Re: [agi] Computer Vision not as hard as I thought!
On Wed, Aug 4, 2010 at 9:27 AM, David Jones davidher...@gmail.com wrote: *So, why computer vision? Why can't we just enter knowledge manually? *a) The knowledge we require for AI to do what we want is vast and complex and we can prove that it is completely ineffective to enter the knowledge we need manually. b) Computer vision is the most effective means of gathering facts about the world. Knowledge and experience can be gained from analysis of these facts. c) Language is not learned through passive observation. The associations that words have to the environment and our common sense knowledge of the environment/world are absolutely essential to language learning, understanding and disambiguation. When visual information is available, children use visual cues from their parents and from the objects they are interacting with to figure out word-environment associations. If visual info is not available, touch is essential to replace the visual cues. Touch can provide much of the same info as vision, but it is not as effective because not everything is in reach and it provides less information than vision. There is some very good documentation out there on how children learn language that supports this. One example is How Children Learn Language by William O'grady. d) The real world cannot be predicted blindly. It is absolutely essential to be able to directly observe it and receive feedback. e) Manual entry of knowledge, even if possible, would be extremely slow and would be a very serious bottleneck(it already is). This is a major reason we want AI... to increase our man power and remove man-power related bottlenecks. Discovering a way to get a computer program to interpret a human language is a difficult problem. The feeling that an AI program might be able to attain a higher level of intelligence if only it could examine data from a variety of different kinds of sensory input modalities it is not new. It has been tried and tried during the past 35 years. But there is no experimental data (that I have heard of) that suggests that this method is the only way anyone will achieve intelligence. I have tried to explain that I believe the problem is twofold. First of all, there have been quite a few AI programs that worked real well as long as the problem was simple enough. This suggests that the complexity of what is trying to be understood is a critical factor. This in turn suggests that using different input modalities, would not -in itself- make AI possible. Secondly, there is a problem of getting the computer to accurately model that which it can know in such a way that it could be effectively utilized for higher degrees of complexity. I consider this to be a conceptual integration problem. We do not know how to integrate different kinds of ideas (or idea-like knowledge) in an effective manner, and as a result we have not seen the gradual advancement in AI programming that we would expect to see given all the advances in computer technology that have been occurring. Both visual analysis and linguistic analysis are significant challenges in AI programming. The idea that combining both of them would make the problem 1/2 as hard may not be any crazier than saying that it would make the problem 2 times as hard, but without experimental evidence it isn't any saner either. Jim Bromer On Wed, Aug 4, 2010 at 9:27 AM, David Jones davidher...@gmail.com wrote: :D Thanks Jim for paying attention! One very cool thing about the human brain is that we use multiple feedback mechanisms to correct for such problems as observer movement. For example, the inner ear senses your bodies movement and provides feedback for visual processing. This is why we get nauseous when the ear disagrees with the eyes and other senses. As you said, eye muscles also provide feedback about how the eye itself has moved. In example papers I have read, such as Object Discovery through Motion, Appearance and Shape, the researchers know the position of the camera (I'm not sure how) and use that to determine which moving features are closest to the cameras movement, and therefore are not actually moving. Once you know how much the camera moved, you can try to subtract this from apparent motion. You're right that I should attempt to implement the system. I think I will in fact, but it is difficult because I have limited time and resources. My main goal is to make sure it is accomplished, even if not by me. So, sometimes I think that it is better to prove that it can be done than to actually spend a much longer amount of time to actually do it myself. I am struggling to figure out how I can gather the resources or support to accomplish the monstrous task. I think that I should work on the theoretical basis in addition to the actual implementation. This is likely important to make sure that my design is well grounded and reflects reality. It
Re: [agi] Computer Vision not as hard as I thought!
On Fri, Aug 6, 2010 at 7:37 PM, Jim Bromer jimbro...@gmail.com wrote: On Wed, Aug 4, 2010 at 9:27 AM, David Jones davidher...@gmail.com wrote: *So, why computer vision? Why can't we just enter knowledge manually? * a) The knowledge we require for AI to do what we want is vast and complex and we can prove that it is completely ineffective to enter the knowledge we need manually. b) Computer vision is the most effective means of gathering facts about the world. Knowledge and experience can be gained from analysis of these facts. c) Language is not learned through passive observation. The associations that words have to the environment and our common sense knowledge of the environment/world are absolutely essential to language learning, understanding and disambiguation. When visual information is available, children use visual cues from their parents and from the objects they are interacting with to figure out word-environment associations. If visual info is not available, touch is essential to replace the visual cues. Touch can provide much of the same info as vision, but it is not as effective because not everything is in reach and it provides less information than vision. There is some very good documentation out there on how children learn language that supports this. One example is How Children Learn Language by William O'grady. d) The real world cannot be predicted blindly. It is absolutely essential to be able to directly observe it and receive feedback. e) Manual entry of knowledge, even if possible, would be extremely slow and would be a very serious bottleneck(it already is). This is a major reason we want AI... to increase our man power and remove man-power related bottlenecks. Discovering a way to get a computer program to interpret a human language is a difficult problem. The feeling that an AI program might be able to attain a higher level of intelligence if only it could examine data from a variety of different kinds of sensory input modalities it is not new. It has been tried and tried during the past 35 years. But there is no experimental data (that I have heard of) that suggests that this method is the only way anyone will achieve intelligence. if only it could examine data from a variety of different kinds of sensory input modalities That statement suggests that such different kinds of input have no meaningful relationship to the problem at hand. I'm not talking about different kinds of input. I'm talking about explicitly and deliberately extracting facts about the environment from sensory perception, specifically remote perception or visual perception. The input modalities are not what is important. It is the facts that you can extract from computer vision that are useful in understanding what is out there in the world, what relationships and associations exist, and how is language associated with the environment. It is well documented that children learn language by interacting with adults around them and using cues from them to learn how the words they speak are associated with what is going on. It is not hard to support the claim that extensive knowledge about the world is important for understanding and interpreting human language. Nor is it hard to support the idea that such knowledge can be gained from computer vision. I have tried to explain that I believe the problem is twofold. First of all, there have been quite a few AI programs that worked real well as long as the problem was simple enough. This suggests that the complexity of what is trying to be understood is a critical factor. This in turn suggests that using different input modalities, would not -in itself- make AI possible. Your conclusion isn't supported by your arguments. I'm not even saying it makes AI possible. I'm saying that a system can make reasonable inferences and come to reasonable conclusions with sufficient knowledge. Without sufficient knowledge, there is reason to believe that it is significantly harder and often impossible to come to correct conclusions. Therefore, gaining knowledge about how things are related is not just helpful in making correct inferences, it is required. So, different input modalities which can give you facts about the world, which in turn would give you knowledge about the world, do make correct reasoning possible, when it otherwise would not be possible. You see, it has nothing to do with the source of the info or whether it is more info or not. It has everything to do with the relationships that information have. Just calling them different input modalities is not correct. Secondly, there is a problem of getting the computer to accurately model that which it can know in such a way that it could be effectively utilized for higher degrees of complexity. This is an engineering problem, not necessarily a problem that can't be solved. When we get
Re: [agi] Computer Vision not as hard as I thought!
On Tue, Aug 3, 2010 at 11:52 AM, David Jones davidher...@gmail.com wrote: I've suddenly realized that computer vision of real images is very much solvable and that it is now just a matter of engineering... I've also realized that I don't actually have to implement it, which is what is most difficult because even if you know a solution to part of the problem has certain properties and issues, implementing it takes a lot of time. Whereas I can just assume I have a less than perfect solution with the properties I predict from other experiments. Then I can solve the problem without actually implementing every last detail... *First*, existing methods find observations that are likely true by themselves. They find data patterns that are very unlikely to occur by coincidence, such as many features moving together over several frames of a video and over a statistically significant distance. They use thresholds to ensure that the observed changes are likely transformations of the original property observed or to ensure the statistical significance of an observation. These are highly likely true observations and not coincidences or noise. -- Just looking at these statements, I can find three significant errors. (I do agree with some of what you said, like the significance of finding observations that are likely true in themselves.) When the camera moves (in a rotation or pan) many features will appear 'to move together over a statistically significant distance'. One might suppose that the animal can sense the movement of his own eyes but then again, he can rotate his head and use his vision to gauge the rotation of his head. So right away there is some kind of serious error in your statement. It might be resolvable, it is just that your statement does not really do the resolution. I do believe that computer vision is possible with contemporary computers but you are also saying that while you can't get your algorithms to work the way you had hoped, it doesn't really matter because you can figure it all out without the work of implementation. My point of view is that these represent major errors in reasoning. I hope to get back to actual visual processing experiments again. Although I don't think that computer vision is necessary for AGI, I do think that there is so much to be learned from experimenting with computer vision that it is a serious mistake not to take advantage of opportunity. Jim Bromer On Tue, Aug 3, 2010 at 11:52 AM, David Jones davidher...@gmail.com wrote: I've suddenly realized that computer vision of real images is very much solvable and that it is now just a matter of engineering. I was so stuck before because you can't make the simple assumptions in screenshot computer vision that you can in real computer vision. This makes experience probably necessary to effectively learn from screenshots. Objects in real images to not change drastically in appearance, position or other dimensions in unpredictable ways. The reason I came to the conclusion that it's a lot easier than I thought is that I found a way to describe why existing solutions work, how they work and how to come up with even better solutions. I've also realized that I don't actually have to implement it, which is what is most difficult because even if you know a solution to part of the problem has certain properties and issues, implementing it takes a lot of time. Whereas I can just assume I have a less than perfect solution with the properties I predict from other experiments. Then I can solve the problem without actually implementing every last detail. *First*, existing methods find observations that are likely true by themselves. They find data patterns that are very unlikely to occur by coincidence, such as many features moving together over several frames of a video and over a statistically significant distance. They use thresholds to ensure that the observed changes are likely transformations of the original property observed or to ensure the statistical significance of an observation. These are highly likely true observations and not coincidences or noise. *Second*, they make sure that the other possible explanations of the observations are very unlikely. This is usually done using a threshold, and a second difference threshold from the first match to the second match. This makes sure that second best matches are much farther away than the best match. This is important because it's not enough to find a very likely match if there are 1000 very likely matches. You have to be able to show that the other matches are very unlikely, otherwise the specific match you pick may be just a tiny bit better than the others, and the confidence of that match would be very low. So, my initial design plans are as follows. Note: I will probably not actually implement the system because the engineering part dominates the time. I'd rather convert real
Re: [agi] Computer Vision not as hard as I thought!
:D Thanks Jim for paying attention! One very cool thing about the human brain is that we use multiple feedback mechanisms to correct for such problems as observer movement. For example, the inner ear senses your bodies movement and provides feedback for visual processing. This is why we get nauseous when the ear disagrees with the eyes and other senses. As you said, eye muscles also provide feedback about how the eye itself has moved. In example papers I have read, such as Object Discovery through Motion, Appearance and Shape, the researchers know the position of the camera (I'm not sure how) and use that to determine which moving features are closest to the cameras movement, and therefore are not actually moving. Once you know how much the camera moved, you can try to subtract this from apparent motion. You're right that I should attempt to implement the system. I think I will in fact, but it is difficult because I have limited time and resources. My main goal is to make sure it is accomplished, even if not by me. So, sometimes I think that it is better to prove that it can be done than to actually spend a much longer amount of time to actually do it myself. I am struggling to figure out how I can gather the resources or support to accomplish the monstrous task. I think that I should work on the theoretical basis in addition to the actual implementation. This is likely important to make sure that my design is well grounded and reflects reality. It is very hard for me to balance everything that has to be done though. This definitely should be done by a much larger team of people. As for your belief that computer vision is not necessary for AGI, I just finished writing an email to someone else who had similar questions regarding why computer vision helps with AGI. I will append them here. I hope you find them helpful. *Appended Below: Why do I think computer vision is so important for AGI.** Someone asked, if I solved computer vision, why would it help in higher reasoning and learning? Why do I think computer vision so important for AGI. *Regarding higher reasoning and learning, I'll try to explain my views a bit here:* *When I talk about higher reasoning and learning, I'm referring to all of the following: * * language learning, * language disambiguation and interpretation * learning about cause and effect * learning about object/environment behavior and mechanisms regarding how or why they behave certain ways * explanatory reasoning that requires common sense knowledge * learning common sense knowledge at increasing levels of abstraction. * trial and error learning * learning to predict the environment. This is extremely important for the purposes of goal pursuit, which is the whole point of AI I think. * inductive learning on examples from observation. This is needed for language learning. This also helps with predicting the behavior of new object instances. * rule induction from observed examples * etc. etc. etc. *So, what am I really using computer vision for?* I'm using computer vision to gather *knowledge*, including common sense knowledge. It is very clear to me, and probably many others, that knowledge is required to solve the problems we want AI to solve. The core problem of AGI is knowledge. There are many other supporting problems such as machine learning, planning, language disambiguation, etc., but without knowledge it is much harder than it needs to be. We need it, we want it, but we haven't been able to get enough of it. Knowledge also makes it easier to solve these problems, making it possible to use simpler algorithms and to learn from fewer examples. Computer vision isn't just for knowledge though. It's also for goal pursuit. Many things we want an AI to do require exploration of the environment, trial and error learning, exploration in general, interaction with the environment, unsupervised learning, etc. These require the ability to perceive and understand the environment. The environment is too complex to predict blindly. It is absolutely essential to be able to directly observe it and receive feedback. *So, why computer vision? Why can't we just enter knowledge manually?* Explaining this requires several supporting arguments that will have to be argued separately. So I will list them below: a) The knowledge we require for AI to do what we want is vast and complex and we can prove that it is completely ineffective to enter the knowledge we need manually. b) Computer vision is the most effective means of gathering facts about the world. Knowledge and experience can be gained from analysis of these facts. c) Language is not learned through passive observation. The associations that words have to the environment and our common sense knowledge of the environment/world are absolutely essential to language learning, understanding and disambiguation. When visual information is available, children use visual cues from their parents and from the objects they
Re: [agi] Computer Vision not as hard as I thought!
Steve, I wouldn't say that's an accurate description of what I wrote. What a wrote was a way to think about how to solve computer vision. My approach to artificial intelligence is a Neat approach. See http://en.wikipedia.org/wiki/Neats_vs._scruffies The paper you attached is a Scruffy approach. Neat approaches are characterized by deliberate algorithms that are analogous to the problem and can sometimes be shown to be provably correct. An example of a Neat approach is the use of features in the paper I mentioned. One can describe why the features are calculated and manipulated the way they are. An example of a scruffies approach would be neural nets, where you don't know the rules by which it comes up with an answer and such approaches are not very scalable. Neural nets require manually created training data and the knowledge generated is not in a form that can be used for other tasks. The knowledge isn't portable. I also wouldn't say I switched from absolute values to rates of change. That's not really at all what I'm saying here. Dave On Wed, Aug 4, 2010 at 2:32 PM, Steve Richfield steve.richfi...@gmail.comwrote: David, It appears that you may have reinvented the wheel. See the attached article. There is LOTS of evidence, along with some good math, suggesting that our brains work on rates of change rather than absolute values. Then, temporal learning, which is otherwise very difficult, falls out as the easiest of things to do. In effect, your proposal shifts from absolute values to rates of change. Steve === On Tue, Aug 3, 2010 at 8:52 AM, David Jones davidher...@gmail.com wrote: I've suddenly realized that computer vision of real images is very much solvable and that it is now just a matter of engineering. I was so stuck before because you can't make the simple assumptions in screenshot computer vision that you can in real computer vision. This makes experience probably necessary to effectively learn from screenshots. Objects in real images to not change drastically in appearance, position or other dimensions in unpredictable ways. The reason I came to the conclusion that it's a lot easier than I thought is that I found a way to describe why existing solutions work, how they work and how to come up with even better solutions. I've also realized that I don't actually have to implement it, which is what is most difficult because even if you know a solution to part of the problem has certain properties and issues, implementing it takes a lot of time. Whereas I can just assume I have a less than perfect solution with the properties I predict from other experiments. Then I can solve the problem without actually implementing every last detail. *First*, existing methods find observations that are likely true by themselves. They find data patterns that are very unlikely to occur by coincidence, such as many features moving together over several frames of a video and over a statistically significant distance. They use thresholds to ensure that the observed changes are likely transformations of the original property observed or to ensure the statistical significance of an observation. These are highly likely true observations and not coincidences or noise. *Second*, they make sure that the other possible explanations of the observations are very unlikely. This is usually done using a threshold, and a second difference threshold from the first match to the second match. This makes sure that second best matches are much farther away than the best match. This is important because it's not enough to find a very likely match if there are 1000 very likely matches. You have to be able to show that the other matches are very unlikely, otherwise the specific match you pick may be just a tiny bit better than the others, and the confidence of that match would be very low. So, my initial design plans are as follows. Note: I will probably not actually implement the system because the engineering part dominates the time. I'd rather convert real videos to pseudo test cases or simulation test cases and then write a psuedo design and algorithm that can solve it. This would show that it works without actually spending the time needed to implement it. It's more important for me to prove it works and show what it can do than to actually do it. If I can prove it, there will be sufficient motivation for others to do it with more resources and man power than I have at my disposal. *My Design* *First, we use high speed cameras and lidar systems to gather sufficient data with very low uncertainty because the changes possible between data points can be assumed to be very low, allowing our thresholds to be much smaller, which eliminates many possible errors and ambiguities. *Second*, *we have to gain experience from high confidence observations. These are gathered as follows: 1) Describe allowable transformations(thresholds) and what they mean.
Re: [agi] Computer Vision not as hard as I thought!
David, You are correct in that I keep bad company. My approach to NNs is VERY different than other people's approaches. I insist on reasonable math being performed on quantities that I understand, which sets me apart from just about everyone else. Your neat approach isn't all that neat, and is arguably scruffier than mine. At least I have SOME math to back up my approach. Further, note that we are self-organizing systems, and that this process is poorly understood. I am NOT particularly interest in people-programmed systems because of their very fundamental limitations. Yes, self-organization is messy, but it fits the neat definition better than it meets the scruffy definition. Scruffy has more to do with people-programmed ad hoc approaches (like most of AGI), which I agree are a waste of time. Steve On Wed, Aug 4, 2010 at 12:43 PM, David Jones davidher...@gmail.com wrote: Steve, I wouldn't say that's an accurate description of what I wrote. What a wrote was a way to think about how to solve computer vision. My approach to artificial intelligence is a Neat approach. See http://en.wikipedia.org/wiki/Neats_vs._scruffies The paper you attached is a Scruffy approach. Neat approaches are characterized by deliberate algorithms that are analogous to the problem and can sometimes be shown to be provably correct. An example of a Neat approach is the use of features in the paper I mentioned. One can describe why the features are calculated and manipulated the way they are. An example of a scruffies approach would be neural nets, where you don't know the rules by which it comes up with an answer and such approaches are not very scalable. Neural nets require manually created training data and the knowledge generated is not in a form that can be used for other tasks. The knowledge isn't portable. I also wouldn't say I switched from absolute values to rates of change. That's not really at all what I'm saying here. Dave On Wed, Aug 4, 2010 at 2:32 PM, Steve Richfield steve.richfi...@gmail.com wrote: David, It appears that you may have reinvented the wheel. See the attached article. There is LOTS of evidence, along with some good math, suggesting that our brains work on rates of change rather than absolute values. Then, temporal learning, which is otherwise very difficult, falls out as the easiest of things to do. In effect, your proposal shifts from absolute values to rates of change. Steve === On Tue, Aug 3, 2010 at 8:52 AM, David Jones davidher...@gmail.comwrote: I've suddenly realized that computer vision of real images is very much solvable and that it is now just a matter of engineering. I was so stuck before because you can't make the simple assumptions in screenshot computer vision that you can in real computer vision. This makes experience probably necessary to effectively learn from screenshots. Objects in real images to not change drastically in appearance, position or other dimensions in unpredictable ways. The reason I came to the conclusion that it's a lot easier than I thought is that I found a way to describe why existing solutions work, how they work and how to come up with even better solutions. I've also realized that I don't actually have to implement it, which is what is most difficult because even if you know a solution to part of the problem has certain properties and issues, implementing it takes a lot of time. Whereas I can just assume I have a less than perfect solution with the properties I predict from other experiments. Then I can solve the problem without actually implementing every last detail. *First*, existing methods find observations that are likely true by themselves. They find data patterns that are very unlikely to occur by coincidence, such as many features moving together over several frames of a video and over a statistically significant distance. They use thresholds to ensure that the observed changes are likely transformations of the original property observed or to ensure the statistical significance of an observation. These are highly likely true observations and not coincidences or noise. *Second*, they make sure that the other possible explanations of the observations are very unlikely. This is usually done using a threshold, and a second difference threshold from the first match to the second match. This makes sure that second best matches are much farther away than the best match. This is important because it's not enough to find a very likely match if there are 1000 very likely matches. You have to be able to show that the other matches are very unlikely, otherwise the specific match you pick may be just a tiny bit better than the others, and the confidence of that match would be very low. So, my initial design plans are as follows. Note: I will probably not actually implement the system because the engineering part dominates the time. I'd rather convert real
Re: [agi] Computer Vision not as hard as I thought!
Steve, Sorry if I misunderstood your approach. I do not really understand how it would work though because it is not clear how you go from inputs to output goals. It likely will still have many of the same problems as other neural networks including 1) poor knowledge portability 2) difficult to extend, augment or understand how it works 3) requires manually created training data, which is a major problem. 4) is designed with biological hardware in mind, not necessarily existing hardware and software. These are my main reasons, at least that I can remember, that I avoid biologically inspired methods. It's not to say that they are wrong. But they don't meet my requirements. It is also very unclear how to implement the system and make it work. My approach is very deliberate, so the steps required to make it work are pretty clear to me. It is not that your approach is bad. It is just different and I really prefer methods that are not biologically inspired, but are designed specifically with goals and requirements in mind as the most important design motivator. Dave On Wed, Aug 4, 2010 at 3:54 PM, Steve Richfield steve.richfi...@gmail.comwrote: David, You are correct in that I keep bad company. My approach to NNs is VERY different than other people's approaches. I insist on reasonable math being performed on quantities that I understand, which sets me apart from just about everyone else. Your neat approach isn't all that neat, and is arguably scruffier than mine. At least I have SOME math to back up my approach. Further, note that we are self-organizing systems, and that this process is poorly understood. I am NOT particularly interest in people-programmed systems because of their very fundamental limitations. Yes, self-organization is messy, but it fits the neat definition better than it meets the scruffy definition. Scruffy has more to do with people-programmed ad hoc approaches (like most of AGI), which I agree are a waste of time. Steve On Wed, Aug 4, 2010 at 12:43 PM, David Jones davidher...@gmail.comwrote: Steve, I wouldn't say that's an accurate description of what I wrote. What a wrote was a way to think about how to solve computer vision. My approach to artificial intelligence is a Neat approach. See http://en.wikipedia.org/wiki/Neats_vs._scruffies The paper you attached is a Scruffy approach. Neat approaches are characterized by deliberate algorithms that are analogous to the problem and can sometimes be shown to be provably correct. An example of a Neat approach is the use of features in the paper I mentioned. One can describe why the features are calculated and manipulated the way they are. An example of a scruffies approach would be neural nets, where you don't know the rules by which it comes up with an answer and such approaches are not very scalable. Neural nets require manually created training data and the knowledge generated is not in a form that can be used for other tasks. The knowledge isn't portable. I also wouldn't say I switched from absolute values to rates of change. That's not really at all what I'm saying here. Dave On Wed, Aug 4, 2010 at 2:32 PM, Steve Richfield steve.richfi...@gmail.com wrote: David, It appears that you may have reinvented the wheel. See the attached article. There is LOTS of evidence, along with some good math, suggesting that our brains work on rates of change rather than absolute values. Then, temporal learning, which is otherwise very difficult, falls out as the easiest of things to do. In effect, your proposal shifts from absolute values to rates of change. Steve === On Tue, Aug 3, 2010 at 8:52 AM, David Jones davidher...@gmail.comwrote: I've suddenly realized that computer vision of real images is very much solvable and that it is now just a matter of engineering. I was so stuck before because you can't make the simple assumptions in screenshot computer vision that you can in real computer vision. This makes experience probably necessary to effectively learn from screenshots. Objects in real images to not change drastically in appearance, position or other dimensions in unpredictable ways. The reason I came to the conclusion that it's a lot easier than I thought is that I found a way to describe why existing solutions work, how they work and how to come up with even better solutions. I've also realized that I don't actually have to implement it, which is what is most difficult because even if you know a solution to part of the problem has certain properties and issues, implementing it takes a lot of time. Whereas I can just assume I have a less than perfect solution with the properties I predict from other experiments. Then I can solve the problem without actually implementing every last detail. *First*, existing methods find observations that are likely true by themselves. They find data patterns that are very unlikely to occur by
Re: [agi] Computer Vision not as hard as I thought!
David On Wed, Aug 4, 2010 at 1:16 PM, David Jones davidher...@gmail.com wrote: 3) requires manually created training data, which is a major problem. Where did this come from. Certainly, people are ill equipped to create dP/dt type data. These would have to come from sensors. 4) is designed with biological hardware in mind, not necessarily existing hardware and software. The biology is just good to help the math over some humps. So far, I have not been able to identify ANY neuronal characteristic that hasn't been refined to near-perfection, once the true functionality was fully understood. Anyway, with the math, you can build a system anyway you want. Without the math, you are just wasting your time and electricity. The math comes first, and all other things follow. Steve === These are my main reasons, at least that I can remember, that I avoid biologically inspired methods. It's not to say that they are wrong. But they don't meet my requirements. It is also very unclear how to implement the system and make it work. My approach is very deliberate, so the steps required to make it work are pretty clear to me. It is not that your approach is bad. It is just different and I really prefer methods that are not biologically inspired, but are designed specifically with goals and requirements in mind as the most important design motivator. Dave On Wed, Aug 4, 2010 at 3:54 PM, Steve Richfield steve.richfi...@gmail.com wrote: David, You are correct in that I keep bad company. My approach to NNs is VERY different than other people's approaches. I insist on reasonable math being performed on quantities that I understand, which sets me apart from just about everyone else. Your neat approach isn't all that neat, and is arguably scruffier than mine. At least I have SOME math to back up my approach. Further, note that we are self-organizing systems, and that this process is poorly understood. I am NOT particularly interest in people-programmed systems because of their very fundamental limitations. Yes, self-organization is messy, but it fits the neat definition better than it meets the scruffy definition. Scruffy has more to do with people-programmed ad hoc approaches (like most of AGI), which I agree are a waste of time. Steve On Wed, Aug 4, 2010 at 12:43 PM, David Jones davidher...@gmail.comwrote: Steve, I wouldn't say that's an accurate description of what I wrote. What a wrote was a way to think about how to solve computer vision. My approach to artificial intelligence is a Neat approach. See http://en.wikipedia.org/wiki/Neats_vs._scruffies The paper you attached is a Scruffy approach. Neat approaches are characterized by deliberate algorithms that are analogous to the problem and can sometimes be shown to be provably correct. An example of a Neat approach is the use of features in the paper I mentioned. One can describe why the features are calculated and manipulated the way they are. An example of a scruffies approach would be neural nets, where you don't know the rules by which it comes up with an answer and such approaches are not very scalable. Neural nets require manually created training data and the knowledge generated is not in a form that can be used for other tasks. The knowledge isn't portable. I also wouldn't say I switched from absolute values to rates of change. That's not really at all what I'm saying here. Dave On Wed, Aug 4, 2010 at 2:32 PM, Steve Richfield steve.richfi...@gmail.com wrote: David, It appears that you may have reinvented the wheel. See the attached article. There is LOTS of evidence, along with some good math, suggesting that our brains work on rates of change rather than absolute values. Then, temporal learning, which is otherwise very difficult, falls out as the easiest of things to do. In effect, your proposal shifts from absolute values to rates of change. Steve === On Tue, Aug 3, 2010 at 8:52 AM, David Jones davidher...@gmail.comwrote: I've suddenly realized that computer vision of real images is very much solvable and that it is now just a matter of engineering. I was so stuck before because you can't make the simple assumptions in screenshot computer vision that you can in real computer vision. This makes experience probably necessary to effectively learn from screenshots. Objects in real images to not change drastically in appearance, position or other dimensions in unpredictable ways. The reason I came to the conclusion that it's a lot easier than I thought is that I found a way to describe why existing solutions work, how they work and how to come up with even better solutions. I've also realized that I don't actually have to implement it, which is what is most difficult because even if you know a solution to part of the problem has certain properties and issues, implementing it takes a lot of time. Whereas I can just
Re: [agi] Computer Vision not as hard as I thought!
Steve, I replace your need for math with my need to understand what the system is doing and why it is doing it. It's basically the same thing. But you are approaching it at an extremely low level. It doesn't seem to me that you are clear on how this math makes the system work the way we want it to work. So, make the math as perfect as you like, if you don't understand why you need the math and how it makes the system do what you want, then it's not going to do you any good. Understanding what you are trying to accomplish and how you want the system to work comes first, not math. If your neural net doesn't require training data, I don't understand how it works or why you expect it to do what you want it to do if it is self organized. How do you tell it how to process inputs correctly? What guides the processing and analysis? Dave On Wed, Aug 4, 2010 at 4:33 PM, Steve Richfield steve.richfi...@gmail.comwrote: David On Wed, Aug 4, 2010 at 1:16 PM, David Jones davidher...@gmail.com wrote: 3) requires manually created training data, which is a major problem. Where did this come from. Certainly, people are ill equipped to create dP/dt type data. These would have to come from sensors. 4) is designed with biological hardware in mind, not necessarily existing hardware and software. The biology is just good to help the math over some humps. So far, I have not been able to identify ANY neuronal characteristic that hasn't been refined to near-perfection, once the true functionality was fully understood. Anyway, with the math, you can build a system anyway you want. Without the math, you are just wasting your time and electricity. The math comes first, and all other things follow. Steve === These are my main reasons, at least that I can remember, that I avoid biologically inspired methods. It's not to say that they are wrong. But they don't meet my requirements. It is also very unclear how to implement the system and make it work. My approach is very deliberate, so the steps required to make it work are pretty clear to me. It is not that your approach is bad. It is just different and I really prefer methods that are not biologically inspired, but are designed specifically with goals and requirements in mind as the most important design motivator. Dave On Wed, Aug 4, 2010 at 3:54 PM, Steve Richfield steve.richfi...@gmail.com wrote: David, You are correct in that I keep bad company. My approach to NNs is VERY different than other people's approaches. I insist on reasonable math being performed on quantities that I understand, which sets me apart from just about everyone else. Your neat approach isn't all that neat, and is arguably scruffier than mine. At least I have SOME math to back up my approach. Further, note that we are self-organizing systems, and that this process is poorly understood. I am NOT particularly interest in people-programmed systems because of their very fundamental limitations. Yes, self-organization is messy, but it fits the neat definition better than it meets the scruffy definition. Scruffy has more to do with people-programmed ad hoc approaches (like most of AGI), which I agree are a waste of time. Steve On Wed, Aug 4, 2010 at 12:43 PM, David Jones davidher...@gmail.comwrote: Steve, I wouldn't say that's an accurate description of what I wrote. What a wrote was a way to think about how to solve computer vision. My approach to artificial intelligence is a Neat approach. See http://en.wikipedia.org/wiki/Neats_vs._scruffies The paper you attached is a Scruffy approach. Neat approaches are characterized by deliberate algorithms that are analogous to the problem and can sometimes be shown to be provably correct. An example of a Neat approach is the use of features in the paper I mentioned. One can describe why the features are calculated and manipulated the way they are. An example of a scruffies approach would be neural nets, where you don't know the rules by which it comes up with an answer and such approaches are not very scalable. Neural nets require manually created training data and the knowledge generated is not in a form that can be used for other tasks. The knowledge isn't portable. I also wouldn't say I switched from absolute values to rates of change. That's not really at all what I'm saying here. Dave On Wed, Aug 4, 2010 at 2:32 PM, Steve Richfield steve.richfi...@gmail.com wrote: David, It appears that you may have reinvented the wheel. See the attached article. There is LOTS of evidence, along with some good math, suggesting that our brains work on rates of change rather than absolute values. Then, temporal learning, which is otherwise very difficult, falls out as the easiest of things to do. In effect, your proposal shifts from absolute values to rates of change. Steve === On Tue, Aug 3, 2010 at 8:52 AM, David Jones
Re: [agi] Computer Vision not as hard as I thought!
David, On Wed, Aug 4, 2010 at 1:45 PM, David Jones davidher...@gmail.com wrote: Understanding what you are trying to accomplish and how you want the system to work comes first, not math. It's all the same. First comes the qualitative, then comes the quantitative. If your neural net doesn't require training data, Sure it needs training data -real-world interactive sensory input training data, rather than static manually prepared training data. I don't understand how it works or why you expect it to do what you want it to do if it is self organized. How do you tell it how to process inputs correctly? What guides the processing and analysis? Bingo - you have just hit on THE great challenge in AI/AGI., and the source of much past debate. Some believe in maximizing the information content of the output. Some believe in other figures of merit, e.g. success in interacting with a test environment, success in forming a layered structure, etc. This particular sub-field is still WIDE open and waiting for some good answers. Note that this same problem presents itself, regardless of approach, e.g. AGI. Steve === On Wed, Aug 4, 2010 at 4:33 PM, Steve Richfield steve.richfi...@gmail.com wrote: David On Wed, Aug 4, 2010 at 1:16 PM, David Jones davidher...@gmail.comwrote: 3) requires manually created training data, which is a major problem. Where did this come from. Certainly, people are ill equipped to create dP/dt type data. These would have to come from sensors. 4) is designed with biological hardware in mind, not necessarily existing hardware and software. The biology is just good to help the math over some humps. So far, I have not been able to identify ANY neuronal characteristic that hasn't been refined to near-perfection, once the true functionality was fully understood. Anyway, with the math, you can build a system anyway you want. Without the math, you are just wasting your time and electricity. The math comes first, and all other things follow. Steve === These are my main reasons, at least that I can remember, that I avoid biologically inspired methods. It's not to say that they are wrong. But they don't meet my requirements. It is also very unclear how to implement the system and make it work. My approach is very deliberate, so the steps required to make it work are pretty clear to me. It is not that your approach is bad. It is just different and I really prefer methods that are not biologically inspired, but are designed specifically with goals and requirements in mind as the most important design motivator. Dave On Wed, Aug 4, 2010 at 3:54 PM, Steve Richfield steve.richfi...@gmail.com wrote: David, You are correct in that I keep bad company. My approach to NNs is VERY different than other people's approaches. I insist on reasonable math being performed on quantities that I understand, which sets me apart from just about everyone else. Your neat approach isn't all that neat, and is arguably scruffier than mine. At least I have SOME math to back up my approach. Further, note that we are self-organizing systems, and that this process is poorly understood. I am NOT particularly interest in people-programmed systems because of their very fundamental limitations. Yes, self-organization is messy, but it fits the neat definition better than it meets the scruffy definition. Scruffy has more to do with people-programmed ad hoc approaches (like most of AGI), which I agree are a waste of time. Steve On Wed, Aug 4, 2010 at 12:43 PM, David Jones davidher...@gmail.comwrote: Steve, I wouldn't say that's an accurate description of what I wrote. What a wrote was a way to think about how to solve computer vision. My approach to artificial intelligence is a Neat approach. See http://en.wikipedia.org/wiki/Neats_vs._scruffies The paper you attached is a Scruffy approach. Neat approaches are characterized by deliberate algorithms that are analogous to the problem and can sometimes be shown to be provably correct. An example of a Neat approach is the use of features in the paper I mentioned. One can describe why the features are calculated and manipulated the way they are. An example of a scruffies approach would be neural nets, where you don't know the rules by which it comes up with an answer and such approaches are not very scalable. Neural nets require manually created training data and the knowledge generated is not in a form that can be used for other tasks. The knowledge isn't portable. I also wouldn't say I switched from absolute values to rates of change. That's not really at all what I'm saying here. Dave On Wed, Aug 4, 2010 at 2:32 PM, Steve Richfield steve.richfi...@gmail.com wrote: David, It appears that you may have reinvented the wheel. See the attached article. There is LOTS of evidence, along with some good math, suggesting that our brains work on
Re: [agi] Computer Vision not as hard as I thought!
On Wed, Aug 4, 2010 at 6:17 PM, Steve Richfield steve.richfi...@gmail.comwrote: David, On Wed, Aug 4, 2010 at 1:45 PM, David Jones davidher...@gmail.com wrote: Understanding what you are trying to accomplish and how you want the system to work comes first, not math. It's all the same. First comes the qualitative, then comes the quantitative. If your neural net doesn't require training data, Sure it needs training data -real-world interactive sensory input training data, rather than static manually prepared training data. You design is not described well enough or succinctly enough for me to comment on then. I don't understand how it works or why you expect it to do what you want it to do if it is self organized. How do you tell it how to process inputs correctly? What guides the processing and analysis? Bingo - you have just hit on THE great challenge in AI/AGI., and the source of much past debate. Some believe in maximizing the information content of the output. Some believe in other figures of merit, e.g. success in interacting with a test environment, success in forming a layered structure, etc. This particular sub-field is still WIDE open and waiting for some good answers. Note that this same problem presents itself, regardless of approach, e.g. AGI. Ah, but I think that this problem is much more solvable and better defined with a more deliberate approach that does not depend on emergence. Emergence is wishful thinking. I hope you do not include such wishful thinking in your design :) Once the AI has the tools and knowledge needed to solve a problem, which I expect to get from computer vision, then it can reason about user stated goals (in natural language) and we can work on how the goal pursuit part works. Much work has already been done on planning and execution. But, all that work was done with insufficient knowledge on narrow problems. All the research needs to be re-evaluated and studied with sufficient knowledge about the world. It changes everything. This is another mile marker on my roadmap to general AI. Dave --- 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] Computer Vision not as hard as I thought!
David Jones wrote: I've suddenly realized that computer vision of real images is very much solvable and that it is now just a matter of engineering. [...] Would you (or anyone else on this list) be interested in learning Forth and working on http://code.google.com/p/mindforth/wiki/VisRecog for the MindForth artificial intelligence? There would be no pay other than AI glory. You have already shown a keen AI interest at http://www.practicalai.org and so you could put your code and documentation up there. Arthur -- http://www.scn.org/~mentifex/mindforth.txt http://www.scn.org/~mentifex/AiMind.html http://AIMind-i.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
