E-Waste Challenges of Generative Artificial Intelligence
Generative AI contributes to growing e-waste, but circular economy strategies could significantly reduce its environmental impact.
AI & Environment Resource HubMining and material supply chains through hardware reuse and electronic waste.
Browse all 136 resources →Counts reflect provisional metadata classification, not measured environmental impact. A record may belong to multiple topics.
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Research papers and scholarly literature on impacts, applications, and methods.
Generative AI contributes to growing e-waste, but circular economy strategies could significantly reduce its environmental impact.
A globally consolidated asset-level dataset for cement production, incorporating plant age and raw material sourcing, enhances emissions tracking, revealing inefficiencies in supply chains while leveraging geospatial computer vision and Large Language Models for comprehensive industry analysis.
Phi-1, a compact 1.3B parameter Transformer-based language model for code, achieves strong performance on HumanEval (50.6%) and MBPP (55.5%) despite its small scale, benefiting from high-quality web data and GPT-3.5-generated training materials.
This study applies machine learning, including deep learning with LSTM, to classify and predict Dhaka's Air Quality Index (AQI), incorporating daily temperature as a parameter and demonstrating optimal AQI forecasting performance.
Missing metadata is a curation task, not evidence of absent research or activity. The Resource Hub does not currently contain a validated incident dataset, expert survey, or measurements of net environmental benefit.