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 ------------------------------------------------------------------------------ agi | Archives | Modify Your Subscription ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=120640061-aded06 Powered by Listbox: http://www.listbox.com