It’s Time to Prioritize Energy Efficient Green Artificial Intelligence

It’s Time to Prioritize Energy Efficient Green Artificial Intelligence

As artificial intelligence research continues to progress, organizations must integrate best platforms, tools and methodologies to build models that are easy to access and reproducible, leading to continuous improvements in energy-efficient Green AI.

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.

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