One more thing. If an AGI program worked then it would effectively be able to operate with more than one meta-cognitive level regardless of whether it was specifically coded that way. And even if it was coded to only work with one meta-cognitive level it would still effectively work on more than one level by the nature of the specific uses (the runtime instantiation of the code). So in a sense my argument for multiple levels of meta-cognition is not based on a necessary specification (even if my reasoning in these messages was correct.) But look at what this is saying. The premise here contains the desired conclusion. If the AGI program worked... I am saying that in order for a program to work as a true AGI program some process of meta-cognition has to be able to work with specific types of derived products. The program has to be able to develop these derived products and turn combinations of simple derived products into types so that it can try variations of these types in similar circumstances. So the methods that it will develop to analyze and respond to the IO data environment are going to be creative and it can refer to them symbolically. It will take kinds of events and refer to them as symbols and it will be able to then use these symbolic references in order to apply higher abstractions on them. I am saying that this is the essence of creative general intelligence and since it is, it is very likely going to be the secret sauce which can develop insightful ways to analyze data. So, sure, if a carefully designed system was able to achieve true general intelligence, then, if this theory is right, it would be able to do what I am saying is necessary for true general intelligence regardless of how the programmer thought about it. But I think if this theory is right then it is much more likely that the programmer who thinks about these things explicitly will be more likely to make some progress with them. The parts of the reasoning processes have to be used by meta-cognitive processes explicitly and symbolically. However, there is no reason for details of these trial and error processes to come to the surface attention of the mind unless they are recognized as being important in some way. So I am taking the ego-argument that while many of us have thought about these sorts of things before none of us have thought about it in just this way until now. Since a meta-cognitive process will be instanced there may be many executive functions (meta-cognition) operating and the most conscious executive function does not need to know exactly what the creative instances are doing at any one moment. This means, for example, that many good preliminary analytical functions may be independently derived by the program rather than be fully specified by the programmer. Jim Bromer From: [email protected] To: [email protected] Subject: RE: [agi] A Very Simple AGI Project Date: Sun, 21 Jul 2013 13:29:19 -0400
I was trying to say that in my paradigm the parts of the acquired (learned) objects of analysis, recognition, and response have to be treated as being potentially distinct so that the run time (or acquired-learned) meta-cognitive functions can use them explicitly. However, these meta-level cognitive functions may not be available directly to the highest level of meta-awareness so the program would not be able to directly report on these functions. So I guess I am saying that there must be different depths of meta-awareness in my paradigm as well. Of course other theories include various functions (or partially-defined-functions) which can use the data from an observation or from derived data to play various roles, such as being used in a comparison (which then may also be used in different ways), but, my conclusion is that since no one has ever discussed this sort of thing with me they must either have concluded that the implied distinctions and utilizations of their theories were adequate (because they did not have to consider this as being a part of a trial and error meta-cognition) or because they had never thought of these parts of processes as explicit *types* of output that some mechanism of analysis might create and be used explicitly and creatively. Either way it is the same thing. For some reason people just have not thought of the various internal processes as being comprised of typed parts which could be used in explicit strings or multi-dimensional spaces of meta-cognitive processes. There are many cases where these different parts of the intelligent process just cannot be combined without explicitly recognizing the significance and roles of the various parts. Jim Bromer From: [email protected] To: [email protected] Subject: RE: [agi] A Very Simple AGI Project Date: Sun, 21 Jul 2013 11:03:10 -0400 I cannot give an easy example of the problem where Bayesian systems are not able to distinguish between situations when information from different sources can be combined and when it can't because I am not that familiar with the language of statistics so I can't use simple references that might point you in the direction of my ideas. When a human being uses applied statistics he can combine his general knowledge of the world with his specialized knowledge of the science of statistics to find ways to make his research more effective and insightful. But in statistical-based-AGI we are asking our automated computer programs to use statistical analysis to effectively learn how to create better models about reality. The belief that this makes sense as a basis for AGI strikes me as absurd. So while Bayesian methods and weighted reasoning in general are almost certainly important tools for AGI, they do not constitute a sound basis - in themselves - for methods that can produce self-improving insights about the world. An analogous criticism may be applied to any kind of mathematical or logical method. I am not saying that math and logic has no place in AGI or something like, that I am saying that we have to come up with other (effective) algorithms to deal with the problem of getting automated learning systems to use these algorithms effectively. Suppose that some composite source of data, which was expressed or could be derived into appropriate Bayesian form, was used to derive theories about the world. Without knowing that the data source was a composite there is little that the Bayesian (theoretical or abstract) formula could (in itself) do to detect it. So we have to rely on other ways to be able to find the composition of extracted streams of data (which superficially seems to be integral). But there are also other kinds of problems. Most AGI data is not in appropriate Bayesian form. So here the Bayesian-AGI guys would typically find a substitute characterization of a situation so that the data which could be expressed in Bayesian forms. An analogous criticism can be directed at any mathematical or logical form used as a presumptive AGI method. I am amazed at how far AI has advanced using these clunky models but it is clear to me that the fundamental failure of narrow methods to set as the base for AGI can be found right here. When we try to design an AGI program we are not locked into dealing with the IO data environment in just one way. We can - and do - try various methods to enhance that data. Most of the ways that are in common use are direct recharacterizations of the data. They are not explicitly based on program acquired theoretical recharacterizations. So if you are dealing with images you might try to increase the contrast or use a Gaussian method to try to detect the edges of the shapes in the image. Or, alternatively, you might imagine a neural network which is trained to detect edges. These are not an acquired theoretical-recharacterizations because they do not rely on the AGI program to create its own theories (with or without outside influences) which it might apply to the problem. When you start thinking of the problem from this basis a number of familiar parts of the problem-solving algorithms start to change. The programmer does not have a clear distinction between the parts of the acquired theory since all the possible theories that might be acquired can not all be foreseen. And particular data from an extraction of data taken from the IO data environment might be applied directly to parts of the extraction in one acquired (or learned) method but only indirectly in another acquired method. Suppose that an AGI program developed a 'theory' that it should use different video analysis methods when the light from the camera is bright and when it is dim. This distinction might be derived from the static parts of the background of the scene as seen at different times of the day. So the overall light from the static background might not be used as the direct object of further analysis (in this particular part of the algorithm) but as a conditional. Now suppose the program subsequently realizes that this method does not always work. Perhaps some of the details of some image objects go into shadow and then come out and it might act on the observation of this apparent change to investigate it further. At this point, parts of the static background might be used in a conditional and parts as comparative objects to compare with the target object (to compare shadows for example). Many people have pointed out that they do not think that babies create theories. Perhaps the phrase "acquired theoretical-recharacterization" does not accurately describe the kind of thing that I am thinking about. I believe that human beings develop implicit theories, or theory-like objects of thought. It was once said that Neural Networks work the way the mind work and you might say that neural networks develop implicit-theory-like relations. And I believe that Bayesian Networks are also able to develop implicit-theory-like relations. What is different in my theory of AGI is that these parts of the theory-like object and the implementation of the theory-like functions must in some cases be distinct and be open to precise activation by the artificial mind even if this internal operation might not be fully available to the mind at a level of meta-awareness. In this model, values or references may in some cases be combined, they might be combined indirectly, they might only be combined as distinct parts of a thought-object (or thought-like algorithm), or they might not be combinable. To the best of my memory I have never had this conversation with an enthusiast of weighted reasoning. This lack of interest might be because I am working on an idea which is still new enough to be a little elusive or it might be due directly to the mistaken belief that weighted reasoning is the solution to inadequacy of discrete reasoning paradigms. Jim Bromer Date: Sat, 20 Jul 2013 12:30:41 -0500 From: [email protected] To: [email protected] Subject: Re: [agi] A Very Simple AGI Project On 7/20/2013 9:14 AM, Jim Bromer wrote: Text seems brittle because it was tried and it did not work. But neither did visual, robotic, or other sensor-based AGI. If the brittleness criticism was based on a lack of substantial achievement in spite of the effort, then the brittleness criticism would have to be applied to all AGI modalities. Of course knowledge that is gathered only through text is going to be brittle in the sense that it would not be able to achieve the range of understanding that human beings can achieve, but the use of cell phones or robotics are not going to create genuine human experiences either. The only conclusion, based on the acceptance of a general lack of substantial advancement in the field, is that we do not have basic AGI because computers cannot achieve general intelligence or general intelligence needs even more advanced hardware than we have or there has been something important missing in AGI research. Something that Bayesian enthusiasts never talk about in these discussion groups is how can a mostly independent learning system make the distinction between those kinds of situations where Bayesian methods can be used to combine different sources of data from those cases where different sources of weighted values can't be combined or have to be combined in a certain way. I wonder if you could describe an example of what you mean here? As you may know, the Microsoft Troubleshooter uses a Bayesian approach... 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