MIT students are making AI more environmental friendly

AI-powered models can monitor greenhouse gasses and predict extreme weather events. But what is the environmental cost of powering such energy-heavy models?

Humanity is possibly at the precipice of creating the most powerful technology yet – artificial general intelligence – when machine learning models will be able to consciously think and emote like human beings. But as this technology stands today, can it address the most pertinent issue of our times – climate change?

"Energy-aware computing is not really a research area, because everyone's been holding on to their data," says Vijay Gadepally, senior staff in the LLSC who leads energy-aware research efforts. "Somebody has to start, and we're hoping others will follow." By capping the power, the researchers were able to reduce the energy consumption of an AI model by 12-15%. But there was a downside to this. The models now took more time to train. In an experiment, the team trained Google’s BERT language model and limited its GPU power to 150 watts. This saw a two-hour increase in training time .The team also built software that lets data center owners set limits across their system or on a job-by-job basis.

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