Artificial Intelligence as a Positive and Negative Factor in Global Risk
Artificial Intelligence as a Positive and Negative Factor in Global Risk
Eliezer Yudkowsky
(yudkowsky@singinst.org) Forthcoming in Global Catastrophic Risks, eds. Nick Bostrom and Milan Cirkovic Draft of August 31, 2006
Singularity Institute for Artificial Intelligence Palo Alto, CA Introduction 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. (Answer: 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. In my other chapter for Global Catastrophic Risks, "Cognitive biases potentially affecting judgment of global risks", I opened by remarking that few people would deliberately choose to destroy the world; a scenario in which the Earth is destroyed by mistake is therefore very worrisome. Few people would push a button that they clearly knew would cause a global catastrophe. But if people are liable to confidently believe that the button does something quite different from its actual consequence, that is cause indeed for alarm. It is far more difficult to write about global
Eliezer Yudkowsky
(yudkowsky@singinst.org) Forthcoming in Global Catastrophic Risks, eds. Nick Bostrom and Milan Cirkovic Draft of August 31, 2006
Singularity Institute for Artificial Intelligence Palo Alto, CA Introduction 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. (Answer: 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. In my other chapter for Global Catastrophic Risks, "Cognitive biases potentially affecting judgment of global risks", I opened by remarking that few people would deliberately choose to destroy the world; a scenario in which the Earth is destroyed by mistake is therefore very worrisome. Few people would push a button that they clearly knew would cause a global catastrophe. But if people are liable to confidently believe that the button does something quite different from its actual consequence, that is cause indeed for alarm. It is far more difficult to write about global
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