machine brains

Alexander Solntsev solntsev at wt.net
Thu Nov 13 14:43:27 EST 1997


In article <MPG.ed4ed5b4d7b67a398968a at news3.wt.net>, solntsev at wt.net 
says...
> I want to make a couple of points.
> 
> First on the subject of neural nets: one should not try to extend the 
> capabilities of such computing device to its use as a model of a brain 
> simply on the basis both apply similar terminology. For it is exactly that 
> which some researchers (and others) appear to be doing. A neural net is an 
> interesting computing device that is both inartistically parallel and
............................................^intrinsically (autocorrected)
> distributed. Its operation is determined by multiple interconnected nodes, 
> not unlike that of a brain. Yet it is not the only device that exhibits 
> such properties. Actually, any system of multiple cooperating 
> interconnected nodes could look like a brain. We should avoid making 
> parallels between computing devices and brain unless we can reasonably show 
> such devices as explaining more than one aspect of brain’s operation.
> 
> Second on the subject of linearity: most of us have became trapped in the 
> Von Neumann world of sequential computation with only neural nets seen as 
> the way out. Well, thank goodness, there are additional inherently parallel 
> and distributed computing devices that could save the day. While there are 
> several such devices, I will name the Dataflow computers specifically for I 
> find them most usefull. Actually, a neural net could be considered a 
> special case of a Dataflow computer. What is a Dataflow computer? It is a 
> device composed on multiple cooperating interconnected nodes that represent 
> a dataflow graph of the computation they perform. Each node can have 
> multiple inputs and outputs connecting it to other nodes. Each node 
> represents a computing function based on those inputs, and that function is 
> executed when these inputs become available. All nodes exchange information 
> using messages that could represent complex data structures.
> 
> Just a couple of points (for now as this message is getting too long :-))
> 
> Alex
> 
> All disclaimers apply
> 
> 



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