The drive for Green AI is gaining momentum, but what does it require to push forward?
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Rapid developments in AI have triggered digital advancements in almost every industry. The technology is capable of construing data contextually to provide requested information, supply analysis, and push events based on findings. Simultaneously, businesses need to meet social, investor and regulatory requirements regarding how they use advanced technologies like AI. Significantly, it is also crucial that organizations must commit to using the technology with a purpose, which leads to the way of sustainable development.
In its recent study, the Allen Institute for AI argued the prioritization of “Green AI” efforts that focus on the energy efficiency of AI systems. The study was based on many high-profile advances in AI that have wavered carbon footprints. According to an OpenAI blog post, the amount of compute required for the largest AI training runs has been surged exponentially by 300,000 times since 2012. This increase in computational requirements leads to the pessimistic environmental impacts of artificial intelligence.
Associate Director of the USC Center for AI in Society and a member of the Association for Computing Machinery, Bistra Dilkina said that environmental problems typically involve complex processes that scientists do not yet fully understand, and for which we have limited available resources. 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 to improve our ability to study current and future trends, including water availability, ecosystem wellbeing, and pollution.
AI can also play a key role in enhancing environmental decisions and policy-making work, by bringing an algorithmic approach to that work, she added.
What is Green AI?
Green AI refers to the part of a broader, long-standing interest in environmentally-friendly scientific research. AI research can be computationally expensive in numerous ways; however, each provides opportunities for efficient improvements. For example, research papers could be required to plot accuracy as a function of computational cost and training set size, giving a baseline for more data-efficient research in the future. Reporting the computational price tag of finding, training, and running models is a key Green AI practice.
Solving Sustainability Challenges with AI
AI has the potential to solve several environmental problems. Google, for instance, uses its own AI expertise to improve its energy efficiency, leveraging DeepMind’s machine learning capabilities that have reduced the amount of energy needed to cool its data centers by 40%. […]