Summary of Leveraging Hybrid Intelligence Towards Sustainable and Energy-efficient Machine Learning, by Daniel Geissler et al.
Leveraging Hybrid Intelligence Towards Sustainable and Energy-Efficient Machine Learning
by Daniel Geissler, Paul Lukowicz
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach for sustainable and energy-aware machine learning is presented, leveraging Hybrid Intelligence by combining human cognitive abilities with artificial intelligence. The proposed method incorporates Large Language Models as smart agents to accelerate machine learning development, focusing on both model performance and optimization efficiency. By incorporating secondary knowledge sources through Human-in-the-loop (HITL) and LLM agents, the approach aims to stress out and resolve inefficiencies in the machine learning process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are getting smarter, but have you ever thought about how they’re made? This paper is all about making those processes more efficient and environmentally friendly. It’s like having a team of super-smart assistants that help humans make better decisions. The idea is to use human brainpower together with artificial intelligence to create the best results possible. |
Keywords
» Artificial intelligence » Machine learning » Optimization
