Re: [agi] draft for comment
Hi,
What I think is that the set of patterns in perceptual and motoric data
has
radically different statistical properties than the set of patterns in
linguistic and mathematical data ... and that the properties of the set
of
patterns in perceptual and motoric data is intrinsically better suited to
the needs of a young, ignorant, developing mind.
Sure it is. Systems with different sensory channels will never fully
understand each other. I'm not saying that one channel (verbal) can
replace another (visual), but that both of them (and many others) can
give symbol/representation/concept/pattern/whatever-you-call-it
meaning. No on is more real than others.
True, but some channels may -- due to the statistical properties of the data
coming across them -- be more conducive to the development of AGI than
others...
All these different domains of pattern display what I've called a dual
network structure ... a collection of hierarchies (of progressively more
and more complex, hierarchically nested patterns) overlayed with a
heterarchy (of overlapping, interrelated patterns). But the statistics
of
the dual networks in the different domains is different. I haven't fully
plumbed the difference yet ... but, among the many differences is that in
perceptual/motoric domains, you have a very richly connected dual network
at
a very low level of the overall dual network hierarchy -- i.e., there's a
richly connected web of relatively simple stuff to understand ... and
then
these simple things are related to (hence useful for learning) the more
complex things, etc.
True, but can you say that the relations among words, or concepts, are
simpler?
I think the set of relations among words (considered in isolation, without
their referents) is less rich than the set of relations among perceptions
of a complex world, and far less rich than the set of relations among
{perceptions of a complex world, plus words referring to these
perceptions}
And I think that this lesser richness makes sequences of words a much worse
input stream for a developing AGI
I realize that quantifying less rich in the above is a significant
challenge, but I'm presenting my intuition anyway...
Also, relatedly and just as critically, the set of perceptions regarding the
body and its interactions with the environment, are well-structured to give
the mind a sense of its own self. This primitive infantile sense of
body-self gives rise to the more sophisticated phenomenal self of the child
and adult mind, which gives rise to reflective consciousness, the feeling of
will, and other characteristic structures of humanlike general
intelligence. A stream of words doesn't seem to give an AI the same kind of
opportunity for self-development
In this short paper, I make no attempt to settle all issues, but just
to point out a simple fact --- a laptop has a body, and is not less
embodied than Roomba or Mindstorms --- that seems have been ignored in
the previous discussion.
I agree with your point, but I wonder if it's partially a straw man
argument. The proponents of embodiment as a key aspect of AGI don't of
course think that Cyc is disembodied in a maximally strong sense -- they
know it interacts with the world via physical means. What they mean by
embodied is something different.
I don't have the details at my finger tips, but I know that Maturana, Varela
and Eleanor Rosch took some serious pains to carefully specify the sense in
which they feel embodiment is critical to intelligence, and to distinguish
their sense of embodiment from the trivial sense of communicating via
physical signals.
I suggest your paper should probably include a careful response to the
characterization of embodiment presented in
http://www.*amazon*.com/*Embodied*-*Mind*
-Cognitive-Science-Experience/dp/0262720213
I note that I do not agree with the arguments of Varela, Rosch, Brooks,
etc. I just think their characterization of embodiment is an interesting
and nontrivial one, and I'm not sure NARS with a text stream as input would
be embodied according to their definition...
-- Ben
---
agi
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Re: [agi] draft for comment
Also, relatedly and just as critically, the set of perceptions regarding the body and its interactions with the environment, are well-structured to give the mind a sense of its own self. This primitive infantile sense of body-self gives rise to the more sophisticated phenomenal self of the child and adult mind, which gives rise to reflective consciousness, the feeling of will, and other characteristic structures of humanlike general intelligence. A stream of words doesn't seem to give an AI the same kind of opportunity for self-development To put it perhaps more clearly: I think that a standard laptop is too lacking in -- proprioceptive perception -- perception of its own relationship to other entities in the world around it to form a physical self-image based on its perceptions ... hence a standard laptop will not likely be driven by its experience to develop a phenomenal self ... hence, I suspect, no generally intelligent mind... -- Ben G --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] A NewMetaphor for Intelligence - the Computer/Organiser
2008/9/4 Mike Tintner [EMAIL PROTECTED]: Terren, If you think it's all been said, please point me to the philosophy of AI that includes it. A programmed machine is an organized structure. A keyboard (and indeed a computer with keyboard) are something very different - there is no organization to those 26 letters etc. They can be freely combined and sequenced to create an infinity of texts. That is the very essence and manifestly, the whole point, of a keyboard. Yes, the keyboard is only an instrument. But your body - and your brain - which use it, are themselves keyboards. They consist of parts which also have no fundamental behavioural organization - that can be freely combined and sequenced to create an infinity of sequences of movements and thought - dances, texts, speeches, daydreams, postures etc. In abstract logical principle, it could all be preprogrammed. But I doubt that it's possible mathematically - a program for selecting from an infinity of possibilities? And it would be engineering madness - like trying to preprogram a particular way of playing music, when an infinite repertoire is possible and the environment, (in this case musical culture), is changing and evolving with bewildering and unpredictable speed. To look at computers as what they are (are you disputing this?) - machines for creating programs first, and following them second, is a radically different way of looking at computers. It also fits with radically different approaches to DNA - moving away from the idea of DNA as coded program, to something that can be, as it obviously can be, played like a keyboard - see Dennis Noble, The Music of Life. It fits with the fact (otherwise inexplicable) that all intelligences have both deliberate (creative) and automatic (routine) levels - and are not just automatic, like purely programmed computers. And it fits with the way computers are actually used and programmed, rather than the essentially fictional notion of them as pure turing machines. And how to produce creativity is the central problem of AGI - completely unsolved. So maybe a new approach/paradigm is worth at least considering rather than more of the same? I'm not aware of a single idea from any AGI-er past or present that directly addresses that problem - are you? You can't create a program out of thin air. So you have to have some sort of program to start with. You probably want to change the initial program in some way as well as perhaps adding more programming. This leads you to recursive self-change and its subset RSI, which is a very tricky business even if you don't think it is going to go FOOM and take over the world. So this very list has been discussing in abstract terms the very thing you want it to be discussing! Will --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] draft for comment.. P.S.
That's if you aim at getting an AGI that is intelligent in the real world. I think some people on this list (incl Ben perhaps) might argue that for now - for safety purposes but also due to costs - it might be better to build an AGI that is intelligent in a simulated environment. Ppl like Ben argue that the concept/engineering aspect of intelligence is *independent of the type of environment*. That is, given you understand how to make it in a virtual environment you can then tarnspose that concept into a real environment more safely. Some other ppl on the other hand believe intelligence is a property of humans only. So you have to simulate every detail about humans to get that intelligence. I'd say that among the two approaches the first one (Ben's) is safer and more realistic. I am more concerned with the physics aspect of the whole issue I guess. --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: AGI goals (was Re: Information theoretic approaches to AGI (was Re: [agi] The Necessity of Embodiment))
That sounds like a useful purpose. Yeh, I don't believe in fast and quick methods either.. but also humans tend to overestimate their own capabilities, so it will probably take more time than predicted. On 9/3/08, William Pearson [EMAIL PROTECTED] wrote: 2008/8/28 Valentina Poletti [EMAIL PROTECTED]: Got ya, thanks for the clarification. That brings up another question. Why do we want to make an AGI? To understand ourselves as intelligent agents better? It might enable us to have decent education policy, rehabilitation of criminals. Even if we don't make human like AGIs the principles should help us understand ourselves, just as optics of the lens helped us understand the eye and aerodynamics of wings helps us understand bird flight. It could also gives us more leverage, more brain power on the planet to help solve the planets problems. This is all predicated on the idea that fast take off is pretty much impossible. It is possible then all bets are off. Will --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com -- A true friend stabs you in the front. - O. Wilde Einstein once thought he was wrong; then he discovered he was wrong. For every complex problem, there is an answer which is short, simple and wrong. - H.L. Mencken --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] What is Friendly AI?
On 8/31/08, Steve Richfield [EMAIL PROTECTED] wrote: Protective mechanisms to restrict their thinking and action will only make things WORSE. Vlad, this was my point in the control e-mail, I didn't express it quite as clearly, partly because coming from a different background I use a slightly different language. Also, Steve made another good point here: loads of people at any moment do whatever they can to block the advancement and progress of human beings as it is now. How will *those* people react to a progress as advanced as AGI? That's why I keep stressing the social factor in intelligence as very important part to consider. --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] What is Friendly AI?
On Thu, Sep 4, 2008 at 12:02 PM, Valentina Poletti [EMAIL PROTECTED] wrote: Vlad, this was my point in the control e-mail, I didn't express it quite as clearly, partly because coming from a different background I use a slightly different language. Also, Steve made another good point here: loads of people at any moment do whatever they can to block the advancement and progress of human beings as it is now. How will those people react to a progress as advanced as AGI? That's why I keep stressing the social factor in intelligence as very important part to consider. No, it's not important, unless these people start to pose a serious threat to the project. You need to care about what is the correct answer, not what is a popular one, in the case where popular answer is dictated by ignorance. P.S. AGI? I'm again not sure what we are talking about here. -- Vladimir Nesov [EMAIL PROTECTED] http://causalityrelay.wordpress.com/ --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] draft for comment
On Thu, Sep 4, 2008 at 2:10 AM, Ben Goertzel [EMAIL PROTECTED] wrote:
Sure it is. Systems with different sensory channels will never fully
understand each other. I'm not saying that one channel (verbal) can
replace another (visual), but that both of them (and many others) can
give symbol/representation/concept/pattern/whatever-you-call-it
meaning. No on is more real than others.
True, but some channels may -- due to the statistical properties of the data
coming across them -- be more conducive to the development of AGI than
others...
I haven't seen any evidence for that. For human intelligence, maybe,
but for intelligence in general, I doubt it.
I think the set of relations among words (considered in isolation, without
their referents) is less rich than the set of relations among perceptions
of a complex world, and far less rich than the set of relations among
{perceptions of a complex world, plus words referring to these
perceptions}
Not necessarily. Actually some people may even make the opposite
argument: relations among non-linguistic components in experience are
basically temporal or spatial, while the relations among words and
concepts have much more types. I won't go that far, but I guess in
some sense all channels may have the same (potential) richness.
And I think that this lesser richness makes sequences of words a much worse
input stream for a developing AGI
I realize that quantifying less rich in the above is a significant
challenge, but I'm presenting my intuition anyway...
If your condition is true, then your conclusion follows, but the
problem is in that IF.
Also, relatedly and just as critically, the set of perceptions regarding the
body and its interactions with the environment, are well-structured to give
the mind a sense of its own self.
We can say the same for every input/out operation set of an
intelligent system. SELF is defined by what the system can feel and
do.
This primitive infantile sense of
body-self gives rise to the more sophisticated phenomenal self of the child
and adult mind, which gives rise to reflective consciousness, the feeling of
will, and other characteristic structures of humanlike general
intelligence.
Agree.
A stream of words doesn't seem to give an AI the same kind of
opportunity for self-development
If the system just sits there and passively accept whatever words come
into it, what you said is true. If the incoming words is causally
related to its outgoing words, will you still say that?
I agree with your point, but I wonder if it's partially a straw man
argument.
If you read Brooks or Pfeifer, you'll see that most of their arguments
are explicitly or implicitly based on the myth that only a robot has
a body, have real sensor, live in a real world, ...
The proponents of embodiment as a key aspect of AGI don't of
course think that Cyc is disembodied in a maximally strong sense -- they
know it interacts with the world via physical means. What they mean by
embodied is something different.
Whether a system is embodied does not depends on hardware, but on semantics.
I don't have the details at my finger tips, but I know that Maturana, Varela
and Eleanor Rosch took some serious pains to carefully specify the sense in
which they feel embodiment is critical to intelligence, and to distinguish
their sense of embodiment from the trivial sense of communicating via
physical signals.
That is different. The embodiment school in CogSci doesn't focus on
body (they know every human already has one), but on experience.
However, they have their misconception about AI. As I mentioned,
Barsalou and Lakoff both thought strong AI is unlikely because
computer cannot have human experience --- I agree what they said
except their narrow conception of intelligence (CogSci people tend to
take intelligence as human intelligence).
I suggest your paper should probably include a careful response to the
characterization of embodiment presented in
http://www.amazon.com/Embodied-Mind-Cognitive-Science-Experience/dp/0262720213
I note that I do not agree with the arguments of Varela, Rosch, Brooks,
etc. I just think their characterization of embodiment is an interesting
and nontrivial one, and I'm not sure NARS with a text stream as input would
be embodied according to their definition...
If I got the time (and motivation) to extend the paper into a journal
paper, I'll double the length by discussing embodiment in CogSci. In
the current version, as a short conference paper, I'd rather focus on
embodiment in AI, and only attack the robot myth.
Pei
---
agi
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Re: [agi] draft for comment
On Thu, Sep 4, 2008 at 2:12 AM, Ben Goertzel [EMAIL PROTECTED] wrote: Also, relatedly and just as critically, the set of perceptions regarding the body and its interactions with the environment, are well-structured to give the mind a sense of its own self. This primitive infantile sense of body-self gives rise to the more sophisticated phenomenal self of the child and adult mind, which gives rise to reflective consciousness, the feeling of will, and other characteristic structures of humanlike general intelligence. A stream of words doesn't seem to give an AI the same kind of opportunity for self-development To put it perhaps more clearly: I think that a standard laptop is too lacking in -- proprioceptive perception -- perception of its own relationship to other entities in the world around it Obviously you didn't consider the potential a laptop has with its network connection, which in theory can give it all kinds of perception by connecting it to some input/output device. Even if we exclude network, your conclusion is still problematic. Why a touchpad cannot provide proprioceptive perception? I agree it usually doesn't, because the way it is used, but that doesn't mean it cannot, under all possible usage. The same is true for keyboard. The current limitation of the standard computer is more in the way we use them than in the hardware itself. to form a physical self-image based on its perceptions ... hence a standard laptop will not likely be driven by its experience to develop a phenomenal self ... hence, I suspect, no generally intelligent mind... Of course it won't have a visual concept of self, but a system like NARS has the potential to grow into an intelligent operating system, with a notion of self based on what it can feel and do, as well as the causal relations among them --- If there is a file in this folder, then I should have felt it, it cannot be there because I've deleted the contents. I know some people won't agree there is a self in such a system, because it doesn't look like themselves. Too bad human intelligence is the only known example of intelligence ... Pei --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] A NewMetaphor for Intelligence - the Computer/Organiser
Will:You can't create a program out of thin air. So you have to have some sort of program to start with Not out of thin air.Out of a general instruction and desire[s]/emotion[s]. Write me a program that will contradict every statement made to it. Write me a single program that will allow me to write video/multimedia articles/journalism fast and simply. That's what you actually DO. You start with v. general briefs rather than any detailed list of instructions, and fill them in as you go along, in an ad hoc, improvisational way - manifestly *creating* rather than *following* organized structures of behaviour in an initially disorganized way. Do you honestly think that you write programs in a programmed way? That it's not an *art* pace Matt, full of hesitation, halts, meandering, twists and turns, dead ends, detours etc? If you have to have some sort of program to start with, how come there is no sign of that being true, in the creative process of programmers actually writing programs? Do you think that there's a program for improvising on a piano [or other form of keyboard]? That's what AGI's are supposed to do - improvise. So create one that can. Like you. And every other living creature. --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] draft for comment
I agree with Pei in that a robot's experience is not necessarily more real than that of a, say, web-embedded agent - if anything it is closer to the * human* experience of the world. But who knows how limited our own sensory experience is anyhow. Perhaps a better intelligence would comprehend the world better through a different emboyment. However, could you guys be more specific regarding the statistical differences of different types of data? What kind of differences are you talking about specifically (mathematically)? And what about the differences at the various levels of the dual-hierarchy? Has any of your work or research suggested this hypothesis, if so which? --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] What Time Is It? No. What clock is it?
Great articles! On 9/4/08, Brad Paulsen [EMAIL PROTECTED] wrote: Hey gang... It's Likely That Times Are Changing http://www.sciencenews.org/view/feature/id/35992/title/It%E2%80%99s_Likely_That_Times_Are_Changing A century ago, mathematician Hermann Minkowski famously merged space with time, establishing a new foundation for physics; today physicists are rethinking how the two should fit together. A PDF of a paper presented in March of this year, and upon which the article is based, can be found at http://arxiv.org/abs/0805.4452. It's a free download. Lots of equations, graphs, oh my! Cheers, Brad --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] A NewMetaphor for Intelligence - the Computer/Organiser
Programming definitely feels like an art to me - I get the same feelings as when I am painting. I always wondered why. On the phylosophical side in general technology is the ability of humans to adapt the environment to themselves instead of the opposite - adapting to the environment. The environment acts on us and we act on it - we absorb information from it and we change it while it changes us. When we want to step further and create an AGI I think we want to externalize the very ability to create technology - we want the environment to start adapting to us by itself, spontaneously by gaining our goals. Vale On 9/4/08, Mike Tintner [EMAIL PROTECTED] wrote: Will:You can't create a program out of thin air. So you have to have some sort of program to start with Not out of thin air.Out of a general instruction and desire[s]/emotion[s]. Write me a program that will contradict every statement made to it. Write me a single program that will allow me to write video/multimedia articles/journalism fast and simply. That's what you actually DO. You start with v. general briefs rather than any detailed list of instructions, and fill them in as you go along, in an ad hoc, improvisational way - manifestly *creating* rather than *following* organized structures of behaviour in an initially disorganized way. Do you honestly think that you write programs in a programmed way? That it's not an *art* pace Matt, full of hesitation, halts, meandering, twists and turns, dead ends, detours etc? If you have to have some sort of program to start with, how come there is no sign of that being true, in the creative process of programmers actually writing programs? Do you think that there's a program for improvising on a piano [or other form of keyboard]? That's what AGI's are supposed to do - improvise. So create one that can. Like you. And every other living creature. --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] draft for comment
Obviously you didn't consider the potential a laptop has with its network connection, which in theory can give it all kinds of perception by connecting it to some input/output device. yes, that's true ... I was considering the laptop w/ only a power cable as the AI system in question. Of course my point does not apply to a laptop that's being used as an on-board control system for an android robot, or a laptop that's connected to a network of sensors and actuators via the net, etc. Sorry I did not clarify my terms better! Similarly the human brain lacks much proprioception and control in isolation, and probably would not be able to achieve a high level of general intelligence without the right peripherals (such as the rest of the human body ;-) Even if we exclude network, your conclusion is still problematic. Why a touchpad cannot provide proprioceptive perception? I agree it usually doesn't, because the way it is used, but that doesn't mean it cannot, under all possible usage. The same is true for keyboard. The current limitation of the standard computer is more in the way we use them than in the hardware itself. I understand that a keyboard and touchpad do provide proprioceptive input, but I think it's too feeble, and too insensitively respondent to changes in the environment and the relation btw the laptop and the environment, to serve as the foundation for a robust self-model or a powerful general intelligence. to form a physical self-image based on its perceptions ... hence a standard laptop will not likely be driven by its experience to develop a phenomenal self ... hence, I suspect, no generally intelligent mind... Of course it won't have a visual concept of self, but a system like NARS has the potential to grow into an intelligent operating system, with a notion of self based on what it can feel and do, as well as the causal relations among them --- If there is a file in this folder, then I should have felt it, it cannot be there because I've deleted the contents. My suggestion is that the file system lacks the complexity of structure and dynamics to support the emergence of a robust self-model, and powerful general intelligence... Not in principle ... potentially a file system *could* display the needed complexity, but I don't think any file systems on laptops now come close... Whether the Internet as a whole contains the requisite complexity is a subtler question. I know some people won't agree there is a self in such a system, because it doesn't look like themselves. Too bad human intelligence is the only known example of intelligence ... I would call a self any internal, explicit model that a system creates that allows it to predict its own behaviors in a sufficient variety of contexts This need not have a visual aspect nor a great similarity to a human self. -- Ben --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: Computation as an explanation of the universe (was Re: [agi] Recursive self-change: some definitions)
OK, then the observable universe has a finite description length. We don't need to describe anything else to model it, so by universe I mean only the observable part. But, what good is it to only have finite description of the observable part, since new portions of the universe enter the observable portion continually? Physics cannot then be modeled as a computer program, because computer programs do not increase in Kolmogorov complexity as they run (except by a logarithmic term to count how long it has been running). I am saying that the universe *is* deterministic. It has a definite quantum state, but we would need about 10^122 bits of memory to describe it. Since we can't do that, we have to resort to approximate models like quantum mechanics. Yes, I understood that you were suggesting a deterministic universe. What I'm saying is that it seems plausible for us to be able to have an accurate knowledge of that deterministic physics, lacking only the exact knowledge of particle locations et cetera. We would be forced to use probabilistic methods as you argue, but they would not necessarily be built into our physical theories; instead, our physical theories act as a deterministic function that is given probabilistic input and therefore yields probabilistic output. I believe there is a simpler description. First, the description length is increasing with the square of the age of the universe, since it is proportional to area. So it must have been very small at one time. Second, the most efficient way to enumerate all possible universes would be to run each B-bit machine for 2^B steps, starting with B = 0, 1, 2... until intelligent life is found. For our universe, B ~ 407. You could reasonably argue that the algorithmic complexity of the free parameters of string theory and general relativity is of this magnitude. I believe that Wolfram also argued that the (observable) universe is a few lines of code. I really do not understand your willingness to restrict universe to observable universe. The description length of the observable universe was very small at one time because at that time none of the basic stuffs of the universe had yet interacted, so by definition the description length of the observable universe for each basic entity is just the description length of that entity. As time moves forward, the entities interact and the description lengths of their observable universes increase. Similarly, today, one might say that the observable universe for each person is slightly different, and indeed the universe observable from my right hand would be slightly different then the one observable from my left. They could have differing description lengths. In short, I think you really want to apply your argument to the actual universe, not merely observable subsets... or if you don't, you should, because otherwise it seems like a very strange argument. But even if we discover this program it does not mean we could model the universe deterministically. We would need a computer larger than the universe to do so. Agreed... partly thanks to your argument below. There is a simple argument using information theory. Every system S has a Kolmogorov complexity K(S), which is the smallest size that you can compress a description of S to. A model of S must also have complexity K(S). However, this leaves no space for S to model itself. In particular, if all of S's memory is used to describe its model, there is no memory left over to store any results of the simulation. Point conceded. --Abram --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] draft for comment
Hi Pei,
I think your point is correct that the notion of embodiment presented by
Brooks and some other roboticists is naive. I'm not sure whether their
actual conceptions are naive, or whether they just aren't presenting their
foundational philosophical ideas clearly in their writings (being ultimately
more engineering-oriented people, and probably not that accustomed to the
philosophical style of discourse in which these sorts of definitional
distinctions need to be more precisely drawn). I do think (in approximate
concurrence with your paper) that ANY control system physically embodied in
a physical system S, that has an input and output stream, and whose input
and output stream possess correlation with the physical state of S, should
be considered as psychologically embodied. Clearly, whether it's a robot
or a laptop (w/o network connection if you like), such a system has the
basic property of embodiment. Furthermore S doesn't need to be a physical
system ... it could be a virtual system inside some virtual world (and
then there's the question of what properties characterize a valid virtual
world ... but let's leave that for another email thread...)
However, I think that not all psychologically-embodied systems possess a
sufficiently rich psychological-embodiment to lead to significantly general
intelligence My suggestion is that a laptop w/o network connection or
odd sensor-peripherals, probably does not have sufficiently rich
correlations btw its I/O stream and its physical state, to allow it to
develop a robust self-model of its physical self (which can then be used as
a basis for a more general phenomenal self).
I think that Varela and crew understood the value of this rich network of
correlations, but mistakenly assumed it to be a unique property of
biological systems...
I realize that the points you made in your paper do not contradict the
suggestions I've made in this email. I don't think anything significant in
your paper is wrong, actually. It just seems to me not to address the most
interesting aspects of the embodiment issue as related to AGI.
-- Ben G
On Thu, Sep 4, 2008 at 7:06 AM, Pei Wang [EMAIL PROTECTED] wrote:
On Thu, Sep 4, 2008 at 2:10 AM, Ben Goertzel [EMAIL PROTECTED] wrote:
Sure it is. Systems with different sensory channels will never fully
understand each other. I'm not saying that one channel (verbal) can
replace another (visual), but that both of them (and many others) can
give symbol/representation/concept/pattern/whatever-you-call-it
meaning. No on is more real than others.
True, but some channels may -- due to the statistical properties of the
data
coming across them -- be more conducive to the development of AGI than
others...
I haven't seen any evidence for that. For human intelligence, maybe,
but for intelligence in general, I doubt it.
I think the set of relations among words (considered in isolation,
without
their referents) is less rich than the set of relations among
perceptions
of a complex world, and far less rich than the set of relations among
{perceptions of a complex world, plus words referring to these
perceptions}
Not necessarily. Actually some people may even make the opposite
argument: relations among non-linguistic components in experience are
basically temporal or spatial, while the relations among words and
concepts have much more types. I won't go that far, but I guess in
some sense all channels may have the same (potential) richness.
And I think that this lesser richness makes sequences of words a much
worse
input stream for a developing AGI
I realize that quantifying less rich in the above is a significant
challenge, but I'm presenting my intuition anyway...
If your condition is true, then your conclusion follows, but the
problem is in that IF.
Also, relatedly and just as critically, the set of perceptions regarding
the
body and its interactions with the environment, are well-structured to
give
the mind a sense of its own self.
We can say the same for every input/out operation set of an
intelligent system. SELF is defined by what the system can feel and
do.
This primitive infantile sense of
body-self gives rise to the more sophisticated phenomenal self of the
child
and adult mind, which gives rise to reflective consciousness, the feeling
of
will, and other characteristic structures of humanlike general
intelligence.
Agree.
A stream of words doesn't seem to give an AI the same kind of
opportunity for self-development
If the system just sits there and passively accept whatever words come
into it, what you said is true. If the incoming words is causally
related to its outgoing words, will you still say that?
I agree with your point, but I wonder if it's partially a straw man
argument.
If you read Brooks or Pfeifer, you'll see that most of their arguments
are explicitly or implicitly based on the myth that only a robot has
a
Re: [agi] draft for comment
However, could you guys be more specific regarding the statistical differences of different types of data? What kind of differences are you talking about specifically (mathematically)? And what about the differences at the various levels of the dual-hierarchy? Has any of your work or research suggested this hypothesis, if so which? Sorry I've been fuzzy on this ... I'm engaging in this email conversation in odd moments while at a conference (Virtual Worlds 2008, in Los Angeles...) Specifically I think that patterns interrelating the I/O stream of system S with the relation between the system S's embodiment and its environment, are important. It is these patterns that let S build a self-model of its physical embodiment, which then leads S to a more abstract self-model (aka Metzinger's phenomenal self) Considering patterns in the above category, it seems critical to have a rich variety of patterns at varying levels of complexity... so that the patterns at complexity level L are largely approximable as compositions of patterns at complexity less than L. This way a mind can incrementally build up its self-model via recognizing slightly complex self-related patterns, then acting based on these patterns, then recognizing somewhat more complex self-related patterns involving its recent actions, and so forth. It seems that a human body's sensors and actuators are suited to create and recognize patterns of the above sort whereas the sensors and actuators of a laptop w/o network cables or odd peripherals are not... -- Ben G --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] draft for comment
On 9/4/08, Ben Goertzel [EMAIL PROTECTED] wrote: However, could you guys be more specific regarding the statistical differences of different types of data? What kind of differences are you talking about specifically (mathematically)? And what about the differences at the various levels of the dual-hierarchy? Has any of your work or research suggested this hypothesis, if so which? Sorry I've been fuzzy on this ... I'm engaging in this email conversation in odd moments while at a conference (Virtual Worlds 2008, in Los Angeles...) Specifically I think that patterns interrelating the I/O stream of system S with the relation between the system S's embodiment and its environment, are important. It is these patterns that let S build a self-model of its physical embodiment, which then leads S to a more abstract self-model (aka Metzinger's phenomenal self) So in short you are saying that the main difference between I/O data by a motor embodyed system (such as robot or human) and a laptop is the ability to interact with the data: make changes in its environment to systematically change the input? Considering patterns in the above category, it seems critical to have a rich variety of patterns at varying levels of complexity... so that the patterns at complexity level L are largely approximable as compositions of patterns at complexity less than L. This way a mind can incrementally build up its self-model via recognizing slightly complex self-related patterns, then acting based on these patterns, then recognizing somewhat more complex self-related patterns involving its recent actions, and so forth. Definitely. It seems that a human body's sensors and actuators are suited to create and recognize patterns of the above sort whereas the sensors and actuators of a laptop w/o network cables or odd peripherals are not... Agree. --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: Computation as an explanation of the universe (was Re: [agi] Recursive self-change: some definitions)
To clarify what I mean by observable universe, I am including any part that could be observed in the future, and therefore must be modeled to make accurate predictions. For example, if our universe is computed by one of an enumeration of Turing machines, then the other enumerations are outside our observable universe. -- Matt Mahoney, [EMAIL PROTECTED] --- On Thu, 9/4/08, Abram Demski [EMAIL PROTECTED] wrote: From: Abram Demski [EMAIL PROTECTED] Subject: Re: Computation as an explanation of the universe (was Re: [agi] Recursive self-change: some definitions) To: agi@v2.listbox.com Date: Thursday, September 4, 2008, 9:43 AM OK, then the observable universe has a finite description length. We don't need to describe anything else to model it, so by universe I mean only the observable part. But, what good is it to only have finite description of the observable part, since new portions of the universe enter the observable portion continually? Physics cannot then be modeled as a computer program, because computer programs do not increase in Kolmogorov complexity as they run (except by a logarithmic term to count how long it has been running). I am saying that the universe *is* deterministic. It has a definite quantum state, but we would need about 10^122 bits of memory to describe it. Since we can't do that, we have to resort to approximate models like quantum mechanics. Yes, I understood that you were suggesting a deterministic universe. What I'm saying is that it seems plausible for us to be able to have an accurate knowledge of that deterministic physics, lacking only the exact knowledge of particle locations et cetera. We would be forced to use probabilistic methods as you argue, but they would not necessarily be built into our physical theories; instead, our physical theories act as a deterministic function that is given probabilistic input and therefore yields probabilistic output. I believe there is a simpler description. First, the description length is increasing with the square of the age of the universe, since it is proportional to area. So it must have been very small at one time. Second, the most efficient way to enumerate all possible universes would be to run each B-bit machine for 2^B steps, starting with B = 0, 1, 2... until intelligent life is found. For our universe, B ~ 407. You could reasonably argue that the algorithmic complexity of the free parameters of string theory and general relativity is of this magnitude. I believe that Wolfram also argued that the (observable) universe is a few lines of code. I really do not understand your willingness to restrict universe to observable universe. The description length of the observable universe was very small at one time because at that time none of the basic stuffs of the universe had yet interacted, so by definition the description length of the observable universe for each basic entity is just the description length of that entity. As time moves forward, the entities interact and the description lengths of their observable universes increase. Similarly, today, one might say that the observable universe for each person is slightly different, and indeed the universe observable from my right hand would be slightly different then the one observable from my left. They could have differing description lengths. In short, I think you really want to apply your argument to the actual universe, not merely observable subsets... or if you don't, you should, because otherwise it seems like a very strange argument. But even if we discover this program it does not mean we could model the universe deterministically. We would need a computer larger than the universe to do so. Agreed... partly thanks to your argument below. There is a simple argument using information theory. Every system S has a Kolmogorov complexity K(S), which is the smallest size that you can compress a description of S to. A model of S must also have complexity K(S). However, this leaves no space for S to model itself. In particular, if all of S's memory is used to describe its model, there is no memory left over to store any results of the simulation. Point conceded. --Abram --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] draft for comment
So in short you are saying that the main difference between I/O data by a motor embodyed system (such as robot or human) and a laptop is the ability to interact with the data: make changes in its environment to systematically change the input? Not quite ... but, to interact w/ the data in a way that gives rise to a hierarchy of nested, progressively more complex patterns that correlate the system and its environment (and that the system can recognize and act upon) ben --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: Computation as an explanation of the universe (was Re: [agi] Recursive self-change: some definitions)
On Thu, Sep 4, 2008 at 10:53 AM, Matt Mahoney [EMAIL PROTECTED] wrote: To clarify what I mean by observable universe, I am including any part that could be observed in the future, and therefore must be modeled to make accurate predictions. For example, if our universe is computed by one of an enumeration of Turing machines, then the other enumerations are outside our observable universe. -- Matt Mahoney, [EMAIL PROTECTED] OK, that works. But, you cannot invoke current physics to argue that this sort of observable universe is finite (so far as I know). Of course, that is not central to your point anyway. The universe might be spatially infinite while still having a finite description length. So, my only remaining objection is that while the universe *could* be computable, it seems unwise to me to totally rule out the alternative. As you said, the idea is something that makes testable predictions. So, it is something to be decided experimentally, not philosophically. -Abram --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] draft for comment
Hi Ben, You may have stated this explicitly in the past, but I just want to clarify - you seem to be suggesting that a phenomenological self is important if not critical to the actualization of general intelligence. Is this your belief, and if so, can you provide a brief justification of that? (I happen to believe this myself.. just trying to understand your philosophy better.) Terren --- On Thu, 9/4/08, Ben Goertzel [EMAIL PROTECTED] wrote: However, I think that not all psychologically-embodied systems possess a sufficiently rich psychological-embodiment to lead to significantly general intelligence My suggestion is that a laptop w/o network connection or odd sensor-peripherals, probably does not have sufficiently rich correlations btw its I/O stream and its physical state, to allow it to develop a robust self-model of its physical self (which can then be used as a basis for a more general phenomenal self). --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Real vs. simulated environments (was Re: [agi] draft for comment.. P.S.)
--- On Thu, 9/4/08, Valentina Poletti [EMAIL PROTECTED] wrote: Ppl like Ben argue that the concept/engineering aspect of intelligence is independent of the type of environment. That is, given you understand how to make it in a virtual environment you can then tarnspose that concept into a real environment more safely. Some other ppl on the other hand believe intelligence is a property of humans only. So you have to simulate every detail about humans to get that intelligence. I'd say that among the two approaches the first one (Ben's) is safer and more realistic. The issue is not what is intelligence, but what do you want to create? In order for machines to do more work for us, they may need language and vision, which we associate with human intelligence. But building artificial humans is not necessarily useful. We already know how to create humans, and we are doing so at an unsustainable rate. I suggest that instead of the imitation game (Turing test) for AI, we should use a preference test. If you prefer to talk to a machine vs. a human, then the machine passes the test. Prediction is central to intelligence. If you can predict a text stream, then for any question Q and any answer A, you can compute the probability distribution P(A|Q) = P(QA)/P(Q). This passes the Turing test. More importantly, it allows you to output max_A P(QA), the most likely answer from a group of humans. This passes the preference test because a group is usually more accurate than any individual member. (It may fail a Turing test for giving too few wrong answers, a problem Turing was aware of in 1950 when he gave an example of a computer incorrectly answering an arithmetic problem). Text compression is equivalent to AI because we have already solved the coding problem. Given P(x) for string x, we know how to optimally and efficiently code x in log_2(1/P(x)) bits (e.g. arithmetic coding). Text compression has an advantage over the Turing or preference tests in that that incremental progress in modeling can be measured precisely and the test is repeatable and verifiable. If I want to test a text compressor, it is important to use real data (human generated text) rather than simulated data, i.e. text generated by a program. Otherwise, I know there is a concise code for the input data, which is the program that generated it. When you don't understand the source distribution (i.e. the human brain), the problem is much harder, and you have a legitimate test. I understand that Ben is developing AI for virtual worlds. This might produce interesting results, but I wouldn't call it AGI. The value of AGI is on the order of US $1 quadrillion. It is a global economic system running on a smarter internet. I believe that any attempt to develop AGI on a budget of $1 million or $1 billion or $1 trillion is just wishful thinking. -- Matt Mahoney, [EMAIL PROTECTED] --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] A NewMetaphor for Intelligence - the Computer/Organiser
On Thu, Sep 4, 2008 at 12:47 AM, Mike Tintner [EMAIL PROTECTED] wrote: Terren, If you think it's all been said, please point me to the philosophy of AI that includes it. I believe what you are suggesting is best understood as an interaction machine. General references: http://www.cs.brown.edu/people/dqg/Papers/wurzburg.ps http://www.cs.brown.edu/people/pw/papers/ficacm.ps http://www.la-acm.org/Archives/laacm9912.html The concept that seems most relevant to AI is the learning theory provided by inductive turing machines, but I cannot find a good single reference for that. (I am not knowledgable on this subject, I just have heard the idea before.) --Abram --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: Computation as an explanation of the universe (was Re: [agi] Recursive self-change: some definitions)
--- On Thu, 9/4/08, Abram Demski [EMAIL PROTECTED] wrote: So, my only remaining objection is that while the universe *could* be computable, it seems unwise to me to totally rule out the alternative. You're right. We cannot prove that the universe is computable. We have evidence like Occam's Razor (if the universe is computable, then algorithmically simple models are to be preferred), but that is not proof. At one time our models of physics were not computable. Then we discovered atoms, quantization of electric charge, general relativity (which bounds density and velocity), the big bang (history is finite) and quantum mechanics. Our models would still not be computable (requiring infinite description length) if any one of these events did not occur. -- Matt Mahoney, [EMAIL PROTECTED] --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] A NewMetaphor for Intelligence - the Computer/Organiser
Abram, Thanks for reply. But I don't understand what you see as the connection. An interaction machine from my brief googling is one which has physical organs. Any factory machine can be thought of as having organs. What I am trying to forge is a new paradigm of a creative, free machine as opposed to that exemplified by most actual machines, which are rational, deterministic machines. The latter can only engage in any task in set ways - and therefore engage and combine their organs in set combinations and sequences. Creative machines have a more or less infinite range of possible ways of going about things, and can combine their organs in a virtually infinite range of combinations, (which gives them a slight advantage, adaptively :) ). Organisms *are* creative machines; computers and robots *could* be (and are, when combined with humans), AGI's will *have* to be. (To talk of creative machines, more specifically, as I did, as keyboards/organisers is to focus on the mechanics of this infinite combinativity of organs). Interaction machines do not seem in any way then to entail what I'm talking about - creative machines - keyboards/ organisers - infinite combinativity - or the *creation,* as quite distinct from *following* of programs/algorithms and routines.. Abram/MT: If you think it's all been said, please point me to the philosophy of AI that includes it. I believe what you are suggesting is best understood as an interaction machine. General references: http://www.cs.brown.edu/people/dqg/Papers/wurzburg.ps http://www.cs.brown.edu/people/pw/papers/ficacm.ps http://www.la-acm.org/Archives/laacm9912.html The concept that seems most relevant to AI is the learning theory provided by inductive turing machines, but I cannot find a good single reference for that. (I am not knowledgable on this subject, I just have heard the idea before.) --Abram --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] A NewMetaphor for Intelligence - the Computer/Organiser
Mike, Thanks for the reference to Dennis Noble, he sounds very interesting and his views on Systems Biology as expressed on his Wikipedia page are perfectly in line with my own thoughts and biases. I agree in spirit with your basic criticisms regarding current AI and creativity. However, it must be pointed out that if you abandon determinism, you find yourself in the world of dualism, or worse. There are several ways out of this conundrum, one involves complexity/emergence (global behavior cannot be understood in terms of reduction to local behavior), another involves algorithmic complexity (or complicatedness, behavior cannot be predicted due to limitations of our inborn abilities to mentally model such complicatedness), although either can be predicted in principle with sufficient computational resources. This is true of humans as well - and if you think it isn't, once again, you're committing yourself to some kind of dualistic position (e.g., we are motivated by our spirit). If you accept the proposition that the appearance of free will in an agent comes down to one's ability to predict its behavior, then either of the schemes above serves to produce free will (or the illusion of it, if you prefer). Thus is creativity possible while preserving determinism. Of course, you still need to have an explanation for how creativity emerges in either case, but in contrast to what you said before, some AI folks have indeed worked on this issue. Terren --- On Thu, 9/4/08, Mike Tintner [EMAIL PROTECTED] wrote: From: Mike Tintner [EMAIL PROTECTED] Subject: Re: [agi] A NewMetaphor for Intelligence - the Computer/Organiser To: agi@v2.listbox.com Date: Thursday, September 4, 2008, 12:47 AM Terren, If you think it's all been said, please point me to the philosophy of AI that includes it. A programmed machine is an organized structure. A keyboard (and indeed a computer with keyboard) are something very different - there is no organization to those 26 letters etc. They can be freely combined and sequenced to create an infinity of texts. That is the very essence and manifestly, the whole point, of a keyboard. Yes, the keyboard is only an instrument. But your body - and your brain - which use it, are themselves keyboards. They consist of parts which also have no fundamental behavioural organization - that can be freely combined and sequenced to create an infinity of sequences of movements and thought - dances, texts, speeches, daydreams, postures etc. In abstract logical principle, it could all be preprogrammed. But I doubt that it's possible mathematically - a program for selecting from an infinity of possibilities? And it would be engineering madness - like trying to preprogram a particular way of playing music, when an infinite repertoire is possible and the environment, (in this case musical culture), is changing and evolving with bewildering and unpredictable speed. To look at computers as what they are (are you disputing this?) - machines for creating programs first, and following them second, is a radically different way of looking at computers. It also fits with radically different approaches to DNA - moving away from the idea of DNA as coded program, to something that can be, as it obviously can be, played like a keyboard - see Dennis Noble, The Music of Life. It fits with the fact (otherwise inexplicable) that all intelligences have both deliberate (creative) and automatic (routine) levels - and are not just automatic, like purely programmed computers. And it fits with the way computers are actually used and programmed, rather than the essentially fictional notion of them as pure turing machines. And how to produce creativity is the central problem of AGI - completely unsolved. So maybe a new approach/paradigm is worth at least considering rather than more of the same? I'm not aware of a single idea from any AGI-er past or present that directly addresses that problem - are you? Mike, There's nothing particularly creative about keyboards. The creativity comes from what uses the keyboard. Maybe that was your point, but if so the digression about a keyboard is just confusing. In terms of a metaphor, I'm not sure I understand your point about organizers. It seems to me to refer simply to that which we humans do, which in essence says general intelligence is what we humans do. Unfortunately, I found this last email to be quite muddled. Actually, I am sympathetic to a lot of your ideas, Mike, but I also have to say that your tone is quite condescending. There are a lot of smart people on this list, as one would expect, and a little humility and respect on your part would go a long way. Saying things like You see, AI-ers simply don't understand computers, or understand only half of them. More often than not you position
Re: [agi] draft for comment
--- On Wed, 9/3/08, Pei Wang [EMAIL PROTECTED] wrote: TITLE: Embodiment: Who does not have a body? AUTHOR: Pei Wang ABSTRACT: In the context of AI, ``embodiment'' should not be interpreted as ``giving the system a body'', but as ``adapting to the system's experience''. Therefore, being a robot is neither a sufficient condition nor a necessary condition of being embodied. What really matters is the assumption about the environment for which the system is designed. URL: http://nars.wang.googlepages.com/wang.embodiment.pdf The paper seems to argue that embodiment applies to any system with inputs and outputs, and therefore all AI systems are embodied. However, there are important differences between symbolic systems like NARS and systems with external sensors such as robots and humans. The latter are analog, e.g. the light intensity of a particular point in the visual field, or the position of a joint in an arm. In humans, there is a tremendous amount of data reduction from the senses, from 137 million rods and cones in each eye each firing up to 300 pulses per second, down to 2 bits per second by the time our high level visual perceptions reach long term memory. AI systems have traditionally avoided this type of processing because they lacked the necessary CPU power. IMHO this has resulted in biologically implausible symbolic language models with only a small number of connections between concepts, rather than the tens of thousands of connections per neuron. Another aspect of embodiment (as the term is commonly used), is the false appearance of intelligence. We associate intelligence with humans, given that there are no other examples. So giving an AI a face or a robotic body modeled after a human can bias people to believe there is more intelligence than is actually present. -- Matt Mahoney, [EMAIL PROTECTED] --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
[agi] open models, closed models, priors
A closed model is one that is interpreted as representing all truths about that which is modeled. An open model is instead interpreted as making a specific set of assertions, and leaving the rest undecided. Formally, we might say that a closed model is interpreted to include all of the truths, so that any other statements are false. This is also known as the closed-world assumption. A typical example of an open model is a set of statements in predicate logic. This could be changed to a closed model simply by applying the closed-world assumption. A possibly more typical example of a closed-world model is a computer program that outputs the data so far (and predicts specific future output), as in Solomonoff induction. These two types of model are very different! One important difference is that we can simply *add* to an open model if we need to account for new data, while we must always *modify* a closed model if we want to account for more information. The key difference I want to ask about here is: a length-based bayesian prior seems to apply well to closed models, but not so well to open models. First, such priors are generally supposed to apply to entire joint states; in other words, probability theory itself (and in particular bayesian learning) is built with an assumption of an underlying space of closed models, not open ones. Second, an open model always has room for additional stuff somewhere else in the universe, unobserved by the agent. This suggests that, made probabilistic, open models would generally predict universes with infinite description length. Whatever information was known, there would be an infinite number of chances for other unknown things to be out there; so it seems as if the probability of *something* more being there would converge to 1. (This is not, however, mathematically necessary.) If so, then taking that other thing into account, the same argument would still suggest something *else* was out there, and so on; in other words, a probabilistic open-model-learner would seem to predict a universe with an infinite description length. This does not make it easy to apply the description length principle. I am not arguing that open models are a necessity for AI, but I am curious if anyone has ideas of how to handle this. I know that Pei Wang suggests abandoning standard probability in order to learn open models, for example. --Abram Demski --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] A NewMetaphor for Intelligence - the Computer/Organiser
Mike, The reason I decided that what you are arguing for is essentially an interactive model is this quote: But that is obviously only the half of it.Computers are obviously much more than that - and Turing machines. You just have to look at them. It's staring you in the face. There's something they have that Turing machines don't. See it? Terren? They have - a keyboard. A keyboard is precisely what the interaction theorists are trying to account for! Plus the mouse, the ethernet port, et cetera. Moreover, your general comments fit into the model if interpreted judiciously. You make a distinction between rule-based and creative behavior; rule-based behavior could be thought of as isolated processing of input (receive input, process without interference, output result) while creative behavior is behavior resulting from continual interaction with and exploration of the external world. Your concept of organisms as organizers only makes sense when I see it in this light: a human organizes the environment by interaction with it, while a Turing machine is unable to do this because it cannot explore/experiment/discover. -Abram On Thu, Sep 4, 2008 at 1:07 PM, Mike Tintner [EMAIL PROTECTED] wrote: Abram, Thanks for reply. But I don't understand what you see as the connection. An interaction machine from my brief googling is one which has physical organs. Any factory machine can be thought of as having organs. What I am trying to forge is a new paradigm of a creative, free machine as opposed to that exemplified by most actual machines, which are rational, deterministic machines. The latter can only engage in any task in set ways - and therefore engage and combine their organs in set combinations and sequences. Creative machines have a more or less infinite range of possible ways of going about things, and can combine their organs in a virtually infinite range of combinations, (which gives them a slight advantage, adaptively :) ). Organisms *are* creative machines; computers and robots *could* be (and are, when combined with humans), AGI's will *have* to be. (To talk of creative machines, more specifically, as I did, as keyboards/organisers is to focus on the mechanics of this infinite combinativity of organs). Interaction machines do not seem in any way then to entail what I'm talking about - creative machines - keyboards/ organisers - infinite combinativity - or the *creation,* as quite distinct from *following* of programs/algorithms and routines.. Abram/MT: If you think it's all been said, please point me to the philosophy of AI that includes it. I believe what you are suggesting is best understood as an interaction machine. General references: http://www.cs.brown.edu/people/dqg/Papers/wurzburg.ps http://www.cs.brown.edu/people/pw/papers/ficacm.ps http://www.la-acm.org/Archives/laacm9912.html The concept that seems most relevant to AI is the learning theory provided by inductive turing machines, but I cannot find a good single reference for that. (I am not knowledgable on this subject, I just have heard the idea before.) --Abram --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] open models, closed models, priors
In a closed model, every statement is either true or false. In an open model, every statement is either true or uncertain. In reality, all statements are uncertain, but we have a means to assign them probabilities (not necessarily accurate probabilities). A closed model is unrealistic, but an open model is even more unrealistic because you lack a means of assigning likelihoods to statements like the sun will rise tomorrow or the world will end tomorrow. You absolutely must have a means of guessing probabilities to do anything at all in the real world. -- Matt Mahoney, [EMAIL PROTECTED] --- On Thu, 9/4/08, Abram Demski [EMAIL PROTECTED] wrote: From: Abram Demski [EMAIL PROTECTED] Subject: [agi] open models, closed models, priors To: agi@v2.listbox.com Date: Thursday, September 4, 2008, 2:19 PM A closed model is one that is interpreted as representing all truths about that which is modeled. An open model is instead interpreted as making a specific set of assertions, and leaving the rest undecided. Formally, we might say that a closed model is interpreted to include all of the truths, so that any other statements are false. This is also known as the closed-world assumption. A typical example of an open model is a set of statements in predicate logic. This could be changed to a closed model simply by applying the closed-world assumption. A possibly more typical example of a closed-world model is a computer program that outputs the data so far (and predicts specific future output), as in Solomonoff induction. These two types of model are very different! One important difference is that we can simply *add* to an open model if we need to account for new data, while we must always *modify* a closed model if we want to account for more information. The key difference I want to ask about here is: a length-based bayesian prior seems to apply well to closed models, but not so well to open models. First, such priors are generally supposed to apply to entire joint states; in other words, probability theory itself (and in particular bayesian learning) is built with an assumption of an underlying space of closed models, not open ones. Second, an open model always has room for additional stuff somewhere else in the universe, unobserved by the agent. This suggests that, made probabilistic, open models would generally predict universes with infinite description length. Whatever information was known, there would be an infinite number of chances for other unknown things to be out there; so it seems as if the probability of *something* more being there would converge to 1. (This is not, however, mathematically necessary.) If so, then taking that other thing into account, the same argument would still suggest something *else* was out there, and so on; in other words, a probabilistic open-model-learner would seem to predict a universe with an infinite description length. This does not make it easy to apply the description length principle. I am not arguing that open models are a necessity for AI, but I am curious if anyone has ideas of how to handle this. I know that Pei Wang suggests abandoning standard probability in order to learn open models, for example. --Abram Demski --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] open models, closed models, priors
Matt, My intention here is that there is a basic level of well-defined, crisp models which probabilities act upon; so in actuality the system will never be using a single model, open or closed... (in a hurry now, more comments later) --Abram On Thu, Sep 4, 2008 at 2:47 PM, Matt Mahoney [EMAIL PROTECTED] wrote: In a closed model, every statement is either true or false. In an open model, every statement is either true or uncertain. In reality, all statements are uncertain, but we have a means to assign them probabilities (not necessarily accurate probabilities). A closed model is unrealistic, but an open model is even more unrealistic because you lack a means of assigning likelihoods to statements like the sun will rise tomorrow or the world will end tomorrow. You absolutely must have a means of guessing probabilities to do anything at all in the real world. -- Matt Mahoney, [EMAIL PROTECTED] --- On Thu, 9/4/08, Abram Demski [EMAIL PROTECTED] wrote: From: Abram Demski [EMAIL PROTECTED] Subject: [agi] open models, closed models, priors To: agi@v2.listbox.com Date: Thursday, September 4, 2008, 2:19 PM A closed model is one that is interpreted as representing all truths about that which is modeled. An open model is instead interpreted as making a specific set of assertions, and leaving the rest undecided. Formally, we might say that a closed model is interpreted to include all of the truths, so that any other statements are false. This is also known as the closed-world assumption. A typical example of an open model is a set of statements in predicate logic. This could be changed to a closed model simply by applying the closed-world assumption. A possibly more typical example of a closed-world model is a computer program that outputs the data so far (and predicts specific future output), as in Solomonoff induction. These two types of model are very different! One important difference is that we can simply *add* to an open model if we need to account for new data, while we must always *modify* a closed model if we want to account for more information. The key difference I want to ask about here is: a length-based bayesian prior seems to apply well to closed models, but not so well to open models. First, such priors are generally supposed to apply to entire joint states; in other words, probability theory itself (and in particular bayesian learning) is built with an assumption of an underlying space of closed models, not open ones. Second, an open model always has room for additional stuff somewhere else in the universe, unobserved by the agent. This suggests that, made probabilistic, open models would generally predict universes with infinite description length. Whatever information was known, there would be an infinite number of chances for other unknown things to be out there; so it seems as if the probability of *something* more being there would converge to 1. (This is not, however, mathematically necessary.) If so, then taking that other thing into account, the same argument would still suggest something *else* was out there, and so on; in other words, a probabilistic open-model-learner would seem to predict a universe with an infinite description length. This does not make it easy to apply the description length principle. I am not arguing that open models are a necessity for AI, but I am curious if anyone has ideas of how to handle this. I know that Pei Wang suggests abandoning standard probability in order to learn open models, for example. --Abram Demski --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] open models, closed models, priors
Matt, I'm confused here. What I mean is that in real life, the probabilities are mathematically incalculable, period, a good deal of the time - you cannot go, as you v. helpfully point out, much beyond saying this is fairly probable, may happen, there's some chance.. And those words are fairly good reflections of how we actually reason and anti-calculate probabilities -*without* numbers or any maths... And such non-mathematical vagueness seems foundational for AGI. You can't, for example, calculate mathematically the likeness or the truthfulness of metaphorical terms - of storms and swirling milk in a teacup. Not even provisionally. My understanding is that AGI-ers still persist in trying to use numbers, and you seem, in your first sentence, to be advocating the same. Matt: I mean that you have to assign likelihoods to beliefs, even if the numbers are wrong. Logic systems where every statement is true or false simply are too brittle to scale beyond toy problems. Everything in life is uncertain, including the degree of uncertainty. That's why we use terms like probably, maybe, etc. instead of numbers. -- Matt:You absolutely must have a means of guessing probabilities to do anything at all in the real world MT: Do you mean mathematically? Estimating chances as roughly, even if provisionally, 0.70? If so, manifestly, that is untrue. What are your chances that you will get lucky tonight? Will an inability to guess the probability stop you trying? Most of the time, arguably, we have to and do, act on the basis of truly vague magnitudes - a mathematically horrendously rough sense of probability. Or just: what the heck - what's the worst that can happen? Let's do it. And let's just pray it works out. How precise a sense of the probabilities attending his current decisions does even a professionally mathematical man like Bernanke have? Only AGI's in a virtual world can live with cosy, mathematically calculable uncertainty. Living in the real world is as Kauffman points out to a great extent living with *mystery*. What are the maths of mystery? Do you think Ben has the least realistic idea of the probabilities affecting his AGI projects? That's not how most creative projects get done, or life gets lived. Quadrillions, Matt, schmazillions. --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] open models, closed models, priors
Mike, standard Bayesianism somewhat accounts for this-- exact-number probabilities are defined by the math, but in no way are they seen as the real probability values. A subjective prior is chosen, which defines all further probabilities, but that prior is not believed to be correct. Subsequent experience tends to outweigh the prior, so the probabilities after experience are much more accurate than before, even though they are still not perfectly accurate. And while we may not be able to articulate our exact belief levels in English, I could still argue that the level of activation in the brain is a precise value. So, just because an AI uses probabilities at the implementation level does not mean it would be able to articulate exact numbers consciously. Of course, none of this is an argument *for* the use of probabilities... --Abram On Thu, Sep 4, 2008 at 5:29 PM, Mike Tintner [EMAIL PROTECTED] wrote: Matt, I'm confused here. What I mean is that in real life, the probabilities are mathematically incalculable, period, a good deal of the time - you cannot go, as you v. helpfully point out, much beyond saying this is fairly probable, may happen, there's some chance.. And those words are fairly good reflections of how we actually reason and anti-calculate probabilities -*without* numbers or any maths... And such non-mathematical vagueness seems foundational for AGI. You can't, for example, calculate mathematically the likeness or the truthfulness of metaphorical terms - of storms and swirling milk in a teacup. Not even provisionally. My understanding is that AGI-ers still persist in trying to use numbers, and you seem, in your first sentence, to be advocating the same. Matt: I mean that you have to assign likelihoods to beliefs, even if the numbers are wrong. Logic systems where every statement is true or false simply are too brittle to scale beyond toy problems. Everything in life is uncertain, including the degree of uncertainty. That's why we use terms like probably, maybe, etc. instead of numbers. -- Matt:You absolutely must have a means of guessing probabilities to do anything at all in the real world MT: Do you mean mathematically? Estimating chances as roughly, even if provisionally, 0.70? If so, manifestly, that is untrue. What are your chances that you will get lucky tonight? Will an inability to guess the probability stop you trying? Most of the time, arguably, we have to and do, act on the basis of truly vague magnitudes - a mathematically horrendously rough sense of probability. Or just: what the heck - what's the worst that can happen? Let's do it. And let's just pray it works out. How precise a sense of the probabilities attending his current decisions does even a professionally mathematical man like Bernanke have? Only AGI's in a virtual world can live with cosy, mathematically calculable uncertainty. Living in the real world is as Kauffman points out to a great extent living with *mystery*. What are the maths of mystery? Do you think Ben has the least realistic idea of the probabilities affecting his AGI projects? That's not how most creative projects get done, or life gets lived. Quadrillions, Matt, schmazillions. --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] draft for comment
On Thu, Sep 4, 2008 at 8:56 AM, Valentina Poletti [EMAIL PROTECTED] wrote: I agree with Pei in that a robot's experience is not necessarily more real than that of a, say, web-embedded agent - if anything it is closer to the human experience of the world. But who knows how limited our own sensory experience is anyhow. Perhaps a better intelligence would comprehend the world better through a different emboyment. Exactly, the world to a system is always limited by the system's I/O channels, and for systems with different I/O channels, their worlds are different in many aspects, but no one is more real than the others. However, could you guys be more specific regarding the statistical differences of different types of data? What kind of differences are you talking about specifically (mathematically)? And what about the differences at the various levels of the dual-hierarchy? Has any of your work or research suggested this hypothesis, if so which? It is Ben who suggested the statistical differences and the dual-hierarchy, while I'm still not convinced about their value. My own constructive work on this topic can be found in http://nars.wang.googlepages.com/wang.semantics.pdf Pei --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] A NewMetaphor for Intelligence - the Computer/Organiser
On Wednesday 03 September 2008, Mike Tintner wrote: And as a matter of scientific, historical fact, computers are first and foremost keyboards - i.e.devices for CREATING programs on keyboards, - and only then following them. [Remember how AI gets almost everything about intelligence back to front?] There is not and never has been a program that wasn't first created on a keyboard. Indisputable fact. Almost everything that happens in computers happens via the keyboard. http://heybryan.org/mediawiki/index.php/Egan_quote So what exactly is a keyboard? Well, like all keyboards whether of computers, musical instruments or typewriters, it is a creative instrument. And what makes it creative is that it is - you could say - an organiser. Then you're starting to get into (some well needed) complexity science. A device with certain organs (in this case keys) that are designed to be creatively organised - arranged in creative, improvised (rather than programmed) sequences of action/ association./organ play. Yes, but the genotype isn't the phenotype and the translation from the 'code', the intentions of the programmer and so on to the expressions is 'hard' - people get so caught up in folk psychology that it's maddening. And an extension of the body. Of the organism. All organisms are organisers - devices for creatively sequencing actions/ associations./organs/ nervous systems first and developing fixed, orderly sequences/ routines/ programs second. Some (I) say that neural systems are somewhat like optimizers, which are heavily used in compilers that are compiling your programs anyway, so be careful: the difference might not be that broad. - Bryan http://heybryan.org/ Engineers: http://heybryan.org/exp.html irc.freenode.net #hplusroadmap --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] A NewMetaphor for Intelligence - the Computer/Organiser
Abram, Thanks. V. helpful and interesting. Yes, on further examination, these interactionist guys seem, as you say, to be trying to take into account the embeddedness of the computer. But no, there's still a huge divide between them and me. I would liken them in the context of this discussion, to Pei who tries to argue that NARS is non-algorithmic, because the program is continuously changing. - and therefore satisfies the objections of classical objectors to AI/AGI. Well, both these guys and Pei are still v. much algorithmic in any reasonable sense of the word - still following *structures,* if v. sophisticated (and continuously changing) structures, of thought. And what I am asserting is a paradigm of a creative machine, which starts as, and is, NON-algorithmic and UNstructured in all its activities, albeit that it acquires and creates a multitude of algorithms, or routines/structures, for *parts* of those activities. For example, when you write a post, nearly every word and a great many phrases and even odd sentences, will be automatically, algorithmically produced. But the whole post, and most paras will *not* be - and *could not* be. A creative machine has infinite combinative potential. An algorithmic, programmed machine has strictly limited combinativity.. And a keyboard is surely the near perfect symbol of infinite, unstructured combinativity. It is being, and has been, used in endlessly creative ways - and is, along with the blank page and pencil, the central tool of our civilisation's creativity. Those randomly arranged letters - clearly designed to be infinitely recombined - are the antithesis of a programmed machine. So however those guys account for that keyboard, I don't see them as in any way accounting for it in my sense, or in its true, full usage. But thanks for your comments. (Oh and I did understand re Bayes - I was and am still arguing he isn't valid in many cases, period). Mike, The reason I decided that what you are arguing for is essentially an interactive model is this quote: But that is obviously only the half of it.Computers are obviously much more than that - and Turing machines. You just have to look at them. It's staring you in the face. There's something they have that Turing machines don't. See it? Terren? They have - a keyboard. A keyboard is precisely what the interaction theorists are trying to account for! Plus the mouse, the ethernet port, et cetera. Moreover, your general comments fit into the model if interpreted judiciously. You make a distinction between rule-based and creative behavior; rule-based behavior could be thought of as isolated processing of input (receive input, process without interference, output result) while creative behavior is behavior resulting from continual interaction with and exploration of the external world. Your concept of organisms as organizers only makes sense when I see it in this light: a human organizes the environment by interaction with it, while a Turing machine is unable to do this because it cannot explore/experiment/discover. -Abram On Thu, Sep 4, 2008 at 1:07 PM, Mike Tintner [EMAIL PROTECTED] wrote: Abram, Thanks for reply. But I don't understand what you see as the connection. An interaction machine from my brief googling is one which has physical organs. Any factory machine can be thought of as having organs. What I am trying to forge is a new paradigm of a creative, free machine as opposed to that exemplified by most actual machines, which are rational, deterministic machines. The latter can only engage in any task in set ways - and therefore engage and combine their organs in set combinations and sequences. Creative machines have a more or less infinite range of possible ways of going about things, and can combine their organs in a virtually infinite range of combinations, (which gives them a slight advantage, adaptively :) ). Organisms *are* creative machines; computers and robots *could* be (and are, when combined with humans), AGI's will *have* to be. (To talk of creative machines, more specifically, as I did, as keyboards/organisers is to focus on the mechanics of this infinite combinativity of organs). Interaction machines do not seem in any way then to entail what I'm talking about - creative machines - keyboards/ organisers - infinite combinativity - or the *creation,* as quite distinct from *following* of programs/algorithms and routines.. Abram/MT: If you think it's all been said, please point me to the philosophy of AI that includes it. I believe what you are suggesting is best understood as an interaction machine. General references: http://www.cs.brown.edu/people/dqg/Papers/wurzburg.ps http://www.cs.brown.edu/people/pw/papers/ficacm.ps http://www.la-acm.org/Archives/laacm9912.html The concept that seems most relevant to AI is the learning theory provided by inductive turing machines, but I cannot find a good single
Re: [agi] A NewMetaphor for Intelligence - the Computer/Organiser
On Thursday 04 September 2008, Terren Suydam wrote: Thus is creativity possible while preserving determinism. Of course, you still need to have an explanation for how creativity emerges in either case, but in contrast to what you said before, some AI folks have indeed worked on this issue. http://heybryan.org/mediawiki/index.php/Egan_quote Egan solved that particular problem. It's about creation -- even if you have the most advanced mathematical theory of the universe, you just made it slightly more recursive and so on just by shuffling around neurotransmitters in your head. - Bryan http://heybryan.org/ Engineers: http://heybryan.org/exp.html irc.freenode.net #hplusroadmap --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] Recursive self-change: some definitions
On Wednesday 03 September 2008, Mike Tintner wrote: I think this is a good important point. I've been groping confusedly here. It seems to me computation necessarily involves the idea of using a code (?). But the nervous system seems to me something capable of functioning without a code - directly being imprinted on by the world, and directly forming movements, (even if also involving complex hierarchical processes), without any code. I've been wondering whether computers couldn't also be designed to function without a code in somewhat similar fashion. Any thoughts or ideas of your own? Hold on there -- the brain most certainly has a code, if you will remember the gene expression and the general neurophysical nature of it all. I think partly the difference you might be seeing here is how much more complex and grown the brain is in comparison to somewhat fragile circuits and the ecological differences between the WWW and the combined evolutionary history keeping your neurons healthy each day. Anyway, because of the quantified nature of energy in general, the brain must be doing something physical and operating on a code, or i.e. have an actual nature to it. I would like to see alternatives to this line of reasoning, of course. As for computers that don't have to be executing code all of the time. I've been wondering about machines that could also imitate the biological ability to recover from errors and not spontaneously burst into flames when something goes wrong in the Source. Clearly there's something of interest here. - Bryan who has gone 36 hours without sleep. Why am I here? http://heybryan.org/ Engineers: http://heybryan.org/exp.html irc.freenode.net #hplusroadmap --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] A NewMetaphor for Intelligence - the Computer/Organiser
On Thursday 04 September 2008, Mike Tintner wrote: And what I am asserting is a paradigm of a creative machine, which starts as, and is, NON-algorithmic and UNstructured in all its activities, albeit that it acquires and creates a multitude of algorithms, or routines/structures, for *parts* of those activities. For example, when you write a post, nearly every word and a great many phrases and even odd sentences, will be automatically, algorithmically produced. But the whole post, and most paras will *not* be - and *could not* be. Here's an alternative formulation for you to play with, Mike. I suspect it is still possible to consider it a creative machine even with an algorithmic basis *because* it is the nature of reality itself to compute these things; there is nothing that can have as much information about the moment than the moment itself, and thus why there's still this element of stochasticity and creativity that we see, even if we say that the brain is deterministic and so on. - Bryan http://heybryan.org/ Engineers: http://heybryan.org/exp.html irc.freenode.net #hplusroadmap --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] A NewMetaphor for Intelligence - the Computer/Organiser
On Wednesday 03 September 2008, Mike Tintner wrote: And how to produce creativity is the central problem of AGI - completely unsolved. So maybe a new approach/paradigm is worth at least considering rather than more of the same? I'm not aware of a single idea from any AGI-er past or present that directly addresses that problem - are you? Mike, one of the big problems in computer science is the prediction of genotypes from phenotypes in general problem spaces. So far, from what I've learned, we haven't a way to guarantee that a resulting process is going to be creative. So it's not going to be solved per-se in the traditional sense of hey look, here's a foolproof equivalency of creativity. I truly hope I am wrong. This is a good way to be wrong about the whole thing, I must admit. - Bryan http://heybryan.org/ Engineers: http://heybryan.org/exp.html irc.freenode.net #hplusroadmap --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] A NewMetaphor for Intelligence - the Computer/Organiser
On Thursday 04 September 2008, Mike Tintner wrote: Do you honestly think that you write programs in a programmed way? That it's not an *art* pace Matt, full of hesitation, halts, meandering, twists and turns, dead ends, detours etc? If you have to have some sort of program to start with, how come there is no sign of that being true, in the creative process of programmers actually writing programs? Two notes on this one. I'd like to see fMRI studies of programmers having at it. I've seen this of authors, but not of programmers per-se. It would be interesting. But this isn't going to work because it'll just show you lots of active regions of the brain and what good does that do you? Another thing I would be interested in showing to people is all of those dead ends and turns that one makes when traveling down those paths. I've sometimes been able to go fully into a recording session where I could write about a few minutes of decisions for hours on end afterwards, but it's just not efficient to getting the point across. I've sometimes wanted to do this for web crawling, when I do my browsing and reading, and at least somewhat track my jumps from page to page and so on, or even in my own grammar and writing so that I can make sure I optimize it :-) and so that I can see where I was going or not going :-) but any solution that requires me to type even /more/ will be a sort of contradiction, since then I will have to type even more, and more. Bah, unused data in the brain should help work with this stuff. Tabletop fMRI and EROS and so on. Fun stuff. Neurobiofeedback. - Bryan http://heybryan.org/ Engineers: http://heybryan.org/exp.html irc.freenode.net #hplusroadmap --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] draft for comment
On Thu, Sep 4, 2008 at 9:35 AM, Ben Goertzel [EMAIL PROTECTED] wrote: I understand that a keyboard and touchpad do provide proprioceptive input, but I think it's too feeble, and too insensitively respondent to changes in the environment and the relation btw the laptop and the environment, to serve as the foundation for a robust self-model or a powerful general intelligence. Compared to what? Of course the human sensors are much more complicated, but many robot sensors are no better, so why they are considered as real, while keyboard and touchpad are not? Of course I'm not really arguing that keyboard and touchpad are all we'll need for AGI (I plan to play with robots myself), but that there is no fundamental difference between what we call 'robot' and what we call 'computer', as far as the 'embodiment' discussion is concerned. Robot is just special-purpose computer with I/O not designed for human users. Of course it won't have a visual concept of self, but a system like NARS has the potential to grow into an intelligent operating system, with a notion of self based on what it can feel and do, as well as the causal relations among them --- If there is a file in this folder, then I should have felt it, it cannot be there because I've deleted the contents. My suggestion is that the file system lacks the complexity of structure and dynamics to support the emergence of a robust self-model, and powerful general intelligence... Sure. I just used file managing as a simple example. What if the AI have full control of the system's hardware and software, and can use them in novel ways to solve all kinds of problems unknown to it previously, without human involvement? I would call a self any internal, explicit model that a system creates that allows it to predict its own behaviors in a sufficient variety of contexts This need not have a visual aspect nor a great similarity to a human self. I'd rather not call it a 'model', though won't argue on this topic --- 'embodiment' is already confusing enough, so 'self' is better to wait, otherwise someone will even add 'consciousness' into the discussion. ;-) Pei --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] A NewMetaphor for Intelligence - the Computer/Organiser
On Thursday 04 September 2008, Valentina Poletti wrote: When we want to step further and create an AGI I think we want to externalize the very ability to create technology - we want the environment to start adapting to us by itself, spontaneously by gaining our goals. There is a sense of resilience in the whole scheme of things. It's not hard to show how stupid each one of us can be in a single moment; but luckily our stupid decisions don't blow us up [often] - it's not so much luck as it might be resilience. In an earlier email to which I replied today, Mike was looking for a resilient computer that didn't need code. On another note: goals are an interesting folk psychology mechanism. I've seen other cultures afflict their own goals upon their environment, sort of how the brain contains a map of the skin for sensory representation, the same with the environment to their own goals and aspirations in life. What alternatives to goals could you do when doing programming? Otherwise you'll not end up with Mike's requested 'resilient computer' as I'm calling it. - Bryan http://heybryan.org/ Engineers: http://heybryan.org/exp.html irc.freenode.net #hplusroadmap --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] open models, closed models, priors
On Thursday 04 September 2008, Matt Mahoney wrote: A closed model is unrealistic, but an open model is even more unrealistic because you lack a means of assigning likelihoods to statements like the sun will rise tomorrow or the world will end tomorrow. You absolutely must have a means of guessing probabilities to do anything at all in the real world. I don't assign or guess probabilities and I seem to get things done. What gives? - Bryan http://heybryan.org/ Engineers: http://heybryan.org/exp.html irc.freenode.net #hplusroadmap --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] draft for comment
On Thursday 04 September 2008, Matt Mahoney wrote: Another aspect of embodiment (as the term is commonly used), is the false appearance of intelligence. We associate intelligence with humans, given that there are no other examples. So giving an AI a face or a robotic body modeled after a human can bias people to believe there is more intelligence than is actually present. I'm still waiting until you guys could show me a psychometric test that has a one-to-one correlation with the bioinformatics and neuroinformatics and then thus could be approached with a physical model down at the biophysics. Otherwise the 'false appearance of intelligence' is a truism - intelligence is false. What then? (Would you give up making brains and such systems? I'm just wondering. It's an interesting scenario.) - Bryan http://heybryan.org/ Engineers: http://heybryan.org/exp.html irc.freenode.net #hplusroadmap --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] open models, closed models, priors
On Thursday 04 September 2008, Abram Demski wrote: My intention here is that there is a basic level of well-defined, crisp models which probabilities act upon; so in actuality the system will never be using a single model, open or closed... I think Mike's model is one more of approach, creativity and action rather than a formalized system existing in some quasi-state between open and closed. I'm not sure if the epistemiologies are meshing here. Hrm. - Bryan http://heybryan.org/ Engineers: http://heybryan.org/exp.html irc.freenode.net #hplusroadmap --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] draft for comment
On Thu, Sep 4, 2008 at 10:04 AM, Ben Goertzel [EMAIL PROTECTED] wrote: Hi Pei, I think your point is correct that the notion of embodiment presented by Brooks and some other roboticists is naive. I'm not sure whether their actual conceptions are naive, or whether they just aren't presenting their foundational philosophical ideas clearly in their writings (being ultimately more engineering-oriented people, and probably not that accustomed to the philosophical style of discourse in which these sorts of definitional distinctions need to be more precisely drawn). To a large extent, their position is an reaction to the 'disembodied' symbolic AI, though they get the issue wrong. The symbolic AI is indeed 'disembodied', but it is not because computers have no body (or sensorimotor devices), but that the systems are designed to ignore their body and their experience. Therefore, the solution should not be to get a (robotic) body, but to take experience into account. I do think (in approximate concurrence with your paper) that ANY control system physically embodied in a physical system S, that has an input and output stream, and whose input and output stream possess correlation with the physical state of S, should be considered as psychologically embodied. Clearly, whether it's a robot or a laptop (w/o network connection if you like), such a system has the basic property of embodiment. Yes, though I'd neither say possess correlation with the physical state (which is the terminology of model-theoretic semantics), nor psychologically embodied (which still sounds like a second-rate substitute of physically embodied). Furthermore S doesn't need to be a physical system ... it could be a virtual system inside some virtual world (and then there's the question of what properties characterize a valid virtual world ... but let's leave that for another email thread...) Every system (in this discussion) is a physical system. It is just that sometimes we can ignore its physical properties. However, I think that not all psychologically-embodied systems possess a sufficiently rich psychological-embodiment to lead to significantly general intelligence My suggestion is that a laptop w/o network connection or odd sensor-peripherals, probably does not have sufficiently rich correlations btw its I/O stream and its physical state, to allow it to develop a robust self-model of its physical self (which can then be used as a basis for a more general phenomenal self). That is a separate issue. If a system's I/O devices are very simple, it cannot produce rich behaviors. However, the problem is not caused by 'disembodiment'. We cannot say that a body much reach a certain complexity to be called a 'body'. I think that Varela and crew understood the value of this rich network of correlations, but mistakenly assumed it to be a unique property of biological systems... Agree. I realize that the points you made in your paper do not contradict the suggestions I've made in this email. I don't think anything significant in your paper is wrong, actually. It just seems to me not to address the most interesting aspects of the embodiment issue as related to AGI. Understand. Pei --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] A NewMetaphor for Intelligence - the Computer/Organiser
Terren: I agree in spirit with your basic criticisms regarding current AI and creativity. However, it must be pointed out that if you abandon determinism, you find yourself in the world of dualism, or worse. Nah. One word (though it would take too long here to explain) ; nondeterministic programming. Terren: you still need to have an explanation for how creativity emerges in either case, but in contrast to what you said before, some AI folks have indeed worked on this issue. Oh, they've done loads of work, often fine work, i.e. produced impressive but 'hack' variations on themes, musical, artistic, scripting etc. But the people actually producing those creative/hack variations, will agree, when pressed that they are not truly creative. And actual AGI-ers, to repeat, AFAIK have not produced a single idea about how machines can be creative. Not even a proposal, however wrong. Please point to one. P.S. Glad to see your evolutionary perspective includes the natural kind - I had begun to think, obviously wrongly, that it didn't. --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] draft for comment
On Thu, Sep 4, 2008 at 2:22 PM, Matt Mahoney [EMAIL PROTECTED] wrote: The paper seems to argue that embodiment applies to any system with inputs and outputs, and therefore all AI systems are embodied. No. It argues that since every system has inputs and outputs, 'embodiment', as a non-trivial notion, should be interpreted as taking experience into account when behaves. Therefore, traditional symbolic AI systems, like CYC, is still disembodied. However, there are important differences between symbolic systems like NARS and systems with external sensors such as robots and humans. NARS, when implemented, has input/output, and therefore has external sensors. I guess you still see NARS as using model-theoretic semantics, so you call it symbolic and contrast it with system with sensors. This is not correct --- see http://nars.wang.googlepages.com/wang.semantics.pdf and http://nars.wang.googlepages.com/wang.AI_Misconceptions.pdf The latter are analog, e.g. the light intensity of a particular point in the visual field, or the position of a joint in an arm. In humans, there is a tremendous amount of data reduction from the senses, from 137 million rods and cones in each eye each firing up to 300 pulses per second, down to 2 bits per second by the time our high level visual perceptions reach long term memory. Within a certain accuracy, 'digital' and 'analog' have no fundamental difference. I hope you are not arguing that only analog system can be embodied. AI systems have traditionally avoided this type of processing because they lacked the necessary CPU power. IMHO this has resulted in biologically implausible symbolic language models with only a small number of connections between concepts, rather than the tens of thousands of connections per neuron. You have made this point on CPU power several times, and I'm still not convinced that the bottleneck of AI is hardware capacity. Also, there is no reason to believe an AGI must be designed in a biologically plausible way. Another aspect of embodiment (as the term is commonly used), is the false appearance of intelligence. We associate intelligence with humans, given that there are no other examples. So giving an AI a face or a robotic body modeled after a human can bias people to believe there is more intelligence than is actually present. I agree with you on this point, though will not argue so in the paper --- it is like to call the roboticists cheating, even though it is indeed the case that works in robotics are much easier to get public attention. Pei --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] open models, closed models, priors
Abram, I agree with the spirit of your post, and I even go further to include being open in my working definition of intelligence --- see http://nars.wang.googlepages.com/wang.logic_intelligence.pdf I also agree with your comment on Solomonoff induction and Bayesian prior. However, I talk about open system, not open model, because I think model-theoretic semantics is the wrong theory to be used here --- see http://nars.wang.googlepages.com/wang.semantics.pdf Pei On Thu, Sep 4, 2008 at 2:19 PM, Abram Demski [EMAIL PROTECTED] wrote: A closed model is one that is interpreted as representing all truths about that which is modeled. An open model is instead interpreted as making a specific set of assertions, and leaving the rest undecided. Formally, we might say that a closed model is interpreted to include all of the truths, so that any other statements are false. This is also known as the closed-world assumption. A typical example of an open model is a set of statements in predicate logic. This could be changed to a closed model simply by applying the closed-world assumption. A possibly more typical example of a closed-world model is a computer program that outputs the data so far (and predicts specific future output), as in Solomonoff induction. These two types of model are very different! One important difference is that we can simply *add* to an open model if we need to account for new data, while we must always *modify* a closed model if we want to account for more information. The key difference I want to ask about here is: a length-based bayesian prior seems to apply well to closed models, but not so well to open models. First, such priors are generally supposed to apply to entire joint states; in other words, probability theory itself (and in particular bayesian learning) is built with an assumption of an underlying space of closed models, not open ones. Second, an open model always has room for additional stuff somewhere else in the universe, unobserved by the agent. This suggests that, made probabilistic, open models would generally predict universes with infinite description length. Whatever information was known, there would be an infinite number of chances for other unknown things to be out there; so it seems as if the probability of *something* more being there would converge to 1. (This is not, however, mathematically necessary.) If so, then taking that other thing into account, the same argument would still suggest something *else* was out there, and so on; in other words, a probabilistic open-model-learner would seem to predict a universe with an infinite description length. This does not make it easy to apply the description length principle. I am not arguing that open models are a necessity for AI, but I am curious if anyone has ideas of how to handle this. I know that Pei Wang suggests abandoning standard probability in order to learn open models, for example. --Abram Demski --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
[agi] How to Guarantee Creativity...
Mike Tintner wrote: And how to produce creativity is the central problem of AGI - completely unsolved. So maybe a new approach/paradigm is worth at least considering rather than more of the same? I'm not aware of a single idea from any AGI-er past or present that directly addresses that problem - are you? Bryan; Mike, one of the big problems in computer science is the prediction of genotypes from phenotypes in general problem spaces. So far, from what I've learned, we haven't a way to guarantee that a resulting process is going to be creative. So it's not going to be solved per-se in the traditional sense of hey look, here's a foolproof equivalency of creativity. I truly hope I am wrong. This is a good way to be wrong about the whole thing, I must admit. Bryan, Thanks for comments. First, you definitely sound like you will enjoy and benefit from Kauffman's Reinventing the Sacred - v. much extending your 1st sentence. Second, you have posed a fascinating challenge. How can one guarantee creativity? I was going to say but of course not, you can only guarantee non-creativity by using programs and rational systems. True creativity can be extremely laborious and involve literally far-fetched associations. But actually, yes, I think you may be able to guarantee creativity with a high degree of probability. That is, low-level creativity. Not social creativity - creative associations that no one in society has thought of before. But personal creativity. Novel personal associations that if not striking fit the definition. Let's see. Prepare to conduct an experiment. I will show you a series of associations - you will quickly grasp the underlying principle - you must, *thinking visually*, continue freely associating with the last one (or, actually, any one). See what your mind comes up with - and let's judge the results. (Everyone else is encouraged to try this too - in the interests of scientific investigation). http://www.bearskinrug.co.uk/_articles/2005/09/16/doodle/hero.jpg [Alternatively, simply start with an image of a snake, and freely, visually associate with that.] P.S. You will notice, Bryan, that this test - these metamorphoses - are related to the nature of the evolution of new species from old. --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] A NewMetaphor for Intelligence - the Computer/Organiser
Bryan, You start v. constructively thinking how to test the non-programmed nature of - or simply record - the actual writing of programs, and then IMO fail to keep going. There have to be endless more precise ways than trying to look at their brain. Verbal protocols. Ask them to use the keyboard for everything - (how much do you guys use the keyboard vs say paper or other things?) - and you can automatically record key-presses. If they use paper, find a surface that records the pen strokes. Combine with a camera recording them. Come on, you must be able to give me still more ways - there are multiple possible recording technologies, no? Hasn't anyone done this in any shape or form? It might sound as if it would produce terribly complicated results, but my guess is that they would be fascinating just to look at (and compare technique) as well as analyse. Bryan/MT: Do you honestly think that you write programs in a programmed way? That it's not an *art* pace Matt, full of hesitation, halts, meandering, twists and turns, dead ends, detours etc? If you have to have some sort of program to start with, how come there is no sign of that being true, in the creative process of programmers actually writing programs? Two notes on this one. I'd like to see fMRI studies of programmers having at it. I've seen this of authors, but not of programmers per-se. It would be interesting. But this isn't going to work because it'll just show you lots of active regions of the brain and what good does that do you? Another thing I would be interested in showing to people is all of those dead ends and turns that one makes when traveling down those paths. I've sometimes been able to go fully into a recording session where I could write about a few minutes of decisions for hours on end afterwards, but it's just not efficient to getting the point across. I've sometimes wanted to do this for web crawling, when I do my browsing and reading, and at least somewhat track my jumps from page to page and so on, or even in my own grammar and writing so that I can make sure I optimize it :-) and so that I can see where I was going or not going :-) but any solution that requires me to type even /more/ will be a sort of contradiction, since then I will have to type even more, and more. Bah, unused data in the brain should help work with this stuff. Tabletop fMRI and EROS and so on. Fun stuff. Neurobiofeedback. - Bryan http://heybryan.org/ Engineers: http://heybryan.org/exp.html irc.freenode.net #hplusroadmap --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] open models, closed models, priors
--- On Thu, 9/4/08, Bryan Bishop [EMAIL PROTECTED] wrote: On Thursday 04 September 2008, Matt Mahoney wrote: A closed model is unrealistic, but an open model is even more unrealistic because you lack a means of assigning likelihoods to statements like the sun will rise tomorrow or the world will end tomorrow. You absolutely must have a means of guessing probabilities to do anything at all in the real world. I don't assign or guess probabilities and I seem to get things done. What gives? Yes you do. Every time you make a decision, you are assigning a higher probability of a good outcome to your choice than to the alternative. -- Matt Mahoney, [EMAIL PROTECTED] --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] Recursive self-change: some definitions
Bryan, How do you know the brain has a code? Why can't it be entirely impression-istic - a system for literally forming, storing and associating sensory impressions (including abstracted, simplified, hierarchical impressions of other impressions)? 1). FWIW some comments from a cortically knowledgeable robotics friend: The issue mentioned below is a major factor for die-hard card-carrying Turing-istas, and to me is also their greatest stumbling-block. You called it a code, but I see computation basically involves setting up a model or description of something, but many people think this is actually synonomous with the real-thing. It's not, but many people are in denial about this. All models involves tons of simplifying assumptions. EG, XXX is adamant that the visual cortex performs sparse-coded [whatever that means] wavelet transforms, and not edge-detection. To me, a wavelet transform is just one possible - and extremely simplistic (meaning subject to myriad assumptions) - mathematical description of how some cells in the VC appear to operate. Real biological systems are immensely more complex than our simple models. Eg, every single cell in the body contains the entire genome, and genes are being turned on+off continually during normal operation, and based upon an immense #feedback loops in the cells, and not just during reproduction. On and on. 2) I vaguely recall de Bono having a model of an imprintable surface that was non-coded: http://en.wikipedia.org/wiki/The_Mechanism_of_the_Mind (But I think you may have to read the book. Forgive me if I'm wrong). 3) Do you know anyone who has thought of using or designing some kind of computer as an imprintable rather than just a codable medium? Perhaps that is somehow possible. PS Go to bed. :) Bryan/MT : I think this is a good important point. I've been groping confusedly here. It seems to me computation necessarily involves the idea of using a code (?). But the nervous system seems to me something capable of functioning without a code - directly being imprinted on by the world, and directly forming movements, (even if also involving complex hierarchical processes), without any code. I've been wondering whether computers couldn't also be designed to function without a code in somewhat similar fashion. Any thoughts or ideas of your own? Hold on there -- the brain most certainly has a code, if you will remember the gene expression and the general neurophysical nature of it all. I think partly the difference you might be seeing here is how much more complex and grown the brain is in comparison to somewhat fragile circuits and the ecological differences between the WWW and the combined evolutionary history keeping your neurons healthy each day. Anyway, because of the quantified nature of energy in general, the brain must be doing something physical and operating on a code, or i.e. have an actual nature to it. I would like to see alternatives to this line of reasoning, of course. As for computers that don't have to be executing code all of the time. I've been wondering about machines that could also imitate the biological ability to recover from errors and not spontaneously burst into flames when something goes wrong in the Source. Clearly there's something of interest here. - --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] A NewMetaphor for Intelligence - the Computer/Organiser
OK, I'll bite: what's nondeterministic programming if not a contradiction? --- On Thu, 9/4/08, Mike Tintner [EMAIL PROTECTED] wrote: Nah. One word (though it would take too long here to explain) ; nondeterministic programming. --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] open models, closed models, priors
Pei, I sympathize with your care in wording, because I'm very aware of the strange meaning that the word model takes on in formal accounts of semantics. While a cognitive scientist might talk about a person's model of the world, a logician would say that the world is a model of a first-order theory. I do want to avoid the second meaning. But, I don't think I could fare well by saying system instead, because the models are only a part of the larger system... so I'm not sure there is a word that is both neutral and sufficiently meaningful. Do you think it is impossible to apply probability to open models/theories/systems, or merely undesirable? On Thu, Sep 4, 2008 at 8:10 PM, Pei Wang [EMAIL PROTECTED] wrote: Abram, I agree with the spirit of your post, and I even go further to include being open in my working definition of intelligence --- see http://nars.wang.googlepages.com/wang.logic_intelligence.pdf I also agree with your comment on Solomonoff induction and Bayesian prior. However, I talk about open system, not open model, because I think model-theoretic semantics is the wrong theory to be used here --- see http://nars.wang.googlepages.com/wang.semantics.pdf Pei On Thu, Sep 4, 2008 at 2:19 PM, Abram Demski [EMAIL PROTECTED] wrote: A closed model is one that is interpreted as representing all truths about that which is modeled. An open model is instead interpreted as making a specific set of assertions, and leaving the rest undecided. Formally, we might say that a closed model is interpreted to include all of the truths, so that any other statements are false. This is also known as the closed-world assumption. A typical example of an open model is a set of statements in predicate logic. This could be changed to a closed model simply by applying the closed-world assumption. A possibly more typical example of a closed-world model is a computer program that outputs the data so far (and predicts specific future output), as in Solomonoff induction. These two types of model are very different! One important difference is that we can simply *add* to an open model if we need to account for new data, while we must always *modify* a closed model if we want to account for more information. The key difference I want to ask about here is: a length-based bayesian prior seems to apply well to closed models, but not so well to open models. First, such priors are generally supposed to apply to entire joint states; in other words, probability theory itself (and in particular bayesian learning) is built with an assumption of an underlying space of closed models, not open ones. Second, an open model always has room for additional stuff somewhere else in the universe, unobserved by the agent. This suggests that, made probabilistic, open models would generally predict universes with infinite description length. Whatever information was known, there would be an infinite number of chances for other unknown things to be out there; so it seems as if the probability of *something* more being there would converge to 1. (This is not, however, mathematically necessary.) If so, then taking that other thing into account, the same argument would still suggest something *else* was out there, and so on; in other words, a probabilistic open-model-learner would seem to predict a universe with an infinite description length. This does not make it easy to apply the description length principle. I am not arguing that open models are a necessity for AI, but I am curious if anyone has ideas of how to handle this. I know that Pei Wang suggests abandoning standard probability in order to learn open models, for example. --Abram Demski --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?; Powered by Listbox: http://www.listbox.com --- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244id_secret=111637683-c8fa51 Powered by Listbox: http://www.listbox.com
Re: [agi] A NewMetaphor for Intelligence - the Computer/Organiser
Mike, In that case I do not see how your view differs from simplistic dualism, as Terren cautioned. If your goal is to make a creativity machine, in what sense would the machine be non-algorithmic? Physical random processes? --Abram On Thu, Sep 4, 2008 at 6:59 PM, Mike Tintner [EMAIL PROTECTED] wrote: Abram, Thanks. V. helpful and interesting. Yes, on further examination, these interactionist guys seem, as you say, to be trying to take into account the embeddedness of the computer. But no, there's still a huge divide between them and me. I would liken them in the context of this discussion, to Pei who tries to argue that NARS is non-algorithmic, because the program is continuously changing. - and therefore satisfies the objections of classical objectors to AI/AGI. Well, both these guys and Pei are still v. much algorithmic in any reasonable sense of the word - still following *structures,* if v. sophisticated (and continuously changing) structures, of thought. And what I am asserting is a paradigm of a creative machine, which starts as, and is, NON-algorithmic and UNstructured in all its activities, albeit that it acquires and creates a multitude of algorithms, or routines/structures, for *parts* of those activities. For example, when you write a post, nearly every word and a great many phrases and even odd sentences, will be automatically, algorithmically produced. But the whole post, and most paras will *not* be - and *could not* be. A creative machine has infinite combinative potential. An algorithmic, programmed machine has strictly limited combinativity.. And a keyboard is surely the near perfect symbol of infinite, unstructured combinativity. It is being, and has been, used in endlessly creative ways - and is, along with the blank page and pencil, the central tool of our civilisation's creativity. Those randomly arranged letters - clearly designed to be infinitely recombined - are the antithesis of a programmed machine. So however those guys account for that keyboard, I don't see them as in any way accounting for it in my sense, or in its true, full usage. But thanks for your comments. (Oh and I did understand re Bayes - I was and am still arguing he isn't valid in many cases, period). Mike, The reason I decided that what you are arguing for is essentially an interactive model is this quote: But that is obviously only the half of it.Computers are obviously much more than that - and Turing machines. You just have to look at them. It's staring you in the face. There's something they have that Turing machines don't. See it? Terren? They have - a keyboard. A keyboard is precisely what the interaction theorists are trying to account for! Plus the mouse, the ethernet port, et cetera. Moreover, your general comments fit into the model if interpreted judiciously. You make a distinction between rule-based and creative behavior; rule-based behavior could be thought of as isolated processing of input (receive input, process without interference, output result) while creative behavior is behavior resulting from continual interaction with and exploration of the external world. Your concept of organisms as organizers only makes sense when I see it in this light: a human organizes the environment by interaction with it, while a Turing machine is unable to do this because it cannot explore/experiment/discover. -Abram On Thu, Sep 4, 2008 at 1:07 PM, Mike Tintner [EMAIL PROTECTED] wrote: Abram, Thanks for reply. But I don't understand what you see as the connection. An interaction machine from my brief googling is one which has physical organs. Any factory machine can be thought of as having organs. What I am trying to forge is a new paradigm of a creative, free machine as opposed to that exemplified by most actual machines, which are rational, deterministic machines. The latter can only engage in any task in set ways - and therefore engage and combine their organs in set combinations and sequences. Creative machines have a more or less infinite range of possible ways of going about things, and can combine their organs in a virtually infinite range of combinations, (which gives them a slight advantage, adaptively :) ). Organisms *are* creative machines; computers and robots *could* be (and are, when combined with humans), AGI's will *have* to be. (To talk of creative machines, more specifically, as I did, as keyboards/organisers is to focus on the mechanics of this infinite combinativity of organs). Interaction machines do not seem in any way then to entail what I'm talking about - creative machines - keyboards/ organisers - infinite combinativity - or the *creation,* as quite distinct from *following* of programs/algorithms and routines.. Abram/MT: If you think it's all been said, please point me to the philosophy of AI that includes it. I believe what you are suggesting is best understood as
