[论文解读] Computational Power and the Social Impact of Artificial Intelligence

Tim Hwang|arXiv (Cornell University)|Mar 23, 2018

Ethics and Social Impacts of AI被引用 32

一句话总结

本文研究了计算能力——由芯片和半导体等硬件进步所推动——在塑造人工智能的发展、部署及社会影响方面所起的关键作用。文章认为，硬件限制与创新直接影响机器学习方法、模型设计以及实际应用，并呼吁在人工智能政策与社会风险评估中更深入地整合硬件考量因素。

ABSTRACT

Machine learning is a computational process. To that end, it is inextricably tied to computational power - the tangible material of chips and semiconductors that the algorithms of machine intelligence operate on. Most obviously, computational power and computing architectures shape the speed of training and inference in machine learning, and therefore influence the rate of progress in the technology. But, these relationships are more nuanced than that: hardware shapes the methods used by researchers and engineers in the design and development of machine learning models. Characteristics such as the power consumption of chips also define where and how machine learning can be used in the real world. Despite this, many analyses of the social impact of the current wave of progress in AI have not substantively brought the dimension of hardware into their accounts. While a common trope in both the popular press and scholarly literature is to highlight the massive increase in computational power that has enabled the recent breakthroughs in machine learning, the analysis frequently goes no further than this observation around magnitude. This paper aims to dig more deeply into the relationship between computational power and the development of machine learning. Specifically, it examines how changes in computing architectures, machine learning methodologies, and supply chains might influence the future of AI. In doing so, it seeks to trace a set of specific relationships between this underlying hardware layer and the broader social impacts and risks around AI.

研究动机与目标

• 分析硬件基础设施（尤其是计算能力）如何塑造机器学习与人工智能发展的轨迹。
• 弥补现有人工智能影响分析中忽视底层计算架构与供应链作用的空白。
• 探讨计算硬件的转变如何影响机器学习方法论与实际部署约束。
• 审视由硬件驱动的人工智能进步所带来的社会与伦理影响，特别是可及性与公平性问题。
• 倡导将硬件视为人工智能社会影响基础因素的更深层次整合性理解。

提出的方法

• 分析计算能力与机器学习性能之间的关系，重点关注训练与推理速度。
• 考察硬件特性（如功耗与芯片效率）如何制约或推动特定人工智能应用。
• 追踪机器学习模型随计算架构与可用硬件变化而演变的过程。
• 研究供应链动态与硬件获取差异，这些因素影响谁能够开发和部署人工智能系统。
• 利用人工智能进展的案例研究与趋势，说明硬件限制如何塑造研究重点与模型设计选择。
• 提出一个将硬件能力与人工智能更广泛社会结果（包括公平性、可持续性与可扩展性）相联系的框架。

实验结果

研究问题

• RQ1 计算能力在多大程度上影响机器学习发展的速度与方向？
• RQ2 硬件限制以何种方式塑造机器学习模型的设计与部署？
• RQ3 为何硬件考量在当前人工智能社会影响分析中仍被低估？
• RQ4 计算资源在供应链与获取方面的差异如何影响全球人工智能发展的公平性？
• RQ5 由硬件驱动的人工智能进步所带来的长期社会与伦理影响是什么？

主要发现

• 计算能力不仅仅是人工智能进步的促进因素，更是决定哪些机器学习方法可行且可扩展的关键因素。
• 功耗等硬件特性直接影响人工智能模型在现实场景中的部署地点与方式。
• 人工智能方法论的演进与计算架构的进步密切相关，新型芯片设计推动了新型模型架构的出现。
• 当前的人工智能影响评估往往忽视硬件因素，导致对能源使用、环境影响与获取不平等风险的理解不完整。
• 高性能计算资源获取的不平等造成了进入壁垒，加剧了人工智能开发中既有的权力失衡。
• 未来的人工智能政策与风险评估必须整合硬件考量，以确保技术的公平与可持续发展。