Jim: I am talking about my own theories now, please try to remain calm: Boris: It's hard, because you're maddeningly vague :). And you can't help being vague, because you don't get that complexity of representation, & all relevant definitions, must be incremental. You can only be explicit if you start from minimal complexity.
Jim: I think that it is important to be able to store or to find the data that represents the basis of a concept or grouping of related concepts in order to resolve some issues that will become apparent as the AGI program learns about the concept or as it relates it to other concepts. However, the program will not be able to store all input that it is exposed to, and this basis has to be derived from, or represent, a collection of variations on the primary subject, so for those reasons the concept has to be composed of generalizations and variations. Boris: Yes, I call those match & miss :). Jim: Are you thinking of storing representations of all primitives that would be used by your program (raw sensory data) so that comparisons might be later made against some of them? Boris: That would be buffering, it's optional for inputs that are pruned-out, - not selected for immediate search. The same cost-benefit analysis applies, but the cost of buffering is a lot lower than that of search. This is done on all levels. Jim: Or are the compressions going to be taken from generalizations of the variations of sensory data that commonly represent a particular event to be gauged? Boris: Generalization is compression. There're all kinds of possible variations, - syntactic complexity of inputs is incremental, & individual variables are pruned just like multi-variable inputs. Jim: Are comparisons going to be made against partial decompressions of previously compressed representations? Boris: This would be a comparison to feedback, & that's only cost-efficient if the feedback is aggregated over all inputs of higher-level search span. I call it evaluation for elevation, rather than comparison. Jim: You don't have to continue if you don't want to, however, I am curious about what you are talking about. Boris: I'd love to continue, as long as we're talking substance :). Jim: I guess you must mind my being rude since you are not able to appreciate the substance of my criticisms. Boris: I think it's the other way around :). It should be obvious to both of us that I am a lot rudder than you. What we disagree on is which one of us doesn't appreciate the substance :). > Boris: > My whole approach is about cognitive economics, I quantify costs & benefits > on the lowest level of representation (& consistently translated on > incremental higher levels). > That's the basis for predictive search pruning, which is what scalability is > all about. Are you saying that you consistently use costs and benefits derived at the lowest level of representation through all incremented higher levels? Boris: Yes, except that "opportunity cost" of utilized computational resources is a feedback from relatively higher levels. Benefit is projected match: current match also adjusted by such downward feedback. And then you are saying that is the basis of pruning searches based on predictions...(of what is being looked for?) Boris: Yes, inputs are forwarded to higher levels if their additive projected match exceeds opportunity cost of thus- expanded search. You're maximizing predictive power of a whole system. I have a lot more details in my intro: http://www.cognitivealgorithm.info/2012/01/cognitive-algorithm.html From: Jim Bromer Sent: Saturday, August 18, 2012 12:41 PM To: AGI Subject: Re: [agi] *Subtraction* is the Engine of Computation I am talking about my own theories now, please try to remain calm: I think that it is important to be able to store or to find the data that represents the basis of a concept or grouping of related concepts in order to resolve some issues that will become apparent as the AGI program learns about the concept or as it relates it to other concepts. However, the program will not be able to store all input that it is exposed to, and this basis has to be derived from, or represent, a collection of variations on the primary subject, so for those reasons the concept has to be composed of generalizations and variations. Are you thinking of storing representations of all primitives that would be used by your program (raw sensory data) so that comparisons might be later made against some of them? Or are the compressions going to be taken from generalizations of the variations of sensory data that commonly represent a particular event to be gauged? Are comparisons going to be made against partial decompressions of previously compressed representations? You don't have to continue if you don't want to, however, I am curious about what you are talking about. Boris: I don't mind "rude", as long as it's interesting. Which it would be if you did address my weak points, but you can't unless you have a stronger alternative. I think the weak points are problems I am currently working on, but I can't explain them if you don't understand those that I already solved. Jim: I guess you must mind my being rude since you are not able to appreciate the substance of my criticisms. So, to repeat what I have already said, sorry. Boris: > My whole approach is about cognitive economics, I quantify costs & benefits > on the lowest level of representation (& consistently translated on > incremental higher levels). > That's the basis for predictive search pruning, which is what scalability is > all about. Are you saying that you consistently use costs and benefits derived at the lowest level of representation through all incremented higher levels? And then you are saying that is the basis of pruning searches based on predictions...(of what is being looked for?) Jim Bromer On Sat, Aug 18, 2012 at 11:29 AM, Boris Kazachenko <bori...@verizon.net> wrote: > Boris: We are a living proof that in our world effective scalability *is* feasible. Jim: I was talking about artificial general intelligence. Boris: The distinction *is* artificial, it's all about algorithms. Jim: I am interested in discovering the weak points of your theory so that I am better able to understand it. Sorry if that is rude. Boris: I don't mind "rude", as long as it's interesting. Which it would be if you did address my weak points, but you can't unless you have a stronger alternative. I think the weak points are problems I am currently working on, but I can't explain them if you don't understand those that I already solved. Jim: I was asking you why you think that your approach to scalability would make your AGI method feasible. I agree that we have to be able to examine the foundations of an (artificial) idea to make a convergence of (artificial) thoughts scalable -in some cases- but I was also saying that the reference to raw sensory data is not generally sufficient for general (artificial) reasoning. Boris: You must've missed this: > I said it's necessary, not sufficient. > My whole approach is about cognitive economics, I quantify costs & benefits on the lowest level of representation (& consistently translated on incremental higher levels). > That's the basis for predictive search pruning, which is what scalability is all about. If you agree with this, show me who else is doing it. If there isn't anyone, then I am a frontrunner. Jim: So you are saying that ontological hierarchy always piggybacks epistemological hierarchy Boris: No, I said nothing of the sort, & in fact it's the reverse: ontological hierarchy is the external reality, epistemological hierarchy is the sequence in which we discover infinitesimal subset of the that, via iterative application of *unsupervised* pattern discovery algorithm, that *always* starts with analog uncompressed data. If it's not analog, then it's already part of our collective epistemological hierarchy. Jim: Aren't you effectively saying that ontological hierarchy has to be reduced to raw sensory data since that has been the basis of your scalability argument? Boris: On the opposite, it's manifested to us via raw sensory data... From: Jim Bromer Sent: Saturday, August 18, 2012 10:14 AM To: AGI Subject: Re: [agi] *Subtraction* is the Engine of Computation Boris: We are a living proof know that in our world effective scalability *is* feasible. Jim: I was talking about artificial general intelligence. I am interested in discovering the weak points of your theory so that I am better able to understand it. Sorry if that is rude. The philosophical issue that I was discussing is whether or not you actually have some evidence - or really good reasons - to think that your approach to AGI will eventually work (without some major advancement in computer science outside of your work.) Your response that "we are living proof..." really did not answer my question in this case. I am not arguing against the possibility of artificial general intelligence but I was asking you why you think that your approach to scalability would make your AGI method feasible. I agree that we have to be able to examine the foundations of an (artificial) idea to make a convergence of (artificial) thoughts scalable -in some cases- but I was also saying that the reference to raw sensory data is not generally sufficient for general (artificial) reasoning. Take a look at what you said in response to my comments: Boris: You are confusing ontological hierarchy, in which we always start from some arbitrary point, & epistemological hierarchy, in which the brain ) civilization of brains *always* starts with analog / un-encoded / uncompressed data. GI is the algorithm of *unsupervised* pattern discovery, supervised education always piggybacks on the former done by prior generations. Jim: So you are saying that ontological hierarchy always piggybacks epistemological hierarchy which (I believe you are saying) is the algorithm of *unsupervised* pattern discovery based on analog uncompressed data. Aren't you effectively saying that ontological hierarchy has to be reduced to raw sensory data since that has been the basis of your scalability argument? I wasn't confusing ontological hierarchy with epistemological hierarchy by the way. The question which is irrelevant to your presentation but relevant to my effort to understand the substance of your presentation is whether or not you realized that I hadn't. if it was a mistake that you made then ok, but if you were misrepresenting my views in order to dismiss my comments then I would discontinue taking that tact with you because I have learned that it is almost hopeless to continue with people who do that. In the one case, you simply misunderstood what I was saying, in the other, you will insist that I am the one who does not understand a foundation of what we are talking about in order to avoid dealing with an issue of relative complexity that no one has solved. Jim Bromer On Sat, Aug 18, 2012 at 9:51 AM, Boris Kazachenko <bori...@verizon.net> wrote: Jim, It would help if you tried to address specific points that I made. > You and I do not need to understand the particle physics of cellular microbiology in order to study an introductory text of biology. And in order to learn what the text is presenting, we do not need to reduce everything mentioned in the text to the order of particle physics... You are confusing ontological hierarchy, in which we always start from some arbitrary point, & epistemological hierarchy, in which the brain ) civilization of brains *always* starts with analog / un-encoded / uncompressed data. GI is the algorithm of *unsupervised* pattern discovery, supervised education always piggybacks on the former done by prior generations. > For example, to really understand what is presented in the biology text we do not need to recall the sensory experience of reading. Sensory experience is how we learn phonemes, alphabet, words, & the concepts behind basic words in the first place. > Also, some scalability issues cannot be resolved just by having the foundations of the subject (or object) handy. I said it's necessary, not sufficient. My whole approach is about cognitive economics, I quantify costs & benefits on the lowest level of representation. That's the basis for predictive search pruning, which is what scalability is all about. > The potential complexity of interrelations (as in derivable interrelations) may make scalability infeasible. We are a living proof know that in our world effective scalability *is* feasible. http://www.cognitivealgorithm.info/2012/01/cognitive-algorithm.html From: Jim Bromer Sent: Saturday, August 18, 2012 8:44 AM To: AGI Subject: Re: [agi] *Subtraction* is the Engine of Computation Boris, You and I do not need to understand the particle physics of cellular microbiology in order to study an introductory text of biology. And in order to learn what the text is presenting, we do not need to reduce everything mentioned in the text to the order of particle physics. So while I agree that we need to go to the basis of knowledge to resolve some scalability issues, and derived knowledge is often based on raw sensory experience, the point that I am trying to make is that the basis of knowledge that we have to use in many scalability scenarios are not raw sensory experience. For example, to really understand what is presented in the biology text we do not need to recall the sensory experience of reading. (I guess it would be nice to be able to do that but it is not necessary for the problem of learning to understand what the text referred to.) So we really do not need to reduce all problems to primitive forms. Also, some scalability issues cannot be resolved just by having the foundations of the subject (or object) handy. The potential complexity of interrelations (as in derivable interrelations) may make scalability infeasible. 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