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Technological advancement is a must in most industries, including machine learning. Whether it is self-driving cars, automated translating, image recognition and more, machine learning models are only getting more complex, sophisticated and effective. Unfortunately, as these models develop so do their carbon footprint. But how is machine learning really having an impact on our environment? 

Some experts have already been developing tools capable of monitoring the carbon impact behind machine learning models, as well as laying down the groundwork for mitigation approaches like carbon-aware computing. According to thenewstack.com, most of these tools measuring AI’s carbon footprint are still in relatively early stages of development. 

A team of researchers from several universities, amongst them the Hebrew University, have made it their mission to develop a more sophisticated approach to accurately measure operational carbon emissions of popular AI models. 

During their experiments, the team came up with some eye-opening findings. For instance, they found that the carbon emissions generated in training some of the lighter AI models were equivalent to that of charging a cellphone, whereas one of the larger models tested was trained to only 13% completion, and yet produced a “staggering” amount of carbon equal to powering a house for a year in the United States. 

The team found that the biggest factor in reducing emissions was selecting the best geographical location. The team found that emissions could be halved by training AIs using renewable energy sources located in countries like Norway or France.

The researchers found that even the time of day when the training occurred had an impact; training a model in Washington state during the night resulted in lower emissions, as that is when electricity is produced by hydroelectric power alone, rather than being mixed with energy from gas-fired power stations during the day.

While the team’s work focuses solely on the operational carbon emissions of training AI models and doesn’t take into account the carbon emissions associated with building the hardware, cooling data centers and so on, the team nevertheless pointed out that more comprehensive carbon-aware approaches will become vital in ensuring the future sustainability of machine learning models.

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