On 19/10/2007, John G. Rose <[EMAIL PROTECTED]> wrote:
> > From: William Pearson [mailto:[EMAIL PROTECTED]
> > Subject: Re: [agi] An AGI Test/Prize
> >
> > I do not think such things are possible. Any problem that we know
> > about and can define, can be solved with a giant look up table, or
> > more realistically, calculated by an unlearning TM. Unless you are of
> > the opinion that learning is unnecessary for intelligence? In which
> > case what you want may be possible.
> >
> > Any appearance of learning can also be faked by GLUT and unlearning
> > TMs, using time as an input. If you want to rigorously define
> > intelligence, you will need to look at how the internals change and
> > base a definition on that. My current thinking is based on which
> > search spaces the system moves through while trying to map input to
> > output, and how it makes use of information from the outside to change
> > what it does.
> >
>
> Whether or not learning is necessary for intelligence would depend on the
> exact definition of it. The minimally intelligent engine would contain
> internal information.

We may be misinterpreting each other. What I mean by learning being
necessary for intelligence is that a system that cannot learn is not
intelligent. Unless you posit some omnipotent, omniscient entity. Not
that a system must learn before it becomes intelligent.

> What is the minimal internal state it would need to
> start with if any? Is the system, before any input, intelligent?

I'm not sure what you are getting at here. I am tempted to answer
with, "Can a plane, before it has left the ground, fly?"

> There could
> be a very simple mathematical definition of intelligence.
>
This is also a bit opaque to me, are you talking about a definition on
the ability to solve problems or a mathematical definition of the
internal structure/dynamics? One I think possible, the other... not so
much.

 Will Pearson

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