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.
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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 book from Professor Munther Dahleh details the creation of a unique kind of transdisciplinary center, uniting many specialties through a common need for data science.
The system automatically learns to adapt to unknown disturbances such as gusting winds.
PhD student Sarah Alnegheimish wants to make machine learning systems accessible.
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Sendhil Mullainathan brings a lifetime of unique perspectives to research in behavioral economics and machine learning.
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“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.
New theoretical approach for generating quantum states could lead to improved accuracy and reliability of information and decision systems.
Engineers developed a planning tool that can help independent entities decide when they should invest in joint projects.
Annual award honors early-career researchers for creativity, innovation, and research accomplishments.