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Given that AI, like any other technology, requires an energy supply, how do we assess its impact on the organization’s carbon footprint? Careful attention is needed to separate the myths attached to AI’s energy impact and the actual reality.
In organizations today, AI has yet to reach maturity: only 13% of companies have implemented AI on an industrial scale. However, while its use may not be widespread, the fact remains that AI consumes energy like any other technology and will do so once it is a mature technology that is routinely used across organizations:
This is where the carbon footprint cost can emerge:
But to build a precise picture of the energy consumption of AI in business, we need to bear in mind a number of reality checks: There are many ways to implement AI and some projects do not involve the training of models. In fact, they only require a dozen parameters to be adjusted. Thanks to statistical and descriptive analyses – coupled with a good understanding of business requirements – it is now possible, using techniques such as regression or clustering, to achieve a range of goals, from optimizing warehouse stock to detecting fraud in finance or public services. In our recently published research, we tried to ascertain the GHG footprint of some of the popular AI use cases. Our analysis shows, for instance, that the GHG emissions produced in training and executing these AI systems amount to only a few kilograms (1–10) of CO2 equivalent.5 This is very small in comparison with the overall GHG emissions of large organizations which typically range in millions of tons of CO2 equivalent per year. We must keep in mind that the need to train complex models only applies to a few of the AI solutions now deployed at scale. When more complex neural network techniques must be used – such as in image recognition – the modelsused are often open source ones that have already been trained. Transfer learning techniques are then applied so that the results obtained with the training data are adapted to the client’s data. This means you can avoid the need to retrain a model. This technological “recycling” helps to limit the impact on energy resources of a massive deployment of artificial intelligence projects in companies.
Broadly speaking, AI solutions, digitization, and increasing data volumes (including the production of new data) rightly raise questions about the future energy impact of tech and data innovations. But one reality is clear: the carbon footprint of technologies does not follow the samegrowth curve as data volume,6 not least because significant advances in energy efficiency have helped to limit the impact.
Towards “sustainable AI” and a convergence of technological and ecological transitions With a clear sense of what is myth and reality in terms of AI’s energy impact, we can turn to the bigger question: how can we accelerate the age of green AI. Researchers and engineers around the world are working to optimize the energy consumption of AI solutions. This ambition is driven by a clear purpose: for AI to achieve the same level of performance as human intelligence (i.e., being capable of performing thousands of trillions of operations per second) while only consuming 20 watts of energy.
There are many initiatives underway to achieve that goal:
As well as minding its own footprint, AI is also a transformational technology that has the power to positively influence sustainabledevelopment. With an almost 80% positive impact on sustainable development goals,12 AI is a critical technology in implementing climate strategies to reduce organizations’ greenhouse gas emissions by an average of 13%.
This focus on positive outcomes for society as a whole reflects the importance of ethical AI. Key stakeholders – from governments to academic experts – agree on the need to adopt an ethical approach14 to trusted artificial intelligence. This means improving people’s lives while not exacerbating existing problems or creating new ones. In other words, the colossal power of artificial intelligence must be placed at the service of sustainable development.
This points to a reality where AI is used across the value chain to help companies achieve their sustainable goals: designing new environmentally responsible products, calculating carbon impact from resource extraction to distribution, optimizing logistics, improving energy efficiency at factories and warehouses, and reducing inefficiencies and waste through the promotion of recycling and the circular economy. The reality is that while AI has an energy impact, the opportunity for ethical and green AI to drive a sustainable future far outweighs any immediate cost.
Just as AI must be ethical, it must be sustainable.
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