"Research Priorities for Robust and Beneficial Artificial Intelligence: an Open Letter"
Future of Life Institute
RESEARCH PRIORITIES FOR ROBUST AND BENEFICIAL ARTIFICIAL INTELLIGENCE
Artificial intelligence (AI) research has explored a variety of problems and approaches since its inception, but for the last 20 years or so has been focused on the problems surrounding the construction of intelligent agents – systems that perceive and act in some environment. In this context, “intelligence” is related to statistical and economic notions of rationality – colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic and decision-theoretic representations and statistical learning methods has led to a large degree of integration and cross-fertilization among AI, machine learning, statistics, control theory, neuroscience, and other fields. The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable successes in various component tasks such as speech recognition, image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems.
As capabilities in these areas and others cross the threshold from laboratory research to economically valuable technologies, a virtuous cycle takes hold whereby even small improvements in performance are worth large sums of money, prompting greater investments in research. There is now a broad consensus that AI research is progressing steadily, and that its impact on society is likely to increase. The potential benefits are huge, since everything that civilization has to offer is a product of human intelligence; we cannot predict what we might achieve when this intelligence is magnified by the tools AI may provide, but the eradication of disease and poverty are not unfathomable. Because of the great potential of AI, it is important to research how to reap its benefits while avoiding potential pitfalls.
The progress in AI research makes it timely to focus research not only on making AI more capable, but also on maximizing the societal benefit of AI. Such considerations motivated the AAAI 2008-09 Presidential Panel on Long-Term AI Futures and other projects on AI impacts, and constitute a significant expansion of the field of AI itself, which up to now has focused largely on techniques that are neutral with respect to purpose. We recommend expanded research aimed at ensuring that increasingly capable AI systems are robust and beneficial: our AI systems must do what we want them to do. The attached research priorities document gives many examples of such research directions that can help maximize the societal benefit of AI. This research is by necessity interdisciplinary, because it involves both society and AI. It ranges from economics, law and philosophy to computer security, formal methods and, of course, various branches of AI itself.
In summary, we believe that research on how to make AI systems robust and beneficial is both important and timely, and that there are concrete research directions that can be pursued today.
人工智能（AI）研究从开始至今就探索了各种各样的问题和方向。但在过去的20多年中，AI课题一直围绕着智能代理（在某种环境中感知并作出反应的系统)的构建。在这种背景下，“智能”与统计和经济理性概念有所挂钩 – 而理性概念简单来说就是理性做出良好决策、计划或推论的能力。概率论和决策理论表征与统计学习方法的采用使得人工智能、机器学习、统计学、控制理论、神经科学等领域大幅度地交叉与融合。共享理论框架的建立，结合数据的累计和数据处理能力的产生，让人工智能在多种任务上,例如：语音识别、图像分类、自主驾驶、机器翻译、机械有腿移动和问答系统，取得耀眼的成绩。
以目前人工智能研究的进展来看，研究不应该只限于扩展AI的能力，而是也需要把AI对社会的效益发挥至最高点。这些考虑促成了人工智能促进协会于 2008-09 讨论AI长期未来的院长小组以及其他AI影响项目，并大大地扩大了AI本身的领域（AI的领域到目前为止主要集中在成立技术中立的目标）。我们建议扩展研究，以确保日渐强大的AI系统是有效和有益的：我们的AI系统必须做我们想要它们做的事情。此处的研究优先考量文件对AI研究方向提供了许多可以帮助增进AI在社会上取得最大效益的建议。因为这项研究涉及社会和人工智能, 这份文件提出的方案也理所当然跨越多门学科。它概括了经济学、法律、哲学、计算机安全、形式化方法，和AI的各个分支。
[Edited at 2018-03-24 14:25 GMT]