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Artificial intelligence (AI) is rapidly transforming industries, but its growth raises significant environmental concerns. The training and deployment of large-scale AI models demand substantial computational resources, leading to increased energy consumption and carbon emissions. A comprehensive study by Tao Xie et al. explores sustainable practices in data acquisition, processing, and system optimization to mitigate these environmental impacts.
Data acquisition is crucial for AI systems, yet it poses sustainability challenges due to the environmental footprint of large datasets. The study highlights innovative solutions like crowdsourcing and active learning to enhance data collection while minimizing ecological costs. Additionally, the research emphasizes the importance of automation and advanced techniques to improve data processing efficiency and reduce waste.
• AI model training can produce carbon emissions equivalent to five cars' lifetime emissions.
• Innovative solutions like crowdsourcing can enhance data collection sustainably.
Crowdsourcing enables scalable data collection but requires quality control for reliability.
Synthetic data generation helps train AI models on rare diseases, reducing real-world data needs.
DSAs optimize hardware for specific AI tasks, significantly reducing energy consumption.
Amazon Mechanical Turk is a crowdsourcing platform that facilitates scalable data collection.
Waymo uses synthetic data to train and test AI models for autonomous vehicles.
NVIDIA employs synthetic data in diverse scenarios to enhance AI model training efficiency.
Google's Tensor Processing Units (TPUs) are designed to optimize AI task performance and energy use.
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