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.

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.

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.

By eliminating redundant computations, a new data-driven method can streamline processes like scheduling trains, routing delivery drivers, or assigning airline crews.

This new framework leverages a model’s reasoning abilities to create a “smart assistant” that finds the optimal solution to multistep problems.