A faster way to estimate AI power consumption
The “EnergAIzer” method generates reliable results in seconds, enabling data center operators to efficiently allocate resources and reduce wasted energy.
The “EnergAIzer” method generates reliable results in seconds, enabling data center operators to efficiently allocate resources and reduce wasted energy.
MIT Sea Grant works with the Woodwell Climate Research Center and other collaborators to demonstrate a deep learning-based system for fish monitoring.
From early motion-sensing platforms to environmental monitoring, the professor and head of the Program in Media Arts and Sciences has turned decades of cross-disciplinary research into real-world impact.
AquaCulture Shock program, in collaboration with MIT-Scandinavia MISTI, offers international internships for AI and autonomy in aquaculture
MIT PhD student and CSAIL researcher Justin Kay describes his work combining AI and computer vision systems to monitor the ecosystems that support our planet.
Four new professors join the Department of Architecture and MIT Media Lab.
The LOBSTgER research initiative at MIT Sea Grant explores how generative AI can expand scientific storytelling by building on field-based photographic data.
With demand for cement alternatives rising, an MIT team uses machine learning to hunt for new ingredients across the scientific literature.
Assistant Professor Sara Beery is using automation to improve monitoring of migrating salmon in the Pacific Northwest.
Rapid development and deployment of powerful generative AI models comes with environmental consequences, including increased electricity demand and water consumption.