RE: [agi] WordNet and NARS
Ben said: However, we need to remember that the knowledge in an AGI should be *experientially grounded*. . . . but it needs to turn this knowledge into knowledge by crosslinking a decent fraction of it with perceptual and procedural patterns . . . Can a color-blind man understand yellow? Perhaps not in the same way a normal person can. But he could easily know more about yellow than many. Its wavelength, its history of use in fine arts, its psychological impact, and so on. He could even effectively use yellow in graphics, perhaps with a tool to identify yellow with a special texture. So, even though the color-blind (or an AI entity) never actually sees yellow, he can experience yellow by way of external knowledge. Perhaps the limit to this grounding by knowledge phenomenon is very high. Maybe as Ben says, the grounding can be by procedural patterns. WordNet type knowledge (implemented in a system such as NARS) could be a link to human knowledge. A yellow filter in the sights of a target rifle makes the target-sight image more distinct in low light. While I have never experienced it myself, the book in which I found this information is a standard reference for Olympic caliber competitors. So as a NARS based intelligence, I give this belief f and c values of .99 :-) Kevin Copple To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] What is Thought? Book announcement
On Wed, 21 Jan 2004, Eric Baum wrote: New Book: What is Thought? Eric B. Baum What a great book. --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] What is Thought? Book announcement
Thanks Bill for the Eric Baum reference. Deep thinker that I am, I've just read the book review on Amazon and that has orientated me to some of the key ideas in the book (I hope!) so I'm happy to start speculating without having actually read the book. (See the review below.) It seems that Baum is arguing that biological minds are amazingly quick at making sense of the world because, as a result of evolution, the structure of the brain is set up with inbuilt limitations/assumptions based on likely possibilities in the real world - thus cutting out vast areas for speculative but ultimately fruitless computation - but presumably limiting biological minds' ability to understand phenomena that go beyond common sense that has been structurally summarised by evolved shortcuts. (That must be why non-Newtonian phisics always makes my brain hurt!) I'm sure that most people on the list who are heavily into developing AGIs will have traversed this ground before. But I wondered.. (By the waywhat follows is most likely not of any interest to people well versed in this issue..what I'm doing is feeding back to the list my understanding of this issue in the hope that somebody who knows all this stuff can can tell me if I'm on the right track...so I'm really hoping I can learn something from both my own cogitations and from the feedback others can offer someone still very much in the AGI sandbox.) So here we go..On the face of it, any AGI that is not designed with all these short cuts and assumptions in place has a huge amount of catching up to do to develop (or learn) efficient rules of thumb (heuristics?). Given the flexibility of AGIs and their advantages of computation speed and accuracy, the 4000 million years of evolutionary learning could perhaps be recapitulated in rather less time. But how much less? Would it only take I million years? 100,000 years, 100 years? I'm sure, Ben that you won't want to be sitting around traiing a baby Novamente for that long. Perhaps AGI's need to be structured so that their minds can do two things: - absorb rules of thumb from observations of other players in the world around them (like children picking up ways of thinking from grown ups around them) or utilise rules of thumb that are donated to it via data dumps. - be prepared to and be capable of challenging absorbed rules of thumb and be able to revert to a systematic, relatively unbiased exploration of an issue when rules of thumb turn up anomalous results or when the AGI simply feels curious to go beyond the current rules of thumb Maybe all the databases of common sense relationships that Cyc is developing and the Wordnet database etc. can be considered to be huge sets of inherited rules of thumb ie. they are not derived from the experience of the AGI. The biggest problem for an AGI starting to learn seems to me to be able to simply get to first base whereby the AGI can make *any* sense of its basic sensory input. It seems to me that this is the AGIs hardest task if it doesn't have any built in rules of thumb to orientate it. Maybe an AGI does have to see the world through the lens of inherited rules of thumb in it's first hours and even years in order to boost it's competence at interpreting the world around it and then it can go about replacing inherited rules of thumb with its own grounded self-generated rules of thumb? Maybe it needs to have an inbuilt program a bit like an optical character recognition program that takes each class of incoming data and sifts it into pre-recognised categies of data - ie. patterns can be letters, numbers, colours, shapes, spacial orientation (up, down, left right, forward, back etc,). Once the AGI is used to dealing with these preset categories it could be fed more abiguous data where it has to perhaps invent new categories of its own. Presumably this is all very obvious, but from comments Ben has made over a fair length of time, it seems he's very reluctant to fill an AGI's head full of downloaded data/rules of thum or whatever. Ben, the language you use suggests that you'd be happy to start with none of this downloaded stuff. But it seems to me that an new Novamente would struggle really badly, perhaps floundering endlessly in its effort to interpret incoming data unless it's primed to make some good guesses and to have some preset notions of what to do with this incoming data. It seems to me that a new-born Novamente needs to be able to use lots of preset rules related to its first learning environment so that of the data coming in, a very large amount of it already makes sense at some level so that the AGI can apply most of it's brain power to resolving a few very simple ambiguities - like we do when solving a jigsaw puzzle. It seems to me the key learning experience comes from successfully mastering these very minor areas of ambiguity thus starting to build up some personally
RE: [agi] WordNet and NARS
I agree that not all knowledge in a mind needs to be grounded. However, I think that a mind needs to have a LOT of grounded knowledge, in order to learn to reason usefully. It can then transfer some of the thinking-ability (and some of the concrete relationships) learned on the grounded domains, to help it think about its ungrounded knowledge... ben g -Original Message-From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]On Behalf Of [EMAIL PROTECTED]Sent: Wednesday, February 04, 2004 3:23 AMTo: [EMAIL PROTECTED]Subject: RE: [agi] WordNet and NARS Ben said: However, we need to remember that the knowledge in an AGI should be *experientially grounded*. . . . but it needs to turn this "knowledge" into knowledge by crosslinking a decent fraction of it with perceptual and procedural patterns . . . Can a color-blind man understand yellow? Perhaps not in the same way a normal person can. But he could easily know more about yellow than many. Its wavelength, its history of use in fine arts, its psychological impact, and so on. He could even effectively use yellow in graphics, perhaps with a tool to identify yellow with a special texture. So, even though the color-blind (or an AI entity) never actually sees yellow, he can experience yellow by way of external knowledge. Perhaps the limit to this grounding by knowledge phenomenon is very high. Maybe as Ben says, the grounding can be by procedural patterns. WordNet type knowledge (implemented in a system such as NARS) could be a link to human knowledge. A yellow filter in the sights of a target rifle makes the target-sight image more distinct in low light. While I have never experienced it myself, the book in which I found this information is a standard reference for Olympic caliber competitors. So as a NARS based intelligence, I give this belief f and c values of .99 :-) Kevin Copple To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED] To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
RE: [agi] What is Thought? Book announcement
Philip, I have mixed feelings on this issue (filling an AI mind with knowledge from DB's). I'd prefer to start with a tabula rasa AI and have it learn everything via sensorimotor experience -- and only LATER experiment with feeding DB knowledge directly into its knowledge-store However, for our current software-application work with our partly-done Novamente, we are in fact loading a bunch of knowledge into the Novamente software system and doing Novamente-based inference on it using special narrow-AI-ish control schemata. So it seems likely that -- once the full Novamente AGI design is implemented and we *finally* start with experiential learning experiments-- we will initially experiment with a Novamente that has pre-loaded knowledge in its brain, and draws on this knowledge as appropriate in the course of its experiential learning. If the pre-loaded knowledge winds up not helping, due to its ungrounded nature, then we'll revert to the tabula rasa approach -- Ben G Presumably this is all very obvious, but from comments Ben has made over a fair length of time, it seems he's very reluctant to fill an AGI's head full of downloaded data/rules of thum or whatever. Ben, the language you use suggests that you'd be happy to start with none of this downloaded stuff. But it seems to me that an new Novamente would struggle really badly, perhaps floundering endlessly in its effort to interpret incoming data unless it's primed to make some good guesses and to have some preset notions of what to do with this incoming data. It seems to me that a new-born Novamente needs to be able to use lots of preset rules related to its first learning environment so that of the data coming in, a very large amount of it already makes sense at some level so that the AGI can apply most of it's brain power to resolving a few very simple ambiguities - like we do when solving a jigsaw puzzle. It seems to me the key learning experience comes from successfully mastering these very minor areas of ambiguity thus starting to build up some personally grounded understanding - which can be added to (exponentially?) as the AGI retests the validity of its understanding based on inherited rules of thumb and as it builds a more and more complex picture of what's around it - at each level gaining mastery through resolving minor ambiguities at the new level of understanding. If this model is right then perhaps it shouldn't matter if the AGI has been given a humungous pile of downloaded data/rules of thumb? It would just call on data in the databanks as these seem to be have some useful connection to the data/rules of thumb that the AGI has mastered. Initially the AGI would understand so little that virtually all of the data in it storages would be just so much noise. It would only be able to work it's way into the data as it mastered some initial concepts and concept labels. So in that sense an infant AGI wouldn't be burdened with having too much downloaded ungrounded data - because most of that data would be efectively invisible to it. Isn't this pretty much like a child that has grown up in a house with a huge library, the contents of which only make sense very slowly as the child builds level after level and area after area of base knowledge? Anyway enough for now. If anyone has time for a babe in the sand box I'd love to know what you think of these musings! Cheers, Philip --- (source) What Is Thought? by Eric B. Baum (Author) Publisher: MIT Press; (January 1, 2004) ISBN: 0262025485 Review: In What Is Thought? Eric Baum proposes a computational explanation of thought. Just as Erwin Schr?ger in his classic 1944 work What Is Life? argued ten years before the discovery of DNA that life must be explainable at a fundamental level by physics and chemistry, Baum contends that the present-day inability of computer science to explain thought and meaning is no reason to doubt there can be such an explanation. Baum argues that the complexity of mind is the outcome of evolution, which has built thought processes that act unlike the standard algorithms of computer science and that to understand the mind we need to understand these thought processes and the evolutionary process that produced them in computational terms. Baum proposes that underlying mind is a complex but compact program that corresponds to the underlying structure of the world. He argues further that the mind is essentially programmed by DNA. We learn more rapidly than computer scientists have so far been able to explain because the DNA code has programmed the mind to deal only with meaningful possibilities. Thus the mind understands by exploiting semantics, or meaning, for the purposes of computation; constraints are built in so that
Re: [agi] What is Thought? Book announcement
It seems that Baum is arguing that biological minds are amazingly quick at making sense of the world because, as a result of evolution, the structure of the brain is set up with inbuilt limitations/assumptions based on likely possibilities in the real world - thus cutting out vast areas for speculative but ultimately fruitless computation - but presumably limiting biological minds' ability to understand phenomena that go beyond common sense that has been structurally summarised by evolved shortcuts. . . . Maybe all the databases of common sense relationships that Cyc is developing and the Wordnet database etc. can be considered to be huge sets of inherited rules of thumb ie. . . . Before this discussion gets too far off track, Baum's book talks about inductive biases that are built into human and animal learning by evolution, not the sort of specific knowledge that is coded in Cyc. In fact, Baum discusses Cyc and is pessimistic about its prospects. As examples of inductive biases Baum discusses 3-D structure, causality, language, and cheating detection, with many specific examples from human and animal behavior. He doesn't say we are born knowing about these things, but that our brains are primed for learning about them. He sees reinforcement learning as fundamental to brains, and discusses his Hayek experiments. He also sees evolution, culture and individual brains as three interacting levels of learning. Cheers, Bill --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
RE: [agi] WordNet and NARS
Philip, I think it's important for a mind to master SOME domain (preferably more than one), because advanced and highly effective cognitive schemata are only going to be learned in domains that have been mastered. These cognitive schemata can then be applied in other domains as well, which are understood only to a lesser degree of mastery. And, as you say, in order for the AI to master some domain, it needs a lot of grounded knowledge in that domain. So, I am skeptical that an AI can really think effectively in ANY domain unless it has done a lot of learning based on grounded knowledge in SOME domain first; because I think advanced cognitive schemata will evolve only through learning based on grounded knowledge... -- Ben So the way you describe things seems to fit the domain where an AGI is trying to build mastery but I'm not convinced that the AGI absolutely needs a high level of grounded knowledge in areas where it is not building mastery. But in areas where the AGI is not building or better still has not achieved mastery it should exercise humility and caution and not make any rash decisions that could affect others - because it really doesn't know how sensible its inherited knowledge is. This seems to me to be an area where ethics intersects with mind dvelopment and the use of mind. Cheers, Philip --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED] --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
[agi] Simulation and cognition
Philip, You and I have chatted a bit about the role of simulation in cognition, in the past. I recently had a dialogue on this topic with a colleague (Debbie Duong), which I think was somewhat clarifying. Attached is a message I recently sent to her on the topic. -- ben Debbie, Let's say that a mind observes a bunch of patterns in a system S: P1, P2,...,Pn. Then, suppose the mind wants to predict the degree to which a new pattern, P(n+1), will occur in the system S. There are at least two approaches it can take: 1) reverse engineer a simulation S' of the system, with the property that if the simulation S' runs, it will display patterns P1, P2, ..., Pn. There are many possible simulations S' that will display these patterns, so you pick the simplest one you can find in a reasonable amount of effort. 2) Do probabilistic reasoning based on background knowledge, to derive the probability that P(n+1) will occur, conditional on the occurence of P1,...,Pn My contention is that process 2 (inference) is the default one, with process 1 (simulation) followed only in cases where a) fully understanding the system S is very important to the mind, so that it's worth spending the large amount of effort required to build a simulation of it [inference being much computationally cheaper] b) the system S is very similar to systems that have previously been modeled, so that building a simulation model of S can quickly be done by analogy About the simulation process. Debbie, you call this process simulation; in the Novamente design it's called predicate-driven schema learning, the simulation S' being the a SchemaNode and the conjunction P1 P2 ... Pn being a PredicateNode. We plan to do this simulation-learning using two methods * combinator-BOA, where both the predicate and schema are represented as CombinatorTrees. * analogical inference, modifying existing simulation models to deal with new contexts, as in case b) above If we have a disagreement, perhaps it is just about the relative frequency of processes 1 and 2 in the mind. You seem to think 1 is more frequent whereas I seem to think 2 is much more frequent. I think we both agree that both processes exist. I think that our reasoning about other peoples' actions is generally a mix of 1 and 2. We are much better at simulating other humans than we are at simulating nearly anything else, because we essentially re-use the wiring used to control *ourselves*, in order to simulate others. This re-use of self-wiring for simulation-of-others, as Eliezer Yudkowsky has pointed out, may be largely responsible for the feeling of empathy we get sometimes (i.e., if you're using your self-wiring to simulate someone else, and you simulate someone else's emotions, then due to the use of your self-wiring you're gonna end up feeling their (simulated) emotions to some extent... presto! empathy...). --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
RE: [agi] WordNet and NARS
Hi Ben, So, I am skeptical that an AI can really think effectively in ANY domain unless it has done a lot of learning based on grounded knowledge in SOME domain first; because I think advanced cognitive schemata will evolve only through learning based on grounded knowledge... OK. I think we're getting close to agreement on most of this except what could be the key starting point. My intuition is that, if an AGI is to avoid (an admittedly accelerated) recapitulation of 3500 billion year evolution of functioning mind, it will have to start thinking *first* in one domain using inherited rules of thumb for interpreting data (and it might help to download some initial ungrounded data that otherwise would have had to be accumulated through exposure to its surroundings). Once the infant AGI has some competence using these implanted rules of thumb it can then go through the job of building it's own grounded rules of thumb for data intepretation and substituting them for the rules of thumb provided at the outset by its creators/trainers. So my guess is that the fastest (and still effective) path to learning would be: - *first* a partially grounded experience - *then* a fully grounded mastery - then a mixed learning strategy of grounded and non-grounded as need and oportunity dictates Cheers, Philip --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
RE: [agi] WordNet and NARS
So my guess is that the fastest (and still effective) path to learning would be: - *first* a partially grounded experience - *then* a fully grounded mastery - then a mixed learning strategy of grounded and non-grounded as need and oportunity dictates Cheers, Philip Well, this appears to be the order we're going to do for the Novamente project -- in spite of my feeling that this isn't ideal -- simply due to the way the project is developing via commercial applications of the half-completed system. And, it seems likely that the initial partially grounded experience will largely be in the domain of molecular biology... at least, that's a lot of what our Novamente code is thinking about these days... -- Ben G --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
Re: [agi] Simulation and cognition
Hi Ben, What you said to Debbie Duong sound intuitively right to me. I think that most human intuition would be inferential rather than a simulation. but it seems that higher primates store a huge amount of data on the members of their clan - so my guess is that we do a lot of simulating of the in-group. Maybe your comment about empathy throw intersting light on this. If we simulate our in-group but use crude inferential intuition for most of the outgroup (except favourite enemies that we fixate on!!) then maybe that explains why we have so little empathy for the outgroup (and can so easily treat them abominably). Given that simulation is much more computationally intensive it gives us a really strong reason for emphasising this capacityy in AGIs precisely because they may be able to escape our limitations in this area to great extent. AGIs with strong simulation capacity could therefore be very valuable partners (complementors) for humans. The empathy issue is interesting in the ethical context. We can feel empathy because we can simulate the emotions of others. Maybe the AllSeing AI needs to make an effort to not only simulate the 'thinking of other beings but also their emotions as well. I guess you'd have to do that anyway since emotions affect thinking so strongly in many (most?) beings. Cheers, Philip You and I have chatted a bit about the role of simulation in cognition, in the past. I recently had a dialogue on this topic with a colleague (Debbie Duong), which I think was somewhat clarifying. Attached is a message I recently sent to her on the topic. -- ben Debbie, Let's say that a mind observes a bunch of patterns in a system S: P1, P2,...,Pn. Then, suppose the mind wants to predict the degree to which a new pattern, P(n+1), will occur in the system S. There are at least two approaches it can take: 1) reverse engineer a simulation S' of the system, with the property that if the simulation S' runs, it will display patterns P1, P2, ..., Pn. There are many possible simulations S' that will display these patterns, so you pick the simplest one you can find in a reasonable amount of effort. 2) Do probabilistic reasoning based on background knowledge, to derive the probability that P(n+1) will occur, conditional on the occurence of P1,...,Pn My contention is that process 2 (inference) is the default one, with process 1 (simulation) followed only in cases where a) fully understanding the system S is very important to the mind, so that it's worth spending the large amount of effort required to build a simulation of it [inference being much computationally cheaper] b) the system S is very similar to systems that have previously been modeled, so that building a simulation model of S can quickly be done by analogy About the simulation process. Debbie, you call this process simulation; in the Novamente design it's called predicate-driven schema learning, the simulation S' being the a SchemaNode and the conjunction P1 P2 ... Pn being a PredicateNode. We plan to do this simulation-learning using two methods * combinator-BOA, where both the predicate and schema are represented as CombinatorTrees. * analogical inference, modifying existing simulation models to deal with new contexts, as in case b) above If we have a disagreement, perhaps it is just about the relative frequency of processes 1 and 2 in the mind. You seem to think 1 is more frequent whereas I seem to think 2 is much more frequent. I think we both agree that both processes exist. I think that our reasoning about other peoples' actions is generally a mix of 1 and 2. We are much better at simulating other humans than we are at simulating nearly anything else, because we essentially re-use the wiring used to control *ourselves*, in order to simulate others. This re-use of self-wiring for simulation-of-others, as Eliezer Yudkowsky has pointed out, may be largely responsible for the feeling of empathy we get sometimes (i.e., if you're using your self-wiring to simulate someone else, and you simulate someone else's emotions, then due to the use of your self-wiring you're gonna end up feeling their (simulated) emotions to some extent... presto! empathy...). --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED] --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
RE: [agi] WordNet and NARS
Hi Ben, Well, this appears to be the order we're going to do for the Novamente project -- in spite of my feeling that this isn't ideal -- simply due to the way the project is developing via commercial applications of the half-completed system. And, it seems likely that the initial partially grounded experience will largely be in the domain of molecular biology... at least, that's a lot of what our Novamente code is thinking about these days... The order might be the same but I don't think the initial content will be right - unless you intend to that a conscious Novababy is born into a molecular biology world/sandbox! What were imagining the Novababy's firs simulated or real world would be? A world with a blue square and a sim-self with certain senses and actuators? Or whatever. Then that is the world I think you'll need to help the Novababy understand bu giving it ready-made rules of thumb for interpreting the data generated in that precise world. I'd be inclined to move on to a molecular biology world a little later in Novababy's life! :) Anyway - you can test my conjectures very easily with a bit of experimentation. Cheers, Philip --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
RE: [agi] Simulation and cognition
What you said to Debbie Duong sound intuitively right to me. I think that most human intuition would be inferential rather than a simulation. but it seems that higher primates store a huge amount of data on the members of their clan - so my guess is that we do a lot of simulating of the in-group. Maybe your comment about empathy throw intersting light on this. If we simulate our in-group but use crude inferential intuition for most of the outgroup (except favourite enemies that we fixate on!!) then maybe that explains why we have so little empathy for the outgroup (and can so easily treat them abominably). Good point. And, simulating the in-group is easier for two reasons: 1) in-group members are similar to us, so we can use our self-models as initial guesses for modeling other in-group members ... whereas if we want to model out-group members, we need to do more learning from scratch 2) in-group is often smaller than the out-group: modeling a smaller range of individuals requires less computational effort Again i come to the conclusion that the root of all evil is not money, but rather limitations on compute power... ben --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
RE: [agi] Simulation and cognition
Hi Ben, Maybe we do simulate a *bit* more with out groups than I first thought - but we do it using caricature stereotypes based on *ungrounded* data - ie. we refuse to use grounded data (from our ingroup), perhaps, since that would make these outgroup people uncomfortably too much like us. Cheers, Philip --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
RE: [agi] WordNet and NARS
From: Ben Goertzel [EMAIL PROTECTED] Well, this appears to be the order we're going to do for the Novamente project -- in spite of my feeling that this isn't ideal -- simply due to the way the project is developing via commercial applications of the half-completed system. And, it seems likely that the initial partially grounded experience will largely be in the domain of molecular biology... at least, that's a lot of what our Novamente code is thinking about these days... Hi Ben I'm very interested in applying automation to experimental molecular biology, especially neurobiology. I think it will help neuroscience a lot if complex experiments can be done automatically by AIs, but I'm not sure about letting an AGI reason about molecular biology in an abstract way. Which are you planning on? I'm also curious why you picked this area. YKY Get advanced SPAM filtering on Webmail or POP Mail ... Get Lycos Mail! http://login.mail.lycos.com/r/referral?aid=27005 --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
RE: [agi] WordNet and NARS
Well, this appears to be the order we're going to do for the Novamente project -- in spite of my feeling that this isn't ideal -- simply due to the way the project is developing via commercial applications of the half-completed system. And, it seems likely that the initial partially grounded experience will largely be in the domain of molecular biology... at least, that's a lot of what our Novamente code is thinking about these days... The order might be the same but I don't think the initial content will be right - unless you intend to that a conscious Novababy is born into a molecular biology world/sandbox! That may well be the case... a robotized bio lab as an AGI playroom... we'll see! What were imagining the Novababy's firs simulated or real world would be? A world with a blue square and a sim-self with certain senses and actuators? Or whatever. Then that is the world I think you'll need to help the Novababy understand bu giving it ready-made rules of thumb for interpreting the data generated in that precise world. I think that in an environment in which the system has decent sensors and actuators, no pre-specified rules of thumb will be needed (though some perceptual preprocessing routines will be needed, just as the human visual and acoustic cortex supply ...). Pre-specified rules are useful for domains where the system's ability to perceive and act are more limited. I'd be inclined to move on to a molecular biology world a little later in Novababy's life! Well, we're already applying the incomplete AI system to molecular biology in more limited, narrow-AI-ish ways, that was my point... ben --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
