On 11/10/07, Bob Mottram <[EMAIL PROTECTED]> wrote:

> On 10/11/2007, Jef Allbright <[EMAIL PROTECTED]> wrote:
> > At the DARPA Urban Challenge last weekend, the optimism and flush of
> > rapid growth was palpable...

> I was saying to someone recently that it's hard to watch something
> like the recent Urban Challenge and argue convincingly that AI is not
> making progress or that it's been a failure.

Yes, just as natural language processing, speech recognition, and
bioinformatics have not been failures, but they've each plateaued  at
a level distinctly below human level capability, for lack of effective
understanding of the broader context of the human-relevant world.


> Admittedly the
> intelligence here is not smart enough to carry out the sort of
> reasoning you describe, such as "I see a large object and predict that
> it may be about to fall so I better move out of the way".  However,
> the path to this sort of ability just involves more accurate 3D
> modelling of the environment

I'm not aware of any theoretical hurdles in this area that would
prevent immediate progress...


> together with intelligent segmentation

Not sure what you mean by segmentation here -- sounds possibly like a
reference to a reductionist approach to understanding via increasingly
understanding of the pieces (?) - but doesn't the inclusion of
"intelligence" in your list of ingredients kinda spoil the surprise of
the soup?  [I may be completely off base here.]

> and some naive physics applied.

Okay, like Second Life, or better, for learning grounded in physical
terms relevant to humans.


> It's the perception accuracy/modeling
> which is key to being able to implement these skills, which a mouse
> may or may not be capable of (I don't know enough about the cognitive
> skills of mice to be able to say).

If we could duplicate the intelligence of a mouse we'd be well on our
way, but it seem to me you're thinking pretty much along the lines of
conventional models of machine learning and leaving out
**********little********** details like a theory of novelty generation
and selection, memory architecture(s), ... and dealing with the
ensuing combinatorial explosion within the constraints of practical
hardware.

I'm impressed with the certainty of some of the views expressed here,
nothing like I get talking to people actually building robots.

- Jef

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