For more sustainability transparency in GenAI! Share your knowledge and reduce energy waste!
Emphasizing the importance of transparency and shared knowledge to enhance sustainability in GenAI.
TL;DR ⏱️
- GenAI involves very large models and significant training efforts
- Transparency can help share emissions and reduce energy waste
- Open source models can optimize future development
GenAI and Sustainability 🌱
- GenAI is implemented through very large models.
- The training can be substantial, such as the 15T tokens of LLAMA 3.1.
- However, these trainings represent only the final training and not all the attempts that led to the final model.
How Transparency helps 📚
- If the models are open or, in the best case, open source, many users and use cases can share the resulting emissions.
- It would also be interesting to see how often models are used for inference to compare the relative share of emissions.
- Open source would be important to share learnings for the next generation of models and thus reduce emissions by reusing hyperparameter optimizations. Please share your findings and let's use our resources and energy wisely.
Our Paper 📄
In our paper “Ethical and Sustainability Considerations for Knowledge Graph based Machine Learning,” we highlight which sustainability and ethical optimizations are possible when working with hardware-intensive Artificial Intelligence approaches. Link: https://lnkd.in/eJjATkTQ
What are you doing to bring AI, ethics, and sustainability together? ❤️
#artificialintelligence #genai #llm #sustainability #aiethics
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Today's Research Proposal - How to achieve "real" thinking and reasoning in GenAI, rather than just relying on a silent Chain of Thought, as seen in ReflectionAI or possibly GPT-o1?