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description: A conference talk transcript covering the environmental impact of AI and machine learning, with practical guidance for reducing carbon emissions across the ML...
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# Green AI: Making Machine Learning Environmentally Sustainable • Charles Humble • YOW! 2025

**[GOTO Conferences](https://daily.dev/sources/goto)** · 42 min read · 1 upvotes · 0 comments

## Summary

A conference talk transcript covering the environmental impact of AI and machine learning, with practical guidance for reducing carbon emissions across the ML lifecycle. Key data points: Microsoft's carbon emissions rose 30% since 2020 and Google's over 50% since 2019, both attributed to generative AI. The IT industry's energy use is comparable to Brazil's national consumption. The talk walks through project planning (questioning whether AI is needed at all), data collection, training, and deployment phases. Techniques covered include demand shifting/shaping using carbon intensity APIs, federated learning, model distillation, quantization, pruning, and speculative decoding. The speaker argues that sustainability is not at odds with performance or cost — halving cloud bills and carbon emissions is often achievable just by turning off unused machines. Open source models are recommended for environmental benchmarking, privacy, and resilience.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.youtube.com/watch?v=5rFuv4SK7OY>

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#llm](https://daily.dev/tags/llm), [#sustainability](https://daily.dev/tags/sustainability)

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