(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.
My own view is that symbol grounding is not a waste of time ... but, **exclusive reliance** on symbol grounding is a waste of time. Novamente utilizes a combination of grounding of symbols in simulated-embodied experience with ingestion of information from existing databases. I believe this sort of combination is optimal, rather than purely relying on data sources with no attention to embodied experience....
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.
This is a tricky point.... For example, it is obvious that Novamente has a general learning capacity in the trivial sense that, given enough computational resources, it can learn anything.... But the same could also be said about a lot of much simpler AI systems. So the real question is how does learning ability scale, in terms of the amount of computational horsepower needed to solve problems of a given complexity... My own view is that all serious learning algorithms are inevitably going to scale exponentially -- so the whole art of AGI design is in figuring out appropriate tricks for making the exponent and the constant outside the exponential function "not too large" for problem classes of practical import... -- Ben G ----- 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]
