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

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.

A new framework from the MIT-IBM Watson AI Lab supercharges language models, so they can reason over, interactively develop, and verify valid, complex travel agendas.

End-of-life planning today includes making sure your loved ones have access to digital photos, email archives and social media accounts.

The system automatically learns to adapt to unknown disturbances such as gusting winds.

Trained with a joint understanding of protein and cell behavior, the model could help with diagnosing disease and developing new drugs.

Researchers share the design and implementation of an incentive-based Space Sustainability Rating.

Words like “no” and “not” can cause this popular class of AI models to fail unexpectedly in high-stakes settings, such as medical diagnosis.

“IntersectionZoo,” a benchmarking tool, uses a real-world traffic problem to test progress in deep reinforcement learning algorithms.

Using diagrams to represent interactions in multipart systems can provide a faster way to design software improvements.