussed on AGI, the main failure modes are probably: (1) insufficient horsepower (echoing Ben) -- I gave up on one design in the 90s for that reason, which I may pick up on again now that I have 1000x the horses to play with... (2) not enough depth and generality in the design. A typical case would be adopting one specific logic or modelling method that limits the domain of discourse the system could even conceivably think about. (3) TOO MUCH depth and generality in the design. This is what happened to Eurisko. If your system ever looks like it's biting itself in the back, it's working in too big a space. (4) wasting time on "symbol grounding." (this wouldn't be a problem for a 10-year, $50M project but the question was to us and for 5 years.) A computer has direct access to enough domains of discourse (such as basic math) that there's no need to try to (a) simulate the physical world and then (b) reduplicate a few billion years evolution working out an appropriate sensory and motor interface. But the failure mode that EVERY attempted AGI has hit to date is: (0) Wind-up toy. They didn't really have a general learning capacity, so they learned to the edges of their built-in potential and stopped. Classic AI example: AM. On Monday 25 September 2006 08:52, Joshua Fox wrote: ... > To those doing AGI development: If, at the end of the development stage of > your project -- say, after approximately five years -- you find that it > has failed technically to the point that it is not salvageable, what do you > think is most likely to have caused it?... ----- 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/[EMAIL PROTECTED]
