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.
翻译:人工智能(AI)是21世纪最具变革性的技术之一,未来的人工智能能力的范围和范围仍然是关键的不确定性,在时间和潜在影响上存在着广泛的分歧。随着国家和技术公司在AI系统中竞争日益复杂和自主,人们对于不透明的AI决策过程的整合和监督程度存在担忧。在机器学习的子领域尤其如此,在机器学习的子领域,系统学会在没有人类援助的情况下优化目标。目标可以不完美地指定或以出乎意料或潜在有害的方式执行。目标可能更加涉及系统增强权力和自主,突然能力突飞猛进可能导致动力动态发生意外变化,甚至灾难性失败。这一研究提出了一个等级复杂的系统框架,以模拟AI风险,并为备选的未来分析提供一个模板。调查数据是从公共和私营部门的域专家收集的,以对AI的影响和可能性进行分类。结果显示,强大的AI代理假设的不确定性、对多剂环境的信心以及对AI调整失败和影响力追求行为更加关切。