A response to:
"I wondered why anyone would deface the
expression of his own thoughts with an emotional and hostile message,"
My theory is that thoughts are generated internally and forced into words via a
babble generator. Then the thoughts are filtered through a screen to remove
any that don't match ones intent, that don't make sense, etc. The value
assigned to each expression is initially dependent on how well it expresses
one's emotional tenor.
Therefore I would guess that all of the verbalizations that the individual
generated which passed the first screen were hostile in nature. From the
remaining sample he filtered those which didn't generate sensible-to-him
scenarios when fed back into his world model. This left him with a much
reduced selection of phrases to choose from when composing his response.
In my model this happens a phrase at a time rather than a sentence at a time.
And there is also a probabilistic element where each word has a certain
probability of being followed by divers other words. I often don't want to
express the most likely probability, as by choosing a less frequently chosen
alternative I (believe I) create the impression a more studied, i.e.
thoughtful, response. But if one wishes to convey a more dynamic style then
one would choose a more likely follower.
Note that in this scenario phrases are generated both randomly and in parallel.
Then they are selected for fitness for expression by passing through various
filter.
Reasonable?
Jim Bromer wrote:
Hi. I will just make a quick response to this message and then I want
to think about the other messages before I reply.
A few weeks ago I decided that I would write a criticism of
ai-probability to post to this group. I wasn't able remember all of
my criticisms so I decided to post a few preliminary sketches to
another group. I wasn't too concerned about how they responded, and
in fact I thought they would just ignore me. The first response I got
was from an irate guy who was quite unpleasant and then finished by
declaring that I slandered the entire ai-probability community! He
had some reasonable criticisms about this but I considered the issue
tangential to the central issue I wanted to discuss. I would have
responded to his more reasonable criticisms if they hadn't been
embedded in his enraged rant. I wondered why anyone would deface the
expression of his own thoughts with an emotional and hostile message,
so I wanted to try the same message on this group to see if anyone who
was more mature would focus on this same issue.
Abram made a measured response but his focus was on the
over-generalization. As I said, this was just a preliminary sketch of
a message that I intended to post to this group after I had worked on
it.
Your point is taken. Norvig seems to say that overfitting is a
general problem. The method given to study the problem is
probabilistic but it is based on the premise that the original data is
substantially intact. But Norvig goes on to mention that with pruning
noise can be tolerated. If you read my message again you may see that
my central issue was not really centered on the issue of whether
anyone in the ai-probability community was aware of the nature of the
science of statistics but whether or not probability can be used as
the fundamental basis to create agi given the complexities of the
problem. So while your example of overfitting certainly does deflate
my statements that no one in the ai-probability community gets this
stuff, it does not actually address the central issue that I was
thinking of.
I am not sure if Norvig's application of a probabilistic method to
detect overfitting is truly directed toward the agi community. In
other words: Has anyone in this grouped tested the utility and clarity
of the decision making of a fully automated system to detect
overfitting in a range of complex IO data fields that one might expect
to encounter in AGI?
Jim Bromer
On Sat, Nov 29, 2008 at 11:32 AM, Abram Demski <[EMAIL PROTECTED]> wrote:
Jim,
There is a large body of literature on avoiding overfitting, ie,
finding patterns that work for more then just the data at hand. Of
course, the ultimate conclusion is that you can never be 100% sure;
but some interesting safeguards have been cooked up anyway, which help
in practice.
My point is, the following paragraph is unfounded:
This is a problem any AI method has to deal with, it is not just a
probability thing. What is wrong with the AI-probability group
mind-set is that very few of its proponents ever consider the problem
of statistical ambiguity and its obvious consequences.
The "AI-probability group" definitely considers such problems.
--Abram
On Sat, Nov 29, 2008 at 10:48 AM, Jim Bromer <[EMAIL PROTECTED]> wrote:
One of the problems that comes with the casual use of analytical
methods is that the user becomes inured to their habitual misuse. When
a casual familiarity is combined with a habitual ignorance of the
consequences of a misuse the user can become over-confident or
unwisely dismissive of criticism regardless of how on the mark it
might be.
The most proper use of statistical and probabilistic methods is to
base results on a strong association with the data that they were
derived from. The problem is that the AI community cannot afford this
strong a connection to original source because they are trying to
emulate the mind in some way and it is not reasonable to assume that
the mind is capable of storing all data that it has used to derive
insight.
This is a problem any AI method has to deal with, it is not just a
probability thing. What is wrong with the AI-probability group
mind-set is that very few of its proponents ever consider the problem
of statistical ambiguity and its obvious consequences.
All AI programmers have to consider the problem. Most theories about
the mind posit the use of similar experiences to build up theories
about the world (or to derive methods to deal effectively with the
world). So even though the methods to deal with the data environment
are detached from the original sources of those methods, they can
still be reconnected by the examination of similar experiences that
may subsequently occur.
But still it is important to be able to recognize the significance and
necessity of doing this from time to time. It is important to be able
to reevaluate parts of your theories about things. We are not just
making little modifications from our internal theories about things
when we react to ongoing events, we must be making some sort of
reevaluation of our insights about the kind of thing that we are
dealing with as well.
I realize now that most people in these groups probably do not
understand where I am coming from because their idea of AI programming
is based on a model of programming that is flat. You have the program
at one level and the possible reactions to the data that is input as
the values of the program variables are carefully constrained by that
level. You can imagine a more complex model of programming by
appreciating the possibility that the program can react to IO data by
rearranging subprograms to make new kinds of programs. Although a
subtle argument can be made that any program that conditionally reacts
to input data is rearranging the execution of its subprograms, the
explicit recognition by the programmer that this is useful tool in
advanced programming is probably highly correlated with its more
effective use. (I mean of course it is highly correlated with its
effective use!) I believe that casually constructed learning methods
(and decision processes) can lead to even more uncontrollable results
when used with this self-programming aspect of advanced AI programs.
The consequences then of failing to recognize that mushed up decision
processes that are never compared against the data (or kinds of
situations) that they were derived from will be the inevitable
emergence of inherently illogical decision processes that will mush up
an AI system long before it gets any traction.
Jim Bromer
-------------------------------------------
agi
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