Presumably, the program would have the potential to understand things. If
it only used text, then a good AGI program would have to have the potential
to acquire a lot of knowledge about a lot of different things using words.
The problem is that because of the ambiguity of words and the ambiguity of
the use of words, it would have a lot of trouble interpreting the meaning
for a sentence. The different ways you can speak of your fondness for
apples demonstrates a little bit about the extent of this knowledge. My
theory is that we should use this range of knowledge in determining the
meaning of words. Since this is the potential, the problem and
computationally feasible I think it might work. I consider this method to
be a way to form good judgement.
I am really thinking of a general AI program, not just a translator. But, I
think that this method is relevant. For example, all your sentences about
your fondness of apples reveal something about the subject and those
sentences could subsequently used for a variety of situations that use some
of the same words - so long as the program had some good way to start
learning about sentences.
By exploring the different ways people talk about things that they like, by
making distinctions between different kinds of likable things, and by
integrating this knowledge with other kinds of knowledge, I believe that a
program could choose good translations for the particular situation that it
is in.
Jim Bromer
On Fri, Apr 15, 2011 at 9:03 AM, Daniel <daniel.burke@yahoo.com> wrote:
>
>
> I like the create and test idea.
>
> If a system created say 5 possibles sentence as appropriate for a
> translation. How would it test each one. There would need to be a scoring
> system of some sort. Since language can be infinitely complex and extendable
> the number of possible ways to say the same thing could be huge.
>
> E.G
> I like apples
> I am fond of Apples
> Apples are nice
> I love Apples
> Apples are fabulous
> Apples are so sweet
> I am in love with the Apple
> I have an Apple passion
> My passion is Apples
>
> Etc. Etc. And this is for a three word base sentence. Can you imagine the
> different ways to say a longer sentence?
>
> The list goes on and on, how do you tell which one is "best"?
>
> Dan
>
>
> --- In artificialintelligencegroup@yahoogroups.com, "bromer2007"
> <jimbromer@...> wrote:
> >
> > > So how do we make sentences in the first place? Any ideas anyone? I
> have a few but wondered what others felt.
> > >
> > > Dan
> >
> > Of course we use memories of events as well as memories of how to use
> words and sentences. However, since we have to comprehend ongoing events in
> the terms of past events it seems clear that the methods we use to
> comprehend what is going on are built from some kind of compounds of
> generalities. Although using language is different than general
> conceptualization, I believe that there are many similarities.
> >
> > So this means, for example, that I don't think iconic grounding is
> necessary (absolutely necessary) for higher intelligence, and so far, there
> is no evidence suggesting that it is.
> >
> > The problem as I see it is just one of complexity. If a computer program
> could examine many different possible expressions that might be used to
> describe a situation it might be able, after a lot of learning, to decide
> which one is most appropriate. But because of the problem of referential
> ambiguity the number of possible combinations of meanings currently increase
> at a rate that is nearly intractable as the amount of knowledge learned
> increases.
> >
> > If this were not the case, it would be easy to test different strategies.
> >
> > My basic strategy would be for the program to create a possible sentence
> and then examine it using a variety of analytical methods that are related
> to the subject (the subjects) of the sentence. Since we are talking about a
> situation where words, word-phrases and sentences may take on different
> meanings, this method of testing an expression makes a lot of sense. In
> other words, the program is not testing every possible interpretation of a
> sentence, but it does have to examine a great many of them.
> >
> > Computers work well with mathematical problems in which a narrow
> resultant of a sequence of computations is then used as the input of the
> next step in a problem that can be eventually solved with a narrow solution.
> (A narrow solution is a solution with a feasible number of precise correct
> evaluations.) General AI (or AGI) does not seem to reduce to systems of
> problems that all have narrowly correct values. If it did, it would be easy
> to test different strategies.
> >
> > Jim Bromer
> >
> >
> > --- In artificialintelligencegroup@yahoogroups.com, "Daniel"
> <daniel.burke@> wrote:
> > >
> > > In order to translate from one language to another obviously the person
> needs to know both languages well. It started me think about how language is
> stored in your head and how you generate sentences in the first place.
> > > Seems to me the translation process works like this.
> > >
> > > Take source material, read it through and make sure you understand the
> MEANING of all the sentence.
> > >
> > > Then in the target language make sentence that have the same meaning.
> > >
> > > This is why machine translation is so hard since word that individually
> mean the same may not mean the same when in bigger language chunks such as
> phrases or sayings or local terminology.
> > >
> > > How do we make sure the meaning is the same in the source and target
> language.
> > > And finally how do we ensure the correct word order in the target
> language?
> > >
> > > So how do we make sentences in the first place? Any ideas anyone? I
> have a few but wondered what others felt.
> > >
> > > Dan
> > >
> >
>
>
>
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