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A study from Caltech and UC Riverside highlights the severe health implications of AI's energy consumption, predicting 1,300 premature deaths in the U.S. by 2030 and $20 billion in health costs annually. The research, titled 'The Unpaid Toll: Quantifying the Public Health Impact of AI,' emphasizes the overlooked environmental consequences of AI advancements. As data centers proliferate, their energy demands lead to increased air pollution from power plants and diesel generators.
The findings reveal that low-income communities near these facilities are disproportionately affected, facing significant health risks from harmful emissions. The study calls for greater corporate accountability from tech companies, urging them to report not only carbon emissions but also harmful air pollutants. This urgent public health issue necessitates immediate action to mitigate the environmental damage caused by AI technologies.
• AI's energy demands could lead to 1,300 premature deaths annually by 2030.
• Training large AI models generates pollution equivalent to thousands of car trips.
AI energy consumption refers to the electricity required to power AI systems, which significantly impacts air quality.
Air pollution from AI-related activities includes harmful emissions that affect public health, especially in vulnerable communities.
1, requires substantial energy for training, contributing to environmental pollution.
Meta develops AI technologies, including large language models, which significantly contribute to energy consumption and pollution.
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