
Contents
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15.1 Introduction 15.1 Introduction
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15.2 Anthropomorphic bias 15.2 Anthropomorphic bias
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15.3 Prediction and design 15.3 Prediction and design
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15.4 Underestimating the power of intelligence 15.4 Underestimating the power of intelligence
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15.5 Capability and motive 15.5 Capability and motive
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15.5.1 Optimization processes 15.5.1 Optimization processes
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15.5.2 Aiming at the target 15.5.2 Aiming at the target
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15.6 Friendly Artificial Intelligence 15.6 Friendly Artificial Intelligence
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15.7 Technical failure and philosophical failure 15.7 Technical failure and philosophical failure
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15.7.1 An example of philosophical failure 15.7.1 An example of philosophical failure
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15.7.2 An example of technical failure 15.7.2 An example of technical failure
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15.8 Rates of intelligence increase 15.8 Rates of intelligence increase
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15.9 Hardware 15.9 Hardware
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15.10 Threats and promises 15.10 Threats and promises
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15.11 Local and Majoritarian Strategies 15.11 Local and Majoritarian Strategies
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15.12 Interactions of Artificial Intelligence with other technologies 15.12 Interactions of Artificial Intelligence with other technologies
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15.13 Making progress on Friendly Artificial Intelligence 15.13 Making progress on Friendly Artificial Intelligence
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15.14 Conclusion 15.14 Conclusion
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Acknowledgement Acknowledgement
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References References
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30815 Artificial Intelligence as a positive and negative factor in global risk
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Published:July 2008
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Abstract
By far the greatest danger of Artificial Intelligence (AI) 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). 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 AI than they actually do. It may be tempting to ignore Artificial Intelligence because, of all the global risks discussed in this book, AI is probably hardest to discuss. We cannot consult actuarial statistics to assign small annual probabilities of catastrophe, as with asteroid strikes. We cannot use calculations from a precise, precisely confirmed model to rule out events or place infinitesimal upper bounds on their probability, as with proposed physics disasters. But this makes AI catastrophes more worrisome, not less. The effect of many cognitive biases has been found to increase with time pressure, cognitive busyness, or sparse information. Which is to say that the more difficult the analytic challenge, the more important it is to avoid or reduce bias. Therefore I strongly recommend reading my other chapter (Chapter 5) in this book before continuing with this chapter. When something is universal enough in our everyday lives, we take it for granted to the point of forgetting it exists. Imagine a complex biological adaptation with ten necessary parts. If each of the ten genes is independently at 50% frequency in the gene pool – each gene possessed by only half the organisms in that species – then, on average, only 1 in 1024 organisms will possess the full, functioning adaptation.
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