Re: What did that thread indicate?

On 24 Sep 2005 16:15:01 GMT, curt@xxxxxxxx (Curt Welch) wrote:

>Traveler <traveler@xxxxxxxxxx> wrote:
>> On 23 Sep 2005 23:52:01 GMT, curt@xxxxxxxx (Curt Welch) wrote:
>>
>> >Traveler <traveler@xxxxxxxxxx> wrote:
>> >> On 22 Sep 2005 16:41:03 -0700, humiguel@xxxxxxx wrote:

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>> Yes, a pattern is just a concept. However, I disagree with current
>> approach to pattern recognition that uses a strictly feed-forward,
>> pyramid-type hierarchical network. As I've mentioned in the past, the
>> biological and psychological evidence refutes this approach. Concept
>> formation is a top-down process, IMO.
>
>It's still very unclear to me how much power my feed forward network has.

By feed-forward, I had the hierarchy in mind, not the connections.
Hawkins' approach is a progressive pyramid that gets more abstract as
you go up the levels in the tree and ultimately gives you a
grandmother-type cell at the top. I think it's nonsense. For one, one
never knows how many levels a grandmother-type cell will require.
Second, reponse time is critical. The system cannot wait for signals
to traverse so many levels. The connectivity diameter of the brain is
six neurons or loess.

I agree that there is a temporal hierarchy in the sensory cortex but
it's limited. You don't get grandmother-type recognition in the visual
cortex. All you get are simple fixed time scale recognition such as
edges, lines, etc... It's really a signal separation/classification
process: if A arrives after B, it goes down this path but if it
arrives after C, it goes down this other path.

Signals from the sesnory cortex feed directly into sequence memory
(varrying time scale correlations) which, in turn, sends its signals
to the motor layer. There is very little time to waste. Concept
formation consists of organizing sequence memory into coherent groups
of related sequences. Thus the concept formation and attention
mechanism sits on top of memory. It can activate and deactivate groups
of sequences using a timing principle that I am still trying to figure
out. In my scheme, concept cells are grandmother-type cells. They do
not generate behavior. They control it.

[BTW, sequence memory stores intervals which are used for prediction.]

>It's clear feedback is needed for multiple reasons (Dan loves the idea of
>lots and lots of feedback for image recognition), but it's unclear how many
>different ways it might be implemented.

Dan is right on this issue. There is a need for massive feedback in
sensory processing. Once you realize what sensory processing is for,
then it everything falls into place, IMO. It's all about signal
separation. Multiple signals in an input fiber are separate and sent
down different paths according to their temporal correlations with
other signals in other fibers. The correlation is a simple 10 ms
contiguity in the human brain and the factor that I use is 10 to 1.

> My training system (because it's a
>reinforcement learning system), is a strong feedback system. So even
>though the data is feedforward, all the training happens in just the
>opposit direction (outputs back to inputs) as a feedback loop. Also,
>because my network is temporal, it natrually has feedback effects without
>feedback paths that non-temporal nets can only get with the help of
>feedback paths.

Well, yes. Training signals go in the opposite direction of sensory
signals.

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>But at the same time, I think other types of actual data feedback paths are
>required for pattern generation and I've not yet experimented with that.
>It might turn out that the same type of feedback is needed just to do
>pattern recognition correctly.

There is a need to use feedback in sensory processing for finding
fixed time scale correlations. This important for signal
separation/classification, which is the only purpose of sensory
processing, IMO.

>And the act of "thinking to ourselves - aka private thoughts" is clearly a
>large internal feedback system of some type at work.
>
>So even though I strongly believe all this will turn out to be the same
>problem in most ways (pattern recognition, concept formation, behavior),
>I'm not as sure if my type of network has what it takes to solve the
>problem or not - I only belive it's a big step in the right direction.

Well, at least it's not GOFAI.

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>My network learns how to get the timing right for all behavior using
>reinforcement learning.

Reinforcement learning is strictly based on pain and pleasure stimuli.
There is a shitload of motor learning taking place that does use pain
and pleasure as corrective signals. Something else is used. It's
called motor conflict detection. You can call it reinforcement
learning, if you want, but that is not the conventional meaning of the
term.

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>But, what happens if this system is being punished because the behavior
>showed up late? How does such a system learn that it must do the behavior
>sooner in order to be rewarded? A system which always tends to delay the
>behavior when it is punished is going to have a very hard time learning it
>needs to do just the opposit. So the "delay when punished" option has
>serious problems.
>
>So the question is, how do you "learn" the proper setting of a continous
>value, such as a timing event, with reinforcement learning?

IMO, your system lacks something essential: the ability to anticipate
the future, not only of pain and pleasure stimuli, bit also of normal
events.

>> The final
>> solution will be simple and easy to implement, I'm sure, but finding
>> it is like searching for the proverbial needle in the haystack.
>
>Yeah, it is. for sure.
>
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>The solution was found with the design of my current network.

[cut]

Kurt, I read your post and I don't believe you have the solution.
There are a few things missing in your scheme. But you know something
essential that a generation of GOFAI crackpots (Minsky et al)
completely ignored for fifty years: it's all about timing.

Louis Savain

Why Software Is Bad and What We Can Do to Fix It:
http://www.rebelscience.org/Cosas/Reliability.htm
.

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