Environment
Environment

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.

Augmenting citizen science with computer vision for fish monitoring

MIT Sea Grant works with the Woodwell Climate Research Center and other collaborators to demonstrate a deep learning-based system for fish monitoring.

How Joseph Paradiso’s sensing innovations bridge the arts, medicine, and ecology

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.

MIT Sea Grant students explore the intersection of technology and offshore aquaculture in Norway

AquaCulture Shock program, in collaboration with MIT-Scandinavia MISTI, offers international internships for AI and autonomy in aquaculture

3 Questions: How AI is helping us monitor and support vulnerable ecosystems

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.

School of Architecture and Planning welcomes new faculty for 2025

Four new professors join the Department of Architecture and MIT Media Lab.

Merging AI and underwater photography to reveal hidden ocean worlds

The LOBSTgER research initiative at MIT Sea Grant explores how generative AI can expand scientific storytelling by building on field-based photographic data.

AI stirs up the recipe for concrete in MIT study

With demand for cement alternatives rising, an MIT team uses machine learning to hunt for new ingredients across the scientific literature.

Streamlining data collection for improved salmon population management

Assistant Professor Sara Beery is using automation to improve monitoring of migrating salmon in the Pacific Northwest.

Explained: Generative AI’s environmental impact

Rapid development and deployment of powerful generative AI models comes with environmental consequences, including increased electricity demand and water consumption.