预测AI进展：研究议程
Forecasting AI Progress: A Research Agenda
摘要：
预测人工智能的进展对于减少不确定性至关重要，以便适当规划人工智能安全和人工智能治理的研究工作。虽然这通常被认为是一个重要的主题，但在这方面开展的工作很少，也没有发表的文件对这一领域进行客观的概述。此外，该领域非常多样化，关于其方向没有公开的共识。本文描述了一个预测人工智能进展的研究议程的发展，该议程利用德尔菲技术来收集和汇总专家对优先考虑哪些问题和方法的意见。德尔菲法的结果，其余的文件遵循这些结果的结构，简要回顾相关文献，并建议今后的工作，为每个主题。专家们表示，应该考虑各种各样的方法来预测人工智能的进展。此外，专家们还确定了一些突出的问题，这些问题对于预测人工智能的进展来说既普遍又完全独特。一些最优先的主题包括验证（部分未解决的）预测，如何使预测行动指导和不同的性能指标的质量。虽然统计方法似乎更有前途，但人们也认识到，补充判断技术可能非常有益。
英文摘要：
Forecasting AI progress is essential to reducing uncertainty in order to appropriately plan for research efforts on AI safety and AI governance. While this is generally considered to be an important topic, little work has been conducted on it and there is no published document that gives and objective overview of the field. Moreover, the field is very diverse and there is no published consensus regarding its direction. This paper describes the development of a research agenda for forecasting AI progress which utilized the Delphi technique to elicit and aggregate experts' opinions on what questions and methods to prioritize. The results of the Delphi are presented; the remainder of the paper follow the structure of these results, briefly reviewing relevant literature and suggesting future work for each topic. Experts indicated that a wide variety of methods should be considered for forecasting AI progress. Moreover, experts identified salient questions that were both general and completely unique to the problem of forecasting AI progress. Some of the highest priority topics include the validation of (partially unresolved) forecasts, how to make forecasting action-guiding and the quality of different performance metrics. While statistical methods seem more promising, there is also recognition that supplementing judgmental techniques can be quite beneficial.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
