I think AGI test should fundamentally be a learning ability test. When there's a specified domain in which the system should demonstrate it competency (like 'chatting' or 'playing Go'), it's likely easier to write narrow solution. If system is not a RSI AI already, resulted competency depends on quirks of given domain too much, and it's unclear how improvements in general learning ability translate in competency.
I see such test along the lines of feeding the system a stream of frame-like representations, and then it should be able to fill in the blanks in incomplete representations based on analogies. It's general enough to be AGI-complete, and simple enough to test existing narrow AI systems. Depending on supplied data it can be taken out of reach of algorithms which are too biased towards their narrow domain. Frame-like representations allow to construct tasks of different complexity according to human intuition, and likewise test their feasibility. This input stream shouldn't be too cluttered (it shouldn't include things like cyc database, wikipedia, etc.), but should assume zero knowledge. -- Vladimir Nesov mailto:[EMAIL PROTECTED] ----- 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=55007138-dd3f75
