Re: [agi] G0 theory completed

[David Clark]

You misinterpret my response.  I never said I didn't understand the 30,000 line program.  I said I can't think about the details embodied by the code at the same time.  These are quite different things.  If remembering huge numbers of details at one time is your definition of understanding then humans don't understand almost anything in our world.  We overcome our small short term memory by organization and external augmentation (make a few notes on paper or computer, memorize known solutions, etc).  I think you define understanding much too narrowly.   I don't treat the rest of my program as unpredictable when I am working on just one small module.  In fact, I have a very clear view of what every part of the program will do when I decide to concentrate on that module or the bigger structures I have created.   I don't check function arguments and have never used exceptions except where I want to catch a know condition with that structure.  It makes no sense to have exceptions in general if you don't know what the error will be (so much for unpredictability), because you wouldn't be able to handle it in any meaningful way.   Your comments don't support your conclusion of the non-predictability of an AGI but as I stated in my email, an AGI with close to human intelligence would be no less/more predictable than humans are.  I don't think humans are all that predictable, do you?   My biggest complaint with your email was the idea of stating flat out that a brain cannot model something more complicated than itself.  I disagree with this view.  I think the answer is in the details.  I can model the whole world easily in my limited short term memory but how detailed and how useful that model is, is open to debate.   You wrote: I think to succeed at AGI, we need to understand the theoretical limits of learning [1,2], then develop a system not based on methods that have already been shown not to work.  Then build a system that can learn, give it enough raw data to do so, and set it loose.   Understanding learning in humans is helpful but why would all possible methods of gaining experience and divining solutions be embodied in humans already?  If an AGI is embodied in a computer, and computers have significantly different attributes than humans, then why would human "theoretical limits of learning" be the final say for creating an AGI?  I think most people on this list would agree with the build learning system, add data and go theory but the disagreement is always how much building needs to be done to get to the most optimal set of learning capability.  We have seen many failures of potential AI programs that tried with a single learning method and got nowhere.  If the answer to AGI was simple or easy, I think that solution would already have been found.   The nuances of any past failed attempt at AGI also make your statement about "not based on methods that have already been shown not to work" not very useful.  I don't know of anyone working on AI who thinks that their particular approach is exactly like the ones that have failed.  Many outsiders to those projects might say that their basic approach has been shown not to work but the people on that project disagree.  An example would be the many NN projects that have not produced any intelligence but we know that basically human intelligence is based on NN's.  If there is a simple message in these facts I fail to see it, other than that no approach should be fully discounted until somebody actually succeeds at building an AGI.   David Clark   ----- Original Message ----- From: Matt Mahoney To: agi@v2.listbox.com Sent: Friday, October 06, 2006 7:53 PM Subject: Re: [agi] G0 theory completed Your programming example illustrates this nicely.  You can't understand a 30,000 line program all at once, so you divide it into modules with well defined interfaces.  You can develop, test, debug, model, predict, etc. one small module while treating the rest of the program as unpredictable, even though you know it is really deterministic.  If you didn't model the program this way, you wouldn't need to check function arguments or throw exceptions.  So you are really supporting my argument that you cannot predict (and therefore cannot control) an AGI. 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]