Study: When allocating scarce resources with AI, randomization can improve fairness
Introducing structured randomization into decisions based on machine-learning model predictions can address inherent uncertainties while maintaining efficiency.
Laboratory for Information and Decision Systems (LIDS)
Large language models don’t behave like people, even though we may expect them to
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.
AI model identifies certain breast tumor stages likely to progress to invasive cancer
The model could help clinicians assess breast cancer stage and ultimately help in reducing overtreatment.
How to assess a general-purpose AI model’s reliability before it’s deployed
A new technique enables users to compare several large models and choose the one that works best for their task.
When to trust an AI model
More accurate uncertainty estimates could help users decide about how and when to use machine-learning models in the real world.
School of Engineering welcomes new faculty
Fifteen new faculty members join six of the school’s academic departments.
A crossroads for computing at MIT
The MIT Schwarzman College of Computing building will form a new cluster of connectivity across a spectrum of disciplines in computing and artificial intelligence.
Using generative AI to improve software testing
MIT spinout DataCebo helps companies bolster their datasets by creating synthetic data that mimic the real thing.
Dealing with the limitations of our noisy world
Tamara Broderick uses statistical approaches to understand and quantify the uncertainty that can affect study results.
New AI model could streamline operations in a robotic warehouse
By breaking an intractable problem into smaller chunks, a deep-learning technique identifies the optimal areas for thinning out traffic in a warehouse.