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 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. 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. 
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



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    -- 
    Abram Demski
    http://lo-tho.blogspot.com/
    http://groups.google.com/group/one-logic

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-- 
Abram Demski
http://lo-tho.blogspot.com/
http://groups.google.com/group/one-logic

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