Many researchers work on improving the data efficiency of machine learning. What would happen if they succeed? This paper explores the social-economic impact of increased data efficiency. Specifically, we examine the intuition that data efficiency will erode the barriers to entry protecting incumbent data-rich AI firms, exposing them to more competition from data-poor firms. We find that this intuition is only partially correct: data efficiency makes it easier to create ML applications, but large AI firms may have more to gain from higher performing AI systems. Further, we find that the effect on privacy, data markets, robustness, and misuse are complex. For example, while it seems intuitive that misuse risk would increase along with data efficiency -- as more actors gain access to any level of capability -- the net effect crucially depends on how much defensive measures are improved. More investigation into data efficiency, as well as research into the "AI production function", will be key to understanding the development of the AI industry and its societal impacts.

中文翻译：

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提高数据效率对社会和治理的影响

许多研究人员致力于提高机器学习的数据效率。如果他们成功了会发生什么？本文探讨了提高数据效率的社会经济影响。具体来说，我们研究了这样一种直觉，即数据效率将侵蚀保护现有数据丰富的人工智能公司的进入壁垒，使它们面临来自数据匮乏公司的更多竞争。我们发现这种直觉只是部分正确：数据效率使创建 ML 应用程序变得更容易，但大型人工智能公司可能会从更高性能的人工智能系统中获得更多收益。此外，我们发现对隐私、数据市场、稳健性和滥用的影响是复杂的。例如，虽然滥用风险会随着数据效率的增加而增加，这似乎很直观——随着越来越多的参与者获得任何级别的能力——净效应关键取决于防御措施的改进程度。对数据效率的更多调查，以及对“人工智能生产函数”的研究，将是了解人工智能产业发展及其社会影响的关键。