[agi] Mushed Up Decision Processes
One of the problems that comes with the casual use of analytical methods is that the user becomes inured to their habitual misuse. When a casual familiarity is combined with a habitual ignorance of the consequences of a misuse the user can become over-confident or unwisely dismissive of criticism regardless of how on the mark it might be. The most proper use of statistical and probabilistic methods is to base results on a strong association with the data that they were derived from. The problem is that the AI community cannot afford this strong a connection to original source because they are trying to emulate the mind in some way and it is not reasonable to assume that the mind is capable of storing all data that it has used to derive insight. This is a problem any AI method has to deal with, it is not just a probability thing. What is wrong with the AI-probability group mind-set is that very few of its proponents ever consider the problem of statistical ambiguity and its obvious consequences. All AI programmers have to consider the problem. Most theories about the mind posit the use of similar experiences to build up theories about the world (or to derive methods to deal effectively with the world). So even though the methods to deal with the data environment are detached from the original sources of those methods, they can still be reconnected by the examination of similar experiences that may subsequently occur. But still it is important to be able to recognize the significance and necessity of doing this from time to time. It is important to be able to reevaluate parts of your theories about things. We are not just making little modifications from our internal theories about things when we react to ongoing events, we must be making some sort of reevaluation of our insights about the kind of thing that we are dealing with as well. I realize now that most people in these groups probably do not understand where I am coming from because their idea of AI programming is based on a model of programming that is flat. You have the program at one level and the possible reactions to the data that is input as the values of the program variables are carefully constrained by that level. You can imagine a more complex model of programming by appreciating the possibility that the program can react to IO data by rearranging subprograms to make new kinds of programs. Although a subtle argument can be made that any program that conditionally reacts to input data is rearranging the execution of its subprograms, the explicit recognition by the programmer that this is useful tool in advanced programming is probably highly correlated with its more effective use. (I mean of course it is highly correlated with its effective use!) I believe that casually constructed learning methods (and decision processes) can lead to even more uncontrollable results when used with this self-programming aspect of advanced AI programs. The consequences then of failing to recognize that mushed up decision processes that are never compared against the data (or kinds of situations) that they were derived from will be the inevitable emergence of inherently illogical decision processes that will mush up an AI system long before it gets any traction. 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=120640061-aded06 Powered by Listbox: http://www.listbox.com
Re: [agi] Mushed Up Decision Processes
Jim, There is a large body of literature on avoiding overfitting, ie, finding patterns that work for more then just the data at hand. Of course, the ultimate conclusion is that you can never be 100% sure; but some interesting safeguards have been cooked up anyway, which help in practice. My point is, the following paragraph is unfounded: This is a problem any AI method has to deal with, it is not just a probability thing. What is wrong with the AI-probability group mind-set is that very few of its proponents ever consider the problem of statistical ambiguity and its obvious consequences. The AI-probability group definitely considers such problems. --Abram On Sat, Nov 29, 2008 at 10:48 AM, Jim Bromer [EMAIL PROTECTED] wrote: One of the problems that comes with the casual use of analytical methods is that the user becomes inured to their habitual misuse. When a casual familiarity is combined with a habitual ignorance of the consequences of a misuse the user can become over-confident or unwisely dismissive of criticism regardless of how on the mark it might be. The most proper use of statistical and probabilistic methods is to base results on a strong association with the data that they were derived from. The problem is that the AI community cannot afford this strong a connection to original source because they are trying to emulate the mind in some way and it is not reasonable to assume that the mind is capable of storing all data that it has used to derive insight. This is a problem any AI method has to deal with, it is not just a probability thing. What is wrong with the AI-probability group mind-set is that very few of its proponents ever consider the problem of statistical ambiguity and its obvious consequences. All AI programmers have to consider the problem. Most theories about the mind posit the use of similar experiences to build up theories about the world (or to derive methods to deal effectively with the world). So even though the methods to deal with the data environment are detached from the original sources of those methods, they can still be reconnected by the examination of similar experiences that may subsequently occur. But still it is important to be able to recognize the significance and necessity of doing this from time to time. It is important to be able to reevaluate parts of your theories about things. We are not just making little modifications from our internal theories about things when we react to ongoing events, we must be making some sort of reevaluation of our insights about the kind of thing that we are dealing with as well. I realize now that most people in these groups probably do not understand where I am coming from because their idea of AI programming is based on a model of programming that is flat. You have the program at one level and the possible reactions to the data that is input as the values of the program variables are carefully constrained by that level. You can imagine a more complex model of programming by appreciating the possibility that the program can react to IO data by rearranging subprograms to make new kinds of programs. Although a subtle argument can be made that any program that conditionally reacts to input data is rearranging the execution of its subprograms, the explicit recognition by the programmer that this is useful tool in advanced programming is probably highly correlated with its more effective use. (I mean of course it is highly correlated with its effective use!) I believe that casually constructed learning methods (and decision processes) can lead to even more uncontrollable results when used with this self-programming aspect of advanced AI programs. The consequences then of failing to recognize that mushed up decision processes that are never compared against the data (or kinds of situations) that they were derived from will be the inevitable emergence of inherently illogical decision processes that will mush up an AI system long before it gets any traction. 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/?; 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=120640061-aded06 Powered by Listbox: http://www.listbox.com
Re: [agi] Mushed Up Decision Processes
Jim, YES - and I think I have another piece of your puzzle to consider... A longtime friend of mine, Dave, went on to become a PhD psychologist, who subsequently took me on as a sort of project - to figure out why most people who met me then either greatly valued my friendship, or quite the opposite, would probably kill me if they had the safe opportunity. After much discussion, interviewing people in both camps, etc., he came up with what appears to be a key to decision making in general... It appears that people pigeonhole other people, concepts, situations, etc., into a very finite number of pigeonholes - probably just tens of pigeonholes for other people. Along with the pigeonhole, they keep amendments, like Steve is like Joe, but with Then, there is the pigeonhole labeled other that all the mavericks are thrown into. Not being at all like anyone else that most people have ever met, I was invariably filed into the other pigeonhole, along with Einstein, Ted Bundy, Jack the Ripper, Stephen Hawking, etc. People are safe to the extent that they are predictable, and people in the other pigeonhole got that way because they appear to NOT be predictable, e.g. because of their worldview, etc. Now, does the potential value of the alternative worldview outweigh the potential danger of perceived unpredictability? The answer to this question apparently drove my own personal classification in other people. Dave's goal was to devise a way to stop making enemies, but unfortunately, this model of how people got that way suggested no potential solution. People who keep themselves safe from others having radically different worldviews are truly in a mental prison of their own making, and there is no way that someone whom they distrust could ever release them from that prison. I suspect that recognition, decision making, and all sorts of intelligent processes may be proceeding in much the same way. There may be no grandmother neuron/pidgeonhole, but rather a kindly old person with an amendment that is related. If on the other hand your other grandmother flogged you as a child, the filing might be quite different. Any thoughts? Steve Richfield On 11/29/08, Jim Bromer [EMAIL PROTECTED] wrote: One of the problems that comes with the casual use of analytical methods is that the user becomes inured to their habitual misuse. When a casual familiarity is combined with a habitual ignorance of the consequences of a misuse the user can become over-confident or unwisely dismissive of criticism regardless of how on the mark it might be. The most proper use of statistical and probabilistic methods is to base results on a strong association with the data that they were derived from. The problem is that the AI community cannot afford this strong a connection to original source because they are trying to emulate the mind in some way and it is not reasonable to assume that the mind is capable of storing all data that it has used to derive insight. This is a problem any AI method has to deal with, it is not just a probability thing. What is wrong with the AI-probability group mind-set is that very few of its proponents ever consider the problem of statistical ambiguity and its obvious consequences. All AI programmers have to consider the problem. Most theories about the mind posit the use of similar experiences to build up theories about the world (or to derive methods to deal effectively with the world). So even though the methods to deal with the data environment are detached from the original sources of those methods, they can still be reconnected by the examination of similar experiences that may subsequently occur. But still it is important to be able to recognize the significance and necessity of doing this from time to time. It is important to be able to reevaluate parts of your theories about things. We are not just making little modifications from our internal theories about things when we react to ongoing events, we must be making some sort of reevaluation of our insights about the kind of thing that we are dealing with as well. I realize now that most people in these groups probably do not understand where I am coming from because their idea of AI programming is based on a model of programming that is flat. You have the program at one level and the possible reactions to the data that is input as the values of the program variables are carefully constrained by that level. You can imagine a more complex model of programming by appreciating the possibility that the program can react to IO data by rearranging subprograms to make new kinds of programs. Although a subtle argument can be made that any program that conditionally reacts to input data is rearranging the execution of its subprograms, the explicit recognition by the programmer that this is useful tool in advanced programming is probably highly correlated with its more effective use.
Re: [agi] Mushed Up Decision Processes
Hi. I will just make a quick response to this message and then I want to think about the other messages before I reply. A few weeks ago I decided that I would write a criticism of ai-probability to post to this group. I wasn't able remember all of my criticisms so I decided to post a few preliminary sketches to another group. I wasn't too concerned about how they responded, and in fact I thought they would just ignore me. The first response I got was from an irate guy who was quite unpleasant and then finished by declaring that I slandered the entire ai-probability community! He had some reasonable criticisms about this but I considered the issue tangential to the central issue I wanted to discuss. I would have responded to his more reasonable criticisms if they hadn't been embedded in his enraged rant. I wondered why anyone would deface the expression of his own thoughts with an emotional and hostile message, so I wanted to try the same message on this group to see if anyone who was more mature would focus on this same issue. Abram made a measured response but his focus was on the over-generalization. As I said, this was just a preliminary sketch of a message that I intended to post to this group after I had worked on it. Your point is taken. Norvig seems to say that overfitting is a general problem. The method given to study the problem is probabilistic but it is based on the premise that the original data is substantially intact. But Norvig goes on to mention that with pruning noise can be tolerated. If you read my message again you may see that my central issue was not really centered on the issue of whether anyone in the ai-probability community was aware of the nature of the science of statistics but whether or not probability can be used as the fundamental basis to create agi given the complexities of the problem. So while your example of overfitting certainly does deflate my statements that no one in the ai-probability community gets this stuff, it does not actually address the central issue that I was thinking of. I am not sure if Norvig's application of a probabilistic method to detect overfitting is truly directed toward the agi community. In other words: Has anyone in this grouped tested the utility and clarity of the decision making of a fully automated system to detect overfitting in a range of complex IO data fields that one might expect to encounter in AGI? Jim Bromer On Sat, Nov 29, 2008 at 11:32 AM, Abram Demski [EMAIL PROTECTED] wrote: Jim, There is a large body of literature on avoiding overfitting, ie, finding patterns that work for more then just the data at hand. Of course, the ultimate conclusion is that you can never be 100% sure; but some interesting safeguards have been cooked up anyway, which help in practice. My point is, the following paragraph is unfounded: This is a problem any AI method has to deal with, it is not just a probability thing. What is wrong with the AI-probability group mind-set is that very few of its proponents ever consider the problem of statistical ambiguity and its obvious consequences. The AI-probability group definitely considers such problems. --Abram On Sat, Nov 29, 2008 at 10:48 AM, Jim Bromer [EMAIL PROTECTED] wrote: One of the problems that comes with the casual use of analytical methods is that the user becomes inured to their habitual misuse. When a casual familiarity is combined with a habitual ignorance of the consequences of a misuse the user can become over-confident or unwisely dismissive of criticism regardless of how on the mark it might be. The most proper use of statistical and probabilistic methods is to base results on a strong association with the data that they were derived from. The problem is that the AI community cannot afford this strong a connection to original source because they are trying to emulate the mind in some way and it is not reasonable to assume that the mind is capable of storing all data that it has used to derive insight. This is a problem any AI method has to deal with, it is not just a probability thing. What is wrong with the AI-probability group mind-set is that very few of its proponents ever consider the problem of statistical ambiguity and its obvious consequences. All AI programmers have to consider the problem. Most theories about the mind posit the use of similar experiences to build up theories about the world (or to derive methods to deal effectively with the world). So even though the methods to deal with the data environment are detached from the original sources of those methods, they can still be reconnected by the examination of similar experiences that may subsequently occur. But still it is important to be able to recognize the significance and necessity of doing this from time to time. It is important to be able to reevaluate parts of your theories about things. We are not just making little modifications from our
Re: [agi] Mushed Up Decision Processes
Well, if you're willing to take the step of asking questions about the world that are framed in terms of probabilities and probability distributions ... then modern probability and statistics tell you a lot about overfitting and how to avoid it... OTOH if, like Pei Wang, you think it's misguided to ask questions posed in a probabilistic framework, then that theory will not be directly relevant to you... To me the big weaknesses of modern probability theory lie in **hypothesis generation** and **inference**. Testing a hypothesis against data, to see if it's overfit to that data, is handled well by crossvalidation and related methods. But the problem of: given a number of hypotheses with support from a dataset, generating other interesting hypotheses that will also have support from the dataset ... that is where traditional probabilistic methods (though not IMO the foundational ideas of probability) fall short, providing only unscalable or oversimplified solutions... -- Ben G On Sat, Nov 29, 2008 at 1:08 PM, Jim Bromer [EMAIL PROTECTED] wrote: Hi. I will just make a quick response to this message and then I want to think about the other messages before I reply. A few weeks ago I decided that I would write a criticism of ai-probability to post to this group. I wasn't able remember all of my criticisms so I decided to post a few preliminary sketches to another group. I wasn't too concerned about how they responded, and in fact I thought they would just ignore me. The first response I got was from an irate guy who was quite unpleasant and then finished by declaring that I slandered the entire ai-probability community! He had some reasonable criticisms about this but I considered the issue tangential to the central issue I wanted to discuss. I would have responded to his more reasonable criticisms if they hadn't been embedded in his enraged rant. I wondered why anyone would deface the expression of his own thoughts with an emotional and hostile message, so I wanted to try the same message on this group to see if anyone who was more mature would focus on this same issue. Abram made a measured response but his focus was on the over-generalization. As I said, this was just a preliminary sketch of a message that I intended to post to this group after I had worked on it. Your point is taken. Norvig seems to say that overfitting is a general problem. The method given to study the problem is probabilistic but it is based on the premise that the original data is substantially intact. But Norvig goes on to mention that with pruning noise can be tolerated. If you read my message again you may see that my central issue was not really centered on the issue of whether anyone in the ai-probability community was aware of the nature of the science of statistics but whether or not probability can be used as the fundamental basis to create agi given the complexities of the problem. So while your example of overfitting certainly does deflate my statements that no one in the ai-probability community gets this stuff, it does not actually address the central issue that I was thinking of. I am not sure if Norvig's application of a probabilistic method to detect overfitting is truly directed toward the agi community. In other words: Has anyone in this grouped tested the utility and clarity of the decision making of a fully automated system to detect overfitting in a range of complex IO data fields that one might expect to encounter in AGI? Jim Bromer On Sat, Nov 29, 2008 at 11:32 AM, Abram Demski [EMAIL PROTECTED] wrote: Jim, There is a large body of literature on avoiding overfitting, ie, finding patterns that work for more then just the data at hand. Of course, the ultimate conclusion is that you can never be 100% sure; but some interesting safeguards have been cooked up anyway, which help in practice. My point is, the following paragraph is unfounded: This is a problem any AI method has to deal with, it is not just a probability thing. What is wrong with the AI-probability group mind-set is that very few of its proponents ever consider the problem of statistical ambiguity and its obvious consequences. The AI-probability group definitely considers such problems. --Abram On Sat, Nov 29, 2008 at 10:48 AM, Jim Bromer [EMAIL PROTECTED] wrote: One of the problems that comes with the casual use of analytical methods is that the user becomes inured to their habitual misuse. When a casual familiarity is combined with a habitual ignorance of the consequences of a misuse the user can become over-confident or unwisely dismissive of criticism regardless of how on the mark it might be. The most proper use of statistical and probabilistic methods is to base results on a strong association with the data that they were derived from. The problem is that the AI community cannot afford this strong a connection to original source because they are
Re: [agi] Mushed Up Decision Processes
--- On Sat, 11/29/08, Jim Bromer [EMAIL PROTECTED] wrote: I am not sure if Norvig's application of a probabilistic method to detect overfitting is truly directed toward the agi community. In other words: Has anyone in this grouped tested the utility and clarity of the decision making of a fully automated system to detect overfitting in a range of complex IO data fields that one might expect to encounter in AGI? The general problem of detecting overfitting is not computable. The principle according to Occam's Razor, formalized and proven by Hutter's AIXI model, is to choose the shortest program (simplest hypothesis) that generates the data. Overfitting is the case of choosing a program that is too large. -- 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=120640061-aded06 Powered by Listbox: http://www.listbox.com
Re: [agi] Mushed Up Decision Processes
A response to: I wondered why anyone would deface the expression of his own thoughts with an emotional and hostile message, My theory is that thoughts are generated internally and forced into words via a babble generator. Then the thoughts are filtered through a screen to remove any that don't match ones intent, that don't make sense, etc. The value assigned to each expression is initially dependent on how well it expresses one's emotional tenor. Therefore I would guess that all of the verbalizations that the individual generated which passed the first screen were hostile in nature. From the remaining sample he filtered those which didn't generate sensible-to-him scenarios when fed back into his world model. This left him with a much reduced selection of phrases to choose from when composing his response. In my model this happens a phrase at a time rather than a sentence at a time. And there is also a probabilistic element where each word has a certain probability of being followed by divers other words. I often don't want to express the most likely probability, as by choosing a less frequently chosen alternative I (believe I) create the impression a more studied, i.e. thoughtful, response. But if one wishes to convey a more dynamic style then one would choose a more likely follower. Note that in this scenario phrases are generated both randomly and in parallel. Then they are selected for fitness for expression by passing through various filter. Reasonable? Jim Bromer wrote: Hi. I will just make a quick response to this message and then I want to think about the other messages before I reply. A few weeks ago I decided that I would write a criticism of ai-probability to post to this group. I wasn't able remember all of my criticisms so I decided to post a few preliminary sketches to another group. I wasn't too concerned about how they responded, and in fact I thought they would just ignore me. The first response I got was from an irate guy who was quite unpleasant and then finished by declaring that I slandered the entire ai-probability community! He had some reasonable criticisms about this but I considered the issue tangential to the central issue I wanted to discuss. I would have responded to his more reasonable criticisms if they hadn't been embedded in his enraged rant. I wondered why anyone would deface the expression of his own thoughts with an emotional and hostile message, so I wanted to try the same message on this group to see if anyone who was more mature would focus on this same issue. Abram made a measured response but his focus was on the over-generalization. As I said, this was just a preliminary sketch of a message that I intended to post to this group after I had worked on it. Your point is taken. Norvig seems to say that overfitting is a general problem. The method given to study the problem is probabilistic but it is based on the premise that the original data is substantially intact. But Norvig goes on to mention that with pruning noise can be tolerated. If you read my message again you may see that my central issue was not really centered on the issue of whether anyone in the ai-probability community was aware of the nature of the science of statistics but whether or not probability can be used as the fundamental basis to create agi given the complexities of the problem. So while your example of overfitting certainly does deflate my statements that no one in the ai-probability community gets this stuff, it does not actually address the central issue that I was thinking of. I am not sure if Norvig's application of a probabilistic method to detect overfitting is truly directed toward the agi community. In other words: Has anyone in this grouped tested the utility and clarity of the decision making of a fully automated system to detect overfitting in a range of complex IO data fields that one might expect to encounter in AGI? Jim Bromer On Sat, Nov 29, 2008 at 11:32 AM, Abram Demski [EMAIL PROTECTED] wrote: Jim, There is a large body of literature on avoiding overfitting, ie, finding patterns that work for more then just the data at hand. Of course, the ultimate conclusion is that you can never be 100% sure; but some interesting safeguards have been cooked up anyway, which help in practice. My point is, the following paragraph is unfounded: This is a problem any AI method has to deal with, it is not just a probability thing. What is wrong with the AI-probability group mind-set is that very few of its proponents ever consider the problem of statistical ambiguity and its obvious consequences. The AI-probability group definitely considers such problems. --Abram On Sat, Nov 29, 2008 at 10:48 AM, Jim Bromer [EMAIL PROTECTED] wrote: One of the problems that comes with the casual use of analytical methods is that the user becomes inured to their habitual misuse. When a casual familiarity is combined with a
Re: [agi] who is going to build the wittgenstein-ian AI filter to spot all the intellectual nonsense
A general approach to this that frequently works is to examine the definitions that you are using for ambiguity. Then to look for operational tests. If the only clear meanings lack operational tests, then it's probably worthless to waste computing resources on the problem until those problems have been cleared up. If the level of ambiguity is too high (judgment call) then the first order of business is to ensure that you are talking about the same thing. If you can't do that, then it's probably a waste of time to compute intensively about it. Note that this works, because different people draw their boundaries in different places, so different people spend time on different questions. It results in an approximately reasonable allocation of effort, which changes as knowledge accumulates. If everyone drew the bounds in the same place, then it would be a lamentably narrow area being explored intensively, with lots of double coverage. (There's already lots of double coverage. Patents for the telephone, I believe it was, were filed by two people within the same week. Or look at the history of the airplane. But there's a lot LESS double coverage than if everyone drew the boundary in the same place.) As for What is consciousness?... DEFINE YOUR TERMS. If you define how you recognize consciousness, then I can have a chance of answering your question, otherwise you can reject any answer I give with But that's not what I meant! Ditto for time. Or I could slip levels and tell you that it's a word with four letters (etc.). Also, many people are working intensively on the nature of time. They know in detail what they mean (not that they all necessarily mean the same thing). To say that they are wasting their time because questions about the nature of time are silly is, itself, silly. Your question about the nature of time may be silly, but that's because you don't have a good definition with operational tests. That says nothing about what the exact same words may mean when someone else says them. (E.g. [off the top of my head], time is a locally monotonically increasing measure of state changes within the local environment is a plausible definition of time. It has some redeeming features. It, however, doesn't admit of a test of why it exists. That would need to be posed within the context of a larger theory which implied operational tests.) There are linguistic tricks. E.g., when It's raining, who or what is raining. But generally they are relatively trivial...unless you accept language as being an accurate model of the universe. Or consider Who is the master who makes the grass green? That's not a meaningless question in the proper context. It's an elementary problem for the student. (don't peek) (do you know the answer?) It's intended to cause the student to realize that things do not have inherent properties that are caused by sensations interpreted by the human brain. But other reasonable answers might be the gardener, who waters and fertilizes it or perhaps a particular molecule that resonates in such a manner that the primary light that re-radiates from grass is in that part of the spectrum that we have labeled green. And I'm certain that there are other valid answers. (I have a non-standard answer to The sound of one hand clapping, as I can, indeed, clap with one hand...fingers against the palm. I think it takes large hands.) If one writes off as senseless questions that don't make sense to one, wellwhat is the square root of -1? The very name imaginary tells you how unreasonable most mathematicians thought that question. But it turned out to be rather valuable. And it worked because someone made a series of operational tests and showed that it would work. Up until then the very definition of square root prohibited using negative numbers. So they agreed to change the definition. I don't think that you can rule out any question as nonsensical provided that there are operational tests and unambiguous definitions. And if there aren't, then you can make some. It may not answer the question that you couldn't define...but if you can't sensibly ask the question, then it isn't much of a question (no matter HOW important it feels). Tudor Boloni wrote: I agree that there are many better questions to elucidate the tricks/pitfalls of language. but lets list the biggest time wasters first, and the post showed some real time wasters from various fields that i found valuable to be aware of It implies it is pointless to ask what the essence of time is, but then proceeds to give an explanation of time that is not pointless, and may shed light on its meaning, which is perhaps as much of an essence as time has.. i think the post tries to show that the error is that treating time like an object of reality with an essence is nonsensical and a waste of time;) it seems wonderful to have an AGI
Re: [agi] If aliens are monitoring us, our development of AGI might concern them
Well. The speed of light limitation seems rather secure. So I would propose that we have been visited by roboticized probes, rather than by naturally evolved creatures. And the energetic constraints make it seem likely that they were extremely small and infrequent...though I suppose that they could build larger probes locally. My guess is that UFOs are just that. Unidentified. I suspect that many of them aren't even objects in any normal sense of the word. Temporary plasmas, etc. And others are more or less orthodox flying vehicles seen under unusual conditions. (I remember once being convinced that I'd seen one, but extended observation revealed that it was an advertising blimp seen with the sun behind it, and it was partially transparent. Quite impressive, and not at all blimp like. It even seemed to be moving rapidly, but that was due to the sunlight passing through an interior membrane that was changing in size and shape. It would require rather impressive evidence before I would believe in actual visitations by naturally evolved entities. (Though the concept of MacroLife does provide one reasonable scenario.) Still... I would consider it more plausible to assert that we lived in a virtual world scenario, and were being monitored within it. In any case, I see no operational tests, and thus I don't see any cause for using those possibilities to alter our activities. Ed Porter wrote: Since there have been multiple discussions of aliens lately on this list, I think I should communicate a thought that I have had concerning them that I have not heard any one else say --- although I would be very surprised if others have not thought it --- and it does relate to AGI --- so it is “on list.” As we learn just how common exoplanets are, the possibility that aliens have visited earth seems increasingly scientifically believable, even for a relatively rationalist person like myself. There have, in fact, been many reportings of UFOs from sources that are hard to reject out of hand. An astronaut that NASA respected enough to send to the moon, has publicly stated he has attended government briefings in which he was told there is substantial evidence aliens have repeatedly visited earth. Within the last year Drudge had a report from a Chicago TV station that said sources at the tower of O'Hare airport claimed multiple airline pilots reported to them seeing a large flying-saucer-shaped object hovering over one of the building of the airport and then disappearing. Now, I am not saying these reports are necessarily true, but I am saying that --- (a) given how rapidly life evolved on earth, as soon as it cooled enough that there were large pools of water; (b) there are probably at least a million habitable planets in the Milky Way (a conservative estimates); and (c) if one assumes one in 1000 such planets will have life evolve to AGI super-intelligence --- the chances there are planets with AGI super-intelligence within several thousand light years of earth are very good. And since, at least, mechanical AGIs with super intelligence and the resulting levels of technology should be able to travel through space at one tenth to one thousandth the speed of light for many tens of thousands of years, it is not at all unlikely life and/or machine forms from such planets have had time to reach us --- and perhaps --- not only to reach us --- but also to report back to their home planet and recruit many more of their kind to visit us. This becomes even more likely if one considers that some predict the Milky Way actually had its peak number of habitable planets billions of years ago, meaning that on many planets evolution of intelligent life is millions, or billions, of years ahead of ours, and thus that life/machine forms on many of the planets capable of supporting intelligent life are millions of years beyond their singularities. This would mean their development of extremely powerful super-intelligence and the attendant developments in technologies we know of --- such as nanofabrication, controlled fusion reactions, and quantum computing and engineering --- and technologies we do not yet even know of --- would be way beyond our imagining. All of the above is nothing new, among those who are open minded about (a) the evidence about the commonness of exoplanets; (b) the fact that there are enough accounts of UFO's from reputable sources that such accounts cannot dismissed out of hand as false, and (c) what the singularity and the development of super-intelligence would mean to a civilization. But what I am suggesting that I have never heard before is that it is possible the aliens, if they actually have been visiting us repeatedly are watching us to see when mankind achieves super-intelligence, because only then do we presumably have a chance of becoming their equal. Perhaps this means that only then we can understand them. Or
Re: [agi] Re: JAGI submission
Matt Mahoney wrote: --- On Tue, 11/25/08, Eliezer Yudkowsky [EMAIL PROTECTED] wrote: Shane Legg, I don't mean to be harsh, but your attempt to link Kolmogorov complexity to intelligence is causing brain damage among impressionable youths. ( Link debunked here: http://www.overcomingbias.com/2008/11/complexity-and.html ) Perhaps this is the wrong argument to support my intuition that knowing more makes you smarter, as in greater expected utility over a given time period. How do we explain that humans are smarter than calculators, and calculators are smarter than rocks? ... -- Matt Mahoney, [EMAIL PROTECTED] Each particular instantiation of computing has a certain maximal intelligence that it can express (noting that intelligence is ill-defined). More capacious stores can store more information. Faster processors can process information more quickly. However, information is not, in and of itself, intelligence. Information is the database on which intelligence operates. Information isn't a measure of intelligence, and intelligence isn't a measure of information. We have decent definitions of information. We lack anything corresponding for intelligence. It's certainly not complexity, though intelligence appears to require a certain amount of complexity. And it's not a relationship between information and complexity. I still suspect that intelligence will turn out to be to what we think of as intelligence rather as a symptom is to a syndrome. (N.B., not as a symptom is to a disease!) That INTELLIGENCE will turn out to be composed of many, many, small little tricks that enable one to solve a certain class of problems quick...or even at all. But that the tricks will have no necessary relation ship to each other. One will be something like alpha-beta pruning and another will be hill-climbing and another quick-sort, and another...and another will be a heuristic for classifying a problem as to what tools might help solve it...and another As such, I don't think that any AGI can exist. Something more general than people, and certainly something that thinks more quickly than people and something that knows more than any person can...but not a truly general AI. E.g., where would you put a map colorer for 4-color maps? Certainly an AGI should be able to do it, but would you really expect it to do it more readily (compared to the speed of it's other processes) than people can? If it could, would that really bump your estimate of it's intelligence that much? And yet there are probably an indefinitely large number of such problems. And from what it currently know, it's quite likely that each one would either need n^k or better steps to solve, or a specialized algorithm. Or both. --- 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=120640061-aded06 Powered by Listbox: http://www.listbox.com
Re: [agi] Mushed Up Decision Processes
In response to my message, where I said, What is wrong with the AI-probability group mind-set is that very few of its proponents ever consider the problem of statistical ambiguity and its obvious consequences. Abram noted, The AI-probability group definitely considers such problems. There is a large body of literature on avoiding overfitting, ie, finding patterns that work for more then just the data at hand. Suppose I responded with a remark like, 6341/6344 wrong Abram... A remark like this would be absurd because it lacks reference, explanation and validity while also presenting a comically false numerical precision for its otherwise inherent meaninglessness. Where does the ratio 6341/6344 come from? I did a search in ListBox of all references to the word overfitting made in 2008 and found that out of 6344 messages only 3 actually involved the discussion of the word before Abram mentioned it today. (I don't know how good ListBox is for this sort of thing). So what is wrong with my conclusion that Abram was 6341/6344 wrong? Lots of things and they can all be described using declarative statements. First of all the idea that the conversations in this newsgroup represent an adequate sampling of all ai-probability enthusiasts is totally ridiculous. Secondly, Abram's mention of overfitting was just one example of how the general ai-probability community is aware of the problem that I mentioned. So while my statistical finding may be tangentially relevant to the discussion, the presumption that it can serve as a numerical evaluation of Abram's 'wrongness' in his response is so absurd that it does not merit serious consideration. My skepticism then concerns the question of just how would a fully automated AGI program that relied fully on probability methods be able to avoid getting sucked into the vortex of such absurd mushy reasoning if it wasn't also able to analyze the declarative inferences of its application of statistical methods? I believe that an AI program that is to be capable of advanced AGI has to be capable of declarative assessment to work with any other mathematical methods of reasoning it is programmed with. The ability to reason about declarative knowledge does not necessarily have to be done in text or something like that. That is not what I mean. What I really mean is that an effective AI program is going to have to be capable of some kind of referential analysis of events in the IO data environment using methods other than probability. But if it is to attain higher intellectual functions it has to be done in a creative and imaginative way. Just as human statisticians have to be able to express and analyze the application of their statistical methods using declarative statements that refer to the data subject fields and the methods used, an AI program that is designed to utilize automated probability reasoning to attain greater general success is going to have to be able to express and analyze its statistical assessments in terms of some kind of declarative methods as well. 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=120640061-aded06 Powered by Listbox: http://www.listbox.com
Re: [agi] Mushed Up Decision Processes
On Sat, Nov 29, 2008 at 1:51 PM, Ben Goertzel [EMAIL PROTECTED] wrote: To me the big weaknesses of modern probability theory lie in **hypothesis generation** and **inference**. Testing a hypothesis against data, to see if it's overfit to that data, is handled well by crossvalidation and related methods. But the problem of: given a number of hypotheses with support from a dataset, generating other interesting hypotheses that will also have support from the dataset ... that is where traditional probabilistic methods (though not IMO the foundational ideas of probability) fall short, providing only unscalable or oversimplified solutions... -- Ben G Could you give me a little more detail about your thoughts on this? Do you think the problem of increasing uncomputableness of complicated complexity is the common thread found in all of the interesting, useful but unscalable methods of AI? 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=120640061-aded06 Powered by Listbox: http://www.listbox.com
Re: [agi] Mushed Up Decision Processes
Whether an AI needs to explicitly manipulate declarative statements is a deep question ... it may be that other dynamics that are in some contexts implicitly equivalent to this sort of manipulation will suffice But anyway, there is no contradiction between manipulating explicit declarative statements and using probability theory. Some of my colleagues and I spent a bunch of time during the last few years figuring out nice ways to combine probability theory and formal logic. In fact there are Progic workshops every year exploring these sorts of themes. So, while the mainstream of probability-focused AI theorists aren't doing hard-core probabilistic logic, some researchers certainly are... I've been displeased with the wimpiness of the progic subfield, and its lack of contribution to areas like inference with nested quantifiers, and intensional inference ... and I've tried to remedy these shortcomings with PLN (Probabilistic Logic Networks) ... So, I think it's correct to criticize the mainstream of probability-focused AI theorists for not doing AGI ;-) ... but I don't think they've overlooking basic issues like overfitting and such ... I think they're just focusing on relatively easy problems where (unlike if you want to do explicitly probability theory based AGI) you don't need to merge probability theory with complex logical constructs... ben On Sat, Nov 29, 2008 at 9:15 PM, Jim Bromer [EMAIL PROTECTED] wrote: In response to my message, where I said, What is wrong with the AI-probability group mind-set is that very few of its proponents ever consider the problem of statistical ambiguity and its obvious consequences. Abram noted, The AI-probability group definitely considers such problems. There is a large body of literature on avoiding overfitting, ie, finding patterns that work for more then just the data at hand. Suppose I responded with a remark like, 6341/6344 wrong Abram... A remark like this would be absurd because it lacks reference, explanation and validity while also presenting a comically false numerical precision for its otherwise inherent meaninglessness. Where does the ratio 6341/6344 come from? I did a search in ListBox of all references to the word overfitting made in 2008 and found that out of 6344 messages only 3 actually involved the discussion of the word before Abram mentioned it today. (I don't know how good ListBox is for this sort of thing). So what is wrong with my conclusion that Abram was 6341/6344 wrong? Lots of things and they can all be described using declarative statements. First of all the idea that the conversations in this newsgroup represent an adequate sampling of all ai-probability enthusiasts is totally ridiculous. Secondly, Abram's mention of overfitting was just one example of how the general ai-probability community is aware of the problem that I mentioned. So while my statistical finding may be tangentially relevant to the discussion, the presumption that it can serve as a numerical evaluation of Abram's 'wrongness' in his response is so absurd that it does not merit serious consideration. My skepticism then concerns the question of just how would a fully automated AGI program that relied fully on probability methods be able to avoid getting sucked into the vortex of such absurd mushy reasoning if it wasn't also able to analyze the declarative inferences of its application of statistical methods? I believe that an AI program that is to be capable of advanced AGI has to be capable of declarative assessment to work with any other mathematical methods of reasoning it is programmed with. The ability to reason about declarative knowledge does not necessarily have to be done in text or something like that. That is not what I mean. What I really mean is that an effective AI program is going to have to be capable of some kind of referential analysis of events in the IO data environment using methods other than probability. But if it is to attain higher intellectual functions it has to be done in a creative and imaginative way. Just as human statisticians have to be able to express and analyze the application of their statistical methods using declarative statements that refer to the data subject fields and the methods used, an AI program that is designed to utilize automated probability reasoning to attain greater general success is going to have to be able to express and analyze its statistical assessments in terms of some kind of declarative methods as well. 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/?; Powered by Listbox: http://www.listbox.com -- Ben Goertzel, PhD CEO, Novamente LLC and Biomind LLC Director of Research, SIAI [EMAIL PROTECTED] I intend to live forever, or die trying. --
Re: [agi] Mushed Up Decision Processes
Could you give me a little more detail about your thoughts on this? Do you think the problem of increasing uncomputableness of complicated complexity is the common thread found in all of the interesting, useful but unscalable methods of AI? Jim Bromer Well, I think that dealing with combinatorial explosions is, in general, the great unsolved problem of AI. I think the opencog prime design can solve it, but this isn't proved yet... Even relatively unambitious AI methods tend to get dumbed down further when you try to scale them up, due to combinatorial explosion issues. For instance, Bayes nets aren't that clever to begin with ... they don't do that much ... but to make them scalable, one has to make them even more limited and basically ignore combinational causes and just look at causes between one isolated event-class and another... And of course, all theorem provers are unscalable due to having no scalable methods of inference tree pruning... Evolutionary methods can't handle complex fitness functions because they'd require overly large population sizes... In general, the standard AI methods can't handle pattern recognition problems requiring finding complex interdependencies among multiple variables that are obscured among scads of other variables The human mind seems to do this via building up intuition via drawing analogies among multiple problems it confronts during its history. Also of course the human mind builds internal simulations of the world, and probes these simulations and draws analogies from problems it solved in its inner sim world, to problems it encounters in the outer world... etc. etc. etc. 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=120640061-aded06 Powered by Listbox: http://www.listbox.com
Re: [agi] Mushed Up Decision Processes
On Sat, Nov 29, 2008 at 11:53 AM, Steve Richfield [EMAIL PROTECTED] wrote: Jim, YES - and I think I have another piece of your puzzle to consider... A longtime friend of mine, Dave, went on to become a PhD psychologist, who subsequently took me on as a sort of project - to figure out why most people who met me then either greatly valued my friendship, or quite the opposite, would probably kill me if they had the safe opportunity. After much discussion, interviewing people in both camps, etc., he came up with what appears to be a key to decision making in general... It appears that people pigeonhole other people, concepts, situations, etc., into a very finite number of pigeonholes - probably just tens of pigeonholes for other people. Steve: I found that I used a similar method of categorizing people who I talked to on these newsgroups. I wouldn't call it pigeonholing though. (Actually, I wouldn't call anything pigeonholing, but that is just me.) I would rely on a handful of generalizations that I thought were applicable to different people who tended to exhibit some common characteristics. However, when I discovered that an individual who I thought I understood had another facet to his personality or thoughts that I hadn't seen before I often found that I had to apply another categorical generality to my impression of him. I soon built up generalization categories based on different experiences with different kinds of people, and I eventually realized that although I often saw similar kinds of behaviors in different people, each person seemed to be comprised of different sets (or different strengths) of the various component characteristics that I derived to recall my experiences with people in these groups. So I came to similar conclusions that you and your friend came to. An interesting thing about talking to reactive people in these discussion groups. I found that by eliminating more and more affect from my comments, by refraining from personal comments, innuendos or making meta-discussion analyses and by increasingly emphasizing objectivity in my comments I could substantially reduce any hostility directed at me. My problem is that I do not want to remove all affect from my conversation just to placate some unpleasant person. But I guess I should start using that technique again when necessary. Jim Bromer On Sat, Nov 29, 2008 at 11:53 AM, Steve Richfield [EMAIL PROTECTED] wrote: Jim, YES - and I think I have another piece of your puzzle to consider... A longtime friend of mine, Dave, went on to become a PhD psychologist, who subsequently took me on as a sort of project - to figure out why most people who met me then either greatly valued my friendship, or quite the opposite, would probably kill me if they had the safe opportunity. After much discussion, interviewing people in both camps, etc., he came up with what appears to be a key to decision making in general... It appears that people pigeonhole other people, concepts, situations, etc., into a very finite number of pigeonholes - probably just tens of pigeonholes for other people. Along with the pigeonhole, they keep amendments, like Steve is like Joe, but with Then, there is the pigeonhole labeled other that all the mavericks are thrown into. Not being at all like anyone else that most people have ever met, I was invariably filed into the other pigeonhole, along with Einstein, Ted Bundy, Jack the Ripper, Stephen Hawking, etc. People are safe to the extent that they are predictable, and people in the other pigeonhole got that way because they appear to NOT be predictable, e.g. because of their worldview, etc. Now, does the potential value of the alternative worldview outweigh the potential danger of perceived unpredictability? The answer to this question apparently drove my own personal classification in other people. Dave's goal was to devise a way to stop making enemies, but unfortunately, this model of how people got that way suggested no potential solution. People who keep themselves safe from others having radically different worldviews are truly in a mental prison of their own making, and there is no way that someone whom they distrust could ever release them from that prison. I suspect that recognition, decision making, and all sorts of intelligent processes may be proceeding in much the same way. There may be no grandmother neuron/pidgeonhole, but rather a kindly old person with an amendment that is related. If on the other hand your other grandmother flogged you as a child, the filing might be quite different. Any thoughts? Steve Richfield On 11/29/08, Jim Bromer [EMAIL PROTECTED] wrote: One of the problems that comes with the casual use of analytical methods is that the user becomes inured to their habitual misuse. When a casual familiarity is combined with a habitual ignorance of the consequences of a misuse the user can become
[agi] Seeking CYC critiques
What are the best available critiques of CYC as it exists now (vs. soon after project started)? Robin Hanson [EMAIL PROTECTED] http://hanson.gmu.edu Research Associate, Future of Humanity Institute at Oxford University Associate Professor of Economics, George Mason University MSN 1D3, Carow Hall, Fairfax VA 22030- 703-993-2326 FAX: 703-993-2323 agi | Archives | Modify Your Subscription
Re: [agi] Seeking CYC critiques
Hi Robin, There are no Cyc critiques that I know of in the last few years. I was employed seven years at Cycorp until August 2006 and my non-compete agreement expired a year later. An interesting competition was held by Project Halo in which Cycorp participated along with two other research groups to demonstrate human-level competency answering chemistry questions. Results are here. Although Cycorp performed principled deductive inference giving detailed justifications, it was judged to have performed inferior due to the complexity of its justifications and due to its long running times. The other competitors used special purpose problem solving modules whereas Cycorp used its general purpose inference engine, extended for chemistry equations as needed. My own interest is in natural language dialog systems for rapid knowledge formation. I was Cycorp's first project manager for its participation in the the DARPA Rapid Knowledge Formation project where it performed to DARPA's satisfaction, but subsequently its RKF tools never lived up to Cycorp's expectations that subject matter experts could rapidly extend the Cyc KB without Cycorp ontological engineers having to intervene. A Cycorp paper describing its KRAKEN system is here. I would be glad to answer questions about Cycorp and Cyc technology to the best of my knowledge, which is growing somewhat stale at this point. Cheers. -Steve Stephen L. Reed Artificial Intelligence Researcher http://texai.org/blog http://texai.org 3008 Oak Crest Ave. Austin, Texas, USA 78704 512.791.7860 From: Robin Hanson [EMAIL PROTECTED] To: agi@v2.listbox.com Sent: Saturday, November 29, 2008 9:46:09 PM Subject: [agi] Seeking CYC critiques What are the best available critiques of CYC as it exists now (vs. soon after project started)? Robin Hanson [EMAIL PROTECTED] http://hanson.gmu.edu Research Associate, Future of Humanity Institute at Oxford University Associate Professor of Economics, George Mason University MSN 1D3, Carow Hall, Fairfax VA 22030- 703-993-2326 FAX: 703-993-2323 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=120640061-aded06 Powered by Listbox: http://www.listbox.com
