A faster, better way to train general-purpose robots
Inspired by large language models, researchers develop a training technique that pools diverse data to teach robots new skills.
Making it easier to verify an AI model’s responses
By allowing users to clearly see data referenced by a large language model, this tool speeds manual validation to help users spot AI errors.
Study finds mercury pollution from human activities is declining
Models show that an unexpected reduction in human-driven emissions led to a 10 percent decline in atmospheric mercury concentrations.
Modeling relationships to solve complex problems efficiently
Associate Professor Julian Shun develops high-performance algorithms and frameworks for large-scale graph processing.
How AI is improving simulations with smarter sampling techniques
MIT CSAIL researchers created an AI-powered method for low-discrepancy sampling, which uniformly distributes data points to boost simulation accuracy.
New security protocol shields data from attackers during cloud-based computation
The technique leverages quantum properties of light to guarantee security while preserving the accuracy of a deep-learning model.
Research quantifying “nociception” could help improve management of surgical pain
New statistical models based on physiological data from more than 100 surgeries provide objective, accurate measures of the body’s subconscious perception of pain.
Study: AI could lead to inconsistent outcomes in home surveillance
Researchers find large language models make inconsistent decisions about whether to call the police when analyzing surveillance videos.
Protecting the rights of internet users, in Mexico and worldwide
PhD student Mariel García-Montes researches the internet’s far-reaching impact on society, especially regarding privacy and young people.
A fast and flexible approach to help doctors annotate medical scans
“ScribblePrompt” is an interactive AI framework that can efficiently highlight anatomical structures across different medical scans, assisting medical workers to delineate regions of interest and abnormalities.
Study: Transparency is often lacking in datasets used to train large language models
Researchers developed an easy-to-use tool that enables an AI practitioner to find data that suits the purpose of their model, which could improve accuracy and reduce bias.
Making a measurable economic impact
Saeed Miganeh’s work at MIT is helping him answer important questions about designing effective programs for poverty mitigation and economic growth in African countries.
New open-source tool helps to detangle the brain
The software tool NeuroTrALE is designed to quickly and efficiently process large amounts of brain imaging data semi-automatically.
MIT researchers use large language models to flag problems in complex systems
The approach can detect anomalies in data recorded over time, without the need for any training.
New framework empowers pavement life-cycle decision-making while reducing data collection burden
MIT Concrete Sustainability Hub research presents a streamlined pavement life-cycle assessment framework to enable a large set of stakeholders to conduct environmental analysis of pavements.