On Tue, Aug 3, 2010 at 11:52 AM, David Jones davidher...@gmail.com wrote:
I've suddenly realized that computer vision of real images is very much
solvable and that it is now just a matter of engineering...
I've also realized that I don't actually have to implement it, which is what
is most
:D Thanks Jim for paying attention!
One very cool thing about the human brain is that we use multiple feedback
mechanisms to correct for such problems as observer movement. For example,
the inner ear senses your bodies movement and provides feedback for visual
processing. This is why we get
Steve,
I wouldn't say that's an accurate description of what I wrote. What a wrote
was a way to think about how to solve computer vision.
My approach to artificial intelligence is a Neat approach. See
http://en.wikipedia.org/wiki/Neats_vs._scruffies The paper you attached is a
Scruffy approach.
David,
You are correct in that I keep bad company. My approach to NNs is VERY
different than other people's approaches. I insist on reasonable math being
performed on quantities that I understand, which sets me apart from just
about everyone else.
Your neat approach isn't all that neat, and is
Steve,
Sorry if I misunderstood your approach. I do not really understand how it
would work though because it is not clear how you go from inputs to output
goals. It likely will still have many of the same problems as other neural
networks including 1) poor knowledge portability 2) difficult to
David
On Wed, Aug 4, 2010 at 1:16 PM, David Jones davidher...@gmail.com wrote:
3) requires manually created training data, which is a major problem.
Where did this come from. Certainly, people are ill equipped to create dP/dt
type data. These would have to come from sensors.
4) is designed
Steve,
I replace your need for math with my need to understand what the system is
doing and why it is doing it. It's basically the same thing. But you are
approaching it at an extremely low level. It doesn't seem to me that you are
clear on how this math makes the system work the way we want it
David,
On Wed, Aug 4, 2010 at 1:45 PM, David Jones davidher...@gmail.com wrote:
Understanding what you are trying to accomplish and how you want the system
to work comes first, not math.
It's all the same. First comes the qualitative, then comes the quantitative.
If your neural net
On Wed, Aug 4, 2010 at 6:17 PM, Steve Richfield
steve.richfi...@gmail.comwrote:
David,
On Wed, Aug 4, 2010 at 1:45 PM, David Jones davidher...@gmail.com wrote:
Understanding what you are trying to accomplish and how you want the
system to work comes first, not math.
It's all the same.
Abram,
Thanks for the explanation. I still don't get it. Your function may be
convergent but it is not a probability. You said that Solomonoff's original
construction involved flipping a coin for the next bit. What good does that
do? And how does that prove that his original idea was
I meant I am able to construct an algorithm that is capable of reaching
every expansion of a real number given infinite resources. However, the
algorithm is never able to write any of them completely since they are all
infinite. So in one sense, no computation is able to write any real number,
Jim,
Your function may be convergent but it is not a probability.
True! All the possibilities sum to less than 1. There are ways of addressing
this (ie, multiply by a normalizing constant which must also be approximated
in a convergent manner), but for the most part adherents of Solomonoff
The Wrong Stuff : Error Message: Google Research Director
Peter Norbig on Being Wrong
http://bit.ly/cQpUpx
translates to
http://www.slate.com/blogs/blogs/thewrongstuff/archive/2010/08/03/error-message-google-research-director-peter-norvig-on-being-wrong.aspx
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