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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