Au contraire, I suspect that the fact that biological organisms grow via the same sorts of processes as the biological environment in which the live, causes the organisms' minds to be built with **a lot** of implicit bias that is useful for surviving in the environment...
Some have argued that this kind of bias is **all you need** for evolution... see "Evolution without Selection" by A. Lima de Faria. I think that is wrong, but it's interesting that there's enough evidence to even try to make the argument... ben g On Tue, Oct 28, 2008 at 2:37 PM, Ed Porter <[EMAIL PROTECTED]> wrote: > It appears to me that the assumptions about initial priors used by a self > learning AGI or an evolutionary line of AGI's could be quite minimal. > > My understanding is that once a probability distribution starts receiving > random samples from its distribution the effect of the original prior > becomes rapidly lost, unless it is a rather rare one. Such rare problem > priors would get selected against quickly by evolution. Evolution would > tend to tune for the most appropriate priors for the success of subsequent > generations (either or computing in the same system if it is capable of > enough change or of descendant systems). Probably the best priors would > generally be ones that could be trained moderately rapidly by data. > > So it seems an evolutionary system or line could initially learn priors > without any assumptions for priors other than a random picking of priors. > Over time and multiple generations it might develop hereditary priors, an > perhaps even different hereditary priors for parts of its network connected > to different inputs, outputs or internal controls. > > The use of priors in an AGI could be greatly improved by having a gen/comp > hiearachy in which models for a given concept could be inherited from the > priors of sets of models for similar concepts, and that the set of priors > appropriate could change contextually. It would also seem that the notion > of a prior could be improve by blending information from episodic and > probabilistic models. > > It would appear than in almost any generally intelligent system, being able > to approximate reality in a manner sufficient for evolutionary success with > the most efficient representations would be a characteristic that would be > greatly preferred by evolution, because it would allow systems to better > model more of their environement sufficiently well for evolutionary success > with whatever current modeling capacity they have. > > So, although a completely accurate description of virtually anything may > not > find much use for Occam's Razor, as a practically useful representation it > often will. It seems to me that Occam's Razor is more oriented to deriving > meaningful generalizations that it is exact descriptions of anything. > > Furthermore, it would seem to me that a more simple set of preconditions, > is > generally more probable than a more complex one, because it requires less > coincidence. It would seem to me this would be true under most random sets > of priors for the probabilities of the possible sets of components involved > and Occam's Razor type selection. > > The are the musings of an untrained mind, since I have not spent much time > studying philosophy, because such a high percent of it was so obviously > stupid (such as what was commonly said when I was young, that you can't > have > intelligence without language) and my understanding of math is much less > than that of many on this list. But none the less I think much of what I > have said above is true. > > I think its gist is not totally dissimilar to what Abram has said. > > Ed Porter > > > > > -----Original Message----- > From: Pei Wang [mailto:[EMAIL PROTECTED] > Sent: Tuesday, October 28, 2008 3:05 PM > To: agi@v2.listbox.com > Subject: Re: [agi] Occam's Razor and its abuse > > > Abram, > > I agree with your basic idea in the following, though I usually put it in > different form. > > Pei > > On Tue, Oct 28, 2008 at 2:52 PM, Abram Demski <[EMAIL PROTECTED]> > wrote: > > Ben, > > > > You assert that Pei is forced to make an assumption about the > > regulatiry of the world to justify adaptation. Pei could also take a > > different argument. He could try to show that *if* a strategy exists > > that can be implemented given the finite resources, NARS will > > eventually find it. Thus, adaptation is justified on a sort of "we > > might as well try" basis. (The proof would involve showing that NARS > > searches the state of finite-state-machines that can be implemented > > with the resources at hand, and is more probable to stay for longer > > periods of time in configurations that give more reward, such that > > NARS would eventually settle on a configuration if that configuration > > consistently gave the highest reward.) > > > > So, some form of learning can take place with no assumptions. The > > problem is that the search space is exponential in the resources > > available, so there is some maximum point where the system would > > perform best (because the amount of resources match the problem), but > > giving the system more resources would hurt performance (because the > > system searches the unnecessarily large search space). So, in this > > sense, the system's behavior seems counterintuitive-- it does not seem > > to be taking advantage of the increased resources. > > > > I'm not claiming NARS would have that problem, of course.... just that > > a theoretical no-assumption learner would. > > > > --Abram > > > > On Tue, Oct 28, 2008 at 2:12 PM, Ben Goertzel <[EMAIL PROTECTED]> > > wrote: > >> > >> > >> On Tue, Oct 28, 2008 at 10:00 AM, Pei Wang <[EMAIL PROTECTED]> > >> wrote: > >>> > >>> Ben, > >>> > >>> Thanks. So the other people now see that I'm not attacking a straw > >>> man. > >>> > >>> My solution to Hume's problem, as embedded in the > >>> experience-grounded semantics, is to assume no predictability, but > >>> to justify induction as adaptation. However, it is a separate topic > >>> which I've explained in my other publications. > >> > >> Right, but justifying induction as adaptation only works if the > >> environment is assumed to have certain regularities which can be > >> adapted to. In a random environment, adaptation won't work. So, > >> still, to justify induction as adaptation you have to make *some* > >> assumptions about the world. > >> > >> The Occam prior gives one such assumption: that (to give just one > >> form) sets of observations in the world tend to be producible by > >> short computer programs. > >> > >> For adaptation to successfully carry out induction, *some* vaguely > >> comparable property to this must hold, and I'm not sure if you have > >> articulated which one you assume, or if you leave this open. > >> > >> In effect, you implicitly assume something like an Occam prior, > >> because you're saying that a system with finite resources can > >> successfully adapt to the world ... which means that sets of > >> observations in the world *must* be approximately summarizable via > >> subprograms that can be executed within this system. > >> > >> So I argue that, even though it's not your preferred way to think > >> about it, your own approach to AI theory and practice implicitly > >> assumes some variant of the Occam prior holds in the real world. > >>> > >>> > >>> Here I just want to point out that the original and basic meaning of > >>> Occam's Razor and those two common (mis)usages of it are not > >>> necessarily the same. I fully agree with the former, but not the > >>> latter, and I haven't seen any convincing justification of the > >>> latter. Instead, they are often taken as granted, under the name of > >>> Occam's Razor. > >> > >> I agree that the notion of an Occam prior is a significant conceptual > >> beyond the original "Occam's Razor" precept enounced long ago. > >> > >> Also, I note that, for those who posit the Occam prior as a **prior > >> assumption**, there is not supposed to be any convincing > >> justification for it. The idea is simply that: one must make *some* > >> assumption (explicitly or > >> implicitly) if one wants to do induction, and this is the assumption > that > >> some people choose to make. > >> > >> -- Ben G > >> > >> > >> > >> ________________________________ > >> agi | Archives | Modify Your Subscription > > > > > > ------------------------------------------- > > agi > > Archives: https://www.listbox.com/member/archive/303/=now > > RSS Feed: https://www.listbox.com/member/archive/rss/303/ > > Modify Your Subscription: https://www.listbox.com/member/?&; Powered by > > Listbox: http://www.listbox.com > > > > > ------------------------------------------- > agi > Archives: https://www.listbox.com/member/archive/303/=now > RSS Feed: https://www.listbox.com/member/archive/rss/303/ > Modify Your Subscription: > https://www.listbox.com/member/?&; > Powered by Listbox: http://www.listbox.com > > > > ------------------------------------------- > agi > Archives: https://www.listbox.com/member/archive/303/=now > RSS Feed: https://www.listbox.com/member/archive/rss/303/ > Modify Your Subscription: > https://www.listbox.com/member/?&; > Powered by Listbox: http://www.listbox.com > -- Ben Goertzel, PhD CEO, Novamente LLC and Biomind LLC Director of Research, SIAI [EMAIL PROTECTED] "A human being should be able to change a diaper, plan an invasion, butcher a hog, conn a ship, design a building, write a sonnet, balance accounts, build a wall, set a bone, comfort the dying, take orders, give orders, cooperate, act alone, solve equations, analyze a new problem, pitch manure, program a computer, cook a tasty meal, fight efficiently, die gallantly. Specialization is for insects." -- Robert Heinlein ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=117534816-b15a34 Powered by Listbox: http://www.listbox.com
