"And so I say to you, tender the dead as you would yourself be tendered, now, in what you would describe as your life."
Alter Egos - I Am Done Watching This
Sunday, December 28, 2008
Saturday, December 27, 2008
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
Thursday, December 04, 2008
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.
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.
Monday, November 24, 2008
Dead Beat notes that Kiyoshi Ito, a mathematician whose innovative models of random motion are used today in fields as diverse as finance and biology, died Nov. 17 at a hospital in Kyoto, Japan. He was 93.
Ito is known for his contributions to probability theory, the study of randomness. His work, starting in the 1940s, built on the earlier breakthroughs of Albert Einstein and Norbert Wiener. Mr. His mathematical framework for describing the evolution of random phenomena came to be known as the Ito Calculus.
“People all over realized that what Ito had done explained things that were unexplainable before.”
Tuesday, October 14, 2008
Thursday, October 09, 2008
Posted by Gerard Beirne at 11:48 pm