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The Environmental Impact of AI: A Case Study of Water Consumption by Chat GPT

Authors: George, A.Shaji; A.S.Hovan George; A.S.Gabrio Martin;

The Environmental Impact of AI: A Case Study of Water Consumption by Chat GPT

Abstract

As AI is becoming more a part of our lives, people are starting to worry about the negative consequences it might have on the environment. One of the major issues is its high water consumption. The water[21] consumption of AI models, including Chat GPT, is a major concern and must be managed effectively to reduce environmental harm. This document examines the amount[15] of water that is utilized by Chat GPT and other AI models and investigates the impact that it may have on the environment, as well as possible solutions to control their water usage. The study further considers the plausibility and usefulness of these approaches. The findings imply that although water usage of AI systems is significantly lower compared to other industries, it is still a matter of concern. AI models can have a significant water footprint, but this can be reduced by taking certain measures such as improving energy efficiency, utilizing renewable energy sources, optimizing algorithms and implementing strategies to conserve water. Despite the potential of these solutions, there are still issues to be addressed, such as the expense associated with implementation, and further research is required for optimum utilization. In conclusion, this document emphasizes the relevance of recognizing the water footprint caused by AI models, giving important details regarding potential solutions to minimize their environmental impact.

Related Organizations
Keywords

Water footprint, Artificial intelligence, Data centers, Energy efficiency, Cooling systems, Sustainability, Environmental impact, Resource management.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
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18
Top 10%
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447
253
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