Mind you, I'm surprised that a text based AI can even get this far.

My comments with ** **

Q. In Miss Q's class, 1/3 of the students brought a teddy bear to
school. Each boy to 12 books, each girl took 17 books, and each teddy
bear took 9 books. A total of 305 books were taken out. How many girls
where there in Miss Q's class?

A. Let's call the number of students in Miss Q's class "S". We know that
1/3 of the students brought a teddy bear, so that means there were S/3
teddy bears. Each teddy bear took 9 books, so the total number of books
taken by the teddy bears is (S/3) * 9 = 9S/3.

** OK **

Each boy took 12 books, so the total number of books taken by the boys
is 12 * the number of boys = 12B.

** Yes **

Similarly, each girl took 17 books, so the total number of books taken
by the girls is 17 * the number of girls = 17G.

** Good so far **

Finally, we know that the total number of books taken out is 305, so we
can set up an equation to represent this:

9S/3 + 12B + 17G = 305

** Looks promising **

Now we can solve for the number of girls, G.

First, we'll isolate G by subtracting 9S/3 and 12B from both sides:

17G = 305 - 9S/3 - 12B

** Might simplify 9S/3 to 3S, but never mind **

Expanding the right side:

17G = 305 - (3 * 9S/3) - 12B = 305 - 27S/3 - 12B

** Huh, what? Where did that extra 3 come from? **

Next, we'll simplify the right side of the equation:

** Not much point now. **

17G = 305 - 27S/3 - 12B = 305 - (3 * 9S/3) - 12B = 305 - 27S/3 - 12B

Finally, we'll substitute S = B + G into the equation to eliminate S:

17G = 305 - 27(B + G)/3 - 12B

Expanding the right side:

17G = 305 - 27B/3 - 27G/3 - 12B

Combining like terms:

17G = 305 - 27B/3 - 27G/3 - 12B

Finally, dividing both sides by 17:

G = (305 - 27B/3 - 12B) / 17

So, the number of girls, G, depends on the number of boys, B. To get an
exact answer, we would need to know the number of boys.

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Sylvia Else wrote:

Mind you, I'm surprised that a text based AI can even get this far.

On maths questions, Ive seen it give the wrong method, yet the correct
result; I've also seen it give the correct method with the wrong result
... it's almost like it hasn't got a clue what it's doing :-P

The most impressive thing I've seen it do was when someone asked it to
pretend to be a SQL server, and then gave it commands to create a table,
insert data into it and query it, which it "emulated" did properly, they
then asked it to write a stored procedure to perform an "upsert". The
worst "mistake" it made during the SQL demo was they asked it not to
keep explaining itself, but apparently it couldn't resist.

I still don't like the idea of AI, how will we ever be able to trust
that search engine results aren't made-up bollocks? Oh wait ...

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On 09-Feb-23 7:25 pm, Andy Burns wrote:

Sylvia Else wrote:

Mind you, I'm surprised that a text based AI can even get this far.

On maths questions, Ive seen it give the wrong method, yet the correct result; I've also seen it give the correct method with the wrong result
... it's almost like it hasn't got a clue what it's doing :-P

The most impressive thing I've seen it do was when someone asked it to pretend to be a SQL server, and then gave it commands to create a table, insert data into it and query it, which it "emulated" did properly, they
then asked it to write a stored procedure to perform an "upsert".  The
worst "mistake" it made during the SQL demo was they asked it not to
keep explaining itself, but apparently it couldn't resist.

I still don't like the idea of AI, how will we ever be able to trust
that search engine results aren't made-up bollocks?  Oh wait ...

We may find that AIs make the same mistakes as humans, just faster.

In the above example, I doubt it's possible to understand why it made
this mistake. All one can do is train it some more on algebra, and hope
for the best.

Sylvia.

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On Thursday, 9 February 2023 at 12:08:09 UTC+1, Sylvia Else wrote:

In the above example, I doubt it's possible to understand why it made
this mistake. All one can do is train it some more on algebra, and hope
for the best.

Or read the fine print and realize why it is called "chat" to begin with.

Indeed, we remain far from inventing any strong AI, and the fact that
ML and overall the statistical methods, i.e. the generalized lying with numbers, are becoming ubiquitous and even strategic is quite the
measure of how globally insane and retarded our society is becoming.

Julio

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On Thursday, 9 February 2023 at 15:34:55 UTC+1, Rich wrote:

Andy Burns <use...@andyburns.uk> wrote:

I still don't like the idea of AI, how will we ever be able to trust
that search engine results aren't made-up bollocks? Oh wait ...

Yes, it is not like current search engine results aren't made-up
bollocks. Although at least today one understands why. The bollocks
were paid for by advertisiers who paid for "search placement".

Nope, it's way worse than that. In the early 90's experts were
rather pointing out that "if you look at garbage with a super
powerful lens you are still looking at garbage", and that, for
the web in particular not to quickly explode with everything
and the contrary of everything, game-theoretic protocols for
collaborative relevance and selection were critical: so Google
rather hijacked and destroyed Usenet... Which is just the tip
of the beginning of our globally insane and demented story.

Julio

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Andy Burns <usenet@andyburns.uk> wrote:

I still don't like the idea of AI, how will we ever be able to trust
that search engine results aren't made-up bollocks? Oh wait ...

Yes, it is not like current search engine results aren't made-up
bollocks. Although at least today one understands why. The bollocks
were paid for by advertisiers who paid for "search placement".

With AI, the bollocks will become "weird mystery bollocks".

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Sylvia Else <sylvia@email.invalid> wrote:

We may find that AIs make the same mistakes as humans, just faster.

Based on what I read about an earlier GPT incarnation it seems to
make somewhat different mistakes as well. It can remember much more
than a (normal?) human, so it tends to slip into quoting things
verbatim or with minor tweaks, which is easily mistaken for a real understanding because we can't remember all the source material
that it's reading from. As a result it tends towards detailed
and believeable answers to a slightly different question (or
wrongly derived from an answer to another question).

In the above example, I doubt it's possible to understand why it made
this mistake. All one can do is train it some more on algebra, and hope
for the best.

That's potentially like training a mouse in nuclear physics, you'll
never get there no matter how long you try. I'm sure there are
improvements to the core system design which could produce better
results, hence the different versions (I think I first read about
GPT-2, while I was intending to find info on the GUID Partition
Table).

--
__ __
#_ < |\| |< _#

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On Thursday, 9 February 2023 at 23:38:55 UTC+1, Computer Nerd Kev wrote:

Sylvia Else <syl...@email.invalid> wrote:

All one can do is train it some more on algebra, and hope for the best.

That's potentially like training a mouse in nuclear physics,

No, it isn't at all, e.g. you can exasperate a mouse, but you
cannot "overfit it". ML has just fuck all to do with any actual
learning and/or understanding. -- But people won't get it...

Julio

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