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 <[email protected]> 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 <[email protected]>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 <[email protected]>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 <[email protected]>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> > <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 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
