Steve,
Thanks. I was just looking for a systematic, v basic analysis of the problems
language poses for any program, which I guess mainly come down to multiplicity -
multiple
-word meanings
-word pronunciations
-word spellings
-word endings
-word fonts
-word/letter layout/design
-languages [mixed discourse]
-accents
-dialects
-sentence constructions
to include new and novel
-words
-pronunciations
-spellings
-endings
-layout/design
-languages
-accents
-dialects
-sentence constructions
-all of which are *advantages* for a GI as opposed to a narrow AI. The latter
wants the "right" meaning, the former wants many meanings - enables flexibility
and creativity of explanation and association.
Have I left anything out?
Steve: MT::
I wonder whether you'd like to outline an additional list of
"English/language's shortcomings" here. I've just been reading Gary Marcus'
Kluge - he has a whole chapter on language's shortcomings, and it would be v.
interesting to compare and analyse.
The real world is a wonderful limitless-dimensioned continuum of interrelated
happenings. We have but a limited window to this, and have an even more limited
assortment of words that have very specific meanings. Languages like Arabic
vary pronunciation or spelling to convey additional shades of meaning, and
languages like Chinese convey meaning via joined concepts. These may help, but
they do not remove the underlying problem. This is like throwing pebbles onto a
map and ONLY being able to communicate which pebble is closest to the intended
location. Further, many words have multiple meanings, which is like only being
able to specify certain disjoint multiples of pebbles, leaving it to AI to take
a WAG (Wild Ass Guess) which one was intended.
This becomes glaring obvious in language translation. I learned this stuff
from people on the Russian national language translator project. Words in these
two languages have very different shades of meaning, so that in general, a
sentence in one language can NOT be translated to the other language with
perfect accuracy, simply because the other language lacks words with the same
shading. This is complicated by the fact that the original author may NOT have
intended all of the shades of meaning, but was stuck with the words in the
dictionary.
For example, a man saying "sit down" in Russian to a woman, is conveying
something like an order (and not a request) to "sit down, shut up, and don't
move". To remove that overloading, he might say "please sit down" in Russian.
Then, it all comes down to just how he pronounces the "please" as to what he
REALLY means, but of course, this is all lost in print. So, just how do you
translate "please sit down" so as not to miss the entire meaning?
One of my favorite pronunciation examples is "excuse me".
In Russian, it is approximately "eezveneetsya minya" and is typically spoken
with flourish to emphasize apology.
In Arabic, it is approximately "afwan" without emphasis on either syllable,
and is typically spoken curtly, as if to say "yea, I know I'm an idiot". It is
really hard to pronounce these two syllables without emphases, but with
flourish.
There is much societal casting of meaning to common concepts.
The underlying issue here is the very concept of translation, be it into a
human language, or a table form in an AI engine.. Really good translations have
more footnotes than translation, where these shades of meaning are explained,
yet "modern" translation programs produce no footnotes, which pretty much
consigns them to the "trash translation" pile, even with perfect
disambiguation, which of course is impossible. Even the AI engines, that can
carry these subtle overloadings, are unable to determine what nearby meaning
the author actually intended.
Hence, no finite language can convey specific meanings from within a
limitlessly-dimensional continuum of potential meanings. English does better
than most other languages, but it is still apparently not good enough even for
automated question answering, which was my original point. Everywhere semantic
meaning is touched upon, both within the wetware and within software,
additional errors are introduced. This makes many answers worthless and all
answers suspect, even before they are formed in the mind of the machine.
Have I answered your question?
Steve Richfield
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