Artificial intelligence (AI) is a rapidly growing technology that is being adopted by various industries to enhance their processes, products and services. But while AI can help organisations make quicker and more informed decisions, the energy consumption to train and power AI models is vast. Green AI can help.

Green AI refers to AI models with a reduced carbon footprint, yet without any significant compromise in either accessibility or performance. The process of “greening” AI involves an analysis of where the most energy-intensive areas are in operating an AI model, and then taking steps to reduce the consumption levels.

Understanding the environmental costs

Looking through an environmental lens, AI can help the sustainability efforts of a company by better managing energy allocations and enabling tasks to be completed more efficiently and with fewer resources. However, that doesn’t mean AI has no carbon footprint. Below are two examples which highlight why there is such a need for more adoption of Green AI:

AI pre-training – Before an AI model can be used, it must undergo an energy intensive “training process”, which involves processing a large amount of data. A study by MIT found that the energy consumption of training a single large AI model would generate waste equivalent to the total CO2 emissions of five cars across their entire lifecycles.
Computing power – With the increase in computing power available to developers, technologies such as machine learning are now widespread and used to train AI models. However, the specialised hardware that is necessary to run these processes consumes significant amounts of energy.

The benefits of assessing visuals

There are no hard and fast rules on the use of imagery beyond common sense and ensuring the communications aim is clear. It is important to gauge the tone of an ESG content piece and select an image that is appropriate and truly reflects the messaging.

Impact will be severely diminished by misrepresentation. For example, an oil company putting an image of wind farms set upon green fields on the front page of their ESG report – could be viewed cynically compared to a more realistic image like power storage.

First impressions will be generated by the image and its quality will make a difference to how this is received.

Greening AI is becoming a necessity

Green AI involves a wide range of energy saving measures and an approach that is gaining ground and support. Here are some of the areas in which this field is growing:

Feeding very specific but varied types of data to an AI model – Part of the issue facing today’s AI models is the vast amount of data they need during training. By developing models that are more focused and only require very specific datasets, such as those from video, text or audio data, a given model can reduce its computing power requirements and thereby become more energy efficient.
Only developing larger AI models when necessary – Smaller AI models are not only less energy intensive but are also more accessible and affordable to small and medium sized companies. Changing the development focus to meeting these needs is prudent as there isn’t a one-size-fits all and will lead to broader application of AI models that will have an impact on the economy.

We hope you found this article on the trend of Green AI insightful. If you would like to learn more about how Paradigm can help with your ESG or tech-related communications, please contact us.

How green AI models build sustainability into technology