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.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 >>> right<http://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 >>> square<http://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 >>> appears in each frame and why one disappears. It doesn't expect these >>> observations. In fact, explanation 1 doesn't expect anything that happens >>> because something new happens in each frame, which doesn't give it a chance >>> to confirm its hypotheses in subsequent frames. >>> >>> The power of this method is immediately clear. It is general and it >>> solves the problem very cleanly. >>> >>> *Here is a simplified version of how we solve case study 2:* >>> We expect the original square to move at a similar velocity from left to >>> right because we hypothesized that it did move from left to right and we >>> calculated its velocity. If this expectation is confirmed, then it is more >>> likely than saying that the square suddenly stopped and another started >>> moving. Such a change would be unexpected and such a conclusion would be >>> unjustifiable. >>> >>> I also believe that explanations which generate fewer incorrect >>> expectations should be preferred over those that more incorrect >>> expectations. >>> >>> The idea I came up with earlier this month regarding high frame rates to >>> reduce uncertainty is still applicable. It is important that all generated >>> hypotheses have as low uncertainty as possible given our constraints and >>> resources available. >>> >>> I thought I'd share my progress with you all. I'll be testing the ideas >>> on test cases such as the ones I mentioned in the coming days and weeks. >>> >>> Dave >>> >> >> *agi* | Archives <https://www.listbox.com/member/archive/303/=now> >> <https://www.listbox.com/member/archive/rss/303/> | >> Modify<https://www.listbox.com/member/?&;>Your Subscription >> <http://www.listbox.com> >> > > > > -- > Abram Demski > http://lo-tho.blogspot.com/ > http://groups.google.com/group/one-logic > *agi* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
