You seem to be reaching for something important here, but it isn't at all clear
what you mean.
I would say that any creative activity (incl. pure problemsolving) begins from
a *conceptual paradigm* - a v. rough outline - of the form of that activity and
the form of its end-product or -procedure. As distinct from rational
activities where a formula (and algorithm) define the form of the product (and
activity) with complete precision.
You have a conceptual paradigm of "writing a post" or "shopping for groceries"
or "having a conversation". You couldn't possibly have a formula or algorithm
completely defining every step - every word and sentence, every food, every
topic - you may have or want to take.
And programs as we know them, don't and can't handle *concepts* - despite the
misnomers of "conceptual graphs/spaces" etc wh are not concepts at all. They
can't for example handle "writing" or "shopping" because these can only be
expressed as flexible outlines/schemas as per ideograms.
What do you mean?
.
From: Jim Bromer
Sent: Tuesday, July 13, 2010 2:50 PM
To: agi
Subject: Re: [agi] Re: Huge Progress on the Core of AGI
On Tue, Jul 13, 2010 at 2:29 AM, Abram Demski <abramdem...@gmail.com> wrote:
[The]complaint that probability theory doesn't try to figure out why it was
wrong in the 30% (or whatever) it misses is a common objection. Probability
theory glosses over important detail, it encourages lazy thinking, etc.
However, this all depends on the space of hypotheses being examined.
Statistical methods will be prone to this objection because they are
essentially narrow-AI methods: they don't *try* to search in the space of all
hypotheses a human might consider. An AGI setup can and should have such a
large hypothesis space.
-------------------------------
That is the thing.
We cannot search all possible hypotheses because we could not even write all
possible hypotheses down. This is why hypotheses have to be formed creatively
in response to an analysis of a situation. In my arrogant opinion, this is
best done through a method that creatively uses discreet representations. Of
course it can use statistical or probabilistic data in making those creative
hypotheses when there is good data to be used. But the best way to do this is
through categorization based creativity. But this is an imaginative method,
one which creates imaginative explanations (or other co-relations) for observed
or conjectured events. Those imaginative hypotheses then have to be compared
to a situation through some trial and error methods. Then the tentative
conjectures that seem to withstand initial tests have to be further integrated
into other hypotheses, conjectures and explanations that are related to the
subject of the hypotheses. This process of conceptual integration, a process
which has to rely on both creative methods and rational methods, is a
fundamental part of the process which does not seem to be clearly understood.
Conceptual Integration cannot be accomplished by reducing a concept to True or
False or to some number from 0 to 1 and then combined with other concepts that
were also so reduced. Ideas take on roles when combined with other ideas.
Basically, a new idea has to be fit into a complex of other ideas that are
strongly related to it.
Jim Bromer
On Tue, Jul 13, 2010 at 2:29 AM, Abram Demski <abramdem...@gmail.com> wrote:
PS-- I am not denying that statistics is applied probability theory. :) When
I say they are different, what I mean is that saying "I'm going to use
probability theory" and "I'm going to use statistics" tend to indicate very
different approaches. Probability is a set of axioms, whereas statistics is a
set of methods. The probability theory camp tends to be bayesian, whereas the
stats camp tends to be frequentist.
Your complaint that probability theory doesn't try to figure out why it was
wrong in the 30% (or whatever) it misses is a common objection. Probability
theory glosses over important detail, it encourages lazy thinking, etc.
However, this all depends on the space of hypotheses being examined.
Statistical methods will be prone to this objection because they are
essentially narrow-AI methods: they don't *try* to search in the space of all
hypotheses a human might consider. An AGI setup can and should have such a
large hypothesis space. Note that AIXI is typically formulated as using a space
of crisp (non-probabilistic) hypotheses, though probability theory is used to
reason about them. This means no theory it considers will gloss over detail in
this way: every theory completely explains the data. (I use AIXI as a
convenient example, not because I agree with it.)
--Abram
On Mon, Jul 12, 2010 at 2:42 PM, Abram Demski <abramdem...@gmail.com> wrote:
David,
I tend to think of probability theory and statistics as different things.
I'd agree that statistics is not enough for AGI, but in contrast I think
probability theory is a pretty good foundation. Bayesianism to me provides a
sound way of integrating the elegance/utility tradeoff of explanation-based
reasoning into the basic fabric of the uncertainty calculus. Others advocate
different sorts of uncertainty than probabilities, but so far what I've seen
indicates more a lack of ability to apply probability theory than a need for a
new type of uncertainty. What other methods do you favor for dealing with these
things?
--Abram
On Sun, Jul 11, 2010 at 12:30 PM, David Jones <davidher...@gmail.com> wrote:
Thanks Abram,
I know that probability is one approach. But there are many problems with
using it in actual implementations. I know a lot of people will be angered by
that statement and retort with all the successes that they have had using
probability. But, the truth is that you can solve the problems many ways and
every way has its pros and cons. I personally believe that probability has
unacceptable cons if used all by itself. It must only be used when it is the
best tool for the task.
I do plan to use some probability within my approach. But only when it
makes sense to do so. I do not believe in completely statistical solutions or
completely Bayesian machine learning alone.
A good example of when I might use it is when a particular hypothesis
predicts something with 70% accuracy, well it may be better than any other
hypothesis we can come up with so far. So, we may use that hypothesis. But, the
30% unexplained errors should be explained if possible with the resources and
algorithms available, if at all possible. This is where my method differs from
statistical methods. I want to build algorithms that resolve the 30% and
explain it. For many problems, there are rules and knowledge that will solve
them effectively. Probability should only be used when you cannot find a more
accurate solution.
Basically we should use probability when we don't know the factors
involved, can't find any rules to explain the phenomena or we don't have the
time and resources to figure it out. So you must simply guess at the most
probable event without any rules for figuring out which event is more
applicable under the current circumstances.
So, in summary, probability definitely has its place. I just think that
explanatory reasoning and other more accurate methods should be preferred
whenever possible.
Regarding learning the knowledge being the bigger problem, I completely
agree. That is why I think it is so important to develop machine learning that
can learn by direct observation of the environment. Without that, it is
practically impossible to gather the knowledge required for AGI-type
applications. We can learn this knowledge by analyzing the world automatically
and generally through video.
My step by step approach for learning and then applying the knowledge for
agi is as follows:
1) Understand and learn about the environment(through Computer Vision for
now and other sensory perceptions in the future)
2) learn about your own actions and how they affect the environment
3) learn about language and how it is associated with or related to the
environment.
4) learn goals from language(such as through dedicated inputs).
5) Goal pursuit
6) Other Miscellaneous capabilities as needed
Dave
On Sat, Jul 10, 2010 at 8:40 PM, Abram Demski <abramdem...@gmail.com>
wrote:
David,
Sorry for the slow response.
I agree completely about expectations vs predictions, though I wouldn't
use that terminology to make the distinction (since the two terms are
near-synonyms in English, and I'm not aware of any technical definitions that
are common in the literature). This is why I think probability theory is
necessary: to formalize this idea of expectations.
I also agree that it's good to utilize previous knowledge. However, I
think existing AI research has tackled this over and over; learning that
knowledge is the bigger problem.
--Abram
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http://groups.google.com/group/one-logic
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