Deploying Green AI to Address Environment Issues Across Globe

Advances in AI have given solutions to several business and non-business problems. Now is the high time when technology should come forward to do something beneficial for the environment. According to certain reports, business decision-makers believe in the potential of AI that it can transform environment sustainability for good.

As noted by the Tech Republic, environmental problems typically involve complex processes that scientists do not yet fully understand, and for which we have limited available resources, said Bistra Dilkina, associate director of the USC Center for AI in Society and a member of the Association for Computing Machinery. With advances in machine learning and deep learning, we can now tap the predictive power of AI to make better data-driven models of environmental processes improve our ability to study current and future trends, including water availability, ecosystems wellbeing, and pollution, she added.

AI can also play a key role in enhancing environmental decision and policy-making work, by bringing an algorithmic approach to that work, Dilkina said.

Moreover, the conversation began with a recent study from the Allen Institute for AI that argued for the prioritization of "Green AI" efforts that focus on the energy efficiency of AI systems.

What is Green AI?

The term Green AI refers to AI research that yields novel results without increasing computational cost, and ideally reducing it. Whereas Red AI has resulted in rapidly escalating computational (and thus carbon) costs, Green AI has the opposite effect. If measures of efficiency are widely accepted as important evaluation metrics for research alongside accuracy, then researchers will have the option of focusing on the efficiency of their models with a positive impact on both the environment and inclusiveness.

The vision of Green AI raises many exciting research directions that help to overcome the inclusiveness challenges of Red AI. Progress will reduce the computational expense with a minimal reduction in performance, or even improve performance as more efficient methods are discovered. Also, it would seem that Green AI could be moving us in a more cognitively plausible direction as the brain is highly efficient. The report notes, "It's important to reiterate that we see Green AI as a valuable option, not an exclusive mandate—of course, both Green AI and Red AI have contributions to make. We want to increase the prevalence of Green AI by highlighting its benefits, advocating a standard measure of efficiency. Below, we point to a few important green research directions and highlight a few open questions. Research on building space or time-efficient models is often motivated by fitting a model on a small device (such as a phone) or fast enough to process examples in real-time, such as image captioning for the blind."

AI for Environment in 2020

The 2020s may see incredible advances in AI, but in terms of infrastructure and efficient use of the energy, we're still in the pioneer age. As AI research progresses, we must insist that the best platforms, tools, and methodologies for building models are easy to access and reproducible. That will lead to continuous improvements in energy-efficient AI.