I wouldn't argue that any software system capable of learning human
language, would necessarily be able to learn mathematics

However, I strongly suspect that any software system **with a vaguely
human-mind-like architecture** that is capable of learning human language,
would also be able to learn basic mathematics

ben

On Tue, Oct 21, 2008 at 2:30 AM, Dr. Matthias Heger <[EMAIL PROTECTED]> wrote:

> Sorry, but this was no proof that a natural language understanding system
> is
> necessarily able to solve the equation x*3 = y for arbitrary y.
>
> 1) You have not shown that a language understanding system must
> necessarily(!) have made statistical experiences on the equation x*3 =y.
>
> 2) you give only a few examples. For a proof of the claim, you have to
> prove
> it for every(!) y.
>
> 3) you apply rules such as 5 * 7 = 35 -> 35 / 7 = 5 but you have not shown
> that
> 3a) that a language understanding system necessarily(!) has this rules
> 3b) that a language understanding system necessarily(!) can apply such
> rules
>
> In my opinion a natural language understanding system must have a lot of
> linguistic knowledge.
> Furthermore a system which can learn natural languages must be able to gain
> linguistic knowledge.
>
> But both systems do not have necessarily(!) the ability to *work* with this
> knowledge as it is essential for AGI.
>
> And for this reason natural language understanding is not AGI complete at
> all.
>
> -Matthias
>
>
>
> -----Ursprüngliche Nachricht-----
> Von: Matt Mahoney [mailto:[EMAIL PROTECTED]
> Gesendet: Dienstag, 21. Oktober 2008 05:05
> An: [email protected]
> Betreff: [agi] Language learning (was Re: Defining AGI)
>
>
> --- On Mon, 10/20/08, Dr. Matthias Heger <[EMAIL PROTECTED]> wrote:
>
> > For instance, I doubt that anyone can prove that
> > any system which understands natural language is
> > necessarily able to solve
> > the simple equation x *3 = y for a given y.
>
> It can be solved with statistics. Take y = 12 and count Google hits:
>
> string count
> ------ -----
> 1x3=12 760
> 2x3=12 2030
> 3x3=12 9190
> 4x3=12 16200
> 5x3=12 1540
> 6x3=12 1010
>
> More generally, people learn algebra and higher mathematics by induction,
> by
> generalizing from lots of examples.
>
> 5 * 7 = 35 -> 35 / 7 = 5
> 4 * 6 = 24 -> 24 / 6 = 4
> etc...
> a * b = c -> c = b / a
>
> It is the same way we learn grammatical rules, for example converting
> active
> to passive voice and applying it to novel sentences:
>
> Bob kissed Alice -> Alice was kissed by Bob.
> I ate dinner -> Dinner was eaten by me.
> etc...
> SUBJ VERB OBJ -> OBJ was VERB by SUBJ.
>
> In a similar manner, we can learn to solve problems using logical
> deduction:
>
> All frogs are green. Kermit is a frog. Therefore Kermit is green.
> All fish live in water. A shark is a fish. Therefore sharks live in water.
> etc...
>
> I understand the objection to learning math and logic in a language model
> instead of coding the rules directly. It is horribly inefficient. I
> estimate
> that a neural language model with 10^9 connections would need up to 10^18
> operations to learn simple arithmetic like 2+2=4 well enough to get it
> right
> 90% of the time. But I don't know of a better way to learn how to convert
> natural language word problems to a formal language suitable for entering
> into a calculator at the level of an average human adult.
>
> -- Matt Mahoney, [EMAIL PROTECTED]
>
>
>
> -------------------------------------------
> agi
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> agi
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-- 
Ben Goertzel, PhD
CEO, Novamente LLC and Biomind LLC
Director of Research, SIAI
[EMAIL PROTECTED]

"Nothing will ever be attempted if all possible objections must be first
overcome "  - Dr Samuel Johnson



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agi
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