AI / ML comparing different models and design choices based on their estimated carbon footprint and choosing the most sustainable options within the scope of acceptable model performance .”
Developing more energy-efficient algorithms and optimising AI infrastructure , meanwhile , can significantly reduce the environmental impact of AI systems . Karthik Sj , General Manager , AI at LogicMonitor , recommends specific techniques involving streamlining AI models to reduce their computational requirements without significantly impacting their performance , thus lowering energy consumption and carbon emissions . “ Companies can implement techniques like pruning and quantisation to create more efficient AI models that require less computational power ,” he says .
Investing in and utilising green data centres powered by renewable energy also can dramatically reduce the carbon footprint of AI operations . Many tech giants are leading the way in this area . “ Google has committed to operating carbon-free by 2030 ,” Sujata Kukreja points out , “ and uses AI to optimise the energy use of its data centres , achieving a 30 % increase in efficiency .”
Conducting comprehensive life cycle analyses of AI systems helps identify areas for improvement in sustainability across all stages of development and deployment . Karthik Sj emphasises the importance of this holistic approach : “ Incorporating lifecycle analysis ensures
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