Large language models (LLMs) are being increasingly incorporated into
scientific workflows. However, we have yet to fully grasp the implications of
this integration. How should the advent of large language models affect the
practice of science? For this opinion piece, we have invited four diverse
groups of scientists to reflect on this query, sharing their perspectives and
engaging in debate. Schulz et al. make the argument that working with LLMs is
not fundamentally different from working with human collaborators, while Bender
et al. argue that LLMs are often misused and over-hyped, and that their
limitations warrant a focus on more specialized, easily interpretable tools.
Marelli et al. emphasize the importance of transparent attribution and
responsible use of LLMs. Finally, Botvinick and Gershman advocate that humans
should retain responsibility for determining the scientific roadmap. To
facilitate the discussion, the four perspectives are complemented with a
response from each group. By putting these different perspectives in
conversation, we aim to bring attention to important considerations within the
academic community regarding the adoption of LLMs and their impact on both
current and future scientific practices.

大型语言模型越来越多地被纳入科学工作流程中，但我们还没有完全掌握这种整合的影响。大型语言模型的出现应该如何影响科学实践？本观点文章邀请了四组不同的科学家来思考这个问题，分享他们的观点并进行辩论。

大型语言模型的出现如何影响科学实践？

How should the advent of large language models affect the practice of  science?

Based on 46 in-depth interviews with scientists, engineers, and CEOs, this
document presents a list of concrete machine research problems, progress on
which would directly benefit tech ventures in East Africa.

通过对 46 位科学家、工程师和 CEO 进行深入采访，本文列出了一份具体的机器研究问题清单，进展将直接惠及东非的科技创业公司。

东非科技领域对机器学习研究的一些请求

Some Requests for Machine Learning Research from the East African Tech  Scene

The h-index is a popular bibliometric indicator for assessing individual
scientists. We criticize the h-index from a theoretical point of view. We argue
that for the purpose of measuring the overall scientific impact of a scientist
(or some other unit of analysis) the h-index behaves in a counterintuitive way.
In certain cases, the mechanism used by the h-index to aggregate publication
and citation statistics into a single number leads to inconsistencies in the
way in which scientists are ranked. Our conclusion is that the h-index cannot
be considered an appropriate indicator of a scientist's overall scientific
impact. Based on recent theoretical insights, we discuss what kind of
indicators can be used as an alternative to the h-index. We pay special
attention to the highly cited publications indicator. This indicator has a lot
in common with the h-index, but unlike the h-index it does not produce
inconsistent rankings.

本文从理论角度批判了 h 指数在衡量科学家总体科学影响力时表现出的反直觉特性，并主张高被引用文献指数可以作为一个比 h 指数更适当的替代性指标，因为前者不存在不一致排名的问题。