[agi] Re: Huge Progress on the Core of AGI
I've learned something really interesting today. I realized that general rules of inference probably don't really exists. There is no such thing as complete generality for these problems. The rules of inference that work for one environment would fail in alien environments. So, I have to modify my approach to solving these problems. As I studied over simplified problems, I realized that there are probably an infinite number of environments with their own behaviors that are not representative of the environments we want to put a general AI in. So, it is not ok to just come up with any case study and solve it. The case study has to actually be representative of a problem we want to solve in an environment we want to apply AI. Otherwise the solution required will take too long to develop because of it tries to accommodate too much generality. As I mentioned, such a general solution is likely impossible. So, someone could easily get stuck trying to solve an impossible task of creating one general solution to too many problems that don't allow a general solution. The best course is a balance between the time required to write a very general solution and the time required to write less general solutions for multiple problem types and environments. The best way to do this is to choose representative case studies to solve and make sure the solutions are truth-tropic and justified for the environments they are to be applied. Dave On Sun, Jun 27, 2010 at 1:31 AM, David Jones davidher...@gmail.com wrote: A method for comparing hypotheses in explanatory-based reasoning: * We prefer the hypothesis or explanation that ***expects* more observations. If both explanations expect the same observations, then the simpler of the two is preferred (because the unnecessary terms of the more complicated explanation do not add to the predictive power).* *Why are expected events so important?* They are a measure of 1) explanatory power and 2) predictive power. The more predictive and the more explanatory a hypothesis is, the more likely the hypothesis is when compared to a competing hypothesis. Here are two case studies I've been analyzing from sensory perception of simplified visual input: The goal of the case studies is to answer the following: How do you generate the most likely motion hypothesis in a way that is general and applicable to AGI? *Case Study 1)* Here is a link to an example: animated gif of two black squares move from left to righthttp://practicalai.org/images/CaseStudy1.gif. *Description: *Two black squares are moving in unison from left to right across a white screen. In each frame the black squares shift to the right so that square 1 steals square 2's original position and square two moves an equal distance to the right. *Case Study 2) *Here is a link to an example: the interrupted squarehttp://practicalai.org/images/CaseStudy2.gif. *Description:* A single square is moving from left to right. Suddenly in the third frame, a single black square is added in the middle of the expected path of the original black square. This second square just stays there. So, what happened? Did the square moving from left to right keep moving? Or did it stop and then another square suddenly appeared and moved from left to right? *Here is a simplified version of how we solve case study 1: *The important hypotheses to consider are: 1) the square from frame 1 of the video that has a very close position to the square from frame 2 should be matched (we hypothesize that they are the same square and that any difference in position is motion). So, what happens is that in each two frames of the video, we only match one square. The other square goes unmatched. 2) We do the same thing as in hypothesis #1, but this time we also match the remaining squares and hypothesize motion as follows: the first square jumps over the second square from left to right. We hypothesize that this happens over and over in each frame of the video. Square 2 stops and square 1 jumps over it over and over again. 3) We hypothesize that both squares move to the right in unison. This is the correct hypothesis. So, why should we prefer the correct hypothesis, #3 over the other two? Well, first of all, #3 is correct because it has the most explanatory power of the three and is the simplest of the three. Simpler is better because, with the given evidence and information, there is no reason to desire a more complicated hypothesis such as #2. So, the answer to the question is because explanation #3 expects the most observations, such as: 1) the consistent relative positions of the squares in each frame are expected. 2) It also expects their new positions in each from based on velocity calculations. 3) It expects both squares to occur in each frame. Explanation 1 ignores 1 square from each frame of the video, because it can't match it. Hypothesis #1 doesn't have a reason for why the a new square
[agi] Re: Huge Progress on the Core of AGI
An easy demonstration of this is visual illusions and even visual mistakes like one I sent to this list before. Our eyes sometimes infer things that are not true. It is absolutely necessary for such mistakes to occur because our sensory interpretation system is optimized for the world we expect to encounter, which didn't optical illusions during most of our development. A perfect solution to all visual problems and possible environments is [likely] impossible. It is ok to fail on optical illusions, since the failure has no fatal consequences, other than maybe thinking that there is a water puddle in the middle of the desert :). Dave On Thu, Jul 8, 2010 at 3:17 PM, David Jones davidher...@gmail.com wrote: I've learned something really interesting today. I realized that general rules of inference probably don't really exists. There is no such thing as complete generality for these problems. The rules of inference that work for one environment would fail in alien environments. So, I have to modify my approach to solving these problems. As I studied over simplified problems, I realized that there are probably an infinite number of environments with their own behaviors that are not representative of the environments we want to put a general AI in. So, it is not ok to just come up with any case study and solve it. The case study has to actually be representative of a problem we want to solve in an environment we want to apply AI. Otherwise the solution required will take too long to develop because of it tries to accommodate too much generality. As I mentioned, such a general solution is likely impossible. So, someone could easily get stuck trying to solve an impossible task of creating one general solution to too many problems that don't allow a general solution. The best course is a balance between the time required to write a very general solution and the time required to write less general solutions for multiple problem types and environments. The best way to do this is to choose representative case studies to solve and make sure the solutions are truth-tropic and justified for the environments they are to be applied. Dave On Sun, Jun 27, 2010 at 1:31 AM, David Jones davidher...@gmail.comwrote: A method for comparing hypotheses in explanatory-based reasoning: * We prefer the hypothesis or explanation that ***expects* more observations. If both explanations expect the same observations, then the simpler of the two is preferred (because the unnecessary terms of the more complicated explanation do not add to the predictive power).* *Why are expected events so important?* They are a measure of 1) explanatory power and 2) predictive power. The more predictive and the more explanatory a hypothesis is, the more likely the hypothesis is when compared to a competing hypothesis. Here are two case studies I've been analyzing from sensory perception of simplified visual input: The goal of the case studies is to answer the following: How do you generate the most likely motion hypothesis in a way that is general and applicable to AGI? *Case Study 1)* Here is a link to an example: animated gif of two black squares move from left to righthttp://practicalai.org/images/CaseStudy1.gif. *Description: *Two black squares are moving in unison from left to right across a white screen. In each frame the black squares shift to the right so that square 1 steals square 2's original position and square two moves an equal distance to the right. *Case Study 2) *Here is a link to an example: the interrupted squarehttp://practicalai.org/images/CaseStudy2.gif. *Description:* A single square is moving from left to right. Suddenly in the third frame, a single black square is added in the middle of the expected path of the original black square. This second square just stays there. So, what happened? Did the square moving from left to right keep moving? Or did it stop and then another square suddenly appeared and moved from left to right? *Here is a simplified version of how we solve case study 1: *The important hypotheses to consider are: 1) the square from frame 1 of the video that has a very close position to the square from frame 2 should be matched (we hypothesize that they are the same square and that any difference in position is motion). So, what happens is that in each two frames of the video, we only match one square. The other square goes unmatched. 2) We do the same thing as in hypothesis #1, but this time we also match the remaining squares and hypothesize motion as follows: the first square jumps over the second square from left to right. We hypothesize that this happens over and over in each frame of the video. Square 2 stops and square 1 jumps over it over and over again. 3) We hypothesize that both squares move to the right in unison. This is the correct hypothesis. So, why should we prefer the correct hypothesis, #3 over the other two?
Re: [agi] Re: Huge Progress on the Core of AGI
David, That's why, imho, the rules need to be *learned* (and, when need be, unlearned). IE, what we need to work on is general learning algorithms, not general visual processing algorithms. As you say, there's not even such a thing as a general visual processing algorithm. Learning algorithms suffer similar environment-dependence, but (by their nature) not as severe... --Abram On Thu, Jul 8, 2010 at 3:17 PM, David Jones davidher...@gmail.com wrote: I've learned something really interesting today. I realized that general rules of inference probably don't really exists. There is no such thing as complete generality for these problems. The rules of inference that work for one environment would fail in alien environments. So, I have to modify my approach to solving these problems. As I studied over simplified problems, I realized that there are probably an infinite number of environments with their own behaviors that are not representative of the environments we want to put a general AI in. So, it is not ok to just come up with any case study and solve it. The case study has to actually be representative of a problem we want to solve in an environment we want to apply AI. Otherwise the solution required will take too long to develop because of it tries to accommodate too much generality. As I mentioned, such a general solution is likely impossible. So, someone could easily get stuck trying to solve an impossible task of creating one general solution to too many problems that don't allow a general solution. The best course is a balance between the time required to write a very general solution and the time required to write less general solutions for multiple problem types and environments. The best way to do this is to choose representative case studies to solve and make sure the solutions are truth-tropic and justified for the environments they are to be applied. Dave On Sun, Jun 27, 2010 at 1:31 AM, David Jones davidher...@gmail.comwrote: A method for comparing hypotheses in explanatory-based reasoning: * We prefer the hypothesis or explanation that ***expects* more observations. If both explanations expect the same observations, then the simpler of the two is preferred (because the unnecessary terms of the more complicated explanation do not add to the predictive power).* *Why are expected events so important?* They are a measure of 1) explanatory power and 2) predictive power. The more predictive and the more explanatory a hypothesis is, the more likely the hypothesis is when compared to a competing hypothesis. Here are two case studies I've been analyzing from sensory perception of simplified visual input: The goal of the case studies is to answer the following: How do you generate the most likely motion hypothesis in a way that is general and applicable to AGI? *Case Study 1)* Here is a link to an example: animated gif of two black squares move from left to righthttp://practicalai.org/images/CaseStudy1.gif. *Description: *Two black squares are moving in unison from left to right across a white screen. In each frame the black squares shift to the right so that square 1 steals square 2's original position and square two moves an equal distance to the right. *Case Study 2) *Here is a link to an example: the interrupted squarehttp://practicalai.org/images/CaseStudy2.gif. *Description:* A single square is moving from left to right. Suddenly in the third frame, a single black square is added in the middle of the expected path of the original black square. This second square just stays there. So, what happened? Did the square moving from left to right keep moving? Or did it stop and then another square suddenly appeared and moved from left to right? *Here is a simplified version of how we solve case study 1: *The important hypotheses to consider are: 1) the square from frame 1 of the video that has a very close position to the square from frame 2 should be matched (we hypothesize that they are the same square and that any difference in position is motion). So, what happens is that in each two frames of the video, we only match one square. The other square goes unmatched. 2) We do the same thing as in hypothesis #1, but this time we also match the remaining squares and hypothesize motion as follows: the first square jumps over the second square from left to right. We hypothesize that this happens over and over in each frame of the video. Square 2 stops and square 1 jumps over it over and over again. 3) We hypothesize that both squares move to the right in unison. This is the correct hypothesis. So, why should we prefer the correct hypothesis, #3 over the other two? Well, first of all, #3 is correct because it has the most explanatory power of the three and is the simplest of the three. Simpler is better because, with the given evidence and information, there is no reason to desire a more complicated hypothesis such
Re: [agi] Solomonoff Induction is Not Universal and Probability is not Prediction
Yes, Jim, you seem to be mixing arguments here. I cannot tell which of the following you intend: 1) Solomonoff induction is useless because it would produce very bad predictions if we could compute them. 2) Solomonoff induction is useless because we can't compute its predictions. Are you trying to reject #1 and assert #2, reject #2 and assert #1, or assert both #1 and #2? Or some third statement? --Abram On Wed, Jul 7, 2010 at 7:09 PM, Matt Mahoney matmaho...@yahoo.com wrote: Who is talking about efficiency? An infinite sequence of uncomputable values is still just as uncomputable. I don't disagree that AIXI and Solomonoff induction are not computable. But you are also arguing that they are wrong. -- Matt Mahoney, matmaho...@yahoo.com -- *From:* Jim Bromer jimbro...@gmail.com *To:* agi agi@v2.listbox.com *Sent:* Wed, July 7, 2010 6:40:52 PM *Subject:* Re: [agi] Solomonoff Induction is Not Universal and Probability is not Prediction Matt, But you are still saying that Solomonoff Induction has to be recomputed for each possible combination of bit value aren't you? Although this doesn't matter when you are dealing with infinite computations in the first place, it does matter when you are wondering if this has anything to do with AGI and compression efficiencies. Jim Bromer On Wed, Jul 7, 2010 at 5:44 PM, Matt Mahoney matmaho...@yahoo.com wrote: Jim Bromer wrote: But, a more interesting question is, given that the first digits are 000, what are the chances that the next digit will be 1? Dim Induction will report .5, which of course is nonsense and a whole less useful than making a rough guess. Wrong. The probability of a 1 is p(0001)/(p()+p(0001)) where the probabilities are computed using Solomonoff induction. A program that outputs will be shorter in most languages than a program that outputs 0001, so 0 is the most likely next bit. More generally, probability and prediction are equivalent by the chain rule. Given any 2 strings x followed by y, the prediction p(y|x) = p(xy)/p(x). -- Matt Mahoney, matmaho...@yahoo.com -- *From:* Jim Bromer jimbro...@gmail.com *To:* agi agi@v2.listbox.com *Sent:* Wed, July 7, 2010 10:10:37 AM *Subject:* [agi] Solomonoff Induction is Not Universal and Probability is not Prediction Suppose you have sets of programs that produce two strings. One set of outputs is 00 and the other is 11. Now suppose you used these sets of programs to chart the probabilities of the output of the strings. If the two strings were each output by the same number of programs then you'd have a .5 probability that either string would be output. That's ok. But, a more interesting question is, given that the first digits are 000, what are the chances that the next digit will be 1? Dim Induction will report .5, which of course is nonsense and a whole less useful than making a rough guess. But, of course, Solomonoff Induction purports to be able, if it was feasible, to compute the possibilities for all possible programs. Ok, but now, try thinking about this a little bit. If you have ever tried writing random program instructions what do you usually get? Well, I'll take a hazard and guess (a lot better than the bogus method of confusing shallow probability with prediction in my example) and say that you will get a lot of programs that crash. Well, most of my experiments with that have ended up with programs that go into an infinite loop or which crash. Now on a universal Turing machine, the results would probably look a little different. Some strings will output nothing and go into an infinite loop. Some programs will output something and then either stop outputting anything or start outputting an infinite loop of the same substring. Other programs will go on to infinity producing something that looks like random strings. But the idea that all possible programs would produce well distributed strings is complete hogwash. Since Solomonoff Induction does not define what kind of programs should be used, the assumption that the distribution would produce useful data is absurd. In particular, the use of the method to determine the probability based given an initial string (as in what follows given the first digits are 000) is wrong as in really wrong. The idea that this crude probability can be used as prediction is unsophisticated. Of course you could develop an infinite set of Solomonoff Induction values for each possible given initial sequence of digits. Hey when you're working with infeasible functions why not dream anything? I might be wrong of course. Maybe there is something you guys haven't been able to get across to me. Even if you can think for yourself you can still make mistakes. So if anyone has actually tried writing a program to output all possible programs (up to some feasible point) on a Turing Machine
Re: [agi] Re: Huge Progress on the Core of AGI
It may not be possible to create a learning algorithm that can learn how to generally process images and other general AGI problems. This is for the same reason that completely general vision algorithms are likely impossible. I think that figuring out how to process sensory information intelligently requires either 1) impossible amounts of processing or 2) intelligent design and understanding by us. Maybe you could be more specific about how general learning algorithms would solve problems such as the one I'm tackling. But, I am extremely doubtful it can be done because the problems cannot be effectively described to such an algorithm. If you can't describe the problem, it can't search for solutions. If it can't search for solutions, you're basically stuck with evolution type algorithms, which require prohibitory amounts of processing. The reason that vision is so important for learning is that sensory perception is the foundation required to learn everything else. If you don't start with a foundational problem like this, you won't be representing the real nature of general intelligence problems that require extensive knowledge of the world to solve properly. Sensory perception is required to learn the information needed to understand everything else. Text and language for example, require extensive knowledge about the world to understand and especially to learn about. If you start with general learning algorithms on these unrepresentative problems, you will get stuck as we already have. So, it still makes a lot of sense to start with a concrete problem that does not require extensive amounts of previous knowledge to start learning. In fact, AGI requires that you not pre-program the AI with such extensive knowledge. So, lots of people are working on general learning algorithms that are unrepresentative of what is required for AGI because the algorithms don't have the knowledge needed to learn what they are trying to learn about. Regardless of how you look at it, my approach is definitely the right approach to AGI in my opinion. On Thu, Jul 8, 2010 at 5:02 PM, Abram Demski abramdem...@gmail.com wrote: David, That's why, imho, the rules need to be *learned* (and, when need be, unlearned). IE, what we need to work on is general learning algorithms, not general visual processing algorithms. As you say, there's not even such a thing as a general visual processing algorithm. Learning algorithms suffer similar environment-dependence, but (by their nature) not as severe... --Abram On Thu, Jul 8, 2010 at 3:17 PM, David Jones davidher...@gmail.com wrote: I've learned something really interesting today. I realized that general rules of inference probably don't really exists. There is no such thing as complete generality for these problems. The rules of inference that work for one environment would fail in alien environments. So, I have to modify my approach to solving these problems. As I studied over simplified problems, I realized that there are probably an infinite number of environments with their own behaviors that are not representative of the environments we want to put a general AI in. So, it is not ok to just come up with any case study and solve it. The case study has to actually be representative of a problem we want to solve in an environment we want to apply AI. Otherwise the solution required will take too long to develop because of it tries to accommodate too much generality. As I mentioned, such a general solution is likely impossible. So, someone could easily get stuck trying to solve an impossible task of creating one general solution to too many problems that don't allow a general solution. The best course is a balance between the time required to write a very general solution and the time required to write less general solutions for multiple problem types and environments. The best way to do this is to choose representative case studies to solve and make sure the solutions are truth-tropic and justified for the environments they are to be applied. Dave On Sun, Jun 27, 2010 at 1:31 AM, David Jones davidher...@gmail.comwrote: A method for comparing hypotheses in explanatory-based reasoning: * We prefer the hypothesis or explanation that ***expects* more observations. If both explanations expect the same observations, then the simpler of the two is preferred (because the unnecessary terms of the more complicated explanation do not add to the predictive power).* *Why are expected events so important?* They are a measure of 1) explanatory power and 2) predictive power. The more predictive and the more explanatory a hypothesis is, the more likely the hypothesis is when compared to a competing hypothesis. Here are two case studies I've been analyzing from sensory perception of simplified visual input: The goal of the case studies is to answer the following: How do you generate the most likely motion hypothesis in a way that is general
Re: [agi] Re: Huge Progress on the Core of AGI
David, How I'd present the problem would be predict the next frame, or more generally predict a specified portion of video given a different portion. Do you object to this approach? --Abram On Thu, Jul 8, 2010 at 5:30 PM, David Jones davidher...@gmail.com wrote: It may not be possible to create a learning algorithm that can learn how to generally process images and other general AGI problems. This is for the same reason that completely general vision algorithms are likely impossible. I think that figuring out how to process sensory information intelligently requires either 1) impossible amounts of processing or 2) intelligent design and understanding by us. Maybe you could be more specific about how general learning algorithms would solve problems such as the one I'm tackling. But, I am extremely doubtful it can be done because the problems cannot be effectively described to such an algorithm. If you can't describe the problem, it can't search for solutions. If it can't search for solutions, you're basically stuck with evolution type algorithms, which require prohibitory amounts of processing. The reason that vision is so important for learning is that sensory perception is the foundation required to learn everything else. If you don't start with a foundational problem like this, you won't be representing the real nature of general intelligence problems that require extensive knowledge of the world to solve properly. Sensory perception is required to learn the information needed to understand everything else. Text and language for example, require extensive knowledge about the world to understand and especially to learn about. If you start with general learning algorithms on these unrepresentative problems, you will get stuck as we already have. So, it still makes a lot of sense to start with a concrete problem that does not require extensive amounts of previous knowledge to start learning. In fact, AGI requires that you not pre-program the AI with such extensive knowledge. So, lots of people are working on general learning algorithms that are unrepresentative of what is required for AGI because the algorithms don't have the knowledge needed to learn what they are trying to learn about. Regardless of how you look at it, my approach is definitely the right approach to AGI in my opinion. On Thu, Jul 8, 2010 at 5:02 PM, Abram Demski abramdem...@gmail.comwrote: David, That's why, imho, the rules need to be *learned* (and, when need be, unlearned). IE, what we need to work on is general learning algorithms, not general visual processing algorithms. As you say, there's not even such a thing as a general visual processing algorithm. Learning algorithms suffer similar environment-dependence, but (by their nature) not as severe... --Abram On Thu, Jul 8, 2010 at 3:17 PM, David Jones davidher...@gmail.comwrote: I've learned something really interesting today. I realized that general rules of inference probably don't really exists. There is no such thing as complete generality for these problems. The rules of inference that work for one environment would fail in alien environments. So, I have to modify my approach to solving these problems. As I studied over simplified problems, I realized that there are probably an infinite number of environments with their own behaviors that are not representative of the environments we want to put a general AI in. So, it is not ok to just come up with any case study and solve it. The case study has to actually be representative of a problem we want to solve in an environment we want to apply AI. Otherwise the solution required will take too long to develop because of it tries to accommodate too much generality. As I mentioned, such a general solution is likely impossible. So, someone could easily get stuck trying to solve an impossible task of creating one general solution to too many problems that don't allow a general solution. The best course is a balance between the time required to write a very general solution and the time required to write less general solutions for multiple problem types and environments. The best way to do this is to choose representative case studies to solve and make sure the solutions are truth-tropic and justified for the environments they are to be applied. Dave On Sun, Jun 27, 2010 at 1:31 AM, David Jones davidher...@gmail.comwrote: A method for comparing hypotheses in explanatory-based reasoning: * We prefer the hypothesis or explanation that ***expects* more observations. If both explanations expect the same observations, then the simpler of the two is preferred (because the unnecessary terms of the more complicated explanation do not add to the predictive power).* *Why are expected events so important?* They are a measure of 1) explanatory power and 2) predictive power. The more predictive and the more explanatory a hypothesis is, the more likely the
Re: [agi] Re: Huge Progress on the Core of AGI
Abram, Yeah, I would have to object for a couple reasons. First, prediction requires previous knowledge. So, even if you make that your primary goal, you're still going to have my research goals as the prerequisite: which are to process visual information in a more general way and learn about the environment in a more general way. Second, not everything is predictable. Certainly, we should not try to predict everything. Only after we have experience, can we actually predict anything. Even then, it's not precise prediction, like predicting the next frame of a video. It's more like having knowledge of what is quite likely to occur, or maybe an approximate prediction, but not guaranteed in the least. For example, based on previous experience, striking a match will light it. But, sometimes it doesn't light, and that too is expected to occur sometimes. We definitely don't predict the next image we'll see when it lights though. We just have expectations for what we might see and this helps us interpret the image effectively. We should try to expect certain outcomes or possible outcomes though. You could call that prediction, but it's not quite the same. The things we are more likely to see should be attempted as an explanation first and preferred if not given a reason to think otherwise. Dave On Thu, Jul 8, 2010 at 5:51 PM, Abram Demski abramdem...@gmail.com wrote: David, How I'd present the problem would be predict the next frame, or more generally predict a specified portion of video given a different portion. Do you object to this approach? --Abram On Thu, Jul 8, 2010 at 5:30 PM, David Jones davidher...@gmail.com wrote: It may not be possible to create a learning algorithm that can learn how to generally process images and other general AGI problems. This is for the same reason that completely general vision algorithms are likely impossible. I think that figuring out how to process sensory information intelligently requires either 1) impossible amounts of processing or 2) intelligent design and understanding by us. Maybe you could be more specific about how general learning algorithms would solve problems such as the one I'm tackling. But, I am extremely doubtful it can be done because the problems cannot be effectively described to such an algorithm. If you can't describe the problem, it can't search for solutions. If it can't search for solutions, you're basically stuck with evolution type algorithms, which require prohibitory amounts of processing. The reason that vision is so important for learning is that sensory perception is the foundation required to learn everything else. If you don't start with a foundational problem like this, you won't be representing the real nature of general intelligence problems that require extensive knowledge of the world to solve properly. Sensory perception is required to learn the information needed to understand everything else. Text and language for example, require extensive knowledge about the world to understand and especially to learn about. If you start with general learning algorithms on these unrepresentative problems, you will get stuck as we already have. So, it still makes a lot of sense to start with a concrete problem that does not require extensive amounts of previous knowledge to start learning. In fact, AGI requires that you not pre-program the AI with such extensive knowledge. So, lots of people are working on general learning algorithms that are unrepresentative of what is required for AGI because the algorithms don't have the knowledge needed to learn what they are trying to learn about. Regardless of how you look at it, my approach is definitely the right approach to AGI in my opinion. On Thu, Jul 8, 2010 at 5:02 PM, Abram Demski abramdem...@gmail.comwrote: David, That's why, imho, the rules need to be *learned* (and, when need be, unlearned). IE, what we need to work on is general learning algorithms, not general visual processing algorithms. As you say, there's not even such a thing as a general visual processing algorithm. Learning algorithms suffer similar environment-dependence, but (by their nature) not as severe... --Abram On Thu, Jul 8, 2010 at 3:17 PM, David Jones davidher...@gmail.comwrote: I've learned something really interesting today. I realized that general rules of inference probably don't really exists. There is no such thing as complete generality for these problems. The rules of inference that work for one environment would fail in alien environments. So, I have to modify my approach to solving these problems. As I studied over simplified problems, I realized that there are probably an infinite number of environments with their own behaviors that are not representative of the environments we want to put a general AI in. So, it is not ok to just come up with any case study and solve it. The case study has to actually be representative of a problem we
Re: [agi] Re: Huge Progress on the Core of AGI
Isn't the first problem simply to differentiate the objects in a scene? (Maybe
the most important movement to begin with is not the movement of the object,
but of the viewer changing their POV if only slightly - wh. won't be a factor
if you're looking at a screen)
And that I presume comes down to being able to put a crude, highly tentative,
and fluid outline round them (something that won't be neces. if you're dealing
with squares?) . Without knowing v. little if anything about what kind of
objects they are. As an infant most likely does. {See infants' drawings and how
they evolve v. gradually from a v. crude outline blob that at first can
represent anything - that I'm suggesting is a replay of how visual perception
developed).
The fluid outline or image schema is arguably the basis of all intelligence -
just about everything AGI is based on it. You need an outline for instance not
just of objects, but of where you're going, and what you're going to try and do
- if you want to survive in the real world. Schemas connect everything AGI.
And it's not a matter of choice - first you have to have an outline/sense of
the whole - whatever it is - before you can start filling in the parts.
P.S. It would be mindblowingly foolish BTW to think you can do better than the
way an infant learns to see - that's an awfully big visual section of the brain
there, and it works.
David,
How I'd present the problem would be predict the next frame, or more
generally predict a specified portion of video given a different portion. Do
you object to this approach?
--Abram
On Thu, Jul 8, 2010 at 5:30 PM, David Jones davidher...@gmail.com wrote:
It may not be possible to create a learning algorithm that can learn how to
generally process images and other general AGI problems. This is for the same
reason that completely general vision algorithms are likely impossible. I think
that figuring out how to process sensory information intelligently requires
either 1) impossible amounts of processing or 2) intelligent design and
understanding by us.
Maybe you could be more specific about how general learning algorithms would
solve problems such as the one I'm tackling. But, I am extremely doubtful it
can be done because the problems cannot be effectively described to such an
algorithm. If you can't describe the problem, it can't search for solutions. If
it can't search for solutions, you're basically stuck with evolution type
algorithms, which require prohibitory amounts of processing.
The reason that vision is so important for learning is that sensory
perception is the foundation required to learn everything else. If you don't
start with a foundational problem like this, you won't be representing the real
nature of general intelligence problems that require extensive knowledge of the
world to solve properly. Sensory perception is required to learn the
information needed to understand everything else. Text and language for
example, require extensive knowledge about the world to understand and
especially to learn about. If you start with general learning algorithms on
these unrepresentative problems, you will get stuck as we already have.
So, it still makes a lot of sense to start with a concrete problem that does
not require extensive amounts of previous knowledge to start learning. In fact,
AGI requires that you not pre-program the AI with such extensive knowledge. So,
lots of people are working on general learning algorithms that are
unrepresentative of what is required for AGI because the algorithms don't have
the knowledge needed to learn what they are trying to learn about. Regardless
of how you look at it, my approach is definitely the right approach to AGI in
my opinion.
On Thu, Jul 8, 2010 at 5:02 PM, Abram Demski abramdem...@gmail.com wrote:
David,
That's why, imho, the rules need to be *learned* (and, when need be,
unlearned). IE, what we need to work on is general learning algorithms, not
general visual processing algorithms.
As you say, there's not even such a thing as a general visual processing
algorithm. Learning algorithms suffer similar environment-dependence, but (by
their nature) not as severe...
--Abram
On Thu, Jul 8, 2010 at 3:17 PM, David Jones davidher...@gmail.com wrote:
I've learned something really interesting today. I realized that general
rules of inference probably don't really exists. There is no such thing as
complete generality for these problems. The rules of inference that work for
one environment would fail in alien environments.
So, I have to modify my approach to solving these problems. As I studied
over simplified problems, I realized that there are probably an infinite number
of environments with their own behaviors that are not representative of the
environments we want to put a general AI in.
So, it is not ok to just come up with any case study and solve it. The
case
