Inroads to personalized AI trip planning
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.
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.
With demand for cement alternatives rising, an MIT team uses machine learning to hunt for new ingredients across the scientific literature.
This new machine-learning model can match corresponding audio and visual data, which could someday help robots interact in the real world.
A new method from the MIT-IBM Watson AI Lab helps large language models to steer their own responses toward safer, more ethical, value-aligned outputs.
A new method lets users ask, in plain language, for a new molecule with certain properties, and receive a detailed description of how to synthesize it.
The framework helps clinicians choose phrases that more accurately reflect the likelihood that certain conditions are present in X-rays.
This new framework leverages a model’s reasoning abilities to create a “smart assistant” that finds the optimal solution to multistep problems.
Researchers fuse the best of two popular methods to create an image generator that uses less energy and can run locally on a laptop or smartphone.
Annual award honors early-career researchers for creativity, innovation, and research accomplishments.
A new study shows LLMs represent different data types based on their underlying meaning and reason about data in their dominant language.
Researchers propose a simple fix to an existing technique that could help artists, designers, and engineers create better 3D models.
An AI method developed by Professor Markus Buehler finds hidden links between science and art to suggest novel materials.
MIT and IBM researchers are creating linkage mechanisms to innovate human-AI kinematic engineering.
“Co-LLM” algorithm helps a general-purpose AI model collaborate with an expert large language model by combining the best parts of both answers, leading to more factual responses.