Hi Bob,
Is there a document somewhere describing what is unique about your approach?
Novamente doesn't involve real robotics right now but the design does
involve occupancy grids and "probabilistic simulated robotics", so your
ideas are of some practical interest to me...
Ben
Bob Mottram wrote:
Some of the 3D reconstruction stuff being done now is quite impressive
(I'm thinking of things like photosynth, monoSLAM and Moravec's stereo
vision) and this kind of capability to take raw sensor data and turn
it into useful 3D models which may then be cogitated upon would be a
basic prerequisite for any AGI operating in the real world. I'm sure
that these and other similar methods are soon destined to be fall into
the bracket of being "no longer AI", instead being considered as just
another computational tool.
In the past I've tried many ad-hoc vision experiments, which would
certainly come under the "narrow AI" label, but I now no longer
believe that this kind of approach is a good way to proceed. Far more
straightforward, albeit more computationally demanding, techniques
give a general solution to the vision problem which is not highly
specific to any particular kind of domain or environment. Under this
system applications which are often treated separately, such as visual
navigation and object recognition, actually turn out to be the same
algorithm deployed on different spatial scales (maybe a classic case
of physics envy!).
My own computer vision project can be found here
http://code.google.com/p/sentience/
On 06/03/07, * Andrew Babian* <[EMAIL PROTECTED]
<mailto:[EMAIL PROTECTED]>> wrote:
Listening to a computer vision lecture, I'm impressed out how much
is being
done now with very domain specific techniques. They can take
general pictures
from different viewpoints, and recreate a 3-d representation of
the world.
This is similar to the sort of stereo reconstruction that people
do. We are
perhaps better optimized to our exact hardware, and we can still
use a lot
more general real world knowledge about things than current
computer vision
techniques, but they get pretty impressive results with just data
crunching
sort of techniques. And they make no pretension to being AI at
all, but they
fit the classic definition of "weak AI" as something that would take
intelligence for a human to do (though maybe it's a kind of animal
intelligence). If I can coin a term, it's one of those "post AI"
fields that
maybe used to be thought of as AI, but it no longer is, like speech
recognition, I guess. So what I'm wondering is how much people
who are
interested in general AI want to go back and find general AI
solutions to post
AI problems. The example comes to mind of Jeff Hawkins who seemed
like he was
trying to work on visual recognition tasks for his model. I got
his team's
demo working in Matlab finally (actually because I am doing this
computer
vision thing and I had an excuse to get a copy of matlab). Some
sort of
graphic pattern recognizer. I didn't look too far into it, but I
would almost
be sure that it pales compared to real CV techniques. Sure, there
is a
question of how to generally handle knowledge problems, but it may
just be
that the best way to handle AI is just to individually find the
best ways for
computers to solve the different problems posed to
intelligences. That's
actually one of the ideas that I seem to get from Minsky and his
idea of
"resources", formerly called "agents". And I also am always
concerned about
the tendency towards physics envy among AI folk, that there need
to be simple
unified prinicples underneath intelligence.
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