*Todor:* Mike, while some of the things you say are correct/agree with school of thought me and several others on the list share (BTW, the paper about embodiment was a good one), you fail to recognize this, say things that others agree with you, but always make it like "no, it's not like what I say!". Everybody is against you, and what you say is always "different". :)
And once you promote embodiment (sensori-motor generalizing hierarchies - the prefered school of thought of me, Sergio, Boris, Jeff Hawkins), the next time you cite guys who are definitively AI-niks with a mask (IMHO such as one Hofstadter's paper about fonts you cited and was displaying your confusions about generalization, Hofstadder seems to has confused generalization and specialization just like you and many others still do). Also that guy Bart Kosko and his word plays - I've read a book of his on Fuzzy logic long time ago, and do you know what I understood from it? That fuzzy logic is not that fuzzy as they say, and it's nothing special, it's classical logic in new shoes so it doesn't solve the problem - it's like classical logic, but with more degrees of freedom. I wrote about this in my "Theory of Mind and Universe", Part 4, section: ", first published in early 2004. "21. Fuzzy logic and is it really fuzzy? [Also Truth, comparison … ], p.21 - p.25: http://research.twenkid.com/agi_english/Teenage_Theory_of_Universe_and_Mind_4.pdf or as I re-published the excerpt in the blog: http://artificial-mind.blogspot.com/2012/06/fuzzy-logic-and-is-it-really-fuzzy.html *Notice the definition of "truth" *there: That excerpt says a lot on the topic and about those "fuzzy fuzzy logic" bullshit, explains about principles of recognition and generalization in generalizing sensori-motor hierarchies. ... You're right regarding/my school of thought agree that classical logic etc. are derived and justified based on sensory experience, they are lower resolution, and including that sensory mobility you talk about (adjustment of coordinates) is crucial and important for the development. However once after working rules are derived, Ben's right that they can be applied without using precise simulation at sensory level detail - it's not needed, unless higher level rules happen to fail, then they need to be re-adjusted. Generalization, loss of detail allows working with abstract concepts and solving big abstract problems that cannot be solved directly (like building a spaceship) - yes, abstract domains, high generality, has to be converted (grounded) down to lowest level representations (both sensory and motor), in order to make sense in reality. That's correct, but spaceships and most of engineering for example is initially born in sketches, blueprints and computer models, which are result of derivation from sensory input, but digested, compressed and morphed. Humans do have this hierarchies which allow them to convert loogical expressions into "real world problem solving", by filling the gaps with the experience (lower level regularities, already collected, and the ones experienced at the moment). >This differs from the factual statements we make about >the real world-statements such as "Pine needles are green" or "Chlorophyll >molecules reflect green light." These factual statements are approximations. >They are technically vague or fuzzy. And they often come juxtaposed with >probabilistic uncertainty: "Pine needles are green with high probability." >Note that this last statement involves triple uncertainty. There is first >the vagueness of green pine needles because there is no bright line between >greenness and non-greenness-it is a matter of degree. There is second only a >probability whether pine needles have the vague property of greenness. And >there is last the magnitude of the probability itself. The magnitude is the >vague or fuzzy descriptor "high," because here, too, there is no bright line >between high probability and not-high probability. *Todor:* Sorry to say that about that VIP fuzzy logic guy, but that's fuzzy fuzzy logic blah-blah-ing, see the citation above... - Yes, natural language is abstract and imprecise compared to sensory input that has higher resolution, wider scope/many cases that can't be encompassed in a single sentence - what a discovery. - Yes, if there's not grounding/mapping to sensory experience, and one is using only NL to talk about them, the details cannot be expressed precisely (namely if the colours are represented with one word, and not for example like a numerical interval, photo/video samples in controlled conditions etc., which is sensory data), it's like a blind man who's never seen to talk about colours. NL is an integrating medium for all modalities, its most abstract and it points to labels to records of sensory inputs. That's why for precise needs it's not used, or is used with auxilliary tools (diagrams, sample data, specific terms, video, audio records, motion-capture records etc.) ... There are calibrated devices and physical measurements which are not vague or fuzzy. Measure the pique of the lenght of the wave of the reflection of a light with given frequency in given environment. ... *"Green" and "not green"is a logicist's nonsense* Colours are not about "green" and "not green", it's about green or red, yellow, blue etc., also it's about a context where this is applied, i.e. the adjacent areas, the specific environment. If something is too general, short, superficial or so (a random sentence told from nowhere), it *APPARENTLY* and by definition is not intended or not applicable for practical usage. Colour has precise mapping in wave-lenghts*, which is mapped to differences in the sensation in different types of cells in the retina. It's "technically not vague". "Non-green" is a logicists' bullshit - stones, "running", f*, beautiful, 34859fje8fjw39fj342 - it's all "non green", and what's the meaning and use of this? There are modalities, which are defined by sensory matrices, that have coordinates and intentisities per their elements. In a continuous domain of a sensory modality there are continous steps, "smooth" "greenness" or so - "fuzzy" "logic". However in this cases, if one is referring to those matrices, he should *cite* it in terms of that domain and its resolution, such as: 0x20ff56, or 0x10ff10 or 0x002000, or 0x016A0C etc. - this is 24-bit RGB colours. The ones with biggest difference of the second component compared to the others is the most "green". But this is not logic, this is simple measurement. "Pine needles are green with high probability." That is to say that they are not red or blue, and compared to those steps the difference is discrete enough. ... **Nooo, optical illusions! * :)) Yeah, some blah-blahbers use to use optical illusions to explain how magical brain is. In fact, the optical illusions are predictable and common, which means that they are ruslt of general and repeatable patterns of operation of the brain - different people, different experience, the same "illusions". In my interpretation it is to say that they are not really illusions, and they always appear in ambiguous scenes, which are artificial extremities that don't exist normally in real perceptions. Brain is trained to see scenes, usually spatial scenes with light sources (thus light spreading, according to the objects and geometry of the scene), it's not just a PC connected to two flat-bed scanners who work always under the same lightning conditions in orthogonal projections, and which record exactly the colours as they come on the CCD line... Also even the web-cameras adjust the "White-Balance" and do "colour-corrections", accenting or attenuating one or another colour aspect. Our conscious sees what the spatial shape and colour would be, if it follows reconstructed 3D model and the light has the particular properties reconstructed (supposed) properties. In cases where the sources, intensities and colour of the light, and also the 3D structure of the scene cannot be unambiguously reconstructed, colours may be seen "wrongly" compared to if measured with a "calibrated colourimeter" or so, or compared to the lowest level input to retina if each reconstructed "pixel" is seen individually with the rest set to black. However in fact this is to show that higher levels of the brain cannot access and fix some of the predictions/models at the lowest levels of visual processing - big deal. >In logic, **you already know how to solve the problem.** In real world reasoning, you DO NOT KNOW EXACTLY HOW TO SOLVE THE PROBLEM - you have to decide on an approach. Both are wrong, you know only the methodology to do, in "RWR" you also know the methodology - you may not realize it in its whole (the highest more abstract part of the system may not realize all the details - it usually doesn't such as the marshall to know the coordinates of every single soldier in his army). Every single action of the, my term, "causality-control units", the low-level units (from the given perspective) know what they do, it's not random. I think I've accented this in the past, that every human action, our whole external behavior is eventually reducable to a set of vectors describing the muscle motions in given space-time coordinates. The intelligent output is just a of sequence of muscle moves, which in essence are coordinate adjustments. And every single step in any activity or solving a problem is known "exactly" in its local scope - move a hand so and so, stretch, grab, turn around so and so, etc. ... *There are always criteria for halting* >In logic & algorithms there are normally criteria for halting, in RWR you can go on forever. [Check out tame vs wicked problemsolving, structured vs unstructured). *Todor:* This is also not true. In algorithms you can go forever, too - "iterative algorithms". An AGI and SIGI (Self-improving general intelligence) system should be an iterative algorithm per se, it's not "push-the-button-get-the-result" software, it's supposed to run all the time. You see many of as talk about "hierarchies", that's another thing preventing "halting", if a problem is solved in low level of abstraction, it can go higher (in more cases, wider scope). Or vice-verse - if the problem is solved in an abstract form (low resolution, wide scope), then it have to be converted down to the highest resolution (high resolution, short scope). An example of this is science. The advance of science turns abstract or not precise-enough theories and representations of physical laws or biological laws into representations which are as precise as allowing to build atoms and molecules one-by-one, such as the DNA-builder they created recently. In high level of abstraction it was just an idea that there's something called "genetic information", and it's stored somewhere in the cell. Then they found the molecule. Then they found segments of the molecule. Then they started to understand the implications of different changes in different segments etc. Also while you "can go on forever", that doesn't normally happen in *one iteration*. There are also short-term goals, always there are criteria to stop, even if they are implicit and most people lack enough of self-reflection in order to understand it. I've written about that there (see all the parts): http://artificial-mind.blogspot.com/2010/01/semantic-analysis-of-sentence.html Part 1 (и български):(This Post) Semantic analysis of a sentence. Reflections about the meaning of the meaning and the Artificial Intelligence Part 2 (и български): Causes and reasons for human actions. Searching for causes. Whether higher or lower levels control. Control Units. Reinforcement learning. Part 3 (и български): Motivation is dependent on local and specific stimuli, not general ones. Pleasure and displeasure as goal-state indicators. Reinforcement learning. Part 4 : Intelligence: search for the biggest cumulative reward for a given period ahead, based on given model of the rewards. Reinforcement learning. Regarding art - it's systematic, it's not random wandering, but I understand why many people believe so - The reason that art impresses people is the fact that they cannot imagine and see how the piece of art was created incrementally, with the resolution of perception and causality/control that they believe they should had to be able to imagine, in order not to be impressed (my hypothesis, from a yet not translated old article). For non-artists non-actors, non-writers, non-photographers, non-comedians, etc. art might look like "magic", "spirits give you something which comes from Heaven." For the ones who do all those activities as authors, and have enough of introspective capabilities, and also have technical talents and capabilities, art is systematic and predictable, not magic at all. The variables that define it are beyond average people comprehension or control, but that's problem of their limited cognitive capabilities. There are places to choose directions and where to start or where to end, but choice is never random (and when it is random, it's not intelligence and it doesn't matter what the choice was), they are all systematic and determined by the other decisions, experience, personal details and goals of the piece of art. Random art is meaningless. ... The ones with obscessive-compulsive disorder or maniacs do cycle the same thing "forever", but even they have some "criteria"for finishing parts of the behavior, otherwise they would be having endless convulsions. Notice that I'm talking in embodied perspective - moving the body requires coordination, "algorithms" and criteria for success in very tiny steps. >Bart Kosko: >The catch is that we can really only prove tautologies. The great binary >truths of mathematics are still logically equivalent to the tautology 1 = 1 >or Green is green. *Todor:* Yeah, but he doesn't get that it's about the MAPPING between different levels of generality (the sensory experience "green" is mapped to the word "green" - pronounciation, sound (inter-modality mapping); prevalence in a scene; attention marker as an attribute of the scene that is pointed, emphasized by another person etc.). "Green" doesn't make sense in the mathematical world, it's a pointer to visual modality. "Truth" in logic is not really meaningful in reality if not converted, that's true, see my definition of truth and the discussion about the nonsense pseudo logical paradoxes. Also, all the sensory, mathematical or whatever knowledge in principle is always "available there", however it can't be perceived or thinked all at once, there must be a sequence and focus. Proves or whatever are focus on particular aspects, chains of thought, emphasizes of particular POV etc. The one who proves something initially doesn't know / is not that certain about the outcome which he proves/finds evidences (matches). Both regarding sensory experiences or logical expressions (very compressed and selected sensory experiences), it's about comparison and finding matches. In classical logic it's binary matching, in reality it has more degrees of freedom, more dimensions, more steps, and various resolution, scope, time-span, ... Another thing is that somewhat "tautologies" are what mind does: it finds or predicts correlations/repetitions between already experienced and current and upcoming sensory input, also it tries to make its own plans/predictions to match the real future, planned sensory input to match with the real one. *-- Todor "Tosh/Twenkid" Arnaudov* http://research.twenkid.com http://artificial-mind.blogspot.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
