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.
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The approach can detect anomalies in data recorded over time, without the need for any training.
Introducing structured randomization into decisions based on machine-learning model predictions can address inherent uncertainties while maintaining efficiency.
A new study shows someone’s beliefs about an LLM play a significant role in the model’s performance and are important for how it is deployed.
The model could help clinicians assess breast cancer stage and ultimately help in reducing overtreatment.
A new technique enables users to compare several large models and choose the one that works best for their task.
PhD student Xinyi Zhang is developing computational tools for analyzing cells in the age of multimodal data.
More accurate uncertainty estimates could help users decide about how and when to use machine-learning models in the real world.
Twelve faculty members have been granted tenure in six units across MIT’s School of Engineering.
These models, which can predict a patient’s race, gender, and age, seem to use those traits as shortcuts when making medical diagnoses.
In the new economics course 14.163 (Algorithms and Behavioral Science), students investigate the deployment of machine-learning tools and their potential to understand people, reduce bias, and improve society.
Fifteen new faculty members join six of the school’s academic departments.
MIT professors Roger Levy, Tracy Slatyer, and Martin Wainwright appointed to the 2024 class of “trail-blazing fellows.”
The MIT Schwarzman College of Computing building will form a new cluster of connectivity across a spectrum of disciplines in computing and artificial intelligence.
MIT spinout DataCebo helps companies bolster their datasets by creating synthetic data that mimic the real thing.