Animals
Animals

Animals

Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) Animals Artificial Intelligence Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology computer-vision Data Ecology Electrical engineering and computer science (EECS) Environment machine learning Mechanical engineering MIT Schwarzman College of Computing MIT Sea Grant National Science Foundation (NSF) Oceanography and ocean engineering Research School of Engineering Undergraduate Research Opportunities Program (UROP) Video Volunteering, outreach, public service Water

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.

3-D Animals Artificial Intelligence Biology Cells computer-vision Imaging machine learning Mechanical engineering National Institutes of Health (NIH) Research School of Engineering Self-assembly

Deep-learning model predicts how fruit flies form, cell by cell

The approach could apply to more complex tissues and organs, helping researchers to identify early signs of disease.

Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) Algorithms Animals Artificial Intelligence Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology computer-vision Data Ecology Electrical engineering and computer science (EECS) Environment Graduate, postdoctoral Interview machine learning MIT Schwarzman College of Computing National Science Foundation (NSF) Pollution Research School of Engineering Students Sustainability

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.

Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) Algorithms Animals Artificial Intelligence computer-vision Data Ecology Electrical engineering and computer science (EECS) Environment Faculty Food machine learning Research School of Engineering Sustainability

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.

Algorithms Animals Artificial Intelligence Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology computer-vision Data Ecology Electrical engineering and computer science (EECS) machine learning MIT Schwarzman College of Computing Research School of Engineering

Ecologists find computer vision models’ blind spots in retrieving wildlife images

Biodiversity researchers tested vision systems on how well they could retrieve relevant nature images. More advanced models performed well on simple queries but struggled with more research-specific prompts.