Improving health, one machine learning system at a time
Marzyeh Ghassemi works to ensure health-care models are trained to be robust and fair.
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Marzyeh Ghassemi works to ensure health-care models are trained to be robust and fair.
The technique could make AI systems better at complex tasks that involve variability.
By sidestepping the need for costly interventions, a new method could potentially reveal gene regulatory programs, paving the way for targeted treatments.
Researchers show that even the best-performing large language models don’t form a true model of the world and its rules, and can thus fail unexpectedly on similar tasks.
A new method called Clio enables robots to quickly map a scene and identify the items they need to complete a given set of tasks.
Researchers argue that in health care settings, “responsible use” labels could ensure AI systems are deployed appropriately.
Researchers find large language models make inconsistent decisions about whether to call the police when analyzing surveillance videos.
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.