GALERY2CONPUTER

 Welcome to this artful virtual galery.

                                                                 Brains.
A quick encapsulation: Artificial neural nets (or as they are often simply called, `neural nets') are composed of units or nodes designed to represent neurons, which are connected by links designed to represent dendrites, each of which has a numeric weight. It is usually assumed that some of the units work in symbiosis with the external environment; these units form the sets of input and output units. Each unit has a current activation level, which is its output, and can compute, based on its inputs and weights on those inputs, its activation level at the next moment in time. This computation is entirely local: a unit takes account of but its neighbors in the net. This local computation is calculated in two stages. First, the input function, 30#30, gives the weighted sum of the unit's input values, that is, the sum of the input activations multiplied by their weights:
 
 

31#31

 In the second stage, the activation function, g, takes the input from the first stage as argument and generates the output, or activation level, 32#32:

33#33

 One common (and confessedly elementary) choice for the activation function (which ususally governs all units in a given net) is the step function, which usually has a threshold t that sees to it that a 1 is output when the input is greater than t, and that 0 is output otherwise. This is supposed to be ``brain-like" to some degree, given that 1 represents the firing of a pulse from a neuron through an axon, and 0 represents no firing. As you might imagine, there are many different kinds of neural nets. The main distinction is between feed-forward and recurrent nets. In feed-forward nets, as their name suggests, links move information in one direction, and there are no cycles; recurrent nets allow for cycling back, and can become rather complicated. Recurrent nets underlie the MONA-LISA system we describe below.

.zombies."

As Ned Block has recently pointed out to one of us (Bringsjord), since at least all mammals are probably P-conscious, the accident would had to have happened quite a while ago.
 Rensselaer,

Information can be found
@ http://www.rpi.edu/dept/ppcs/MM/c-agents.html.


 
 

look for lonksWONDER BOY>

Sent-Ts'an:
 If you want to get the plain truth
             Be not concerned with right and wrong.
             The conflict between right and wrong
             is the sickness of the mind.
calvin.htm">                                    g

 Hofstadter, p.281, commentary/reflections (on Riddle of Universe):

 Examples of Riddles:
          "This sentence is false"
          "Thiss sentence contains threee errors."
          "This sentence contains one error."
 
 
 
 

how about it> Leptons in 9 dimension

To the philosopher J.R.Lucas by C.H.Whitely:
          "Lucas cannot consistently assert this sentence."
      or  "Lucas cannot consistently believe this sentence."
 
 

This clusters that I can descrive
 Most of nature is very, very complicated.    How could one descrive a cloud? A cloud is not a sphere.. It is like a ball but very irregular. A mountain? A mountain is not a cone...If you want to speak of clouds, of mountains, of rivers, of ligthtning, the goemetric language of school is inadequate.  Mandelbrot.
 

gluons 9some things are beyond the realm of the blak body radiation
 

                                                                                                .gold.

For a complete treatment of super-computation and related matters, including literary creativity, see [Bringsjord & Zenzen, 1997
[ and Bringsjord, mingb].
 

 computation- A series of rule governed state transitions whose rules can be altered

There are numerous competing definitions of computation. Along with the initial definition provided here, the following three definitions are often encountered:

  1. Rule governed state transitions
  2. Discrete rule governed state transitions
  3. Rule governed state transtitions between interpretable states
The difficulties with these definitions can be summarized as follows:
  1. Admits all physical systems into the class of computational systems, making the definition somewhat vacuous
  2. Excludes all forms of analog computation, perhaps including the sorts of processing taking place in the brain.
  3. Necessitates accepting all computational systems as representational systems. In other words, there is no computation without representation on this definition.
  4. Chris Eliasmit
  5. Were are we bound !
  6. a.bremon@caramail.com
Šalfred bremontŠ