Artificial Intelligence Makes Energy Demand More Complex — And More Achievable
Artificial intelligence, a field known for its expanding uses across society, is also increasingly notorious for the massive amount of energy it needs to function.
In a 2024 paper, researchers from Carnegie Mellon University and machine learning development corporation Hugging Face found that generative AI systems could use as much as 33 times more energy to complete a task than task-specific software would.
“The climate and sustainability challenge can be overwhelming in the amount of new clean technology that we have to deploy and develop, and the ways that the energy system has to evolve,” said Costa Samaras, head of the university-wide Wilton E. Scott Institute for Energy Innovation. “The scale of the challenge alone can be overwhelming to folks.”
However, Carnegie Mellon University’s standing commitment to the United Nations' Sustainable Development Goals and its position as a nationally recognized leader in technologies like artificial intelligence mean that it is uniquely positioned to address growing concerns around energy demand, climate resilience and social good.
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Trustee Professor of Civil and Environmental Engineering
Costa Samaras's research focuses on the pathways to clean, climate-safe, equitable, and secure energy and infrastructure systems.
- Transportation Systems
- Autonomous Driving
- Climate and Energy Decision Making
- Engineering and Public Policy
- Environmental Engineering