Steve Richfield wrote:
Richard,
On 5/18/08, *Richard Loosemore* <[EMAIL PROTECTED]
<mailto:[EMAIL PROTECTED]>> wrote:
Steve Richfield wrote:
"I have a headache. I missed my morning coffee."
From this, Dr. Eliza will see a present-tense headache, a
present-tense negated (presumed consumption) of coffee. A link
definition would look for a headache and no present-tense
coffee, and past-tense coffee. What it presently lacks is seeing
that this also implies that the writer usually drinks coffee in
the morning, which is VERY important. Seeing no (apparent)
mention of usual coffee consumption, Dr. Eliza would then ask
something like "How much coffee do you usually drink on an
average day?" in the hopes that you would provide this
(redundant) information to improve its computation of probability.
Note here that the apparent primary meaning of this sentence -
the linking of a headache to missing morning coffee, was
properly DISCARDED because there was nothing useful that could
be done with this opinion of causality on the part of the user.
What I fail to see is how fully "understanding" the
written/spoken word is of any use to any computer program! What
would you then do with that understanding, since most of it will
be beyond the ability of any computer program to do anything
useful and accurate with?
If you are saying that people have tried and failed to come up with
good methods for extracting the meaning of sentences such as "I have
a headache - I missed my morning coffee", then you would, of course
be correct.
My REAL point was that around half of all sentences suffer from such
problems. If you are only interested in understanding the OTHER half and
accepting a ~50% error rate, then please proceed as you have been.
Note the Dr. Eliza has a high ~20% "error budget" where it will continue
to function usefully until its overall error rate gets to around 20%.
This gets divided up between speech recognition, spelling errors,
grammatical errors, design weaknesses, shallow parsing shortcomings,
etc. Speech recognition related problems are responsible for ~10%, and
everything else adds up to ~10%, so it just barely works with spoken
input. Putting in some common speech mis-recognitions helped a LOT
because it often pushes things back over the 20% point. For example,
under some circumstances it may ask if you are taking "pregnenalone",
which the speech recognition engine usually hears back as "pregnant
alone" (The Bayesian statistics were obviously gathered over OTHER
domains) when you answer the question.
But the whole point of doing research in Artificial General
Intelligence (as opposed to narrow-AI) is because we want to go
beyond past failures and reach a point where we can indeed build
systems that can fully understand sentences such as the
coffee-headache one.
A good first step would seem to be to FULLY understand past failures,
rather than continuing to butt your head up against the same wall, but
in a slightly different way.
Some of us have explicitly made a priority of trying to understand
how this kind of understanding happens,
As I have hopefully explained, I believe that the "understanding" that
you are seeking does NOT occur in humans, and very likely can NOT be
made to work in machines where the input is (technically) nonsense ~50%
of the time.
and some even believe that they re[ally] making progress on the problem.
Only because they haven't looked ABOVE the module to see what is needed
from it. Sure, a few more decades of work may be able to fill in some of
the gapping and may even be able to extract >80% of the stated meaning,
but where is it that this "understanding" module starts automatically
rejecting ~5% of its *_perfectly_* understood input the same as human
listeners do? Any AGI that perfectly accepts and understands its input
would quickly devolve into a psychotic mess.
In light of that, I cannot make any sense of your last paragraph,
above. If you mean this literally [!] then I am at a loss for words.
Perhaps we have mutually stumbled into the REAL problem! Your WORDS are
not what is needed here, but rather your DEED of figuring out just what
is needed from an "understanding" module to be useful to an AGI. What
structure of the "understanding" would lend itself to useful AGI
functionality? I don't believe that any such structure can exist, and am
going to considerable effort to "paradigm shift" my orthogonal
understanding to address you in language that you can process in your
quite different paradigm.
Perhaps you mean something else by it.
Pasting in my last paragraph in italics here, but expanded hopefully
enough to clarify my meaning, hopefully sufficiently to argue the facts...
//
/What I fail to see is how fully "understanding" the written/spoken word
is of any use to any computer program!/
In short, it appears (to me) that the AGI folks here are committing the
greatest sin of all in system design - performing bottom-up design. Of
course, even a good top-down design must sometimes stop and evaluate the
writability of a difficult low-level module by actually writing it (I
have certainly done this many times), but when this effort goes beyond
weeks, it is usually a good sign that the STRUCTURE that it fits into is
wrong. Of course we can't argue your structure here because the AGI
folks here haven't done their most basic homework of stating the
high-level design that NEEDS the full understanding that is being
sought. Surely, if any one of you would attempt to propose a design and
maybe write just a little of the high-level code that would need such a
module, I believe that you would QUICKLY abandon this effort as NOT
being in a path to success in your stated goals.
Following is where I (attempt to) drill down into just WHY I believe
that fully "understanding" a sentence won't help much, in the hopes of
saving you the effort of designing the high-level code mentioned above.
If I fail to make my point below, then you would seem to have little
other rational choice than to STOP working on "understanding" for a week
or two and throw together some high-level trial design to see just what
might be needed from the understanding module.
/What would you then do with that understanding/
My point here is that models have structure, e.g. the figure 6 shape of
problematic cause-and-effect chains. Until you put the input into a
useful structure, like a compiler restates a program in digraphs, you
can't even start to do anything useful. However, random speech/writing
comes from the heads of people who do NOT understand such structure, and
hence the writing itself lacks such structure and often reflects an
erroneous structure reflecting a misunderstanding of the very structures
of reality itself. "Understanding" seems to be an effort to structure
nonsense (e.g. typical writing), somewhat like asking a compiler to make
good code from syntactically incorrect and intractable code.
/since most of it will be beyond the ability of any computer program to
do anything useful and accurate with?/
//
//
For a computer program to handle such input, it would not only have to
fully understand the domain (usually not possible except in VERY mature
domains), but also understand the many common erroneous points of view
of people writing in that domain. Dr. Eliza handles this by only dealing
with what it DOES know about and ignoring the rest (what else can any
program do?!), and by looking for pre-programmed snippets of common
statements of ignorance. I see no way of getting this information into
an AGI by reading text, which would be needed before it can process the
input needed to be able to process the input needed to be able to
process the input needed to be able to process the input needed to be
able to process the input...
With luck we can wring things out at this level. With a little less
luck, a couple of weeks of attempted high-level design will lead you to
these same conclusions. With no luck at all, you will dismiss the need
for high-level design guiding the functionality of low-level modules and
continue on your present bottom-up path, and quite probably spend much
of your life working on this apparently impossible and useless module.
Steve Richfield
Steve,
The above text is gibberish. You make wild, sweeping and sometimes
incoherent statements about people and their work, based on complete
ignorance of what those people are actually doing.
Your "Dr. Eliza" may be a modestly useful program, within its own terms
of reference. But it has nothing to do with AGI.
Richard Loosemore
-------------------------------------------
agi
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