Analyses & Studies

Addressing AI's Carbon Footprint: Promising Solutions Ahead 

While artificial intelligence (AI) holds immense potential for addressing various challenges, including the climate crisis, its energy consumption poses a significant environmental concern. The infrastructure supporting AI, such as data centres, contributes to its substantial carbon footprint. 

However, innovative approaches to AI development offer hope for reducing its environmental impact. Two promising technologies, spiking neural networks and lifelong learning, show potential in this regard. 

AI systems undergo two main phases: training and inference. During training, datasets are used to optimize the system, while inference involves generating predictions based on new data. 

For instance, training an AI for self-driving cars requires extensive datasets of driving scenarios. Following training, the AI can make predictions for effective manoeuvres. While artificial neural networks (ANN) underpin most AI systems, advancements in spiking neural networks and lifelong learning offer avenues for mitigating AI's carbon footprint. 

By embracing these technological innovations, the AI community can work towards more sustainable solutions, ensuring that AI contributes positively to addressing global challenges without exacerbating environmental issues. 

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