Automating enterprise workflows could unlock $4 trillion/year in productivity
gains. Despite being of interest to the data management community for decades,
the ultimate vision of end-to-end workflow automation has remained elusive.
Current solutions rely on process mining and robotic process automation (RPA),
in which a bot is hard-coded to follow a set of predefined rules for completing
a workflow. Through case studies of a hospital and large B2B enterprise, we
find that the adoption of RPA has been inhibited by high set-up costs (12-18
months), unreliable execution (60% initial accuracy), and burdensome
maintenance (requiring multiple FTEs). Multimodal foundation models (FMs) such
as GPT-4 offer a promising new approach for end-to-end workflow automation
given their generalized reasoning and planning abilities. To study these
capabilities we propose ECLAIR, a system to automate enterprise workflows with
minimal human supervision. We conduct initial experiments showing that
multimodal FMs can address the limitations of traditional RPA with (1)
near-human-level understanding of workflows (93% accuracy on a workflow
understanding task) and (2) instant set-up with minimal technical barrier
(based solely on a natural language description of a workflow, ECLAIR achieves
end-to-end completion rates of 40%). We identify human-AI collaboration,
validation, and self-improvement as open challenges, and suggest ways they can
be solved with data management techniques. Code is available at:
this https URL

自动化企业工作流程可以释放每年 4 万亿美元的生产力增益。通过案例研究医院和大型 B2B 企业，我们发现采用机器人流程自动化受到成本高昂（需 12-18 个月），执行不可靠（初始准确率为 60％）和繁琐的维护（需要多个全职员工）的限制。在这篇文章中，我们提出 ECLAIR，一个能够以较少人力监督自动化企业工作流程的系统，通过初步实验表明，多模态基础模型具有解决传统机器人流程自动化的局限性的潜力。我们还确定了人工智能与人类协作、验证和自我提升作为开放性挑战，并提出了通过数据管理技术解决这些挑战的方式。

基于基础模型的企业自动化

Automating the Enterprise with Foundation Models

This paper presents Graph-of-Thought (GoT), a new model for workflow
automation that enhances the flexibility and efficiency of Large Language
Models (LLMs) in complex task execution. GoT advances beyond traditional linear
and tree-like cognitive models with a graph structure that enables dynamic path
selection. The open-source engine GoTFlow demonstrates the practical
application of GoT, facilitating automated, data-driven decision-making across
various domains. Despite challenges in complexity and transparency, GoTFlow's
potential for improving business processes is significant, promising
advancements in both efficiency and decision quality with continuous
development.

本文介绍了 Graph-of-Thought（GoT）模型，这是一种用于工作流自动化的新模型，提高了复杂任务执行中大型语言模型（LLMs）的灵活性和效率。GoT 通过图形结构超越传统的线性和树状认知模型，实现了动态路径选择。开源引擎 GoTFlow 展示了 GoT 的实际应用，促进了在各个领域中的自动化、数据驱动决策制定。尽管存在复杂性和透明度方面的挑战，GoTFlow 在改进业务流程方面具有巨大潜力，通过持续发展能够提高效率和决策质量。

思维之图：运用大型语言模型解决复杂和动态的商业问题

Graph-of-Thought: Utilizing Large Language Models to Solve Complex and  Dynamic Business Problems

This paper presents DeepTSF, a comprehensive machine learning operations
(MLOps) framework aiming to innovate time series forecasting through workflow
automation and codeless modeling. DeepTSF automates key aspects of the ML
lifecycle, making it an ideal tool for data scientists and MLops engineers
engaged in machine learning (ML) and deep learning (DL)-based forecasting.
DeepTSF empowers users with a robust and user-friendly solution, while it is
designed to seamlessly integrate with existing data analysis workflows,
providing enhanced productivity and compatibility. The framework offers a
front-end user interface (UI) suitable for data scientists, as well as other
higher-level stakeholders, enabling comprehensive understanding through
insightful visualizations and evaluation metrics. DeepTSF also prioritizes
security through identity management and access authorization mechanisms. The
application of DeepTSF in real-life use cases of the I-NERGY project has
already proven DeepTSF's efficacy in DL-based load forecasting, showcasing its
significant added value in the electrical power and energy systems domain.

DeepTSF 是一款全面的机器学习操作（MLOps）框架，通过工作流自动化和无代码建模来创新时间序列预测。