会计可以利用 AI 做以下事情：
除了能够帮助回答财务问题外，LLMs还可以帮助金融服务团队改进自己的内部流程，简化财务团队的日常工作流程。尽管金融的几乎每个其他方面都取得了进展，但现代财务团队的日常工作流程仍然依赖于像Excel、电子邮件和需要人工输入的商业智能工具这样的手动流程。由于缺乏数据科学资源，基本任务尚未被自动化，CFO及其直接报告人因此在繁琐的记录和报告任务上花费太多时间，而他们应该专注于[金字塔顶端](https://a16z.com/2020/04/15/new-cfo-tools/)的战略决策。总体而言，生成式AI可以帮助这些团队从更多的数据源中获取数据，并自动化突出趋势、生成预测和报告的过程。以下是一些例子：预测：生成式AI可以帮助编写Excel、SQL和BI工具中的公式和查询，从而实现分析的自动化。此外，这些工具可以帮助发现模式，并从更广泛、更复杂的数据集中为预测建议输入（例如，考虑宏观经济因素），并建议如何更容易地适应这些模型，以便为公司决策提供依据。报告：生成式AI可以帮助自动创建文本、图表、图形等内容，并根据不同的示例调整此类报告，而无需手动将数据和分析整合到外部和内部报告中（例如，董事会材料、投资者报告、周报表）。会计和税务：会计和税务团队需要花时间咨询规则并了解如何应用它们。生成式AI可以帮助综合、总结，并就税法和潜在的扣除项提出可能的答案。采购和应付账款：生成式AI可以帮助自动生成和调整合同、采购订单和发票以及提醒。
May：会计BOT[会计prompt](https://getgaoding.feishu.cn/docx/L87XdsFrcoe5H7x3t8FcU4yqn4c)需求：朋友给我出了一个课题，让我研究一下在会计行业里面，写什么样的prompt可以直接识别会计分类。这是可以批处理的场景，并且专业性很强，可以总结规则。感受：我尝试了不同的方法，AI可以帮我做到准确的识别会计prompt。虽然我不是会计，但是对会计来说，需要专业判断的场景，是可以训练处一个专业领域的AI来提高效率的。[heading2]DAY39 2024.7.28 AI在各个领域的应用[content]May：AI的使用场景[3.1案例：AI产品案例和投稿](https://waytoagi.feishu.cn/wiki/MdNUwjXUZiuKLCkN4YrcNSZcnFb?table=tblwdvsWICkId67f&view=vewJuuzsne)感受：接触了AI这段时间，我也很好奇AI可以在各个领域怎么用。今天看到了这个内容，就存在这里。
125 What is the UK constitution?The Constitution Unit,University College London,2023.55A pro-innovation approach to AI regulation1.84.Tools for trustworthy AI like assurance techniques and technical standards can support supply chain risk management.These tools can also drive the uptake and adoption of AI by building justified trust in these systems,giving users confidence that key AI-related risks have been identified,addressed and mitigated across the supply chain.For example,by describing measures that manufacturers should take to ensure the safety of AI systems,technical standards can provide reassurance to purchasers and users of AI systems that appropriate safety-focused measures have been adopted,ultimately encouraging adoption of AI.2.85.Our evaluation of the framework will assess whether the legal responsibility for AI is effectively and fairly distributed.As we implement the framework,we will continue our extensive engagement to gather evidence from regulators,industry,academia,and civil society on its impact on different actors across the AI life cycle.This will allow us to monitor the effects of our framework on actors across the AI supply chain on an ongoing basis.We will need a particular focus on foundation models given the potential challenges they pose to life cycle accountability,especially when available as open-source.By centrally evaluating whether there are adequate measures for AI accountability,we can assess the need for further interventions into AI liability across the whole economy and AI life cycle.Consultation questions:L1.What challenges might arise when regulators apply the principles across different AI applications and systems?How could we address these challenges through our proposed AI regulatory framework?L2.1.Do you agree that the implementation of our principles through existing legal frameworks will fairly and effectively allocate legal responsibility for AI across the life cycle?L.2.2.How could it be improved,if at all?