Leveraging Hybrid Intelligence Towards Sustainable and Energy-Efficient Machine Learning

TL;DR

This paper explores how hybrid intelligence, combining human input and large language models, can improve the sustainability and energy efficiency of machine learning development by addressing process inefficiencies.

Contribution

It introduces an approach integrating human-in-the-loop and LLM agents to enhance energy-aware machine learning development processes.

Findings

Improved energy efficiency in ML development

Effective human-LLM collaboration strategies

Reduction of computational resource usage

Abstract

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.