Researchers developed a way to make large language models more adaptable to challenging tasks like strategic planning or process optimization.

Developed to analyze new semiconductors, the system could streamline the development of more powerful solar panels.

In an analysis of over 160,000 transplant candidates, researchers found that race is linked to how likely an organ offer is to be accepted on behalf of a patient.

By leveraging reflections from wireless signals like Wi-Fi, the system could allow robots to find and manipulate items that are blocked from view.

MIT CSAIL researchers combined GenAI and a physics simulation engine to refine robot designs. The result: a machine that out-jumped a robot designed by humans.

Rising seniors Avani Ahuja, Julianna Lian, Jacqueline Prawira, and Alex Tang are honored for their academic achievements.

Researchers find nonclinical information in patient messages — like typos, extra white space, and colorful language — reduces the accuracy of an AI model.

Presentations targeted high-impact intersections of AI and other areas, such as health care, business, and education.

The low-cost, scalable technology can seamlessly integrate high-speed gallium nitride transistors onto a standard silicon chip.

In a new study, researchers discover the root cause of a type of bias in LLMs, paving the way for more accurate and reliable AI systems.