Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
This are just some controversial tips/inspirations: Warning: Don't read it if you do not believe that sensory and AGI go together or if you are skeptical. Just ignore it. What to detect? detect inregulaties and store them analysis complexity structure evolution memorization is about memorising the gist, or parts of visual aspects and some spatial aspects, etc., but no sense completely memorize all of it you do not memorize the locations of the objects. you memorize the parts or gist taxonomies classify using subjectively important parts of an object. *subjectively significant* subjective unimportant qualities are unclassified complexity is a product of two factor-independent symbols complexity is the incompatibility between input and output for example, random black and white dots is considered nonrandom because our senses overabstracts the dots as one unit if the symbols have a constraint relation, they can be reduced to a simpler structure using factor analysis factor analysis analyze the factors of a trait. the more ad hoc a trait, the more likely it would be overriden by a general ability evolution prefers traits that generalize many structures knowledge cannot be appraised nor ignored. you must know everything about a topic before you judge taxonomies do not holistic taxonomies are just as leaky as abstractions efficiency is achieved using minimization of structures. if an attribute has changed, then would reorder minimization innovation would do that called creative destruction evolution creates traits that generalize, which cannot be perfect risk is the lack of knowledge. these are minimized by genetic heuristics language are taxonomies there exist empty taxonomies which are contradictions genetic irregulaties language is a different classification system than logic, which is incompatible to each other this difference produces contradictions and ambiguities a contradiction is a partial map between languages an ambiguity is a surjective function entrophy is a subset of complexity it is the information required to convert a surjective function to a bijective function, avoid contradiction the division of knowledge minimizes risk from lacking critical and revolutionary knowledge latency occurs in social interactions the fastest way is to think individually the law of unintended consequences is the product of appraising unforseen knowledge prohibition would let users choose another negative motivations are hard coded, in order to prevent alternatives positive motivations are also hard coded, but not to a high extent, because alternatives are inevitable if there are only negative according to the 100,000 human population it is impossible to hard code all negative motivations. Therefore, a Pavlovian learning capability is replaced However, the pavlovian capability requires learning, perhaps risky Therefore parents would teach them a type of imitation learning develops positive motivations are more likely learned than hard also prominent negative motivations are preserved distributed systems are like division of labor the more labor intensive a task is, the more likely that an individual would not count. The more predictable the labor is. So labor intensive industries would have to maximize efficiency. It is more likely that an invention would help thinking collectively produces latency. Thinking collectively distracts concentration, because humans cannot multitask the faults of abstraction can be found using factor analysis to be motivated to learn, you need positive motivations such as rewarding unexpected things and imitation learning irregularities our nonconscious memory is huge you do not feel pain when sacrificing. laissez-faire is optimal if there is infinite preference most of our daily actions are unconscious doing something you dislike doing is internal conflict between two virtual opposing individuals you automatically pattern recognize emotionally fulfilling parts and do it. that is called motivation emotion is concentration exception recognization entails negative priming from subconscious memory negative priming is an adaptation boredom and interesting is motivation using negative priming of common or boring pavlovian learning does not require object detection. it is subconscious using implicit occurances to associate negative stimuli with it, because explicit pavlovian conditioning is inhibited by inhibition pavlovian learning is automatic association pavlovian/unconditioned learning is automatic, requiring no motivational system subconscious learning requires no motivational system emotion is perpetual concentration automatic emotion recognizers subconsciously recognize emotional effects the emotion recognizers are always searching for emotional rewarding parts emotional pattern recognizers subconscious motor conditioning emotion induced selective focus and negative prime motor learning is
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
Hello Boris, and welcome to the list. I didn't understand your algorithm, you use many terms that you didn't define. It probably would be clearer if you use some kind of pseudocode and systematically describe all occurring procedures. But I think more fundamental questions that need clarifying won't depend on these. What is it that your system tries to predict? Does it predict only specific terminal inputs, values on the ends of its sensors? Or something else? When does prediction occur? What is this prediction for? How does it help? How does the system use it? What use is this ability of the system for us? -- Vladimir Nesov [EMAIL PROTECTED] --- 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
Although I symphathize with some of Hawkin's general ideas about unsupervised learning, his current HTM framework is unimpressive in comparison with state-of-the-art techniques such as Hinton's RBM's, LeCun's convolutional nets and the promising low-entropy coding variants. But it should be quite clear that such methods could eventually be very handy for AGI. For example, many of you would agree that a reliable, computationally affordable solution to Vision is a crucial factor for AGI: much of the world's information, even on the internet, is encoded in audiovisual information. Extracting (sub)symbolic semantics from these sources would open a world of learning data to symbolic systems. An audiovisual perception layer generates semantic interpretation on the (sub)symbolic level. How could a symbolic engine ever reason about the real world without access to such information? Vision may be classified under Narrow AI, but I reckon that an AGI can never understand our physical world without a reliable perceptual system. Therefore, perception is essential for any AGI reasoning about physical entities! Greets, Durk On Sun, Mar 30, 2008 at 4:34 PM, Derek Zahn [EMAIL PROTECTED] wrote: It seems like a reasonable and not uncommon idea that an AI could be built as a mostly-hierarchical autoassiciative memory. As you point out, it's not so different from Hawkins's ideas. Neighboring pixels will correlate in space and time; features such as edges should become principle components given enough data, and so on. There is a bunch of such work on self-organizing the early visual system like this. That overall concept doesn't get you very far though; the trick is to make it work past the first few rather obvious feature extraction stages of sensory data, and to account for things like episodic memory, language use, goal-directed behavior, and all other cognitive activity that is not just statistical categorization. I sympathize with your approach and wish you luck. If you think you have something that produce more than Hawkins has with his HTM, please explain it with enough precision that we can understand the details. -- *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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
On Sun, Mar 30, 2008 at 7:23 PM, Kingma, D.P. [EMAIL PROTECTED] wrote: Although I symphathize with some of Hawkin's general ideas about unsupervised learning, his current HTM framework is unimpressive in comparison with state-of-the-art techniques such as Hinton's RBM's, LeCun's convolutional nets and the promising low-entropy coding variants. But it should be quite clear that such methods could eventually be very handy for AGI. For example, many of you would agree that a reliable, computationally affordable solution to Vision is a crucial factor for AGI: much of the world's information, even on the internet, is encoded in audiovisual information. Extracting (sub)symbolic semantics from these sources would open a world of learning data to symbolic systems. An audiovisual perception layer generates semantic interpretation on the (sub)symbolic level. How could a symbolic engine ever reason about the real world without access to such information? Vision may be classified under Narrow AI, but I reckon that an AGI can never understand our physical world without a reliable perceptual system. Therefore, perception is essential for any AGI reasoning about physical entities! At this point I think that although vision doesn't seem absolutely necessary, it may as well be implemented, if it can run on the same substrate as everything else (it probably can). It may prove to be a good playground for prototyping. If it's implemented with moving fovea (which is essentially what LeCun's hack is about) and relies on selective attention (so that only gist of the scene is perceived, read supported on higher levels), it shouldn't require insanely much resources, compared to the rest of reasoning engine. Alas in this picture I give up my previous assessment (of few months back) that reasoning can be implemented efficiently, so that only few active concepts need to figure into computation each tact. In my current model all concepts compute all the time... -- Vladimir Nesov [EMAIL PROTECTED] --- 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
RE: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
[EMAIL PROTECTED] writes: But it should be quite clear that such methods could eventually be very handy for AGI. I agree with your post 100%, this type of approach is the most interesting AGI-related stuff to me. An audiovisual perception layer generates semantic interpretation on the (sub)symbolic level. How could a symbolic engine ever reason about the real world without access to such information? Even more interesting: How could a symbolic engine ever reason about the real world *with* access to such information? :) --- 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
On 30/03/2008, Kingma, D.P. [EMAIL PROTECTED] wrote: Although I symphathize with some of Hawkin's general ideas about unsupervised learning, his current HTM framework is unimpressive in comparison with state-of-the-art techniques such as Hinton's RBM's, LeCun's convolutional nets and the promising low-entropy coding variants. But it should be quite clear that such methods could eventually be very handy for AGI. For example, many of you would agree that a reliable, computationally affordable solution to Vision is a crucial factor for AGI: much of the world's information, even on the internet, is encoded in audiovisual information. Extracting (sub)symbolic semantics from these sources would open a world of learning data to symbolic systems. An audiovisual perception layer generates semantic interpretation on the (sub)symbolic level. How could a symbolic engine ever reason about the real world without access to such information? So a deafblind person couldn't reason about the real world? Put ear muffs and a blind fold on, see what you can figure out about the world around you. Less certainly, but then you could figure out more about the world if you had magnetic sense like pidgeons. Intelligence is not about the modalities of the data you get, it is about the what you do with the data you do get. All of the data on the web is encoded in electronic form, it is only because of our comfort with incoming photons and phonons that it is translated to video and sound. This fascination with A/V is useful, but does not help us figure out the core issues that are holding us up whilst trying to create AGI. 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
Derek: How could a symbolic engine ever reason about the real world *with*
access to such information?
I hope my work eventually demonstrates a solution to your satisfaction. In the
meantime there is evidence from robotics, specifically driverless cars, that
real world sensor input can be sufficiently combined and abstracted for use by
symbolic route planners.
-Steve
Stephen L. Reed
Artificial Intelligence Researcher
http://texai.org/blog
http://texai.org
3008 Oak Crest Ave.
Austin, Texas, USA 78704
512.791.7860
- Original Message
From: Derek Zahn [EMAIL PROTECTED]
To: agi@v2.listbox.com
Sent: Sunday, March 30, 2008 11:21:52 AM
Subject: RE: [agi] Intelligence: a pattern discovery algorithm of scalable
complexity.
.hmmessage P { margin:0px;padding:0px;} body.hmmessage {
FONT-SIZE:10pt;FONT-FAMILY:Tahoma;} [EMAIL PROTECTED] writes:
But it should be quite clear that such methods could eventually be very
handy for AGI.
I agree with your post 100%, this type of approach is the most interesting
AGI-related stuff to me.
An audiovisual perception layer generates semantic interpretation on the
(sub)symbolic level. How could a symbolic engine ever reason about the real
world without access to such information?
Even more interesting: How could a symbolic engine ever reason about the real
world *with* access to such information? :)
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Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
Durk, Absolutely right about the need for what is essentially an imaginative level of mind. But wrong in thinking: Vision may be classified under Narrow AI You seem to be treating this extra audiovisual perception layer as a purely passive layer. The latest psychology philosophy recognize that this is in fact a level of v. active thought and intelligence. And our culture is only starting to understand imaginative thought generally. Just to begin reorienting your thinking here, I suggest you consider how much time people spend on audiovisual information (esp. tv) vs purely symbolic information (books). And allow for how much and how rapidly even academic thinking is going audiovisual. Know of anyone trying to give computers that extra layer? I saw some vague reference about this recently.of which I have only a confused memory. Durk:Although I symphathize with some of Hawkin's general ideas about unsupervised learning, his current HTM framework is unimpressive in comparison with state-of-the-art techniques such as Hinton's RBM's, LeCun's convolutional nets and the promising low-entropy coding variants. But it should be quite clear that such methods could eventually be very handy for AGI. For example, many of you would agree that a reliable, computationally affordable solution to Vision is a crucial factor for AGI: much of the world's information, even on the internet, is encoded in audiovisual information. Extracting (sub)symbolic semantics from these sources would open a world of learning data to symbolic systems. An audiovisual perception layer generates semantic interpretation on the (sub)symbolic level. How could a symbolic engine ever reason about the real world without access to such information? Vision may be classified under Narrow AI, but I reckon that an AGI can never understand our physical world without a reliable perceptual system. Therefore, perception is essential for any AGI reasoning about physical entities! Greets, Durk On Sun, Mar 30, 2008 at 4:34 PM, Derek Zahn [EMAIL PROTECTED] wrote: It seems like a reasonable and not uncommon idea that an AI could be built as a mostly-hierarchical autoassiciative memory. As you point out, it's not so different from Hawkins's ideas. Neighboring pixels will correlate in space and time; features such as edges should become principle components given enough data, and so on. There is a bunch of such work on self-organizing the early visual system like this. That overall concept doesn't get you very far though; the trick is to make it work past the first few rather obvious feature extraction stages of sensory data, and to account for things like episodic memory, language use, goal-directed behavior, and all other cognitive activity that is not just statistical categorization. I sympathize with your approach and wish you luck. If you think you have something that produce more than Hawkins has with his HTM, please explain it with enough precision that we can understand the details. agi | Archives | Modify Your Subscription -- agi | Archives | Modify Your Subscription -- No virus found in this incoming message. Checked by AVG. Version: 7.5.519 / Virus Database: 269.22.1/1349 - Release Date: 3/29/2008 5:02 PM --- 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
RE: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
Stephen Reed writes: How could a symbolic engine ever reason about the real world *with* access to such information? I hope my work eventually demonstrates a solution to your satisfaction. Me too! In the meantime there is evidence from robotics, specifically driverless cars, that real world sensor input can be sufficiently combined and abstracted for use by symbolic route planners. True enough, that is one answer: by hand-crafting the symbols and the mechanics for instantiating them from subsymbolic structures. We of course hope for better than this but perhaps generalizing these working systems is a practical approach. --- 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
Mike, you seem to have misinterpreted my statement. Perception is certainly not 'passive', as it can be described as active inference using a (mostly actively) learned world model. Inference is done on many levels, and could integrate information from various abstraction levels, so I don't see it as an isolated layer. On Sun, Mar 30, 2008 at 6:27 PM, Mike Tintner [EMAIL PROTECTED] wrote: Durk, Absolutely right about the need for what is essentially an imaginative level of mind. But wrong in thinking: Vision may be classified under Narrow AI You seem to be treating this extra audiovisual perception layer as a purely passive layer. The latest psychology philosophy recognize that this is in fact a level of v. active thought and intelligence. And our culture is only starting to understand imaginative thought generally. Just to begin reorienting your thinking here, I suggest you consider how much time people spend on audiovisual information (esp. tv) vs purely symbolic information (books). And allow for how much and how rapidly even academic thinking is going audiovisual. Know of anyone trying to give computers that extra layer? I saw some vague reference about this recently.of which I have only a confused memory. Durk:Although I symphathize with some of Hawkin's general ideas about unsupervised learning, his current HTM framework is unimpressive in comparison with state-of-the-art techniques such as Hinton's RBM's, LeCun's convolutional nets and the promising low-entropy coding variants. But it should be quite clear that such methods could eventually be very handy for AGI. For example, many of you would agree that a reliable, computationally affordable solution to Vision is a crucial factor for AGI: much of the world's information, even on the internet, is encoded in audiovisual information. Extracting (sub)symbolic semantics from these sources would open a world of learning data to symbolic systems. An audiovisual perception layer generates semantic interpretation on the (sub)symbolic level. How could a symbolic engine ever reason about the real world without access to such information? Vision may be classified under Narrow AI, but I reckon that an AGI can never understand our physical world without a reliable perceptual system. Therefore, perception is essential for any AGI reasoning about physical entities! Greets, Durk On Sun, Mar 30, 2008 at 4:34 PM, Derek Zahn [EMAIL PROTECTED] wrote: It seems like a reasonable and not uncommon idea that an AI could be built as a mostly-hierarchical autoassiciative memory. As you point out, it's not so different from Hawkins's ideas. Neighboring pixels will correlate in space and time; features such as edges should become principle components given enough data, and so on. There is a bunch of such work on self-organizing the early visual system like this. That overall concept doesn't get you very far though; the trick is to make it work past the first few rather obvious feature extraction stages of sensory data, and to account for things like episodic memory, language use, goal-directed behavior, and all other cognitive activity that is not just statistical categorization. I sympathize with your approach and wish you luck. If you think you have something that produce more than Hawkins has with his HTM, please explain it with enough precision that we can understand the details. -- *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 http://www.listbox.com/member/archive/rss/303/ | Modifyhttp://www.listbox.com/member/?;Your Subscription http://www.listbox.com -- No virus found in this incoming message. Checked by AVG. Version: 7.5.519 / Virus Database: 269.22.1/1349 - Release Date: 3/29/2008 5:02 PM -- *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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
On Sun, Mar 30, 2008 at 6:48 PM, William Pearson [EMAIL PROTECTED] wrote: On 30/03/2008, Kingma, D.P. [EMAIL PROTECTED] wrote: An audiovisual perception layer generates semantic interpretation on the (sub)symbolic level. How could a symbolic engine ever reason about the real world without access to such information? So a deafblind person couldn't reason about the real world? Put ear muffs and a blind fold on, see what you can figure out about the world around you. Less certainly, but then you could figure out more about the world if you had magnetic sense like pidgeons. Intelligence is not about the modalities of the data you get, it is about the what you do with the data you do get. All of the data on the web is encoded in electronic form, it is only because of our comfort with incoming photons and phonons that it is translated to video and sound. This fascination with A/V is useful, but does not help us figure out the core issues that are holding us up whilst trying to create AGI. Will Pearson Intelligence is not *only* about the modalities of the data you get, but modalities are certainly important. A deafblind person can still learn a lot about the world with taste, smell, and touch, but the senses one has access to defines the limits to the world model one can build. If I put on ear muffs and a blind fold right now, I can still reason quite well using touch, since I have access to a world model build using e.g. vision. If you were deafblind and paralysed since your birth, would you have any possibility of spatial reasoning? No, maybe except for some extremely crude genetically coded heuristics. Sure, you could argue that an intelligence purely based on text, disconnected from the physical world, could be intelligent, but it would have a very hard time reasoning about interaction of entities in the physicial world. It would be unable to understand humans in many aspects: I wouldn't call that generally intelligent. Perception is a about learning and using a model of our physical world. Input is often high-bandwidth, while output is often low-bandwidth and useful for high-level processing (e.g. reasining and memory). Luckily, efficient methods are arising, so I'm quite optimistic about progress towards this aspect of intelligence. --- 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
On Sun, Mar 30, 2008 at 10:16 PM, Kingma, D.P. [EMAIL PROTECTED] wrote: Intelligence is not *only* about the modalities of the data you get, but modalities are certainly important. A deafblind person can still learn a lot about the world with taste, smell, and touch, but the senses one has access to defines the limits to the world model one can build. If I put on ear muffs and a blind fold right now, I can still reason quite well using touch, since I have access to a world model build using e.g. vision. If you were deafblind and paralysed since your birth, would you have any possibility of spatial reasoning? No, maybe except for some extremely crude genetically coded heuristics. Sure, you could argue that an intelligence purely based on text, disconnected from the physical world, could be intelligent, but it would have a very hard time reasoning about interaction of entities in the physicial world. It would be unable to understand humans in many aspects: I wouldn't call that generally intelligent. Perception is a about learning and using a model of our physical world. Input is often high-bandwidth, while output is often low-bandwidth and useful for high-level processing (e.g. reasining and memory). Luckily, efficient methods are arising, so I'm quite optimistic about progress towards this aspect of intelligence. One of the requirements that I try to satisfy with my design is ability to equivalently perceive information encoded by seemingly incompatible modalities. For example, visual stream can be encoded using a set of pairs tag,color, where tags are unique labels that correspond to positions of pixels. This set of pairs can be shuffled and supplied using serial input (where tags and colors are encoded as binary words of activation), and system must be able to reconstruct representation as good as that supplied by naturally arranged video input. Of course getting to that point requires careful incremental teaching, but after that there should be no real difference (aside from bandwidth, of course). It might be useful to look at all concepts as 'modalities': you can 'see' your thoughts, when you know a certain theory, you can 'see' how it's applied, how its parts interact, what obvious conclusions are. Prewiring sensory input in a certain way merely pushes learning in certain direction, just like inbuilt drives bias action in theirs. This way, for example, it should be possible to teach a 'modality' for understanding simple graphs encoded as text, so that on one hand text-based input is sufficient, and on the other hand system effectively perceives simple vector graphics. This trick can be used to explain spacial concepts from natural language. But, again, video camera might be a simpler and more powerful way to the same end, even if visual processing is severely limited. -- Vladimir Nesov [EMAIL PROTECTED] --- 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
Vladimir, I agree with you on many issues, but... On Sun, Mar 30, 2008 at 9:03 PM, Vladimir Nesov [EMAIL PROTECTED] wrote: This way, for example, it should be possible to teach a 'modality' for understanding simple graphs encoded as text, so that on one hand text-based input is sufficient, and on the other hand system effectively perceives simple vector graphics. This trick can be used to explain spacial concepts from natural language. But, again, video camera might be a simpler and more powerful way to the same end, even if visual processing is severely limited. Vector graphics can indeed be communicated to an AGI by relatively low-bandwidth textual input. But, unfortunately, the physical world is not made of vector graphics, so reducing the physical world to vector graphics is quite lossy (and computationally expensive an sich). I'm not sure whether you're assuming that vector graphics is very useful for AGI, but I would disagree. Prewiring sensory input in a certain way merely pushes learning in certain direction, just like inbuilt drives bias action in theirs. Who said perception needs to be prewired? Perception should be made efficient by exploiting statistical regularities in the data, not assuming them per se. Regularities in the data (captured by your world model) should tell you where to focus your attention on *most* of the time, not *all* the time ;) --- 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
On Sun, Mar 30, 2008 at 11:33 PM, Kingma, D.P. [EMAIL PROTECTED] wrote: Vector graphics can indeed be communicated to an AGI by relatively low-bandwidth textual input. But, unfortunately, the physical world is not made of vector graphics, so reducing the physical world to vector graphics is quite lossy (and computationally expensive an sich). I'm not sure whether you're assuming that vector graphics is very useful for AGI, but I would disagree. I referred to manually providing explanation in conversational format. Of course it's lossy, but whether it's lossy compared to the real world is not an issue, it's much more important how it compares to 'gist' scheme that we extract from full vision. It's clearly not much. Vision allows to attend to any of huge number of details present in the input, but there are only few details seen at a time. When a specific issue needs a spacial explanation, it can be carried out by explicitly specifying its structure in vector graphics. Prewiring sensory input in a certain way merely pushes learning in certain direction, just like inbuilt drives bias action in theirs. Who said perception needs to be prewired? Perception should be made efficient by exploiting statistical regularities in the data, not assuming them per se. Regularities in the data (captured by your world model) should tell you where to focus your attention on *most* of the time, not *all* the time ;) By prewiring I meant a trivial level, like routing signals from the retina to certain places in the brain, from the start suggesting that nearby pixels on the retina are close together, and making temporal synchrony of signals to be approximately the same as in image on the retina. Bad prewiring would consist in sticking signals from pixels on the retina to random parts of the brain, with random delays. It would take much more effort to acquire good visual perception in this case (and would be impossible on brain wetware). -- Vladimir Nesov [EMAIL PROTECTED] --- 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
Alright, agreed with all you say. If I understood correctly, your system (at the moment) assumes scene descriptions at a level higher than pixels, but certainly lower than objects. An application of such system seems be a simulated, virtual world where such descriptions are at hand... Is this indeed the direction you're going? Greets, Durk On Sun, Mar 30, 2008 at 10:00 PM, Vladimir Nesov [EMAIL PROTECTED] wrote: On Sun, Mar 30, 2008 at 11:33 PM, Kingma, D.P. [EMAIL PROTECTED] wrote: Vector graphics can indeed be communicated to an AGI by relatively low-bandwidth textual input. But, unfortunately, the physical world is not made of vector graphics, so reducing the physical world to vector graphics is quite lossy (and computationally expensive an sich). I'm not sure whether you're assuming that vector graphics is very useful for AGI, but I would disagree. I referred to manually providing explanation in conversational format. Of course it's lossy, but whether it's lossy compared to the real world is not an issue, it's much more important how it compares to 'gist' scheme that we extract from full vision. It's clearly not much. Vision allows to attend to any of huge number of details present in the input, but there are only few details seen at a time. When a specific issue needs a spacial explanation, it can be carried out by explicitly specifying its structure in vector graphics. Prewiring sensory input in a certain way merely pushes learning in certain direction, just like inbuilt drives bias action in theirs. Who said perception needs to be prewired? Perception should be made efficient by exploiting statistical regularities in the data, not assuming them per se. Regularities in the data (captured by your world model) should tell you where to focus your attention on *most* of the time, not *all* the time ;) By prewiring I meant a trivial level, like routing signals from the retina to certain places in the brain, from the start suggesting that nearby pixels on the retina are close together, and making temporal synchrony of signals to be approximately the same as in image on the retina. Bad prewiring would consist in sticking signals from pixels on the retina to random parts of the brain, with random delays. It would take much more effort to acquire good visual perception in this case (and would be impossible on brain wetware). -- Vladimir Nesov [EMAIL PROTECTED] --- 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
On Mon, Mar 31, 2008 at 12:21 AM, Kingma, D.P. [EMAIL PROTECTED] wrote: Alright, agreed with all you say. If I understood correctly, your system (at the moment) assumes scene descriptions at a level higher than pixels, but certainly lower than objects. An application of such system seems be a simulated, virtual world where such descriptions are at hand... Is this indeed the direction you're going? I'm far from dealing with high-level stuff, so it's only in design. Vector graphics that I talked about was supposed to be provided manually by a human. For example, it can be a part of explanation of what 'between' word is about. Alternatively a kind of sketchpad can be used. My point is that 'modality' seems to be a learnable thing, that can be stimulated not only by direct sensory input, but also by learned inferences coming from completely different modalities. -- Vladimir Nesov [EMAIL PROTECTED] --- 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
True enough, that is one answer: by hand-crafting the symbols and the mechanics for instantiating them from subsymbolic structures. We of course hope for better than this but perhaps generalizing these working systems is a practical approach. Um. That is what is known as the grounding problem. I'm sure that Richard Loosemore would be more than happy to send references explaining why this is not productive. --- 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
From: Kingma, D.P. [EMAIL PROTECTED] Sure, you could argue that an intelligence purely based on text, disconnected from the physical world, could be intelligent, but it would have a very hard time reasoning about interaction of entities in the physicial world. It would be unable to understand humans in many aspects: I wouldn't call that generally intelligent. Given sufficient bandwidth, why would it have a hard time reasoning about interaction of entities? You could describe vision down to the pixel, hearing down to the pitch and decibel, touch down to the sensation, etc. and the system could internally convert it to exactly what a human feels. You could explain to it all the known theories of psychology and give it the personal interactions of billions of people. Sure, that's a huge amount of bandwidth, but it proves that your statement is inaccurate. --- 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
From: Kingma, D.P. [EMAIL PROTECTED] Vector graphics can indeed be communicated to an AGI by relatively low-bandwidth textual input. But, unfortunately, the physical world is not made of vector graphics, so reducing the physical world to vector graphics is quite lossy (and computationally expensive an sich). Huh? Intelligence is based upon lossyness and the ability to lose rarely relevant (probably incorrect) outlier information is frequently the key to making problems tractable (though it can also set you up for failure when you miss a phase transition by mistaking it for just an odd outlier :-) since it forms the basis of discovery by analogy. Matt Mahoney's failure to recognize this has him trapped in *exact* compression hell.;-) Who said perception needs to be prewired? Perception should be made efficient by exploiting statistical regularities in the data, not assuming them per se. Regularities in the data (captured by your world model) should tell you where to focus your attention on *most* of the time, not *all* the time ;) Which is the correct answer to the grounding problem. Thank you. --- 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
RE: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
Mark Waser writes: True enough, that is one answer: by hand-crafting the symbols and the mechanics for instantiating them from subsymbolic structures. We of course hope for better than this but perhaps generalizing these working systems is a practical approach. Um. That is what is known as the grounding problem. I'm sure that Richard Loosemore would be more than happy to send references explaining why this is not productive. It's not the grounding problem. The symbols crashing around inthese robotic systems are very well grounded. The problem is that these systems are narrow, not that they manipulateungrounded symbols. --- 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
On 30/03/2008, Kingma, D.P. [EMAIL PROTECTED] wrote: Intelligence is not *only* about the modalities of the data you get, but modalities are certainly important. A deafblind person can still learn a lot about the world with taste, smell, and touch, but the senses one has access to defines the limits to the world model one can build. As long as you have one high bandwidth modality you should be able to add on technological gizmos to convert information to that modality, and thus be able to model the phenomenon from that part of the world. Humans manage to convert modalities E.g. http://www.engadget.com/2006/04/25/the-brain-port-neural-tongue-interface-of-the-future/ Using touch on the tongue. We don't do it very well, but that is mainly because we don't have to do it it very often. AIs that are designed to have new modalities added to them, using their major modality of their memory space+interrupts (or other computational modality), may be even more flexible than humans and able to adapt to to a new modality as quickly as a current computer is able to add a new device. If I put on ear muffs and a blind fold right now, I can still reason quite well using touch, since I have access to a world model build using e.g. vision. If you were deafblind and paralysed since your birth, would you have any possibility of spatial reasoning? No, maybe except for some extremely crude genetically coded heuristics. Sure if you don't get any spatial information you won't be able to model spatially. But getting the information is different from having a dedicated modality. My point was that audiovisual is not the only way to get spatial information. It may not even be the best way for what we happen to want to do. So not to get too hung up on any specific modality when discussing intelligence. Sure, you could argue that an intelligence purely based on text, disconnected from the physical world, could be intelligent, but it would have a very hard time reasoning about interaction of entities in the physicial world. It would be unable to understand humans in many aspects: I wouldn't call that generally intelligent. I'm not so much interested in this case, but what about the case where you have a robot with Sonar, Radar and other sensors. But not the normal 2 camera +2 microphone thing people imply when they say audiovisual. 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
Okay, with text, I mean natural language, in it's usual low-bandwidth form. That should clarify my statement. Any data can be represented with text of course, but that's not the point... The point that I was trying to make is that natural language is too low-bandwidth to provide sufficient data to learn a sufficient model about entities embedded in a complex physical world, such as humans. On Sun, Mar 30, 2008 at 10:50 PM, Mark Waser [EMAIL PROTECTED] wrote: From: Kingma, D.P. [EMAIL PROTECTED] Sure, you could argue that an intelligence purely based on text, disconnected from the physical world, could be intelligent, but it would have a very hard time reasoning about interaction of entities in the physicial world. It would be unable to understand humans in many aspects: I wouldn't call that generally intelligent. Given sufficient bandwidth, why would it have a hard time reasoning about interaction of entities? You could describe vision down to the pixel, hearing down to the pitch and decibel, touch down to the sensation, etc. and the system could internally convert it to exactly what a human feels. You could explain to it all the known theories of psychology and give it the personal interactions of billions of people. Sure, that's a huge amount of bandwidth, but it proves that your statement is inaccurate. --- 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
On Sun, Mar 30, 2008 at 11:00 PM, Mark Waser [EMAIL PROTECTED] wrote: From: Kingma, D.P. [EMAIL PROTECTED] Vector graphics can indeed be communicated to an AGI by relatively low-bandwidth textual input. But, unfortunately, the physical world is not made of vector graphics, so reducing the physical world to vector graphics is quite lossy (and computationally expensive an sich). Huh? Intelligence is based upon lossyness and the ability to lose rarely relevant (probably incorrect) outlier information is frequently the key to making problems tractable (though it can also set you up for failure when you miss a phase transition by mistaking it for just an odd outlier :-) since it forms the basis of discovery by analogy. Matt Mahoney's failure to recognize this has him trapped in *exact* compression hell.;-) Agreed with that, exact compression is not the way to go if you ask me. But that doesn't mean any lossy method is OK. Converting a scene to vector graphics will lead you to throwing away much visual information early in the process: visual information (e.g. texture) that might be useful later in the process (for e.g. disambiguation). I'm not stating a vector description is not useful: I'm stating that information is thrown away that could have been used to construct an essential part of a world model that understands physical entities down to the level of e.g. textures. --- 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
(Sorry for triple posting...) On Sun, Mar 30, 2008 at 11:34 PM, William Pearson [EMAIL PROTECTED] wrote: On 30/03/2008, Kingma, D.P. [EMAIL PROTECTED] wrote: Intelligence is not *only* about the modalities of the data you get, but modalities are certainly important. A deafblind person can still learn a lot about the world with taste, smell, and touch, but the senses one has access to defines the limits to the world model one can build. As long as you have one high bandwidth modality you should be able to add on technological gizmos to convert information to that modality, and thus be able to model the phenomenon from that part of the world. Humans manage to convert modalities E.g. http://www.engadget.com/2006/04/25/the-brain-port-neural-tongue-interface-of-the-future/ Using touch on the tongue. Nice article. Apparently even the brain's region for perception of taste is generally adaptable to new input. I'm not so much interested in this case, but what about the case where you have a robot with Sonar, Radar and other sensors. But not the normal 2 camera +2 microphone thing people imply when they say audiovisual. That's an interesting case indeed. AGIs equipped with sonar/radar/ladar instead of 'regular' vision should be perfectly able at certain forms of spatial reasoning, but still unable to understand humans at certain subjects. Still, if you don't need your agents to completely understand humans, audiovisual senses could go out of the window. It depends on your agent's goals, I guess. --- 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
From: Kingma, D.P. [EMAIL PROTECTED] Okay, with text, I mean natural language, in it's usual low-bandwidth form. That should clarify my statement. Any data can be represented with text of course, but that's not the point... The point that I was trying to make is that natural language is too low-bandwidth to provide sufficient data to learn a sufficient model about entities embedded in a complex physical world, such as humans. Ah . . . . but natural language is *NOT* necessarily low-bandwidth. As humans experience it with pretty much just a single focus of attention and only one set of eyes and ears that can only operate so fast -- Yes, it is low bandwidth. But what about an intelligence with a hundred or more foci of attention and the ability to pull that many Wikipedia pages simultaneously at extremely high speed? --- 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
From: Kingma, D.P. [EMAIL PROTECTED] Agreed with that, exact compression is not the way to go if you ask me. But that doesn't mean any lossy method is OK. Converting a scene to vector graphics will lead you to throwing away much visual information early in the process: visual information (e.g. texture) that might be useful later in the process (for e.g. disambiguation). I'm not stating a vector description is not useful: I'm stating that information is thrown away that could have been used to construct an essential part of a world model that understands physical entities down to the level of e.g. textures. I would agree completely except that I would think that there is some way to include texture in the vector graphics in the same way in which color is included. --- 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
I'm going to attack you by questions again :-) You're more than welcome to, sorry for being brisk. I did reply about RSS on the blog, but for some reason the post never made it through. I don't how RSS works, but you can subscribe via bloglines.com. What are 'range' and 'complexity'? Is there a specific architecture of 'levels'? Why should higher-level concepts be multidimensional? Levels are defined incrementally, a comparison adds a set of derivatives to the syntax (complexity) of the template pattern. After sufficient accumulation (range) the pattern is evaluated selectively transfered to a higher level, where these derivatives are also compared, forming yet another level of complexity. Complexity generally corresponds to the range of search ( resulting projection) because it adds cost, which must be justified by the benefit: accumulated match (one of the derivatives). The levels may differ in dimensionality (we do live in a 4D world)b or modality integration, but this doesn't have to be designed-in, the differences can be discovered by the system. What is the dynamics of system's operation in time? Is inference feed-forward and 'instantaneous', measuring by external clock? Can capture time series? Temporal, as well as spatial, range of search ( duration is storage) increases with the level of generality, the feedback (projection) delay increases too. By 'what prediction is for?' I mean connection to action. How does prediction of inputs or features of inputs translate into action? If this prediction activity doesn't lead to any result, it may as well be absent. The intellectual part of action is planning, which technically is a self-prediction. Prediction is a pattern with adjusted focus: coordinates resolution, sent down the hierarchy the changes act as a motor feedback. Using the feedback the system will focus on areas of the environment with the greatest predictive potential. To do so, it will eventualy learn to use tools. Boris, http://scalable-intelligence.blogspot.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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
Re: [agi] Intelligence: a pattern discovery algorithm of scalable complexity.
It seems like a reasonable and not uncommon idea that an AI could be built as a mostly-hierarchical autoassiciative memory. As you point out, it's not so different from Hawkins's ideas. Neighboring pixels will correlate in space and time; features such as edges should become principle components given enough data, and so on. There is a bunch of such work on self-organizing the early visual system like this. That overall concept doesn't get you very far though; the trick is to make it work past the first few rather obvious feature extraction stages of sensory data, and to account for things like episodic memory, language use, goal-directed behavior, and all other cognitive activity that is not just statistical categorization. I sympathize with your approach and wish you luck. If you think you have something that produce more than Hawkins has with his HTM, please explain it with enough precision that we can understand the details. Good questions. I agree with you on Hawkins HTM, but his main problem is conceptual. He seems to be profoundly confused as to what the hierarchy should select for: generality or novelty. He nominates both, apparently not realizing that they're mutually exclusive. This creates a difficulty in defining a quantitative criterion for selection, which is a key for my approach. This internal inconsistency leads to haphazard hacking in the HTM. For example, he starts by comparing 2D frames in a binary fashion, which pretty perverse for an incremental approach. I start from the begining, by comparing pixels: the limit of resolution, I quantify the degree of match right there, as a distinct variable. I also record compare explicit coordinates derivatives, while he simply junks all that information. His approach doesn't scale because it's not consistent incremental enough. I disagree that we need to specifically code episodic memory, language, action, - to me these are emergent properties (damn, I hate that word:)). Boris. http://scalable-intelligence.blogspot.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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
[agi] Intelligence: a pattern discovery algorithm of scalable complexity.
Here's another try: I think the main reason for the failure of AI is that no existing approach is derived from a theoretically consistent definition of intelligence. Some, such as Algorithmic Information Theory, are close but not close enough. Scalable (general) intelligence must recursively self-improve: continuously develop new mechanisms. These mechanisms must be selected according to a universal criterion, which can only be derived from a functional definition of intelligence. I define intelligence as an ability to predict/plan by discovering projecting patterns within an input flow. For an excellent high-level discussion see On intelligence by Jeff Hawkins. We know of one mechanism that did produce an intelligence, although a pretty messed-up one: the evolution. Initially algorithmically very simple, evolution changes heritable traits at random evaluates results for reproductive fitness. But biological evolution is ludicrously inefficient because intelligence is only one element of reproductive fitness, selection is extremely coarse-grained: on the level of a whole genome rather than of individual traits. From my definition, a fitness function specific to intelligence is predictive correspondence of the memory. Correspondence is a representational analog of reproduction, maximized by an internalized evolution: - the heritable traits for evolving predictions are past inputs, - the fitness is their cummulative match to the following inputs. Match (fitness) should be quantified on the lowest level of comparison,- this makes selection more incremental efficient. The lowest level is comparison between two single-variable inputs, the match is a partial identity: a complimentary of the difference, or the smaller of the variables. This is also a measure of analog compression: a sum of bitwise AND between uncompressed comparands (represented by strings of ones). To speedup, the search algorithm must incorporate increasingly more complex shortcuts to discover better predictions (the speed is what it's all about, otherwise we can just sit back let the biological evolution do the job). These more complex predictions (patterns) pattern discovery methods are derived from the past inputs of increasing comparison range order: derivation depth. The most basic shortcuts are based on the assumption that the environment is not random: - Input patterns are decreasingly predictive with the distance. - Pattern is increasingly predictive with the accumulated match, decreasingly so with the difference between constituent inputs. A core algorithm based on these assumptions would be an iterative step that selectively increases range complexity of the patterns in proportion to their projected cummulative match: The original inputs are single variables produced by senses, such as pixels of visual perception. Their subsequent comparison by iterative subtraction adds new variable types: length aggregate value for both partial match miss (derivatives) for each variable of the comparands. The inputs are integrated into patterns (higher-level inputs) if the additional projected match is greater than the system's average for the computational resources necessary to record compare additional syntactic complexity. Each variable of thus-formed patterns is compared on a higher level of search can form its own pattern. On the other hand, if predictive value (projected match) falls below the systems' average, the input pattern is aggregated with adjacent subcritical patterns by iterative addition, into a lower-resolution input. Aggregation results in a fractional projection range for constituent inputs, as opposed to multiple range for matching inputs. By increasing magnitude of the input it increases its projected match: a subset of the magnitude. Aggregation also produces the averages to determine resolution of future inpus evaluate their matches. So, the alternative integrated/aggregated representations of inputs are produced by iterative subtraction/addition (the neural analogs are inhibition excitation), both determined by comparison among the respective inputs. It's a kind of evolution where neither traits nor their change are really produced at random. The inputs are inherently predictive on the average by the virtue of their proximity, the change is introduced either by new inputs (proximity update), or as incremental syntax of the old inputs, produced by their individual predictiveness evaluation: comparison, selectively incremental in distance derivation. The biggest hangup people usually have is that this kind of algorithm is obviously very simple, while working intelligence is obviously very complex. But, as I tried to explain, additional complexity should only improve speed, rather than changing the direction of cognition (although it may save a few zillion years). The main requirement for such algorithm is that it
