--- Ben Goertzel <[EMAIL PROTECTED]> wrote:
> For instance, suppose you ask an AI if chocolate makes a person more
> alert.
> 
> It might read one article saying that coffee makes people more alert,
> and another article saying that chocolate contains theobromine, and another
> article saying that theobromine is related to caffeine, and another article
> saying that coffee contains caffeine ... and then put the pieces together to
> answer YES
> 
> This kind of reasoning
> may sound simple but getting it to work systematically on the large
> scale based on text mining has not been done...
>
> And it does seem w/in the grasp of current tech without any breakthroughs...

It could be done with a simple chain of word associations mined from a text
corpus: alert -> coffee -> caffeine -> theobromine -> chocolate.

But that is not the problem.  The problem is that the reasoning would be
faulty, even with a more sophisticated analysis.  By a similar analysis you
could reason:

- coffee makes you alert.
- coffee contains water.
- water (H20) is related to hydrogen sulfide (H2S).
- rotten eggs produce hydrogen sulfide.
- therefore rotten eggs make you alert.

Long chains of logical reasoning are not very useful outside of mathematics.

> I think there would be a viable path to AGI via
> 
> 1)
> Filling a KB up w/ commensense knowledge via text mining and simple
> inference,
> as I described above
> 
> 2)
> Building an NL conversation system utilizing the KB created in 1
> 
> 3)
> Teaching the AGI the "implicit knowledge" you suggest via conversing with it

I think adding common sense knowledge before language is the wrong approach. 
It didn't work for Cyc.

Natural language evolves to the easiest form for humans to learn, because if a
language feature is hard to learn, people will stop using it because they
aren't understood.  We would be wise to study language learning in humans and
model the process.  The fact is that children learn language in spite of a
lack of common sense.


-- Matt Mahoney, [EMAIL PROTECTED]

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agi
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