Jim,
Since you are using your brain to do all that reasoning, I must conclude that there must be something in your brain that allows you to do that. My attention, therefore, immediately switches away from the reasoning itself, and towards "that" which is in your brain and allows you to reason that way. This statement is in no way a judgement about your ability to reason, or the correctness of your conclusions. It only says that brain comes before reasoning, so I want to know how it came to be before I start using it for reasoning. If I or someone else succeded in explaining how the brain came to be, then, and only then, would I agree to consider reasoning. And my next step would be to reason about AGI, and I would conclude that, knowing how the function of the brain came to be, AGI might also work if we managed to simulate the same function on computers. Now, with all due respect to other beliefs, my belief is that the brain came to be by evolution for the survival of the fittest. I can tone this down even more. I want to assume that the brain came to be because brained creatures survided better than brainless creatures, and examine the consequences of that assumption. I do not need to deny any other assumption. Of course, one could argue against. Brained creatures survived better because they could reason. True, but they needed a brain before they could reason, and they had to test that brain through countless uncertainties they encountered in nature, for a long period of time, and still survive. So the brain came first, and reasoning for survival came as a consequence. I'll stop here. I'll stop because I just did two things, that I am afraid you may not like. I refused to even consider your reasoning, and I proposed a new approach to AGI that turns 60 years of research on its head. I want to hear your reaction, and most of all, I want to know if you are willing to allow me to proceed and examine the consequences of my assumption. The first consequence, is that I would be forced to explain reasoning and intelligence from purely natural causes. As your two presentations were limited to describing the difficulties and failures of the traditional approach to AGI which consists of reasoning and writing computer programs, I think you should allow me to proceed. Sergio From: Jim Bromer [mailto:[email protected]] Sent: Wednesday, August 15, 2012 8:56 PM To: AGI Subject: Re: [agi] Uncertainty, causality, entropy, self-organization, and Schroedinger's cat. Well, I am still sceptical of the theory for one basic reason. The problem, as I see it, is that the complexity of the level of general knowledge that is (probably) required for a basic AGI program is too great for any of these methods to work with anything other than superficial or at best simple examples. So if the day came when one method worked then a great many methods would probably work because it would mean that someone had figured out how to contain combinatorial complexity or had developed hardware sufficient to attain minimal AI. Weighted reasoning was at first believed to be a solution to combinatorial complexity. Why didn't it work? There were a few problems. One was that not everything can be expressed in the terms of a range of values. Secondly, a weighting would, by the very method that it is able to simplify complex relationships, represent different kinds of things and these different things get mushed up and produce sub par results. Finally, these systems have no way to integrate different kinds of relations wisely. Of course, since weighted reasoning is usually used with correlations, one might imagine that correlation points could be developed to represent when these complicated relations can be fused and when they should be divided and how they can be structured and integrated. This would require some trial and error methods to learn how to apply these techniques to real world modelling, but few people actually talk about stuff like this. And there is no reason to think that this sort of method can produce intelligence without first transcending the combinatorial complexity problem. I have thought about taking actions that can be used to minimize the complications of numerous unknowns, but since this strategy has to be based on some method of avoiding the worse outcomes, that means that the strategy cannot be based on a simplistic way to minimize "entropy". Taking an action when facing multiple unknowns has to be derived from a biased method that help the entity avoid the worse outcomes. This in turn implies that biasing strategies could also be used in the hope of increasing the chances of better outcomes based on the projection of insights about the kinds of situation that the intelligent device thought it might be in. Jim Bromer AGI | <https://www.listbox.com/member/archive/303/=now> Archives <https://www.listbox.com/member/archive/rss/303/18883996-f0d58d57> | <https://www.listbox.com/member/?&; ad2> Modify Your Subscription <http://www.listbox.com> ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-c97d2393 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-2484a968 Powered by Listbox: http://www.listbox.com
