Re: Savants and user-interfaces [was Re: [agi] WHAT SORT OF HARDWARE $33K AND $850K BUYS TODAY FOR USE IN AGI
2008/6/30 Vladimir Nesov [EMAIL PROTECTED]: On Tue, Jul 1, 2008 at 8:31 AM, Linas Vepstas [EMAIL PROTECTED] wrote: Why binary? I once skimmed a biography of Ramanujan, he started multiplying numbers in his head as a pre-teen. I suspect it was grindingly boring, but given the surroundings, might have been the most fun thing he could think of. If you're autistic, then focusing obsessively on some task might be a great way to pass the time, but if you're more or less normal, I doubt you'll get very far with obsessive-compulsive self-training -- and that's the problem, isn't it? If the signals have properties of their own, I'm afraid they will start interfering with each other, which won't allow the circuit to execute in real time. Binary signals, on the other hand, can be encoded by the activation of nodes of the circuit, active/inactive. If you have an AND gate that leads from symbols S1 and S2 to S3, you learn to remember S3 only when you see both S1 and S2 What are you trying to accomplish here? I don't see where you are trying to go with this. I don't think a human can consciously train one or two neurons to do something, we train millions at a time. -- I'm guessing savants only employ a few tens of million neurons (give or take a few orders of magnitude) -- to do their stuff. Still, an array of 1K by 1K electrodes is well within current technology, we just don't know where to hook this up to, with the exception of simple motor areas, retina, and bit of the auditory circuits. --linas --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
Re: [agi] the uncomputable
2008/6/16 Abram Demski [EMAIL PROTECTED]: I previously posted here claiming that the human mind (and therefore an ideal AGI) entertains uncomputable models, counter to the AIXI/Solomonoff model. There was little enthusiasm about this idea. :) I missed your earlier posts. However, I believe that there are models of computation can compute things that turing machines cannot, and that this is not arcane, just not widely known or studied. Here is a quick sketch: Topological finite automata, or geometric finite automata, (of which the quantum finite automata is a special case) generalize the notion of non-deterministic finite automata by replacing its powerset of states with a general topological or geometric space (complex projective space in the quantum case). It is important to note that these general spaces are in general uncountable (have the cardinality of the continuum). It is well known that the languages accepted by quantum finite automata are not regular languages, they are bigger and more complex in some ways. I am not sure what is known about the languages accepted by quantum push-down automata, but intuitively these are clearly different (and bigger) than the class of context-free languages. I believe the concepts of topological finite automata extend just fine to a generalization of turing machines, but I also believe this is a poorly-explored area of mathematics. I beleive such machines can compute things that turing machiens can't .. this should not be a surprise, since, after all, these systems have, in general, an uncountably infinite number of internal states (cardinality of the continuum!), and (as a side effect of the definition), perform infinite-precision addition and multiplication in finite time. So yes, I think there are perfectly fine, rather simple definitions for computing machines that can (it seems like) perform calculations that turing machines cannot. It should really be noted that quantum computers fall into this class. Considerably more confusing is the relationship of such machines (and the languages they accept) to lambda calculus, or first-order (or higher-order) logic. This is where the rubber hits the road, and even for the simplest examples, the systems are poorly understood, or not even studied. So, yeah, I think there's plenty of room for the uncomputable in some rather simple mathematical models of generalized computation. --linas --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
Re: Savants and user-interfaces [was Re: [agi] WHAT SORT OF HARDWARE $33K AND $850K BUYS TODAY FOR USE IN AGI
On Tue, Jul 1, 2008 at 10:02 AM, Linas Vepstas [EMAIL PROTECTED] wrote: What are you trying to accomplish here? I don't see where you are trying to go with this. I don't think a human can consciously train one or two neurons to do something, we train millions at a time. -- I'm guessing savants only employ a few tens of million neurons (give or take a few orders of magnitude) -- to do their stuff. Still, an array of 1K by 1K electrodes is well within current technology, we just don't know where to hook this up to, with the exception of simple motor areas, retina, and bit of the auditory circuits. Certainly nothing to do with individual neurons. Basically, it's possible to train a finite state automaton in the mind through association. You see a certain combination of properties, you think the symbol that describes this combination. If such automaton is trained not just to handle natural data (such as language), but to a specifically designed circuit plan, it'll probably be possible to use it as a directly accessible 'add-on' to the brain that implements specific simple function efficiently, such as some operation with numbers using a clever algorithm in a way alien to normal deliberative learning. You don't learn to perform a task, but to execute individual steps of an algorithm that performs a task. -- Vladimir Nesov [EMAIL PROTECTED] http://causalityrelay.wordpress.com/ --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
Re: Savants and user-interfaces [was Re: [agi] WHAT SORT OF HARDWARE $33K AND $850K BUYS TODAY FOR USE IN AGI
I was nearly kicked out of school in seventh grade for coming up with a method of manipulating (multiplying, dividing) large numbers in my head using what I later learned was a shift-reduce method. It was similar to this: http://www.metacafe.com/watch/742717/human_calculator/ My seventh grade math teacher was so upset with me, he almost struck me (physically -- you could get away with that back them). His reason? Wasting valuable math class time. The point is, you can train yourself to do this type of thing and look very savant-like. The above link is just one in a series of videos where the teacher presents this system. It takes practice, but not much more than learning the standard multiplication table. Cheers, Brad Vladimir Nesov wrote: Interesting: is it possible to train yourself to run a specially designed nontrivial inference circuit based on low-base transformations (e.g. binary)? You start by assigning unique symbols to its nodes, train yourself to stably perform associations implementing its junctions, and then assemble it all together by training yourself to generate a problem as a temporal sequence (request), so that it can be handled by the overall circuit, and training to read out the answer and convert it to sequence of e.g. base-10 digits or base-100 words keying pairs of digits (like in mnemonic)? Has anyone heard of this attempted? At least the initial steps look straightforward enough, what kind of obstacles this kind of experiment can run into? On Tue, Jul 1, 2008 at 7:43 AM, Linas Vepstas [EMAIL PROTECTED] wrote: 2008/6/30 Terren Suydam [EMAIL PROTECTED]: savant I've always theorized that savants can do what they do because they've been able to get direct access to, and train, a fairly small number of neurons in their brain, to accomplish highly specialized (and thus rather unusual) calculations. I'm thinking specifically of Ramanujan, the Hindi mathematician. He appears to have had access to a multiply-add type circuit in his brain, and could do symbolic long division and multiplication as a result -- I base this on studying some of the things he came up with -- after a while, it seems to be clear how he came up with it (even if the feat is clearly not reproducible). In a sense, similar feats are possible by using a modern computer with a good algebra system. Simon Plouffe seems to be a modern-day example of this: he noodles around with his systems, and finds various interesting relationships that would otherwise be obscure/unknown. He does this without any particularly deep or expansive training in math (whence some of his friction with real academics). If Simon could get a computer-algebra chip implanted in his brain, (i.e. with a very, very user-freindly user-interface) so that he could work the algebra system just by thinking about it, I bet his output would resemble that of Ramanujan a whole lot more than it already does -- as it were, he's hobbled by a crappy user interface. Thus, let me theorize: by studying savants with MRI and what-not, we may find a way of getting a much better man-machine interface. That is, currently, electrodes are always implanted in motor neurons (or visual cortex, etc) i.e. in places of the brain with very low levels of abstraction from the real word. It would be interesting to move up the level of abstraction, and I think that studying how savants access the magic circuits in thier brain will open up a method for high-level interfaces to external computing machinery. --linas --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
Re: [agi] WHAT SORT OF HARDWARE $33K AND $850K BUYS TODAY FOR USE IN AGI
2008/6/30 Terren Suydam [EMAIL PROTECTED]: Hi Will, --- On Mon, 6/30/08, William Pearson [EMAIL PROTECTED] wrote: The only way to talk coherently about purpose within the computation is to simulate self-organized, embodied systems. I don't think you are quite getting my system. If you had a bunch of programs that did the following 1) created new programs, by trial and error and taking statistics of variables or getting arbitrary code from the outside. 2) communicated with each other to try and find programs that perform services they need. 3) Bid for computer resources, if a program loses its memory resources it is selected against, in a way. Would this be sufficiently self-organised? If not, why not? And the computer programs would be as embodied as your virtual creatures. They would just be embodied within a tacit economy, rather than an artificial chemistry. It boils down to your answer to the question: how are the resources ultimately allocated to the programs? If you're the one specifying it, via some heuristic or rule, then the purpose is driven by you. If resource allocation is handled by some self-organizing method (this wasn't clear in the article you provided), then I'd say that the system's purpose is self-defined. I'm not sure how the system qualifies. It seems to be half way between the two definitions you gave. The programs can have special instructions in that bid for a specific resource with as much credit as they want (see my recent message replying to Vladimir Nesov for more information about banks, bidding and credit). The instructions can be removed or not done, the amount of credit bid can be changed. The credit is given to some programs by a fixed function, but they have instructions they can execute (or not) to give it to other programs forming an economy. What say you, self-organised or not? As for embodiment, my question is, how do your programs receive input? Embodiment, as I define it, requires that inputs are merely reflections of state variables, and not even labeled in any way... i.e. we can't pre-define ontologies. The embodied entity starts from the most unstructured state possible and self-structures whatever inputs it receives. Bits and bytes from the outside world, or bits and bytes from reading other programs programing and data. No particular ontology. That said, you may very well be doing that and be creating embodied programs in this way... if so, that's cool because I hadn't considered that possibility and I'll be interested to see how you fare. It is going to take a while. Virtual machine writing is very unrewarding programming. I have other things to do right now, I'll get back to the rest of the message in a bit. Will Pearson --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
Re: [agi] WHAT SORT OF HARDWARE $33K AND $850K BUYS TODAY FOR USE IN AGI
Terren:It's to make the larger point that we may be so immersed in our own conceptualizations of intelligence - particularly because we live in our models and draw on our own experience and introspection to elaborate them - that we may have tunnel vision about the possibilities for better or different models. Or, we may take for granted huge swaths of what makes us so smart, because it's so familiar, or below the radar of our conscious awareness, that it doesn't even occur to us to reflect on it. No 2 is more relevant - AI-ers don't seem to introspect much. It's an irony that the way AI-ers think when creating a program bears v. little resemblance to the way programmed computers think. (Matt started to broach this when he talked a while back of computer programming as an art). But AI-ers seem to have no interest in the discrepancy - which again is ironic, because analysing it would surely help them with their programming as well as the small matter of understanding how general intelligence actually works. In fact - I just looked - there is a longstanding field on psychology of programming. But it seems to share the deficiency of psychology and cognitive science generally which is : no study of the stream-of-conscious-thought, especially conscious problemsolving. The only AI figure I know who did take some interest here was Herbert Simon who helped establish the use of verbal protocols. --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
Re: [agi] Paper rec: Complex Systems: Network Thinking
Idem dito. On Mon, Jun 30, 2008 at 10:33 PM, Daniel Allen [EMAIL PROTECTED] wrote: Thanks. I have downloaded the paper and pre-ordered the book. -- *agi* | Archives http://www.listbox.com/member/archive/303/=now http://www.listbox.com/member/archive/rss/303/ | Modifyhttp://www.listbox.com/member/?;Your Subscription http://www.listbox.com --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
Re: Savants and user-interfaces [was Re: [agi] WHAT SORT OF HARDWARE $33K AND $850K BUYS TODAY FOR USE IN AGI
2008/7/1 Vladimir Nesov [EMAIL PROTECTED]: On Tue, Jul 1, 2008 at 10:02 AM, Linas Vepstas [EMAIL PROTECTED] wrote: What are you trying to accomplish here? I don't see where you are trying to go with this. I don't think a human can consciously train one or two neurons to do something, we train millions at a time. -- I'm guessing savants only employ a few tens of million neurons (give or take a few orders of magnitude) -- to do their stuff. Still, an array of 1K by 1K electrodes is well within current technology, we just don't know where to hook this up to, with the exception of simple motor areas, retina, and bit of the auditory circuits. Certainly nothing to do with individual neurons. Basically, it's possible to train a finite state automaton in the mind through association. You see a certain combination of properties, you think the symbol that describes this combination. If such automaton is trained not just to handle natural data (such as language), but to a specifically designed circuit plan, it'll probably be possible to use it as a directly accessible 'add-on' to the brain that implements specific simple function efficiently, such as some operation with numbers using a clever algorithm in a way alien to normal deliberative learning. You don't learn to perform a task, but to execute individual steps of an algorithm that performs a task. Yes, but isn't the interesting case in the other direction? We have ordinary computers that can already do quite well computationally. What we *don't* have a a good man-machine interface. For example, modern disk drives hold more bytes than the human mind can. I don't want to train myself for feats of memorization, I want automatic and instant access to a disk drive. So, perhaps by studying savants who are capable of memorization feats, perhaps we can find the sort of neural circuitry needed to interface to a disk drive. It is, perhaps because savants have these unusual abilities, that it sheds light on the kind of wiring that would be needed for electrodes. --linas --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
[agi] Simple example of the complex systems problem, for those in a hurry
John G. Rose wrote: Could you say that it takes a complex system to know a complex system? If an AGI is going to try to say predict the weather, it doesn't have infinite cpu cycles to simulate so it'll have to come up with something better. Sure it can build a probabilistic historical model but that is kind of cheating. So for it to emulate the weather, I think, or to semi-understand it there has to be some complex systems activity going on there in its cognition. No? I'm not sure that this what Richard is taking about but an AGI is going to bump into complex systems all over the place. Also it will encounter what seems to be complex and later on it may determine that it is not. And perhaps, a key component in the cognition engine in order for it to understand complexity differentials in systems from a relationist standpoint it would need some sort of complexity .. not a comparator but a...sort of harmonic leverage. Can't think of the right words Either way this complexity thing is getting rather annoying because on one hand you think it can drasticly enhance an AGI and is required and on the other hand you think it is unnecessary - I'm not talking about creativity or thought emergence or similar but complexity as integral component in a computational cognition system. There has always been a lot of confusion about what exactly I mean by the complex systems problem (CSP), so let me try, once again, to give a quick example of how it could have an impact on AGI, rather than what the argument is. (One thing to bear in mind is that the complex systems problem is about how researchers and engineers should go about building an AGI. The whole point of the CSP is to say that IF intelligent systems are of a certain sort, THEN it will be impossible to build intelligent systems using today's methodology). What I am going to do is give an example of how the CSP might make an impact on intelligent systems. This is only a made-up example, so try to see it is as just an illustration. Suppose that when evolution was trying to make improvements to the design of simple nervous systems, it hit upon the idea of using mechanisms that I will call concept-builder units, or CB units. The simplest way to understand the CB units is to say that each one is forever locked into a peculiar kind of battle with the other units. The CBs spend a lot of energy engaging in the battle with other CB units, but they also sometimes do other things, like fall asleep (in fact, most of them are asleep at any given moment), or have babies (they spawn new CB units) and sometimes they decide to lock onto a small cluster of other CB units and become obsessed with what those other CBs are doing. So you should get the idea that these CB units take part in what can only be described as organized mayhem. Now, if we were able to look inside a CB system and see what the CBs are doing [Note: we can do this, to a limited extent: it is called introspection], we would notice many aspects of CB behavior that were quite regular and sensible. We would say, for example, that the CB units appear to be representing concepts like [chair] and [upside-down] and [desperation], and we would also say that when some CB units have babies, it looks rather like a couple of existing concepts being combined to form a new concept. In fact, we might notice so many regular, ordered, understandable things happening in the CB-system that we would start to believe that the CB units were not engaging in what I just called organized mayhem at all! We might say that the whole thing was pretty comprehensible and ordered. In fact, we might be tempted to try to build a version of the system in which the behaviors were tidied up and cleaned - a system in which the 'meaning' of each CB unit was precisely defined, and in which the building of new CBs always proceeded in a very precise, understandable way. And then, after we started our project to build a cleaned-up version of a CB system, we would say that all we were doing was eliminating a lot of wasteful noise and inefficiency in the original CB system that was built by evolution. But now, here is a little problem that we have to deal with. It turns out that the CB system built by evolution was functioning *because* of all that chaotic, organized mayhem, *not* in spite of it. It was not really a nice, organized, understandable mechanism plus a bit of noise and wastfulness . it was a mechanism whose proper functioning absolutely depended on a proper balance of those fighting CB units. In fact, the overall intelligence of the system would drop like a stone if some of those mechanisms were taken away. It was like an ecology: all the competing species are in perfect balance, not because they are cooperating so that everyone gets the resources they need, but because nobody is cooperating with anyone else at all. Now, here comes a crucial idea that many
Re: [agi] Approximations of Knowledge
On Mon, Jun 30, 2008 at 8:10 PM, Richard Loosemore [EMAIL PROTECTED] wrote: My scepticism comes mostly from my personal observation that each complex systems scientist I come across tends to know about one breed of complex system, and have a great deal to say about that breed, but when I come to think about my preferred breed (AGI, cognitive systems) I cannot seem to relate their generalizations to my case. That's not very surprising if you think about it. Suppose we postulate the existence of a grand theory of complexity. That's a theory of everything that is not simple (in the sense being discussed here) - but a theory that says something about _every nontrivial thing in the entire Tegmark multiverse_ is rather obviously not going to say very much about any particular thing. --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
RE: [agi] Simple example of the complex systems problem, for those in a hurry
Thanks again Richard for continuing to make your view on this topic clear to those who are curious. As somebody who has tried in good faith and with limited but nonzero success to understand your argument, I have some comments. They are just observations offered with no sarcasm or insult intended. 1) The presentations would be a LOT clearer if you did not always start with Suppose that... and then make up a hypothetical situation. As a reader I don't care about the hypothetical situation, and it is frustrating to be forced into trying to figure out if it is somehow a metaphor for what I *am* interested in, or what exactly the reason behind it is. In this case, if you are actually talking about a theory of how evolution produced a significant chunk of human cognition (a society of CBs), then just say so and lead us to the conclusions about the actual world. If you are not theorizing that the evolution/CBs thing is how human minds work, then I do not see the benefit of walking down the path. Note that the basic CB idea you user here strikes me as a good one; it resonates with things like Minsky's Society of Mind, as well as the intent behind things like Hall's Sigmas and Goertzel's subgraphs. 2) Similarly, when you say if we were able to look inside a CB system and see what the CBs are doing [Note: we can do this, to a limited extent: it is called introspection], we would notice many aspects of CB behavior ... It would be a lot better if you left out the if and the would. Say when we look inside this CB system... and we do notice any aspects... if that is what you mean. If again this is some sort of strange hypothetical universe as a reader I am not very interested in speculations about it. 3) When you say But now, here is a little problem that we have to deal with. It turns out that the CB system built by evolution was functioning *because* of all that chaotic, organized mayhem, *not* in spite of it. Assuming that you are actually talking about human minds instead of a hypothetical universe, this is a very strong statement. It is a theory about human intelligence that needs some support. It is not necessarily a theory about intelligence-in-general; linking it to intelligence in general would be another theory requring support. You may or may not think that intelligence in general is a coherent concept; given your recent statements that there can be no formal definition of intelligence, it's hard to say whether intelligence that is not isomorphic to human intelligence can exist in your view. 4) Regarding: Evolution explored the space of possible intelligent mechanisms. In the course of doing so, it discovered a class of systems that work, but it may well be that the ONLY systems in the whole universe that can function as well as a human intelligence involve a small percentage of weirdness that just balances out to make the system work. There may be no cleaned-up versions that work. The natural response is: sure, this may well be, but it just as easily may well not be. This is addressed in your concluding points, which say that it is not definite, but is very likely. As a reader, I do not see a reason to suppose that this is true. You offer only the circumstantial evidence that AI has failed for 50 years, but there are many other possible reasons for this: - maybe it's just hard. many aspects of the universe took more than 50 years to understand, many are still not understood. i personally think that if this is true we are unlikely to be just a few years from the solution, but it does seem like a reasonable viewpoint. - maybe logic just stinks as a tool for modeling the world. it seemed natural but looking at the things and processes in the human universe logically seems like a pretty poor idea to me. maybe probabilistic logic of one sort or another will help. but the point here is that it might not be a complex systems issue, it might just be a knowledge representation and reasoning issue. perhaps generated or evolved program fragments will fare better; perhaps things that look like neural clusters will work, perhaps we haven't discovered a good way to model the universe yet. - maybe we haven't ripped the kitchen sink out of the wall yet... maybe intelligence will turn out to be a conglomeration of 1543 different representation schemes and reasoning tricks, but we've only put a fraction together so far and therefore only covered a small section of what intelligence needs to do. 5) Of course, the argument would be strengthened by a somewhat detailed suggestion of how AI research *should* proceed; you give some arguments for why certain (unspecified) approaches *might* not work, but nothing beyond the barest hint of what to do about it, which doesn't motivate anybody to give much more than a shrug to your comments. I wonder what it is that you expect people to do in response to
RE: [agi] Simple example of the complex systems problem, for those in a hurry
Oh, one last point: I find your thoughts in this message quite interesting personally because I think that puzzling out exactly what concept builders need to do, and how they might be built to do it, is the most interesting thing in the whole world. I am resistant to the idea that it is impossible because all efforts to do so must be destined to result in insufficient results. I admit to stubbornness on this point, and it will take strong deprogramming to stop me from taking an interest in recipes for the philosophers' stone. --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
RE: [agi] Simple example of the complex systems problem, for those in a hurry
Sorry for three messages in short succession. Regarding concept builders, I have been writing in my bumbling way about this (and will continue to muse on fundamental issues) in my little blog: http://agiblog.net --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
RE: [agi] Simple example of the complex systems problem, for those in a hurry
Well I can spend a lot of time replying this since it is a tough subject. The CB system is a good example my thinking doesn't involve CB's yet so the organized mayhem would be of a different form and I was thinking of the complexity being integrated differently. What you are saying makes sense in terms of evolution finding the right combination. The reliance on the complexity, yes sure, possible. What I think of this system you describe is like if you design a complicated electronic circuit with much theory but little hands-on experience you run into complexity issues from component value deviations and environmental factors that need to be tamed and filtered out before your theoretical electronic emergence comes to life. In that case the result is highly dependent on the interoperating components clean design. BUT there are some circuits I believe, can't think of any offhand, where the opposite is true. It just kind of works based on based on complex subsystems interoperational functionality and it was discovered, not designed intentionally. If the CS problem is such that you describe then there is a serious obstacle. I personally think that getting close to the human brain isn't going to do it. A monkey brain is close. Can we get closer with a simulation? Also I think there are other designs that Earth evolution just didn't get. Those others designs may have the complex reliance. Building a complex based intelligence much different from the human brain design but still basically dependant on complexity is not impossible just formidable. Working with software systems that have designed complexity and getting predicted emergence and in this case cognition, well that is something that takes special talent. We have tools now that nature and evolution didn't have. We understand things through collective knowledge accumulated over time. It can be more than trial and error. And the existing trial and error can be narrowed down. The part that I wonder about is why this complex ingredient is there (if it is). Is it because of the complexity spectrum inherent in nature? Is it fully non-understandable, can it be derived based on nature's complexity structure? Or is there such a computational resource barrier that it is just prohibitively inefficient to calculate. Or are we perhaps using the wrong mathematics to try to understand it? Can it be estimated and does it converge to anything we know of or is it just so randomish and exact. I feel though that the human brain had to evolve though that messy data space of nature and what we have is a momentary semi-reflection of that historical environmental complexity. So our form of intelligence is somewhat optimized for that. And if you take an intersecting subset with other theoretical forms of intelligence would the complexity properties somehow correlate or are they highly dependent on the environment of the evolution? Or does our atomic based universe define what that evolutionary cognitive complexity dependency is. I suppose that is the basis of arguments for or against. John From: Richard Loosemore [mailto:[EMAIL PROTECTED] There has always been a lot of confusion about what exactly I mean by the complex systems problem (CSP), so let me try, once again, to give a quick example of how it could have an impact on AGI, rather than what the argument is. (One thing to bear in mind is that the complex systems problem is about how researchers and engineers should go about building an AGI. The whole point of the CSP is to say that IF intelligent systems are of a certain sort, THEN it will be impossible to build intelligent systems using today's methodology). What I am going to do is give an example of how the CSP might make an impact on intelligent systems. This is only a made-up example, so try to see it is as just an illustration. Suppose that when evolution was trying to make improvements to the design of simple nervous systems, it hit upon the idea of using mechanisms that I will call concept-builder units, or CB units. The simplest way to understand the CB units is to say that each one is forever locked into a peculiar kind of battle with the other units. The CBs spend a lot of energy engaging in the battle with other CB units, but they also sometimes do other things, like fall asleep (in fact, most of them are asleep at any given moment), or have babies (they spawn new CB units) and sometimes they decide to lock onto a small cluster of other CB units and become obsessed with what those other CBs are doing. So you should get the idea that these CB units take part in what can only be described as organized mayhem. Now, if we were able to look inside a CB system and see what the CBs are doing [Note: we can do this, to a limited extent: it is called introspection], we would notice many aspects of CB behavior that were quite regular and sensible. We would say, for example, that the CB units appear to be representing
Re: [agi] Simple example of the complex systems problem, for those in a hurry
Derek Zahn wrote: Thanks again Richard for continuing to make your view on this topic clear to those who are curious. As somebody who has tried in good faith and with limited but nonzero success to understand your argument, I have some comments. They are just observations offered with no sarcasm or insult intended. Thanks for the thoughtful comments. I wonder if it would help if I reiterated that this was supposed to be an illustration of the *manner* in which the CSP is likely to manifest itself, rather than the reasons why we should believe it will manifest itself? In other words, what I was trying to achieve was an illustration of the kind of situation that would arise *if* the argument itself was sound. I was trying to do this because there are many people who misinterpret the argument's supposed impact. In particular, many people assume that what I am saying is that intelligence is a completely emergent property of the human mind ... so drastically emergent that it just springs out of apparent chaos. By attempting to give a more detailed example I was hoping to show that the type of situation that could arise might be very subtle - little obvious evidence of 'complexity' - and yet at the same time quite devastating in its impact. It was that contrast between the small complexity footprint and big kick that I was trying toi bring out. Alas, most of your observations and questions bring us back to the background arguments and reasoning (which is what I was trying to leave out). Let me try to address some of them. 1) The presentations would be a LOT clearer if you did not always start with Suppose that... and then make up a hypothetical situation. As a reader I don't care about the hypothetical situation, and it is frustrating to be forced into trying to figure out if it is somehow a metaphor for what I *am* interested in, or what exactly the reason behind it is. In this case, if you are actually talking about a theory of how evolution produced a significant chunk of human cognition (a society of CBs), then just say so and lead us to the conclusions about the actual world. If you are not theorizing that the evolution/CBs thing is how human minds work, then I do not see the benefit of walking down the path. Note that the basic CB idea you user here strikes me as a good one; it resonates with things like Minsky's Society of Mind, as well as the intent behind things like Hall's Sigmas and Goertzel's subgraphs. My strategy was as follows. (1) Suppose that the human mind is built in such-and-such a way. (2) One consequence of it being that way would be that it would be critically dependent on some mechanisms that give it stability without their contribution being at all obvious. (3) Although this hypothetical mind design is just a guess, it illustrates an entire class of designs that can be very, very different from one another, but which all share the common feature that their stability would be dependent on mechanisms that supply stability without doing so in a way that is understandable. (4) Systems in this general class are, of course, the ones that are called complex, and the reason that I chose a simple example to illustrate the class is that there are many other examples in which it is much harder to see the linkage between global behavior and local mechanisms ... so I was just trying to pick an example where it becomes as easy to comprehend as possible. (5) One thing we know for sure is that the human mind has all the ingredients that normally give rise to complexity of the 'mild' sort shown in this example, and so it would be a truly astonishing fact if the human mind did not, in some way, have some global-local disconnects tucked away somewhere. (6) I do not necessarily believe that the particular type of global-local disconnect that I used in my example is exactly the one that manifests itself in the human mind, but if I avoid specific examples and instead talk in the abstract, people find it very hard to imagine what it might mean to say that a small amount of complexity might make it impossible to build an intelligence as good as the human mind. Unfortunately, my example is a little ambiguous: do I really think this is true in the human case, or is it just a made-up example? Well, it is a little bit of both. I actually think that it could be true, but I am not in a position to claim it to be true. So it is partly a metaphor and partly real. I can see how that might be frustrating from the reader's point of view. My bad. It is important to understand, though, that in creating this hypthetical example I was merely trying to illustrate an abstract concept that would otherwise leave many people perplexed. 2) Similarly, when you say if we were able to look inside a CB system and see what the CBs are doing [Note: we can do this, to a limited extent: it is called introspection], we would notice many aspects
Re: [agi] WHAT SORT OF HARDWARE $33K AND $850K BUYS TODAY FOR USE IN AGI
Will, I think the original issue was about purpose. In your system, since a human is the one determining which programs are performing the best, the purpose is defined in the mind of the human. Beyond that, it certainly sounds as if it is a self-organizing system. Terren --- On Tue, 7/1/08, William Pearson [EMAIL PROTECTED] wrote: I'm not sure how the system qualifies. It seems to be half way between the two definitions you gave. The programs can have special instructions in that bid for a specific resource with as much credit as they want (see my recent message replying to Vladimir Nesov for more information about banks, bidding and credit). The instructions can be removed or not done, the amount of credit bid can be changed. The credit is given to some programs by a fixed function, but they have instructions they can execute (or not) to give it to other programs forming an economy. What say you, self-organised or not? --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
[agi] You Say Po-tay-toe, I Sign Po-toe-tay...
Greetings Fellow Knowledge Workers... WHEN USING GESTURES, RULES OF GRAMMAR REMAIN THE SAME http://www.physorg.com/news134065200.html The link title is a bit misleading. You'll see what I mean when you read it. Enjoy, Brad --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
Re: [agi] WHAT SORT OF HARDWARE $33K AND $850K BUYS TODAY FOR USE IN AGI
Hi Mike, My points about the pitfalls of theorizing about intelligence apply to any and all humans who would attempt it - meaning, it's not necessary to characterize AI folks in one way or another. There are any number of aspects of intelligence we could highlight that pose a challenge to orthodox models of intelligence, but the bigger point is that there are fundamental limits to the ability of an intelligence to observe itself, in exactly the same way that an eye cannot see itself. Consciousness and intelligence are present in every possible act of contemplation, so it is impossible to gain a vantage point of intelligence from outside of it. And that's exactly what we pretend to do when we conceptualize it within an artificial construct. This is the principle conceit of AI, that we can understand intelligence in an objective way, and model it well enough to reproduce by design. Terren --- On Tue, 7/1/08, Mike Tintner [EMAIL PROTECTED] wrote: Terren:It's to make the larger point that we may be so immersed in our own conceptualizations of intelligence - particularly because we live in our models and draw on our own experience and introspection to elaborate them - that we may have tunnel vision about the possibilities for better or different models. Or, we may take for granted huge swaths of what makes us so smart, because it's so familiar, or below the radar of our conscious awareness, that it doesn't even occur to us to reflect on it. No 2 is more relevant - AI-ers don't seem to introspect much. It's an irony that the way AI-ers think when creating a program bears v. little resemblance to the way programmed computers think. (Matt started to broach this when he talked a while back of computer programming as an art). But AI-ers seem to have no interest in the discrepancy - which again is ironic, because analysing it would surely help them with their programming as well as the small matter of understanding how general intelligence actually works. In fact - I just looked - there is a longstanding field on psychology of programming. But it seems to share the deficiency of psychology and cognitive science generally which is : no study of the stream-of-conscious-thought, especially conscious problemsolving. The only AI figure I know who did take some interest here was Herbert Simon who helped establish the use of verbal protocols. --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
Re: [agi] Approximations of Knowledge
Nevertheless, generalities among different instances of complex systems have been identified, see for instance: http://en.wikipedia.org/wiki/Feigenbaum_constants Terren --- On Tue, 7/1/08, Russell Wallace [EMAIL PROTECTED] wrote: [EMAIL PROTECTED] wrote: My scepticism comes mostly from my personal observation that each complex systems scientist I come across tends to know about one breed of complex system, and have a great deal to say about that breed, but when I come to think about my preferred breed (AGI, cognitive systems) I cannot seem to relate their generalizations to my case. That's not very surprising if you think about it. Suppose we postulate the existence of a grand theory of complexity. That's a theory of everything that is not simple (in the sense being discussed here) - but a theory that says something about _every nontrivial thing in the entire Tegmark multiverse_ is rather obviously not going to say very much about any particular thing. --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
RE: [agi] Simple example of the complex systems problem, for those in a hurry
--- On Tue, 7/1/08, John G. Rose [EMAIL PROTECTED] wrote: BUT there are some circuits I believe, can't think of any offhand, where the opposite is true. It just kind of works based on based on complex subsystems interoperational functionality and it was discovered, not designed intentionally. Perhaps you are thinking of this: http://www.damninteresting.com/?p=870 The story of a guy who evolved FPGA's to detect specific audio tones. After 4000 generations, his simple 10 by 10 array of logic gates could perfectly discriminate the tones. But the best part, from the article: Dr. Thompson peered inside his perfect offspring to gain insight into its methods, but what he found inside was baffling. The plucky chip was utilizing only thirty-seven of its one hundred logic gates, and most of them were arranged in a curious collection of feedback loops. Five individual logic cells were functionally disconnected from the rest– with no pathways that would allow them to influence the output– yet when the researcher disabled any one of them the chip lost its ability to discriminate the tones. Furthermore, the final program did not work reliably when it was loaded onto other FPGAs of the same type. Turns out the evolutionary process incorporated electromagnetic field effects unique to that particular FPGA chip. I love this story because it illustrates perfectly what I've been saying about the limitations of design versus the creativity of the evolved approach. Terren --- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
