Steve, I know what dimensional analysis is, but it would be great if you could give an example of how it's useful for everyday commonsense reasoning such as, say, a service robot might need to do to figure out how to clean a house...
thx ben On Sun, Jun 27, 2010 at 6:43 PM, Steve Richfield <steve.richfi...@gmail.com>wrote: > Ben, > > What I saw as my central thesis is that propagating carefully conceived > dimensionality information along with classical "information" could greatly > improve the cognitive process, by FORCING reasonable physics WITHOUT having > to "understand" (by present concepts of what "understanding" means) physics. > Hutter was just a foil to explain my thought. Note again my comments > regarding how physicists and astronomers "understand" some processes though > "dimensional analysis" that involves NONE of the sorts of "understanding" > that you might think necessary, yet can predictably come up with the right > answers. > > Are you up on the basics of dimensional analysis? The reality is that it is > quite imperfect, but is often able to yield a short list of "answers", with > the correct one being somewhere in the list. Usually, the wrong answers are > wildly wrong (they are probably computing something, but NOT what you might > be interested in), and are hence easily eliminated. I suspect that neurons > might be doing much the same, as could formulaic implementations like (most) > present AGI efforts. This might explain "natural architecture" and guide > human architectural efforts. > > In short, instead of a "pot of neurons", we might instead have a pot of > dozens of types of neurons that each have their own complex rules regarding > what other types of neurons they can connect to, and how they process > information. "Architecture" might involve deciding how many of each type to > provide, and what types to put adjacent to what other types, rather than the > more detailed concept now usually thought to exist. > > Thanks for helping me wring my thought out here. > > Steve > ============= > On Sun, Jun 27, 2010 at 2:49 PM, Ben Goertzel <b...@goertzel.org> wrote: > >> >> Hi Steve, >> >> A few comments... >> >> 1) >> Nobody is trying to implement Hutter's AIXI design, it's a mathematical >> design intended as a "proof of principle" >> >> 2) >> Within Hutter's framework, one calculates the shortest program that >> explains the data, where "shortest" is measured on Turing machine M. >> Given a sufficient number of observations, the choice of M doesn't matter >> and AIXI will eventually learn any computable reward pattern. However, >> choosing the right M can greatly accelerate learning. In the case of a >> physical AGI system, choosing M to incorporate the correct laws of physics >> would obviously accelerate learning considerably. >> >> 3) >> Many AGI designs try to incorporate prior understanding of the structure & >> properties of the physical world, in various ways. I have a whole chapter >> on this in my forthcoming book on OpenCog.... E.g. OpenCog's design >> includes a physics-engine, which is used directly and to aid with >> inferential extrapolations... >> >> So I agree with most of your points, but I don't find them original except >> in phrasing ;) >> >> ... ben >> >> >> On Sun, Jun 27, 2010 at 2:30 PM, Steve Richfield < >> steve.richfi...@gmail.com> wrote: >> >>> Ben, et al, >>> >>> *I think I may finally grok the fundamental misdirection that current >>> AGI thinking has taken! >>> >>> *This is a bit subtle, and hence subject to misunderstanding. Therefore >>> I will first attempt to explain what I see, WITHOUT so much trying to >>> convince you (or anyone) that it is necessarily correct. Once I convey my >>> vision, then let the chips fall where they may. >>> >>> On Sun, Jun 27, 2010 at 6:35 AM, Ben Goertzel <b...@goertzel.org> wrote: >>> >>>> Hutter's AIXI for instance works [very roughly speaking] by choosing the >>>> most compact program that, based on historical data, would have yielded >>>> maximum reward >>>> >>> >>> ... and there it is! What did I see? >>> >>> Example applicable to the lengthy following discussion: >>> 1 - 2 >>> 2 - 2 >>> 3 - 2 >>> 4 - 2 >>> 5 - ? >>> What is "?". >>> >>> Now, I'll tell you that the left column represents the distance along a >>> 4.5 unit long table, and the right column represents the distance above the >>> floor that you will be as your walk the length of the table. Knowing this, >>> without ANY supporting physical experience, I would guess "?" to be zero, or >>> maybe a little more if I were to step off of the table and land onto >>> something lower, like the shoes that I left there. >>> >>> In an imaginary world where a GI boots up with a complete understanding >>> of physics, etc., we wouldn't prefer the simplest "program" at all, but >>> rather the simplest representation of the real world that is not >>> physics/math *in*consistent with our observations. All observations >>> would be presumed to be consistent with the response curves of our sensors, >>> showing a world in which Newton's laws prevail, etc. Armed with these >>> presumptions, our physics-complete AGI would look for the simplest set of >>> *UN*observed phenomena that explained the observed phenomena. This >>> theory of a physics-complete AGI seems undeniable, but of course, we are NOT >>> born physics-complete - or are we?! >>> >>> This all comes down to the limits of representational math. At great risk >>> of hand-waving on a keyboard, I'll try to explain by pseudo-translating the >>> concepts into NN/AGI terms. >>> >>> We all know about layering and columns in neural systems, and understand >>> Bayesian math. However, let's dig a little deeper into exactly what is being >>> represented by the "outputs" (or "terms" for died-in-the-wool AGIers). All >>> physical quantities are well known to have value, significance, and >>> dimensionality. Neurons/Terms (N/T) could easily be protein-tagged as to the >>> dimensionality that their functionality is capable of producing, so that >>> only compatible N/Ts could connect to them. However, let's dig a little >>> deeper into "dimensionality" >>> >>> Physicists think we live in an MKS (Meters, Kilograms, Seconds) world, >>> and that all dimensionality can be reduced to MKS. For physics purposes they >>> may be right (see challenge below), but maybe for information processing >>> purposes, they are missing some important things. >>> >>> *Challenge to MKS:* Note that some physicists and most astronomers >>> utilize "*dimensional analysis*" where they experimentally play with the >>> dimensions of observations to inductively find manipulations that would >>> yield the dimensions of unobservable quantities, e.g. the mass of a star, >>> and then run the numbers through the same manipulation to see if the results >>> at least have the right exponent. However, many/most such manipulations >>> produce nonsense, so they simply use this technique to jump from >>> observations to a list of prospective results with wildly different >>> exponents, and discard the results with the ridiculous exponents to find the >>> correct result. The frequent failures of this process indirectly >>> demonstrates that there is more to dimensionality (and hence physics) than >>> just MKS. Let's accept that, and presume that neurons must have already >>> dealt with whatever is missing from current thought. >>> >>> Consider, there is some (hopefully finite) set of reasonable >>> manipulations that could be done to Bayesian measures, with the various >>> competing theories of recognition representing part of that set. The >>> reasonable mathematics to perform on spacial features is probably different >>> than the reasonable mathematics to perform on recognized objects, or the >>> recognition of impossible observations, the manipulation of ideas, etc. >>> Hence, N/Ts could also be tagged for this deeper level of dimensionality, so >>> that ideas don't get mixed up with spacial features, etc. >>> >>> Note that we may not have perfected this process, and further, that this >>> process need not be perfected. Somewhere around the age of 12, many of our >>> neurons DIE. Perhaps these were just the victims of insufficiently precise >>> dimensional tagging? >>> >>> Once things can ONLY connect up in mathematically reasonable ways, what >>> remains between a newborn and a physics-complete AGI? Obviously, the >>> physics, which can be quite different on land than in the water. Hence, the >>> physics must also be learned. >>> >>> My point here is that if we impose a fragile requirement for mathematical >>> correctness against a developing system of physics and REJECT simplistic >>> explanations (not observations) that would violate either the mathematics or >>> the physics, then we don't end up with overly simplistic and useless >>> "programs", but rather we find more complex explanations that are physics >>> and mathematically believable. >>> >>> we should REJECT the concept of "pattern matching" UNLESS the discovered >>> pattern is both physics and mathematically correct. In short, the next >>> number in the "2, 2, 2, 2, ?" example sequence would *obviously* (by >>> this methodology) not be "2". >>> >>> OK, the BIG question here is whether a carefully-designed (or evolved >>> over 100 million years) system of representation can FORCE the construction >>> of systems (like us) that work this way, so that our "programs" aren't >>> "simple" at all, but rather are maximally correct? >>> >>> Anyway, I hope you grok the question above, and agree that the search for >>> the simplest "program" (without every possible reasonable physics and math >>> constraint that can be found) may be a considerable misdirection. Once you >>> impose physics and math constraints, which could potentially be done with >>> simplistic real-world mechanisms like protein tagging in neurons, the >>> problems then shifts to finding ANY solution that fits the complex >>> constraints, rather than finding the SIMPLEST solution without such >>> constraints. >>> >>> Once we can get past the questions, hopefully we can discuss prospective >>> answers. >>> >>> Are we in agreement here? >>> >>> Any thoughts? >>> >>> Steve >>> >>> *agi* | Archives <https://www.listbox.com/member/archive/303/=now> >>> <https://www.listbox.com/member/archive/rss/303/> | >>> Modify<https://www.listbox.com/member/?&;>Your Subscription >>> <http://www.listbox.com> >>> >> >> >> >> -- >> Ben Goertzel, PhD >> CEO, Novamente LLC and Biomind LLC >> CTO, Genescient Corp >> Vice Chairman, Humanity+ >> Advisor, Singularity University and Singularity Institute >> External Research Professor, Xiamen University, China >> b...@goertzel.org >> >> " >> “When nothing seems to help, I go look at a stonecutter hammering away at >> his rock, perhaps a hundred times without as much as a crack showing in it. >> Yet at the hundred and first blow it will split in two, and I know it was >> not that blow that did it, but all that had gone before.” >> >> *agi* | Archives <https://www.listbox.com/member/archive/303/=now> >> <https://www.listbox.com/member/archive/rss/303/> | >> Modify<https://www.listbox.com/member/?&;>Your Subscription >> <http://www.listbox.com> >> > > *agi* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > -- Ben Goertzel, PhD CEO, Novamente LLC and Biomind LLC CTO, Genescient Corp Vice Chairman, Humanity+ Advisor, Singularity University and Singularity Institute External Research Professor, Xiamen University, China b...@goertzel.org " “When nothing seems to help, I go look at a stonecutter hammering away at his rock, perhaps a hundred times without as much as a crack showing in it. Yet at the hundred and first blow it will split in two, and I know it was not that blow that did it, but all that had gone before.” ------------------------------------------- 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
