Considering sustainability: Navigating the path toward green Generative AI in Education
With the rapid adoption of generative artificial intelligence tools in education, the talk about achieving sustainability comes into play. The advent of advanced Large Language Models (LLMs) such as GPT-4 and Bard has sparked a revolution in AI capabilities. However, this progress carries a hefty environmental price tag, underscoring the critical importance of steering the field toward more sustainable practices through the learning process, commonly called Green Generative AI in education.
In recent years, a call from researchers to college faculty has been made out loud to focus on questions about the implications of LLMs for student learning and achieving sustainability in education. Those questions are related to the high consumption of energy resources. For instance, How many kilowatt hours of electricity students in a classroom are willing to spend for research and brainstorming ideas for a new essay using LLMs?
In addition, the considerable carbon emissions and water consumption associated with these models make a powerful argument for embedding sustainability into every stage of AI development and use.
In this blog post, we will look closer at the environmental footprint of Large Language Models (LLMs). Thereafter, we’ll discuss strategies for reducing the environmental impacts while creating and using such revolutionary AI applications.
The Ecological Cost of Generative AI
?A recent multimodal news feature in Bloomberg, “AI Is Already Wreaking Havoc on Global Power Systems ” reviews the environmental influence of LLMS. The writer referred to the number of data centers built or being built around the world which has gone from 3,600 in 2015 to more than 7,000 in 2024. Experts expect that, by 2034, these centers may require 1,580 terawatt hours of electricity per year, about as much electricity as the nation of India uses annually.
In addition to the rapidly increasing electricity needs associated with artificial intelligence, Research reveals that the process of training such models is resource-intensive, with GPT-3's training phase alone responsible for emitting 502 tons of carbon dioxide equivalents. This substantial carbon footprint underscores the urgent need for sustainable practices within the field of artificial intelligence, according to Artificial? Intelligence Index Report 2023 .
Furthermore, the operation of LLMs also demands considerable amounts of water . It is estimated that for generating a range of 10 to 50 responses, GPT-3 requires water equivalent to a 500ml bottle.
In data centers , water is used both indirectly, through electricity use, and directly, for cooling, which is crucial because the integrated circuits inside data centers produce heat as a by-product. If they get too hot, they stop working.
Generative AI VS. the goals of Net zero carbon emissions
According to the 2024 Environmental Report , Google endorsed that despite its goal to reach net zero carbon emissions by 2030, its emissions grew 13 percent in 2023, an increase “primarily driven by increased data center energy consumption and supply chain emissions.” Since 2019, Google’s emissions are up 48 percent. Google’s water consumption at its data centers and offices increased 14 percent from 2022 to 2023, an increase the company attributes to data center cooling needs.
The mentioned aspects of the dilemma are shedding light on a frequently neglected dimension of AI's ecological footprint leveraging the need for new approaches to reduce the environmental impact of LLMs in education.
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Eco-friendly strategies for developing and deploying green AI in education
To mitigate the challenge of the significant environmental impact of AI systems in education, a growing focus on "green AI" has emerged throughout the lifecycle of educational AI applications. Here are some strategies suggested by experts for an eco-friendly and AI-based learning process.
Green Computing Infrastructure
Educational institutions need to power their computing facilities with renewable energy sources, such as solar or wind power. Moreover, they must invest in advanced cooling systems and power management technologies to improve the energy efficiency of data centers used for LLM hosting and inference.
Improving Model Architecture
This strategy aims at decreasing computational demands through refined model architecture to ensure high performance with lower energy consumption. On the other hand, optimizing data usage will lead to minimizing energy consumption during model training.
Retrieval-Augmented Generation (RAG)
Incorporating RAG techniques that leverage external knowledge bases efficiently can minimize the need for extensive computational resources, further aligning with sustainability goals.
Conclusion
As the adoption of advanced AI technologies continues to transform the educational landscape, it is crucial to address the significant environmental impact of these systems. By implementing eco-friendly strategies across the entire lifecycle of educational AI applications, we can harness the benefits of these innovative tools while minimizing their ecological footprint. So that we can align the digital revolution in education with the imperative of environmental stewardship.