Re: [agi] Pretty worldchanging
Availibility of the Internet actually makes school grades worse. Of course, grades does not equal education, but I don't see anything worldchanging about education because of this. - Panu Horsmalahti --- 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
RE: [agi] How do we hear music
-Original Message- You have all missed one vital point. Music is repeating and it has a symmetry. In dancing (song and dance) moves are repeated in a symmetrical pattern. Question why are we programmed to find symmetry? This question may be more core to AGI than appears at first sight. Chearly an AGI system will have to look for symmetry and do what Hardy described as beautiful maths. Symmetry is at the heart of everything; without symmetry the universe collapses. Intelligence operates over symmetric verses non-symmetric IMO. But everything is ultimately grounded in symmetry. BTW kind of related, was just watching this neat video - the soundtrack needs to be redone though :) http://www.youtube.com/watch?v=4dpRPTwsKJs Why does the brain have bi-lateral symmetry I wonder and why is the heart not symmetric? Some researchers say consciousness is both heart and brain. John --- 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] Pretty worldchanging
On Sat, Jul 24, 2010 at 5:36 AM, Panu Horsmalahti nawi...@gmail.com wrote: Availibility of the Internet actually makes school grades worse. Of course, grades does not equal education, but I don't see anything worldchanging about education because of this. - Panu Horsmalahti Hmmm I do think the Internet has worldchanging implications for education, many of which are being realized all around us as we speak... School grades are a poor measure of intellectual achievement. And of course, the Internet can be used in either wonderful or idiotic ways -- it obviously DOES have revolutionary implications for education, even if statistically few make use of it in a way that significantly manifests these implications. I see this article http://news.yahoo.com/s/ytech_wguy/20100714/tc_ytech_wguy/ytech_wguy_tc3118 linked from the above article, which provides some (not that much) data that computer or Net access may decrease test scores in some low-income families But as the article itself states, this suggests the problem is not the computers or Net, but rather the inability of many low-income parents to guide their kids in educational use of computers and the Net ... or to give their kids a broad enough general education to enable them to guide themselves in this regard... Similarly, reading has great potential to aid education -- but if all you read are romance novels and People or Fat Biker Chick magazine, you're not going to broaden your mind that much ;p ... Maybe there are some students on this email list, who are wading through all the BS and learning something about AGI, by following links and reading papers mentioned here, etc. Without the Net, how would these students learn about AGI, in practice? Such education would be far harder to come by and less effective without the Net. That's world-changing... ;-) ... Learning about AGI via online resources may not improve your school grades any, because AGI knowledge isn't tested much in school. But students learning about AGI online could change the world... -- Ben G *agi* | Archives https://www.listbox.com/member/archive/303/=now https://www.listbox.com/member/archive/rss/303/ | Modifyhttps://www.listbox.com/member/?;Your Subscription http://www.listbox.com -- Ben Goertzel, PhD CEO, Novamente LLC and Biomind LLC CTO, Genescient Corp Vice Chairman, Humanity+ Advisor, Singularity University and Singularity Institute External Research Professor, Xiamen University, China b...@goertzel.org I admit that two times two makes four is an excellent thing, but if we are to give everything its due, two times two makes five is sometimes a very charming thing too. -- Fyodor Dostoevsky --- 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] Huge Progress on the Core of AGI
lol. thanks Jim :) On Thu, Jul 22, 2010 at 10:08 PM, Jim Bromer jimbro...@gmail.com wrote: I have to say that I am proud of David Jone's efforts. He has really matured during these last few months. I'm kidding but I really do respect the fact that he is actively experimenting. I want to get back to work on my artificial imagination and image analysis programs - if I can ever figure out how to get the time. As I have read David's comments, I realize that we need to really leverage all sorts of cruddy data in order to make good agi. But since that kind of thing doesn't work with sparse knowledge, it seems that the only way it could work is with extensive knowledge about a wide range of situations, like the knowledge gained from a vast variety of experiences. This conjecture makes some sense because if wide ranging knowledge could be kept in superficial stores where it could be accessed quickly and economically, it could be used efficiently in (conceptual) model fitting. However, as knowledge becomes too extensive it might become too unwieldy to find what is needed for a particular situation. At this point indexing becomes necessary with cross-indexing references to different knowledge based on similarities and commonalities of employment. Here I am saying that relevant knowledge based on previous learning might not have to be totally relevant to a situation as long as it could be used to run during an ongoing situation. From this perspective then, knowledge from a wide variety of experiences should actually be composed of reactions on different conceptual levels. Then as a piece of knowledge is brought into play for an ongoing situation, those levels that seem best suited to deal with the situation could be promoted quickly as the situation unfolds, acting like an automated indexing system into other knowledge relevant to the situation. So the ongoing process of trying to determine what is going on and what actions should be made would simultaneously act like an automated index to find better knowledge more suited for the situation. Jim Bromer *agi* | Archives https://www.listbox.com/member/archive/303/=now https://www.listbox.com/member/archive/rss/303/ | Modifyhttps://www.listbox.com/member/?;Your Subscription 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] Re: Huge Progress on the Core of AGI
Abram, I haven't found a method that I think works consistently yet. Basically I was trying methods like the one you suggested, which measures the number of correct predictions or expectations. But, then I ran into the problem of, what if the predictions you are counting are more of the same? Do you count them or not? For example, lets say that we see a piece of paper on a table in an image and we see that the paper looks different but moves with the table. So, we can hypothesize that they are attached. Now what if it is not a piece of paper, but a mural. Do you count every little piece of the mural that moves with the desk as a correct prediction? Is it a single prediction? What about the number of times they move together? It doesn't seem right to count each and every time, but we also have to be careful about coincidental movement together. Just because it seems to move together in one frame out of 1000 does not mean we should consider them temporarily attached. So, quantitatively defining simpler and predictive is quite challenging. I am honestly a bit stumped at how to do it at the moment. I will keep trying to find ways to at least approximate it, but I'm really not sure the best way. Of course, I haven't been working on this specific problem long, but other people have tried to quantify our explanatory methods in other areas and have also failed. I think part of the failure has to do with the fact that the things they want to explain using the same method should probably use different methods and should be more heuristic than mathematically precise. It's all quite overwhelming to analyze sometimes. I may have thought about fractions correct vs. incorrect also. The truth is, I haven't locked on and carefully analyzed the different ideas I've come up with because they all seem to have issues and it is difficult to analyze. I definitely need to try some out and just see what the results are and document them better. Dave On Thu, Jul 22, 2010 at 10:23 PM, Abram Demski abramdem...@gmail.comwrote: David, What are the different ways you are thinking of for measuring the predictiveness? I can think of a few different possibilities (such as measuring number incorrect vs measuring fraction incorrect, et cetera) but I'm wondering which variations you consider significant/troublesome/etc. --Abram On Thu, Jul 22, 2010 at 7:12 PM, David Jones davidher...@gmail.comwrote: It's certainly not as simple as you claim. First, assigning a probability is not always possible, nor is it easy. The factors in calculating that probability are unknown and are not the same for every instance. Since we do not know what combination of observations we will see, we cannot have a predefined set of probabilities, nor is it any easier to create a probability function that generates them for us. That is just as exactly what I meant by quantitatively define the predictiveness... it would be proportional to the probability. Second, if you can define a program ina way that is always simpler when it is smaller, then you can do the same thing without a program. I don't think it makes any sense to do it this way. It is not that simple. If it was, we could solve a large portion of agi easily. On Thu, Jul 22, 2010 at 3:16 PM, Matt Mahoney matmaho...@yahoo.com wrote: David Jones wrote: But, I am amazed at how difficult it is to quantitatively define more predictive and simpler for specific problems. It isn't hard. To measure predictiveness, you assign a probability to each possible outcome. If the actual outcome has probability p, you score a penalty of log(1/p) bits. To measure simplicity, use the compressed size of the code for your prediction algorithm. Then add the two scores together. That's how it is done in the Calgary challenge http://www.mailcom.com/challenge/ and in my own text compression benchmark. -- Matt Mahoney, matmaho...@yahoo.com *From:* David Jones davidher...@gmail.com *To:* agi agi@v2.listbox.com *Sent:* Thu, July 22, 2010 3:11:46 PM *Subject:* Re: [agi] Re: Huge Progress on the Core of AGI Because simpler is not better if it is less predictive. On Thu, Jul 22, 2010 at 1:21 PM, Abram Demski abramdem...@gmail.com wrote: Jim, Why more predictive *and then* simpler? --Abram On Thu, Jul 22, 2010 at 11:49 AM, David Jones davidher...@gmail.com wrote: An Update I think the following gets to the heart of general AI and what it takes to achieve it. It also provides us with evidence as to why general AI is so difficult. With this new knowledge in mind, I think I will be much more capable now of solving the problems and making it work. I've come to the conclusion lately that the best hypothesis is better because it is more predictive and then simpler than other hypotheses (in that order more predictive... then simpler). But, I am amazed at how difficult it is to quantitatively define more predictive and simpler for specific problems. This is
Re: [agi] Huge Progress on the Core of AGI
The Web site of David Jones at http://practicalai.org is quite impressive to me as a kindred spirit building AGI. (Just today I have been coding MindForth AGI :-) For his Practical AI Challenge or similar ventures, I would hope that David Jones is open to the idea of aggregating or archiving representative AI samples from such sources as - TexAI; - OpenCog; - Mentifex AI; - etc.; so that visitors to PracticalAI may gain an overview of what is happening in our field. Arthur -- http://www.scn.org/~mentifex/AiMind.html http://www.scn.org/~mentifex/mindforth.txt lol. thanks Jim :) On Thu, Jul 22, 2010 at 10:08 PM, Jim Bromer jimbro...@gmail.com wrote: I have to say that I am proud of David Jone's efforts. He has really matured during these last few months. I'm kidding but I really do respect the fact that he is actively experimenting. I want to get back to work on my artificial imagination and image analysis programs - if I can ever figure out how to get the time. As I have read David's comments, I realize that we need to really leverage all sorts of cruddy data in order to make good agi. But since that kind of thing doesn't work with sparse knowledge, it seems that the only way it could work is with extensive knowledge about a wide range of situations, like the knowledge gained from a vast variety of experiences. This conjecture makes some sense because if wide ranging knowledge could be kept in superficial stores where it could be accessed quickly and economically, it could be used efficiently in (conceptual) model fitting. However, as knowledge becomes too extensive it might become too unwieldy to find what is needed for a particular situation. At this point indexing becomes necessary with cross-indexing references to different knowledge based on similarities and commonalities of employment. Here I am saying that relevant knowledge based on previous learning might not have to be totally relevant to a situation as long as it could be used to run during an ongoing situation. From this perspective then, knowledge from a wide variety of experiences should actually be composed of reactions on different conceptual levels. Then as a piece of knowledge is brought into play for an ongoing situation, those levels that seem best suited to deal with the situation could be promoted quickly as the situation unfolds, acting like an automated indexing system into other knowledge relevant to the situation. So the ongoing process of trying to determine what is going on and what actions should be made would simultaneously act like an automated index to find better knowledge more suited for the situation. Jim Bromer --- 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
[agi] Clues to the Mind: What do you think is the reason for selective attention
http://www.youtube.com/watch?v=vJG698U2Mvo Can anyone suggest why our brains exhibit this phenomenon? cheers, Deepak --- 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] Re: Huge Progress on the Core of AGI
Abram, I should also mention that I ran into problems mainly because I was having a hard time deciding how to identify objects and determine what is really going on in a scene. This adds a whole other layer of complexity to hypotheses. It's not just about what is more predictive of the observations, it is about deciding what exactly you are observing in the first place. (although you might say its the same problem). I ran into this problem when my algorithm finds matches between items that are not the same. Or it may not find any matches between items that are the same, but have changed. So, how do you decide whether it is 1) the same object, 2) a different object or 3) the same object but it has changed. And how do you decide its relationship to something else... is it 1) dependently attached 2) semi-dependently attached(can move independently, but only in certain ways. Yet also moves dependently) 3) independent 4) sometimes dependent 5) was dependent, but no longer is, 6) was dependent on something else, but then was independent, but now is dependent on something new. These hypotheses are different ways of explaining the same observations, but are complicated by the fact that we aren't sure of the identity of the objects we are observing in the first place. Multiple hypotheses may fit the same observations, and its hard to decide why one is simpler or better than the other. The object you were observing at first may have disappeared. A new object may have appeared at the same time (this is why screenshots are a bit malicious). Or the object you were observing may have changed. In screenshots, sometimes the objects that you are trying to identify as different never appear at the same time because they always completely occlude each other. So, that can make it extremely difficult to decide whether they are the same object that has changed or different objects. Such ambiguities are common in AGI. It is unclear to me yet how to deal with them effectively, although I am continuing to work hard on it. I know its a bit of a mess, but I'm just trying to demonstrate the trouble I've run into. I hope that makes it more clear why I'm having so much trouble finding a way of determining what hypothesis is most predictive and simplest. Dave On Thu, Jul 22, 2010 at 10:23 PM, Abram Demski abramdem...@gmail.comwrote: David, What are the different ways you are thinking of for measuring the predictiveness? I can think of a few different possibilities (such as measuring number incorrect vs measuring fraction incorrect, et cetera) but I'm wondering which variations you consider significant/troublesome/etc. --Abram On Thu, Jul 22, 2010 at 7:12 PM, David Jones davidher...@gmail.comwrote: It's certainly not as simple as you claim. First, assigning a probability is not always possible, nor is it easy. The factors in calculating that probability are unknown and are not the same for every instance. Since we do not know what combination of observations we will see, we cannot have a predefined set of probabilities, nor is it any easier to create a probability function that generates them for us. That is just as exactly what I meant by quantitatively define the predictiveness... it would be proportional to the probability. Second, if you can define a program ina way that is always simpler when it is smaller, then you can do the same thing without a program. I don't think it makes any sense to do it this way. It is not that simple. If it was, we could solve a large portion of agi easily. On Thu, Jul 22, 2010 at 3:16 PM, Matt Mahoney matmaho...@yahoo.com wrote: David Jones wrote: But, I am amazed at how difficult it is to quantitatively define more predictive and simpler for specific problems. It isn't hard. To measure predictiveness, you assign a probability to each possible outcome. If the actual outcome has probability p, you score a penalty of log(1/p) bits. To measure simplicity, use the compressed size of the code for your prediction algorithm. Then add the two scores together. That's how it is done in the Calgary challenge http://www.mailcom.com/challenge/ and in my own text compression benchmark. -- Matt Mahoney, matmaho...@yahoo.com *From:* David Jones davidher...@gmail.com *To:* agi agi@v2.listbox.com *Sent:* Thu, July 22, 2010 3:11:46 PM *Subject:* Re: [agi] Re: Huge Progress on the Core of AGI Because simpler is not better if it is less predictive. On Thu, Jul 22, 2010 at 1:21 PM, Abram Demski abramdem...@gmail.com wrote: Jim, Why more predictive *and then* simpler? --Abram On Thu, Jul 22, 2010 at 11:49 AM, David Jones davidher...@gmail.com wrote: An Update I think the following gets to the heart of general AI and what it takes to achieve it. It also provides us with evidence as to why general AI is so difficult. With this new knowledge in mind, I think I will be much more capable now of solving the problems and making it work. I've
Re: [agi] Comments On My Skepticism of Solomonoff Induction
Solomonoff Induction may require a trans-infinite level of complexity just to run each program. Suppose each program is iterated through the enumeration of its instructions. Then, not only do the infinity of possible programs need to be run, many combinations of the infinite programs from each simulated Turing Machine also have to be tried. All the possible combinations of (accepted) programs, one from any two or more of the (accepted) programs produced by each simulated Turing Machine, have to be tried. Although these combinations of programs from each of the simulated Turing Machine may not all be unique, they all have to be tried. Since each simulated Turing Machine would produce infinite programs, I am pretty sure that this means that Solmonoff Induction is, *by definition,*trans-infinite. Jim Bromer On Thu, Jul 22, 2010 at 2:06 PM, Jim Bromer jimbro...@gmail.com wrote: I have to retract my claim that the programs of Solomonoff Induction would be trans-infinite. Each of the infinite individual programs could be enumerated by their individual instructions so some combination of unique individual programs would not correspond to a unique program but to the enumerated program that corresponds to the string of their individual instructions. So I got that one wrong. Jim Bromer --- 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] Comments On My Skepticism of Solomonoff Induction
On Sat, Jul 24, 2010 at 3:59 PM, Jim Bromer jimbro...@gmail.com wrote: Solomonoff Induction may require a trans-infinite level of complexity just to run each program. Suppose each program is iterated through the enumeration of its instructions. Then, not only do the infinity of possible programs need to be run, many combinations of the infinite programs from each simulated Turing Machine also have to be tried. All the possible combinations of (accepted) programs, one from any two or more of the (accepted) programs produced by each simulated Turing Machine, have to be tried. Although these combinations of programs from each of the simulated Turing Machine may not all be unique, they all have to be tried. Since each simulated Turing Machine would produce infinite programs, I am pretty sure that this means that Solmonoff Induction is, *by definition,*trans-infinite. Jim Bromer All the possible combinations of (accepted) programs, one program taken from any two or more simulated Turing Machines, have to be tried. Since each simulated Turing Machine would produce infinite programs and there are infinite simulated Turing Machines, I am pretty sure that this means that Solmonoff Induction is, *by definition,* trans-infinite. --- 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] Clues to the Mind: What do you think is the reason for selective attention
On Sat, Jul 24, 2010 at 7:07 PM, deepakjnath deepakjn...@gmail.com wrote: http://www.youtube.com/watch?v=vJG698U2Mvo Can anyone suggest why our brains exhibit this phenomenon? May I flag this as AGI irrelevant? The brain at a non-AGI task is not that interesting for AGI, me thinks. Plus, we have loads of specialist opinion on these things. Having just missed the gorilla myself, I would be curious to see the video´s effectiveness with different screen sizes and different prompts though. How about the prompt which of these players is the most intelligent! --- 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] Re: Huge Progress on the Core of AGI
David Jones wrote: I should also mention that I ran into problems mainly because I was having a hard time deciding how to identify objects and determine what is really going on in a scene. I think that your approach makes the problem harder than it needs to be (not that it is easy). Natural language processing is hard, so researchers in an attempt to break down the task into simpler parts, focused on steps like lexical analysis, parsing, part of speech resolution, and semantic analysis. While these problems went unsolved, Google went directly to a solution by skipping them. Likewise, parsing an image into physically separate objects and then building a 3-D model makes the problem harder, not easier. Again, look at the whole picture. You input an image and output a response. Let the system figure out which features are important. If your goal is to count basketball passes, then it is irrelevant whether the AGI recognizes that somebody is wearing a gorilla suit. -- Matt Mahoney, matmaho...@yahoo.com From: David Jones davidher...@gmail.com To: agi agi@v2.listbox.com Sent: Sat, July 24, 2010 2:25:49 PM Subject: Re: [agi] Re: Huge Progress on the Core of AGI Abram, I should also mention that I ran into problems mainly because I was having a hard time deciding how to identify objects and determine what is really going on in a scene. This adds a whole other layer of complexity to hypotheses. It's not just about what is more predictive of the observations, it is about deciding what exactly you are observing in the first place. (although you might say its the same problem). I ran into this problem when my algorithm finds matches between items that are not the same. Or it may not find any matches between items that are the same, but have changed. So, how do you decide whether it is 1) the same object, 2) a different object or 3) the same object but it has changed. And how do you decide its relationship to something else... is it 1) dependently attached 2) semi-dependently attached(can move independently, but only in certain ways. Yet also moves dependently) 3) independent 4) sometimes dependent 5) was dependent, but no longer is, 6) was dependent on something else, but then was independent, but now is dependent on something new. These hypotheses are different ways of explaining the same observations, but are complicated by the fact that we aren't sure of the identity of the objects we are observing in the first place. Multiple hypotheses may fit the same observations, and its hard to decide why one is simpler or better than the other. The object you were observing at first may have disappeared. A new object may have appeared at the same time (this is why screenshots are a bit malicious). Or the object you were observing may have changed. In screenshots, sometimes the objects that you are trying to identify as different never appear at the same time because they always completely occlude each other. So, that can make it extremely difficult to decide whether they are the same object that has changed or different objects. Such ambiguities are common in AGI. It is unclear to me yet how to deal with them effectively, although I am continuing to work hard on it. I know its a bit of a mess, but I'm just trying to demonstrate the trouble I've run into. I hope that makes it more clear why I'm having so much trouble finding a way of determining what hypothesis is most predictive and simplest. Dave On Thu, Jul 22, 2010 at 10:23 PM, Abram Demski abramdem...@gmail.com wrote: David, What are the different ways you are thinking of for measuring the predictiveness? I can think of a few different possibilities (such as measuring number incorrect vs measuring fraction incorrect, et cetera) but I'm wondering which variations you consider significant/troublesome/etc. --Abram On Thu, Jul 22, 2010 at 7:12 PM, David Jones davidher...@gmail.com wrote: It's certainly not as simple as you claim. First, assigning a probability is not always possible, nor is it easy. The factors in calculating that probability are unknown and are not the same for every instance. Since we do not know what combination of observations we will see, we cannot have a predefined set of probabilities, nor is it any easier to create a probability function that generates them for us. That is just as exactly what I meant by quantitatively define the predictiveness... it would be proportional to the probability. Second, if you can define a program ina way that is always simpler when it is smaller, then you can do the same thing without a program. I don't think it makes any sense to do it this way. It is not that simple. If it was, we could solve a large portion of agi easily. On Thu, Jul 22, 2010 at 3:16 PM, Matt Mahoney matmaho...@yahoo.com wrote: David Jones wrote: But, I am amazed at how difficult it is to quantitatively define more
Re: [agi] Comments On My Skepticism of Solomonoff Induction
Jim Bromer wrote: Solomonoff Induction may require a trans-infinite level of complexity just to run each program. Trans-infinite is not a mathematically defined term as far as I can tell. Maybe you mean larger than infinity, as in the infinite set of real numbers is larger than the infinite set of natural numbers (which is true). But it is not true that Solomonoff induction requires more than aleph-null operations. (Aleph-null is the size of the set of natural numbers, the smallest infinity). An exact calculation requires that you test aleph-null programs for aleph-null time steps each. There are aleph-null programs because each program is a finite length string, and there is a 1 to 1 correspondence between the set of finite strings and N, the set of natural numbers. Also, each program requires aleph-null computation in the case that it runs forever, because each step in the infinite computation can be numbered 1, 2, 3... However, the total amount of computation is still aleph-null because each step of each program can be described by an ordered pair (m,n) in N^2, meaning the n'th step of the m'th program, where m and n are natural numbers. The cardinality of N^2 is the same as the cardinality of N because there is a 1 to 1 correspondence between the sets. You can order the ordered pairs as (1,1), (1,2), (2,1), (1,3), (2,2), (3,1), (1,4), (2,3), (3,2), (4,1), (1,5), etc. See http://en.wikipedia.org/wiki/Countable_set#More_formal_introduction Furthermore you may approximate Solomonoff induction to any desired precision with finite computation. Simply interleave the execution of all programs as indicated in the ordering of ordered pairs that I just gave, where the programs are ordered from shortest to longest. Take the shortest program found so far that outputs your string, x. It is guaranteed that this algorithm will approach and eventually find the shortest program that outputs x given sufficient time, because this program exists and it halts. In case you are wondering how Solomonoff induction is not computable, the problem is that after this algorithm finds the true shortest program that outputs x, it will keep running forever and you might still be wondering if a shorter program is forthcoming. In general you won't know. -- Matt Mahoney, matmaho...@yahoo.com From: Jim Bromer jimbro...@gmail.com To: agi agi@v2.listbox.com Sent: Sat, July 24, 2010 3:59:18 PM Subject: Re: [agi] Comments On My Skepticism of Solomonoff Induction Solomonoff Induction may require a trans-infinite level of complexity just to run each program. Suppose each program is iterated through the enumeration of its instructions. Then, not only do the infinity of possible programs need to be run, many combinations of the infinite programs from each simulated Turing Machine also have to be tried. All the possible combinations of (accepted) programs, one from any two or more of the (accepted) programs produced by each simulated Turing Machine, have to be tried. Although these combinations of programs from each of the simulated Turing Machine may not all be unique, they all have to be tried. Since each simulated Turing Machine would produce infinite programs, I am pretty sure that this means that Solmonoff Induction is, by definition, trans-infinite. Jim Bromer On Thu, Jul 22, 2010 at 2:06 PM, Jim Bromer jimbro...@gmail.com wrote: I have to retract my claim that the programs of Solomonoff Induction would be trans-infinite. Each of the infinite individual programs could be enumerated by their individual instructions so some combination of unique individual programs would not correspond to a unique program but to the enumerated program that corresponds to the string of their individual instructions. So I got that one wrong. Jim Bromer agi | Archives | Modify Your Subscription --- 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] Re: Huge Progress on the Core of AGI
Huh, Matt? What examples of this holistic scene analysis are there (or are you thinking about)? From: Matt Mahoney Sent: Saturday, July 24, 2010 10:25 PM To: agi Subject: Re: [agi] Re: Huge Progress on the Core of AGI David Jones wrote: I should also mention that I ran into problems mainly because I was having a hard time deciding how to identify objects and determine what is really going on in a scene. I think that your approach makes the problem harder than it needs to be (not that it is easy). Natural language processing is hard, so researchers in an attempt to break down the task into simpler parts, focused on steps like lexical analysis, parsing, part of speech resolution, and semantic analysis. While these problems went unsolved, Google went directly to a solution by skipping them. Likewise, parsing an image into physically separate objects and then building a 3-D model makes the problem harder, not easier. Again, look at the whole picture. You input an image and output a response. Let the system figure out which features are important. If your goal is to count basketball passes, then it is irrelevant whether the AGI recognizes that somebody is wearing a gorilla suit. --- 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] Comments On My Skepticism of Solomonoff Induction
Abram, II use constructivist's and intuitionist's (and for that matter finitist's) methods when they seem useful to me. I often make mistakes when I am not wary of constructivist issues. Constructist criticisms are interesting because they can be turned against any presumptive method even though they might seem to contradict a constructivist criticism taken from a different presumption. I misused the term computable a few times because I have seen it used in different ways. But it turns out that it can be used in different ways. For example pi is not computable because it is infinite but a limiting approximation to pi is computable. So I would say that pi is computable - given infinite resources. One of my claims here is that I believe there are programs that will run Solomonoff Induction so the method would therefore be computable given infinite resources. However, my other claim is that the much desired function or result where one may compute the probability that a string will be produced given a particular prefix is incomputable. If I lived 500 years ago I might have said that a function that wasn't computable wasn't well-defined. (I might well have been somewhat pompous about such things in 1510). However, because of the efficacy of the theory of limits and other methods of finding bounds on functions, I would not say that now. Pi is well defined, but I don't think that Solmonoff Induction is completely well-defined. But we can still talk about certain aspects of it (using mathematics that are well grounded relative to those aspects of the method that are computable) even though the entire function is not completely well-defined. One way to do this is by using conditional statements. So if it turns out that one or some of my assumptions are wrong, I can see how to revise my theory about the aspect of the function that is computable (or seems computable). Jim Bromer On Thu, Jul 22, 2010 at 10:50 PM, Abram Demski abramdem...@gmail.comwrote: Jim, Aha! So you *are* a constructivist or intuitionist or finitist of some variety? This would explain the miscommunication... you appear to hold the belief that a structure needs to be computable in order to be well-defined. Is that right? If that's the case, then you're not really just arguing against Solomonoff induction in particular, you're arguing against the entrenched framework of thinking which allows it to be defined-- the so-called classical mathematics. If this is the case, then you aren't alone. --Abram On Thu, Jul 22, 2010 at 5:06 PM, Jim Bromer jimbro...@gmail.com wrote: On Wed, Jul 21, 2010 at 8:47 PM, Matt Mahoney matmaho...@yahoo.comwrote: The fundamental method is that the probability of a string x is proportional to the sum of all programs M that output x weighted by 2^-|M|. That probability is dominated by the shortest program, but it is equally uncomputable either way. Also, please point me to this mathematical community that you claim rejects Solomonoff induction. Can you find even one paper that refutes it? You give a precise statement of the probability in general terms, but then say that it is uncomputable. Then you ask if there is a paper that refutes it. Well, why would any serious mathematician bother to refute it since you yourself acknowledge that it is uncomputable and therefore unverifiable and therefore not a mathematical theorem that can be proven true or false? It isn't like you claimed that the mathematical statement is verifiable. It is as if you are making a statement and then ducking any responsibility for it by denying that it is even an evaluation. You honestly don't see the irregularity? My point is that the general mathematical community doesn't accept Solomonoff Induction, not that I have a paper that *refutes it,*whatever that would mean. Please give me a little more explanation why you say the fundamental method is that the probability of a string x is proportional to the sum of all programs M that output x weighted by 2^-|M|. Why is the M in a bracket? On Wed, Jul 21, 2010 at 8:47 PM, Matt Mahoney matmaho...@yahoo.comwrote: Jim Bromer wrote: The fundamental method of Solmonoff Induction is trans-infinite. The fundamental method is that the probability of a string x is proportional to the sum of all programs M that output x weighted by 2^-|M|. That probability is dominated by the shortest program, but it is equally uncomputable either way. How does this approximation invalidate Solomonoff induction? Also, please point me to this mathematical community that you claim rejects Solomonoff induction. Can you find even one paper that refutes it? -- Matt Mahoney, matmaho...@yahoo.com -- *From:* Jim Bromer jimbro...@gmail.com *To:* agi agi@v2.listbox.com *Sent:* Wed, July 21, 2010 3:08:13 PM *Subject:* Re: [agi] Comments On My Skepticism of Solomonoff Induction I should have said, It would be unwise
Re: [agi] Re: Huge Progress on the Core of AGI
Mike Tintner wrote: Huh, Matt? What examples of this holistic scene analysis are there (or are you thinking about)? I mean a neural model with increasingly complex features, as opposed to an algorithmic 3-D model (like video game graphics in reverse). Of course David rejects such ideas ( http://practicalai.org/Prize/Default.aspx ) even though the one proven working vision model uses it. -- Matt Mahoney, matmaho...@yahoo.com From: Mike Tintner tint...@blueyonder.co.uk To: agi agi@v2.listbox.com Sent: Sat, July 24, 2010 6:16:07 PM Subject: Re: [agi] Re: Huge Progress on the Core of AGI Huh, Matt? What examples of this holistic scene analysis are there (or are you thinking about)? From: Matt Mahoney Sent: Saturday, July 24, 2010 10:25 PM To: agi Subject: Re: [agi] Re: Huge Progress on the Core of AGI David Jones wrote: I should also mention that I ran into problems mainly because I was having a hard time deciding how to identify objects and determine what is really going on in a scene. I think that your approach makes the problem harder than it needs to be (not that it is easy). Natural language processing is hard, so researchers in an attempt to break down the task into simpler parts, focused on steps like lexical analysis, parsing, part of speech resolution, and semantic analysis. While these problems went unsolved, Google went directly to a solution by skipping them. Likewise, parsing an image into physically separate objects and then building a 3-D model makes the problem harder, not easier. Again, look at the whole picture. You input an image and output a response. Let the system figure out which features are important. If your goal is to count basketball passes, then it is irrelevant whether the AGI recognizes that somebody is wearing a gorilla suit. agi | Archives | Modify Your Subscription --- 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] Re: Huge Progress on the Core of AGI
Matt, Any method must deal with similar, if not the same, ambiguities. You need to show how neural nets solve this problem or how they solve agi goals while completely skipping the problem. Until then, it is not a successful method. Dave On Jul 24, 2010 7:18 PM, Matt Mahoney matmaho...@yahoo.com wrote: Mike Tintner wrote: Huh, Matt? What examples of this holistic scene analysis are there (or are y... I mean a neural model with increasingly complex features, as opposed to an algorithmic 3-D model (like video game graphics in reverse). Of course David rejects such ideas ( http://practicalai.org/Prize/Default.aspx ) even though the one proven working vision model uses it. -- Matt Mahoney, matmaho...@yahoo.com -- *From:* Mike Tintner tint...@blueyonder.co.uk To: agi agi@v2.listbox.com *Sent:* Sat, July 24, 2010 6:16:07 PM Subject: Re: [agi] Re: Huge Progress on the Core of AGI Huh, Matt? What examples of this holistic scene analysis are there (or are you thinking about)? ... *agi* | Archives https://www.listbox.com/member/archive/303/=now https://www.listbox.com/member/archive/rss/303/ | Modifyhttps://www.listbox.com/member/?;Your Subscription 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] Pretty worldchanging
Maybe there are some students on this email list, who are wading through all the BS and learning something about AGI, by following links and reading papers mentioned here, etc. Without the Net, how would these students learn about AGI, in practice? Such education would be far harder to come by and less effective without the Net. That's world-changing... ;-) ... The Net saves time. Back in the day, one could spend a lifetime sifting through paper in the library, or traveling the world to meet authorities. Now you do some googling, realize that no one has a clue, go on to do some real work on your own. That's if you have the guts, of course. intelligence-as-a-cognitive-algorithm --- 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] Re: Huge Progress on the Core of AGI
Matt: I mean a neural model with increasingly complex features, as opposed to an algorithmic 3-D model (like video game graphics in reverse). Of course David rejects such ideas ( http://practicalai.org/Prize/Default.aspx ) even though the one proven working vision model uses it. Which is? and does what? (I'm starting to consider that vision and visual perception - or perhaps one should say common sense, since no sense in humans works independent of the others - may well be considerably *more* complex than language. The evolutionary time required to develop our common sense perception and conception of the world was vastly greater than that required to develop language. And we are as a culture merely in our babbling infancy in beginning to understand how sensory images work and are processed). --- 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] Re: Huge Progress on the Core of AGI
Mike Tintner wrote: Which is? The one right behind your eyes. -- Matt Mahoney, matmaho...@yahoo.com From: Mike Tintner tint...@blueyonder.co.uk To: agi agi@v2.listbox.com Sent: Sat, July 24, 2010 9:00:42 PM Subject: Re: [agi] Re: Huge Progress on the Core of AGI Matt: I mean a neural model with increasingly complex features, as opposed to an algorithmic 3-D model (like video game graphics in reverse). Of course David rejects such ideas ( http://practicalai.org/Prize/Default.aspx ) even though the one proven working vision model uses it. Which is? and does what? (I'm starting to consider that vision and visual perception - or perhaps one should say common sense, since no sense in humans works independent of the others - may well be considerably *more* complex than language. The evolutionary time required to develop our common sense perception and conception of the world was vastly greater than that required to develop language. And we are as a culture merely in our babbling infancy in beginning to understand how sensory images work and are processed). agi | Archives | Modify Your Subscription --- 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] Re: Huge Progress on the Core of AGI
Check this out! The title Space and time, not surface features, guide object persistence says it all. http://pbr.psychonomic-journals.org/content/14/6/1199.full.pdf Over just the last couple days I have begun to realize that they are so right. My idea before of using high frame rates is also spot on. The brain does not use features as much as we think. First we construct a model of the object, then we probably decide what features to index it with for future search. If we know that the object occurs at a particular location in space, then we can learn a great deal about it with very little ambiguity! Of course, processing images at all is hard, but that's besides the point... The point is that we can automatically learn about the world using high frame rates and a simple heuristic for identifying specific objects in a scene. Because we can reliably identify them, we can learn an extremely large amount in a very short period of time. We can learn about how lighting affects the colors, noise, size, shape, components, attachment relationships, etc. etc. So, it is very likely that screenshots are not simpler than real images! lol. The objects in real images usually don't change as much, as drastically or as quickly as the objects in screenshots. That means that we can use the simple heuristics of size, shape, location and continuity of time to match objects and learn about them. Dave On Sat, Jul 24, 2010 at 9:10 PM, Matt Mahoney matmaho...@yahoo.com wrote: Mike Tintner wrote: Which is? The one right behind your eyes. -- Matt Mahoney, matmaho...@yahoo.com -- *From:* Mike Tintner tint...@blueyonder.co.uk *To:* agi agi@v2.listbox.com *Sent:* Sat, July 24, 2010 9:00:42 PM *Subject:* Re: [agi] Re: Huge Progress on the Core of AGI Matt: I mean a neural model with increasingly complex features, as opposed to an algorithmic 3-D model (like video game graphics in reverse). Of course David rejects such ideas ( http://practicalai.org/Prize/Default.aspx ) even though the one proven working vision model uses it. Which is? and does what? (I'm starting to consider that vision and visual perception - or perhaps one should say common sense, since no sense in humans works independent of the others - may well be considerably *more* complex than language. The evolutionary time required to develop our common sense perception and conception of the world was vastly greater than that required to develop language. And we are as a culture merely in our babbling infancy in beginning to understand how sensory images work and are processed). *agi* | Archives https://www.listbox.com/member/archive/303/=now https://www.listbox.com/member/archive/rss/303/ | Modifyhttps://www.listbox.com/member/?;Your Subscription http://www.listbox.com *agi* | Archives https://www.listbox.com/member/archive/303/=now https://www.listbox.com/member/archive/rss/303/ | Modifyhttps://www.listbox.com/member/?;Your Subscription 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] Re: Huge Progress on the Core of AGI
This is absolutely incredible. The answer was right there in the last paragraph: The present experiments suggest that the computation of object persistence appears to rely so heavily upon spatiotemporal information that it will not (or at least is unlikely to) use otherwise available surface feature information, particularly when there is conflicting spatiotemporal information. This reveals a striking limitation, given various theories that visual perception uses whatever shortcuts, or heuristics, it can to simplify processing, as well as the theory that perception evolves out of a buildup of the statistical nature of our environment (e.g., Purves Lotto, 2003). Instead, it appears that the object file system has “tunnel vision” and turns a blind eye to surface feature information, focusing on spatiotemporal information when computing persistence. So much for Matt's claim that the brain uses hierarchical features LOL Dave On Sat, Jul 24, 2010 at 11:52 PM, David Jones davidher...@gmail.com wrote: Check this out! The title Space and time, not surface features, guide object persistence says it all. http://pbr.psychonomic-journals.org/content/14/6/1199.full.pdf Over just the last couple days I have begun to realize that they are so right. My idea before of using high frame rates is also spot on. The brain does not use features as much as we think. First we construct a model of the object, then we probably decide what features to index it with for future search. If we know that the object occurs at a particular location in space, then we can learn a great deal about it with very little ambiguity! Of course, processing images at all is hard, but that's besides the point... The point is that we can automatically learn about the world using high frame rates and a simple heuristic for identifying specific objects in a scene. Because we can reliably identify them, we can learn an extremely large amount in a very short period of time. We can learn about how lighting affects the colors, noise, size, shape, components, attachment relationships, etc. etc. So, it is very likely that screenshots are not simpler than real images! lol. The objects in real images usually don't change as much, as drastically or as quickly as the objects in screenshots. That means that we can use the simple heuristics of size, shape, location and continuity of time to match objects and learn about them. Dave On Sat, Jul 24, 2010 at 9:10 PM, Matt Mahoney matmaho...@yahoo.comwrote: Mike Tintner wrote: Which is? The one right behind your eyes. -- Matt Mahoney, matmaho...@yahoo.com -- *From:* Mike Tintner tint...@blueyonder.co.uk *To:* agi agi@v2.listbox.com *Sent:* Sat, July 24, 2010 9:00:42 PM *Subject:* Re: [agi] Re: Huge Progress on the Core of AGI Matt: I mean a neural model with increasingly complex features, as opposed to an algorithmic 3-D model (like video game graphics in reverse). Of course David rejects such ideas ( http://practicalai.org/Prize/Default.aspx ) even though the one proven working vision model uses it. Which is? and does what? (I'm starting to consider that vision and visual perception - or perhaps one should say common sense, since no sense in humans works independent of the others - may well be considerably *more* complex than language. The evolutionary time required to develop our common sense perception and conception of the world was vastly greater than that required to develop language. And we are as a culture merely in our babbling infancy in beginning to understand how sensory images work and are processed). *agi* | Archives https://www.listbox.com/member/archive/303/=now https://www.listbox.com/member/archive/rss/303/ | Modifyhttps://www.listbox.com/member/?;Your Subscription http://www.listbox.com *agi* | Archives https://www.listbox.com/member/archive/303/=now https://www.listbox.com/member/archive/rss/303/ | Modifyhttps://www.listbox.com/member/?;Your Subscription 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] Clues to the Mind: What do you think is the reason for selective attention
Thanks Dave, its very interesting. This gives us more clues in to how the brain compresses and uses the relevant information while neglecting the irrelevant information. But as Anast has demonstrated, the brain does need priming inorder to decide what is relevant and irrelevant. :) Cheers, Deepak On Sun, Jul 25, 2010 at 5:34 AM, David Jones davidher...@gmail.com wrote: I also wanted to say that it is agi related because this may be the way that the brain deals with ambiguity in the real world. It ignores many things if it can use expectations to constrain possibilities. It is an important way in which the brain tracks objects and identifies them without analyzing all of an objects features before matching over the whole image. On Jul 24, 2010 7:53 PM, David Jones davidher...@gmail.com wrote: Actually Deepak, this is AGI related. This week I finally found a cool body of research that I previously had no knowledge of. This research area is in psychology, which is probably why I missed it the first time. It has to do with human perception, object files, how we keep track of object, individuate them, match them (the correspondence problem), etc. And I found the perfect article just now for you Deepak: http://www.duke.edu/~mitroff/papers/SimonsMitroff_01.pdfhttp://www.duke.edu/%7Emitroff/papers/SimonsMitroff_01.pdf This article mentions why the brain does not notice things. And I just realized as I was reading it why we don't see the gorilla or other unexpected changes. The reason is this: We have a limited amount of processing power that we can apply to visual tracking and analysis. So, in attention demanding situations such as these, we assign our processing resources to only track the things we are interested in. In fact, we probably do this all the time, but it is only when we need a lot of attention to be applied to a few objects do we notice that we don't see some unexpected events. So, our brain knows where to expect the ball next and our visual processing is very busy tracking the ball and then seeing who is throwing it. As a result, it is unable to also process the movement of other objects. If the unexpected event is drastic enough, it will get our attention. But since some of the people are in black, our brain probably thinks it is just a person in black and doesn't consider it an event that is worthy of interrupting our intense tracking. Dave On Sat, Jul 24, 2010 at 4:58 PM, Anastasios Tsiolakidis sokratis.dk@ gmail.com wrote: On Sat,... *agi* | Archives https://www.listbox.com/member/archive/303/=now https://www.listbox.com/member/archive/rss/303/ | Modifyhttps://www.listbox.com/member/?;Your Subscription http://www.listbox.com -- cheers, Deepak --- 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
[agi] Clues to the Mind: Illusions / Vision
http://www.youtube.com/watch?v=QbKw0_v2clofeature=player_embedded What we see is not really what you see. Its what you see and what you know you are seeing. The brain superimposes the predicted images to the viewed image to actually have a perception of image. cheers, Deepak --- 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
Re: [agi] Clues to the Mind: Illusions / Vision
Yes. I think I may have discovered the keys to crack this puzzle wide open. The brain seems to use simplistic heuristics for depth perception and surface bounding. Once it has that, it can apply the spaciotemporal heuristic I mentioned in other emails to identify and track an object, which allows it to learn a lot with high confidence. So, that model would explain why we see depth perception illusions. Dave On Jul 25, 2010 1:04 AM, deepakjnath deepakjn...@gmail.com wrote: http://www.youtube.com/watch?v=QbKw0_v2clofeature=player_embedded What we see is not really what you see. Its what you see and what you know you are seeing. The brain superimposes the predicted images to the viewed image to actually have a perception of image. cheers, Deepak *agi* | Archives https://www.listbox.com/member/archive/303/=now https://www.listbox.com/member/archive/rss/303/ | Modifyhttps://www.listbox.com/member/?;Your Subscription 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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
