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The Hidden Environmental Costs of Processing Unstructured Data in AI Systems
- AI tools processing unstructured data require significant energy consumption, posing environmental challenges.
- Research shows that over 80% of global data remains unstructured, demanding complex preprocessing.
- Unstructured data processing relies on energy-intensive hardware like GPUs, increasing power consumption.
- AI data centers consume vast amounts of electricity, with the largest ones having consumption equivalent to 1 lakh households.
- Scalable storage solutions and high-performance hardware for unstructured data workflows further escalate energy costs.
- AI's power consumption extends beyond training models to processing unstructured data, leading to infrastructure strain.
- The proliferation of AI data centers indicates a pressing need to address power equipment supply limitations.
- Efficiency in AI hardware and models could potentially reduce data centers' electricity demand by 20% by 2035.
- Transparent energy reporting in AI is crucial to ensure developers prioritize sustainability over scale and popularity.
- Sustainable measures like selective data retention and efficient data curation can mitigate excessive energy usage in AI systems.
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