We analyze how output and wages behave under different scenarios for
technological progress that may culminate in Artificial General Intelligence
(AGI), defined as the ability of AI systems to perform all tasks that humans
can perform. We assume that human work can be decomposed into atomistic tasks
that differ in their complexity. Advances in technology make ever more complex
tasks amenable to automation. The effects on wages depend on a race between
automation and capital accumulation. If the distribution of task complexity
exhibits a sufficiently thick infinite tail, then there is always enough work
for humans, and wages may rise forever. By contrast, if the complexity of tasks
that humans can perform is bounded and full automation is reached, then wages
collapse. But declines may occur even before if large-scale automation outpaces
capital accumulation and makes labor too abundant. Automating productivity
growth may lead to broad-based gains in the returns to all factors. By
contrast, bottlenecks to growth from irreproducible scarce factors may
exacerbate the decline in wages.

我们分析了在可能达到人工智能系统能执行人类所有任务的技术进步背景下，产出和工资的表现，并且假设人类的工作可以分解为不同复杂度的原子任务。技术进步使得越来越复杂的任务能够自动化。工资的影响取决于自动化与资本积累之间的竞赛。如果任务复杂度的分布具有足够厚的无穷尾部，那么总是有足够的工作供人类从事，而工资可能会永久上升。相反，如果人类能够执行的任务复杂度是有界的，并且实现了完全自动化，那么工资会崩溃。但即使在这之前，如果大规模自动化超过资本积累，使得劳动力过剩，工资也可能会下降。自动化生产率的增长可能会导致对所有要素的广泛利益增长。相反，来自不可再生稀缺要素的增长瓶颈可能会加剧工资的下降。

向通用人工智能的过渡场景

Scenarios for the Transition to AGI

This year the International Conference on Natural Language Generation (INLG)
will feature an award for the paper with the best evaluation. The purpose of
this award is to provide an incentive for NLG researchers to pay more attention
to the way they assess the output of their systems. This essay provides a short
introduction to evaluation in NLG, explaining key terms and distinctions.

该论文简要介绍了自然语言生成领域中的评估，解释了关键术语和区别，并提出了一个奖项，以激励研究人员更多地关注他们系统输出的评估方法。