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On 05/28/2014 01:27 PM, Juyang Weng wrote:
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Danko:<br>
<p>What about a primate, an animal, and an insect. How many traverses
does each have?</p>
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John, Danko, all,<br>
That's a good question but difficult to answer (at least for me).<br>
How can certain raven solve 3-step puzzles for getting at some food?<br>
They have never seen the puzzle, they look at the available pieces from
different <br>
points, and they have the solution. Are they consciously reasoning?<br>
Or is it a low-level process in which the rewards of different
combinations<br>
are predicted and summed in order to predict the total reward in order
to access <br>
the food or not? Like chess players using a lot of knowledge,
experience and<br>
intuition, or a chess program that simply goes through all possible
combinations <br>
until a certain depth?<br>
Like us, mens agitat molem, they are tool users. I would put them at our<br>
level because, apart from using tools and solving puzzles, they are
very social:<br>
they can collaborate, they are inventive to hide food while observing
the others, <br>
they can fool others, and they can detect intentions from others. Three
traverses?<br>
So is an ant intelligent? Perhaps a little bit. But the colony is much
more<br>
intelligent.<br>
Is a neuron intelligent? Without any doubt, because the many chemical
reactions<br>
are very complicated and intricate, and it has a function, like an ant.
You don't<br>
need to answer the question whether the neuron's colony is intelligent.<br>
<br>
One of the questions was: is there one equation?<br>
I have been reading a lot about simple reaction-diffusion equations.
Murray's<br>
two-tome book Mathematical Biology, explaining why some zebras are not<br>
striped vertically but horizontally, or even group interactions. Turing
instabilities,<br>
spatial coherence resonance near pattern-forming instabilities, mostly
systems<br>
composed of only two components. I have been experimenting with
Swift-Hohenberg,<br>
where heat conduction in a fluid suddenly jumps to convection loops (if
you are<br>
interested, please see the very nice demos of Michael Cross at
Caltech). I managed<br>
to simulate the Brusselator, and belief me, even using Cross' notes it
was not<br>
easy to get a stable simulation because there is always one critical
value where the<br>
system jumps from one attractor to another. Only TWO components!<br>
<br>
Nevertheless, we can use dynamic neural field (DNF) theory and
attractors to track <br>
multiple objects in complex scenes (vision) and to control limb joints
for e.g. walking.<br>
This is low-level. One level higher DNFs can be used to predict rewards
and to<br>
generate visuomotor sequences. A very complex system of massively
parallel DNFs.<br>
But it is still reactive.<br>
The question, which I touched upon in a previous email, is whether the
same principles<br>
can be used to balance narrow and broad attention, both endogenous and
exogenous.<br>
To balance between excessive control and lack of control. I think this
is possible.<br>
But the ultimate question is whether it could be applied at the highest
level.<br>
This involves so many things. When I read a paper saying "this filter
is implemented<br>
by the following Gaussian" my brain expects to see two times sigma
squared. This is<br>
still experience and different memories. If you want to build an
intelligent machine<br>
which can solve a 3-step puzzle which it has never seen before, only
with the goal<br>
of satisfying a reward, you need a lot of experience and memories etc.
before you<br>
can even start to think about it. But, at the very end, the circuits
which solve the <br>
problem, and those which we think (!) are responsible for taking
conscious decisions,<br>
are all the same. <br>
Do not expect a God in our machine, but: Deus ex machina.<br>
Hmmm, reminds me of the fact that our brain still has a religious
region, which<br>
evolved in our ancestors to explain some natural phenomena and to
establish a few<br>
basic rules for the survival of the tribe.<br>
Hans<br>
<br>
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