Harold Pinter

"And so I say to you, tender the dead as you would yourself be tendered, now, in what you would describe as your life."

Death - Harold Pinter 1930-2008

'Death'.

Where was the dead body found?
Who found the dead body?
Was the dead body dead when found?
How was the dead body found?

Who was the dead body?

Who was the father or daughter or brother
Or uncle or sister or mother or son
Of the dead and abandoned body?

Was the body dead when abandoned?
Was the body abandoned?
By whom had it been abandoned?

Was the dead body naked or dressed for a journey?
What made you declare the dead body dead?
Did you declare the dead body dead?
How well did you know the dead body?

How did you know the dead body was dead?

Did you wash the dead body
Did you close both its eyes
Did you bury the body
Did you leave it abandoned
Did you kiss the dead body

That's Me! That's Me! - A Writing Guide

In 1958 you wrote "Pandemonium". What does pandemonium mean? What was the concept of this?

The concept, "pandemonium" was a word first used by John Milton in a very long English poem called "Paradise Lost". Pandemonium comes from the Greek "pan", meaning all and "demonium", meaning the demons. The idea of pandemonium is that in recognizing something - for example, recognizing a face or a character on a page - we have a little demon for each feature, for each part of the picture. And when the demons see themselves in the picture they shout, That's me! That's me! and then a higher level demon listens to these other demons and decides who shouts the loudest. If you are reading a character, a letter in a word, if the higher level demon hears the "A" demon shout the loudest, then he knows it is an "A". The idea is that we have separate neural nets, say, representing the demons, and what they shout, their output, is the amount of themselves that they see, that they perceive in what they are looking at.

So it's a network of neural networks at the end.

Yes, in the long run neural networks will have to be built up of pieces that are neural networks. But they still have to work together. Then the whole system does not have simple purposes or goals but very complex ones, just like people. In that sense the neural network is very different from the network of computers which we are talking about now because here it is a social thing. In our society not every piece, not every computer wants the same thing. They want to communicate but not because there is a single purpose; they want to communicate because everybody wants to do something different. In the neural network, in the good neural networks, they are all contributing to the same end.

Oliver Selridge: From Cybernetics to Neural Networks

Question 1: What are neural networks?

Answer: A neural network is a model of the way real nerves, real sensors like eyes and ears and brains, work. It tries to imitate so that it will work in the same way and do the same things.


Question 2: E' possibile costruire macchine, computer e altre apparecchiature con le reti neurali?

Answer: It is possible. We believe that our thinking works in a way like that and we want to find out how real brains work, and also to build machines to do some of the same things that our brains, our minds do.

Question 3: But these machines are not programmable. Will they learn by themselves?

Answer: One hopes so. They do learn by themselves, by their own experiences but not as much as people do. They are still very simple. The kinds of tasks that these machines can now do are low-level tasks. As science improves, as the engineers and scientists, the people at SMAU, work them and practice with them they get better, but they are still very far from real people.

Question 4: Can you compare the ability of neural networks with the ability of animals or children?

Answer: It is not an age so much. The neural network in the machine keeps trying, but an intelligent child stops trying after a while and gets bored. Our machines do not get bored yet, which is a sign that they are very elementary indeed. There are tasks which they can do for us. They will keep track of the right way to do a very easy task. But as yet they do not have much sense of purpose of their own beyond what they are given by the people who build them.

Question 5: That is interesting because they have to understand from the environment. How can they understand from the environment?

Answer: That is a very interesting point. It is not that they understand so much, it is that they work with the environment to get something done, to perceive something, to have the right effect. But they do not really understand what the environment is or how it works. So neural networks today do not make a model of the environment in the way that you and I make a model of the environment, instead they merely play with what they can do until it works.

Question 6: And can you compare the goals of cybernetics and the goals of neural networks?

Answer: The goals of neural networks are much more cybernetic than present day computers. Our computers are nearly all programmed, that is, they are told exactly what to do. Neural networks are not told exactly what to do. The study of cybernetics started out with Professor Norbert Wiener at MIT, who was my adviser, studying how gets to a particular place. The word cybernetics comes from the Greek word for the steersman on a boat, who moved the tiller or the rudder to get the boat where he wanted to go. The steersman is performing the goal, the seeking of the goal, the going where he wants to. At a very low level neural networks move their connections and rewire themselves so that the machine will do what it is programmed to want to do. In computers the programs are written so the machine will do what the designer wants them to do. So the machines in computers do not want. Neural networks are beginning to want, to care, to have purpose.

Demons and Pandemonium - The Stuff of Writing

D.B. notes that Oliver Selfridge sadly died in a fall at age 82. Selfridge was a pioneer in early computer science and artificial intelligence. And as D.B. fans know, every writer worth his or her salt needs to understand the complex processes of intelligence. How else to improve the creative processes.

Selfridge himself understood the connection between literature and intelligent processes. Selfridge envisioned the mind as a collection of tiny demons (this idea of the demon came to him after reading Paradise Lost!), each of whom responds to a name -- or something close to it -- being called out by other demons. When one thinks it is being called, it begins to yell out to other demons. The more certain it is that it is being called, the louder it yells, until some other demon thinks it is being called in turn. And so on. Selfridge called this pandemonium.

He used this idea to explain and model the way perceptual systems recognize stuff. For example, the letter R has one vertical line, a "belly" on the upper right, and a "leg" on the lower right. When "feature demons" whose names are "vertical," "belly," and "leg" (and others with names like "one," "upper right," and "lower right") hear their names being called, they begin to to call to the "cognitive demons." The cognitive demons named B and D, for example, may each prick up their ears, since they are "sensitized" to such calls as are given out by the vertical and belly demons. K may be listening, because it is listening for the calls of the vertical and leg demons. But only the R demon recognizes the calls of all three. So while B, D, and K may be calling out to the "decision demon," it will be R who calls the loudest.

As Doctor C. George Boeree says, "This may seem rather silly, but pandemonium provides a very good model for much of what goes on in the mind. The tip-of-the-tongue phenomenon, for example: You are trying to think of the name of that actress in Moulin Rouge. Her name starts with an N, you are certain. Nancy, Nadene, Norah, Natalie... damn. You could say the N demon is yelling, and several names are responding. Nicole! That's it: Nicole Kidman. "

How about the poets amongst us seeking out a rhyme or a particular work, metaphor even - think of the connections.