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?

Organizations of all sizes, across all industries and domains are leveraging
artificial intelligence (AI) technologies to solve some of their biggest
challenges around operations, customer experience, and much more. However, due
to the probabilistic nature of AI, the risks associated with it are far greater
than traditional technologies. Research has shown that these risks can range
anywhere from regulatory, compliance, reputational, and user trust, to
financial and even societal risks. Depending on the nature and size of the
organization, AI technologies can pose a significant risk, if not used in a
responsible way. This position paper seeks to present a brief introduction to
AI governance, which is a framework designed to oversee the responsible use of
AI with the goal of preventing and mitigating risks. Having such a framework
will not only manage risks but also gain maximum value out of AI projects and
develop consistency for organization-wide adoption of AI.

本文探讨了人工智能技术的风险，并介绍了一种名为 AI 治理的框架，以确保人工智能的负责任使用，从而避免和减轻包括监管、合规性、声誉、用户信任、财务和社会风险在内的风险。