Artificial Intelligence

For more information about Artificial Intelligence at UC San Diego, visit the AI webpage.

The Latest

Researchers to Develop Predictive Model for Opioid Addiction in High-Risk Patients

UC San Diego School of Medicine part of $50M initiative to use predictive AI to help fight opioid abuse.

Advanced AI-Based Techniques Scale-up Solving Complex Combinatorial Optimization Problems

A framework based on advanced AI techniques can solve complex, computationally intensive problems faster and in a more more scalable way than state-of-the-art methods, according to a study led by engineers at the University of California San Diego.

UC San Diego Startup Incubator Helps Company Expand Wireless Reach

“The problem World Mobile is solving has global implications,” says Stuart Volkow, World Mobile program manager. “Those without [connectivity] are doomed to relative poverty.”

Franklin Antonio Hall Sparks Research Collaborations

From improving tools for robot-assisted surgery to studying how humans prefer to interact with robots, collaborations across different departments at the Jacobs School of Engineering are in the spotlight in this year’s accepted papers for the 2024 IEEE International Conference on Robotics and Automation.

National AI Researchers Have the Chance to Access San Diego Supercomputer Center Resources through NSF NAIRR Pilot

The U.S. National Science Foundation (NSF) and the Department of Energy have announced the first 35 projects that will be supported with computational time through the National Artificial Intelligence Research Resource (NAIRR) Pilot, marking a significant milestone in fostering responsible AI research across the nation. The initial call for applicants was issued in January 2024.

AI search, circuitry and computing elements superimposed over hands at a laptop keyboard.

Science has an AI problem. This group says they can fix it.

AI holds the potential to help doctors find early markers of disease and accelerate research on other important scientific advances. But a growing body of evidence has revealed deep flaws in how machine learning is used in science, a problem that has swept through dozens of fields and implicated thousands of erroneous papers.