Predicting the old and predictable [incl in shape and form] is narrow AI.
Squaresville.
Adapting to the new and unpredictable [incl in shape and form] is AGI. Rock on.
From: David Jones
Sent: Thursday, July 22, 2010 4:49 PM
To: agi
Subject: [agi] Re: Huge Progress on the Core of AGI
An Update....
I think the following gets to the heart of general AI and what it takes to
achieve it. It also provides us with evidence as to why general AI is so
difficult. With this new knowledge in mind, I think I will be much more capable
now of solving the problems and making it work.
I've come to the conclusion lately that the best hypothesis is better because
it is more predictive and then simpler than other hypotheses (in that order....
more predictive... then simpler). But, I am amazed at how difficult it is to
quantitatively define more predictive and simpler for specific problems. This
is why I have sometimes doubted the truth of the statement.
In addition, the observations that the AI gets are not representative of all
observations! This means that if your measure of "predictiveness" depends on
the number of certain observations, it could make mistakes! So, the specific
observations you are aware of may be unrepresentative of the predictiveness of
a hypothesis relative to the truth. If you try to calculate which hypothesis is
more predictive and you don't have the critical observations that would give
you the right answer, you may get the wrong answer! This all depends of course
on your method of calculation, which is quite elusive to define.
Visual input from screenshots, for example, can be somewhat malicious. Things
can move, appear, disappear or occlude each other suddenly. So, without
sufficient knowledge it is hard to decide whether matches you find between such
large changes are because it is the same object or a different object. This may
indicate that bias and preprogrammed experience should be introduced to the AI
before training. Either that or the training inputs should be carefully chosen
to avoid malicious input and to make them nice for learning.
This is the "correspondence problem" that is typical of computer vision and has
never been properly solved. Such malicious input also makes it difficult to
learn automatically because the AI doesn't have sufficient experience to know
which changes or transformations are acceptable and which are not. It is
immediately bombarded with malicious inputs.
I've also realized that if a hypothesis is more "explanatory", it may be
better. But quantitatively defining explanatory is also elusive and truly
depends on the specific problems you are applying it to because it is a
heuristic. It is not a true measure of correctness. It is not loyal to the
truth. "More explanatory" is really a heuristic that helps us find hypothesis
that are more predictive. The true measure of whether a hypothesis is better is
simply the most accurate and predictive hypothesis. That is the ultimate and
true measure of correctness.
Also, since we can't measure every possible prediction or every last prediction
(and we certainly can't predict everything), our measure of predictiveness
can't possibly be right all the time! We have no choice but to use a heuristic
of some kind.
So, its clear to me that the right hypothesis is "more predictive and then
simpler". But, it is also clear that there will never be a single measure of
this that can be applied to all problems. I hope to eventually find a nice
model for how to apply it to different problems though. This may be the reason
that so many people have tried and failed to develop general AI. Yes, there is
a solution. But there is no silver bullet that can be applied to all problems.
Some methods are better than others. But I think another major reason of the
failures is that people think they can predict things without sufficient
information. By approaching the problem this way, we compound the need for
heuristics and the errors they produce because we simply don't have sufficient
information to make a good decision with limited evidence. If approached
correctly, the right solution would solve many more problems with the same
efforts than a poor solution would. It would also eliminate some of the
difficulties we currently face if sufficient data is available to learn from.
In addition to all this theory about better hypotheses, you have to add on the
need to solve problems in reasonable time. This also compounds the difficulty
of the problem and the complexity of solutions.
I am always fascinated by the extraordinary difficulty and complexity of this
problem. The more I learn about it, the more I appreciate it.
Dave
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