Hybrid intelligence aims to enhance decision-making, problem-solving, and overall system performance by combining the strengths of both, human cognitive abilities and artificial intelligence. With the rise of Large Language Models (LLM), progressively participating as smart agents to accelerate machine learning development, Hybrid Intelligence is becoming an increasingly important topic for effective interaction between humans and machines. This paper presents an approach to leverage Hybrid Intelligence towards sustainable and energy-aware machine learning. When developing machine learning models, final model performance commonly rules the optimization process while the efficiency of the process itself is often neglected. Moreover, in recent times, energy efficiency has become equally crucial due to the significant environmental impact of complex and large-scale computational processes. The contribution of this work covers the interactive inclusion of secondary knowledge sources through Human-in-the-loop (HITL) and LLM agents to stress out and further resolve inefficiencies in the machine learning development process.

翻译：混合智能旨在通过结合人类认知能力与人工智能的优势，来增强决策制定、问题解决及整体系统性能。随着大型语言模型（LLM）日益作为智能体参与并加速机器学习发展，混合智能正成为人机有效交互中愈发重要的话题。本文提出一种利用混合智能实现可持续与节能机器学习的方法。在开发机器学习模型时，最终模型性能通常主导优化过程，而过程本身的效率常被忽视。此外，近期由于复杂大规模计算过程对环境产生的显著影响，能源效率已变得同等关键。本工作的贡献在于，通过人在回路（HITL）与LLM智能体交互式地纳入辅助知识源，以揭示并进一步解决机器学习开发过程中的低效问题。