Artificial Intelligence (AI) is one of the most transformative technologies
of the 21st century. The extent and scope of future AI capabilities remain a
key uncertainty, with widespread disagreement on timelines and potential
impacts. As nations and technology companies race toward greater complexity and
autonomy in AI systems, there are concerns over the extent of integration and
oversight of opaque AI decision processes. This is especially true in the
subfield of machine learning (ML), where systems learn to optimize objectives
without human assistance. Objectives can be imperfectly specified or executed
in an unexpected or potentially harmful way. This becomes more concerning as
systems increase in power and autonomy, where an abrupt capability jump could
result in unexpected shifts in power dynamics or even catastrophic failures.
This study presents a hierarchical complex systems framework to model AI risk
and provide a template for alternative futures analysis. Survey data were
collected from domain experts in the public and private sectors to classify AI
impact and likelihood. The results show increased uncertainty over the powerful
AI agent scenario, confidence in multiagent environments, and increased concern
over AI alignment failures and influence-seeking behavior.

本文使用分层复杂系统框架对人工智能（AI）风险进行建模，并从公共和私营领域的领域专家收集调查数据以分类 AI 影响和可能性，结果显示强大的 AI 代理情景有更多不确定性，对 AI 对齐失败和影响寻求行为的关注增加以及对多智能体环境的信心增强。

探讨高水平人工智能的差异化风险和控制问题

Examining the Differential Risk from High-level Artificial Intelligence and the Question of Control

Imitation can be viewed as a means of enhancing learning in multiagent
environments. It augments an agent's ability to learn useful behaviors by
making intelligent use of the knowledge implicit in behaviors demonstrated by
cooperative teachers or other more experienced agents. We propose and study a
formal model of implicit imitation that can accelerate reinforcement learning
dramatically in certain cases. Roughly, by observing a mentor, a
reinforcement-learning agent can extract information about its own capabilities
in, and the relative value of, unvisited parts of the state space. We study two
specific instantiations of this model, one in which the learning agent and the
mentor have identical abilities, and one designed to deal with agents and
mentors with different action sets. We illustrate the benefits of implicit
imitation by integrating it with prioritized sweeping, and demonstrating
improved performance and convergence through observation of single and multiple
mentors. Though we make some stringent assumptions regarding observability and
possible interactions, we briefly comment on extensions of the model that relax
these restricitions.

本文提出和研究了一种隐含模仿的形式模型，通过观察导师，强化学习代理可以提取关于其自身能力和状态空间中未访问部分的相对价值的信息，并阐述了隐含模仿的好处，通过指导单个和多个导师来证明性能和收敛性有所提高。