ARTIFICIAL INTELLIGENCE AS A POSITIVE AND NEGATIVE FACTOR IN GLOBAL RISK

ARTIFICIAL INTELLIGENCE AS A POSITIVE   AND  NEGATIVE FACTOR IN GLOBAL RISK

Forthcoming in Global Catastrophic Risks, eds. Nick Bostrom and Milan Cirkovic
Draft of August 31, 2006 Eliezer Yudkowsky (yudkowsky@singinst.org)
Singularity Institute for Artificial Intelligence Palo Alto, CA

By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it. Of course this problem is not limited to the field of AI. Jacques Monod wrote: "A curious aspect of the theory of evolution is that everybody thinks he understands it." (Monod 1974.) My father, a physicist, complained about people making up their own theories of physics; he wanted to know why people did not make up their own theories of chemistry. (They do.) Nonetheless the problem seems to be unusually acute in Artificial Intelligence. The field of AI has a reputation for making huge promises and then failing to deliver on them. Most observers conclude that AI is hard; as indeed it is. But the embarrassment does not stem from the difficulty. It is difficult to build a star from hydrogen, but the field of stellar astronomy does not have a terrible reputation for promising to build stars and then failing. The critical inference is not that AI is hard, but that, for some reason, it is very easy for people to think they know far more about Artificial Intelligence than they actually do....................................

Introduction 1 By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it. Of course this problem is not limited to the field of AI. Jacques Monod wrote: "A curious aspect of the theory of evolution is that everybody thinks he understands it." (Monod 1974.) My father, a physicist, complained about people making up their own theories of physics; he wanted to know why people did not make up their own theories of chemistry. (They do.) Nonetheless the problem seems to be unusually acute in Artificial Intelligence. The field of AI has a reputation for making huge promises and then failing to deliver on them. Most observers conclude that AI is hard; as indeed it is. But the embarrassment does not stem from the difficulty. It is difficult to build a star from hydrogen, but the field of stellar astronomy does not have a terrible reputation for promising to build stars and then failing. The critical inference is not that AI is hard, but that, for some reason, it is very easy for people to think they know far more about Artificial Intelligence than they actually do. CLICK HERE TO DOWNLOAD

Artificial Neural Networks

Artificial Neural Networks

What They Are

  A neural network is, in essence, an attempt to simulate the brain. Neural network theory revolves around the idea that certain key properties of biological neurons can be extracted and applied to simulations, thus creating a simulated (and very much simplified) brain. The first important thing to understand then, is that the components of an artificial neural network are an attempt to recreate the computing potential of the brain. The second important thing to understand, however, is that no one has ever claimed to simulate anything as complex as an actual brain. Whereas the human brain is estimated to have something on the order of ten to a hundred billion neurons, a typical artificial neural network (ANN) is not likely to have more than 1,000 artificial neurons.

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