The article discusses the environmental impacts of AI technology, particularly focusing on the research led by Shaolei Ren and his team at UC Riverside. They analyze how the energy consumption associated with AI, particularly large models like GPT-3, disproportionately affects disadvantaged regions. The research aims to develop geographical load balancing solutions that can minimize the environmental costs on these vulnerable areas. This interview highlights the ongoing conversation about the need for cloud service providers and app developers to consider environmental implications in their operations to foster equity in AI utilization.
AI models, especially large generative models like GPT-3, are typically trained on large clusters of power-hungry computers. These models can consume significant amounts of energy, leading to higher carbon emissions.
Our research aims to develop geographical load balancing strategies to ensure that AI's environmental costs do not unfairly burden already disadvantaged regions, promoting a more equitable approach to AI deployment.
Collection
[
|
...
]