论文标题
从学习机器中学习：优化，规则和社会规范
Learning from Learning Machines: Optimisation, Rules, and Social Norms
论文作者
论文摘要
机器学习系统与经济实体之间有一个类比，因为它们都是适应性的，并且它们的行为是以更明确的方式指定的。看来，AI领域与经济实体的行为最类似，这是道德上良好的决策，但这是关于如何在AI系统中实现精确道德行为的一个悬而未决的问题。本文探讨了这两个复杂系统之间的类比，我们建议对这种明显的类比有更清晰的了解可能有助于我们在社会经济领域和AI领域中迈进：经济学的已知结果可能有助于为AI安全性提供可行的解决方案，但在AI中也知道的结果可能会为AI的经济政策提供信息。如果这一主张是正确的，那么AI的最新学习取得的最新成功表明，与解决此类问题的明确规格相比，更多的隐性规范效果更好。
There is an analogy between machine learning systems and economic entities in that they are both adaptive, and their behaviour is specified in a more-or-less explicit way. It appears that the area of AI that is most analogous to the behaviour of economic entities is that of morally good decision-making, but it is an open question as to how precisely moral behaviour can be achieved in an AI system. This paper explores the analogy between these two complex systems, and we suggest that a clearer understanding of this apparent analogy may help us forward in both the socio-economic domain and the AI domain: known results in economics may help inform feasible solutions in AI safety, but also known results in AI may inform economic policy. If this claim is correct, then the recent successes of deep learning for AI suggest that more implicit specifications work better than explicit ones for solving such problems.
