Wireless spectrum regulation is a complex and demanding process due to the
rapid pace of technological progress, increasing demand for spectrum, and a
multitude of stakeholders with potentially conflicting interests, alongside
significant economic implications. To navigate this, regulators must engage
effectively with all parties, keep pace with global technology trends, conduct
technical evaluations, issue licenses in a timely manner, and comply with
various legal and policy frameworks.
In light of these challenges, this paper demonstrates example applications of
Large Language Models (LLMs) to expedite spectrum regulatory processes. We
explore various roles that LLMs can play in this context while identifying some
of the challenges to address. The paper also offers practical case studies and
insights, with appropriate experiments, highlighting the transformative
potential of LLMs in spectrum management.

无线频谱规制是一项复杂而严苛的过程，本文介绍了利用大型语言模型（LLMs）来加速频谱规制过程的示例应用，同时探讨了 LLMs 在此背景下可能扮演的各种角色以及需要解决的一些挑战，并通过实际案例和实验提供了相应的见解，突显了 LLMs 在频谱管理中的转型潜力。

利用大型语言模型（LLMs）加速无线电频谱管理工作流程

Accelerating Radio Spectrum Regulation Workflows with Large Language  Models (LLMs)

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

As artificial intelligence (AI) is integrated into various services and
systems in society, many companies and organizations have proposed AI
principles, policies, and made the related commitments. Conversely, some have
proposed the need for independent audits, arguing that the voluntary principles
adopted by the developers and providers of AI services and systems
insufficiently address risk. This policy recommendation summarizes the issues
related to the auditing of AI services and systems and presents three
recommendations for promoting AI auditing that contribute to sound AI
governance. Recommendation1.Development of institutional design for AI audits.
Recommendation2.Training human resources for AI audits. Recommendation3.
Updating AI audits in accordance with technological progress.
In this policy recommendation, AI is assumed to be that which recognizes and
predicts data with the last chapter outlining how generative AI should be
audited.

人工智能服务和系统的审计问题及其促进良好人工智能治理的三项建议。

推进 AI 审计以加强 AI 治理

Advancing AI Audits for Enhanced AI Governance

Forecasting technological progress is of great interest to engineers, policy
makers, and private investors. Several models have been proposed for predicting
technological improvement, but how well do these models perform? An early
hypothesis made by Theodore Wright in 1936 is that cost decreases as a power
law of cumulative production. An alternative hypothesis is Moore's law, which
can be generalized to say that technologies improve exponentially with time.
Other alternatives were proposed by Goddard, Sinclair et al., and Nordhaus.
These hypotheses have not previously been rigorously tested. Using a new
database on the cost and production of 62 different technologies, which is the
most expansive of its kind, we test the ability of six different postulated
laws to predict future costs. Our approach involves hindcasting and developing
a statistical model to rank the performance of the postulated laws. Wright's
law produces the best forecasts, but Moore's law is not far behind. We discover
a previously unobserved regularity that production tends to increase
exponentially. A combination of an exponential decrease in cost and an
exponential increase in production would make Moore's law and Wright's law
indistinguishable, as originally pointed out by Sahal. We show for the first
time that these regularities are observed in data to such a degree that the
performance of these two laws is nearly tied. Our results show that
technological progress is forecastable, with the square root of the logarithmic
error growing linearly with the forecasting horizon at a typical rate of 2.5%
per year. These results have implications for theories of technological change,
and assessments of candidate technologies and policies for climate change
mitigation.

本文利用 62 个不同技术的成本和生产的最新数据库，测试了六种假设规律预测未来科技成本的能力，并发现了一个以前未被发现的规律，即生产 tends tends to increase exponentially，同时发现 Moore's Law 和 Wright's Law 的表现几乎相同。研究结果对于技术变革的理论、候选技术和气候变化缓解政策的评估具有重要意义。