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Algorithms Artificial Intelligence Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology Data Electrical engineering and computer science (EECS) machine learning MIT Schwarzman College of Computing Research Robotics School of Engineering

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.

Artificial Intelligence cybersecurity Data Electrical engineering and computer science (EECS) Electronics Light machine learning MIT Schwarzman College of Computing Photonics Privacy Research Research Laboratory of Electronics School of Engineering

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.

Algorithms Artificial Intelligence Computer science and technology Data Ethics Human-computer interaction IDSS Laboratory for Information and Decision Systems (LIDS) machine learning MIT Schwarzman College of Computing Research School of Engineering Video

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.

Algorithms Artificial Intelligence Broad Institute Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology Data Electrical Engineering & Computer Science (eecs) Health Care Imaging machine learning MIT Schwarzman College of Computing National Institutes of Health (NIH) Research School of Engineering

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.

Artificial Intelligence Computer science and technology Data Human-computer interaction Law machine learning Media Lab Privacy Research School of Architecture and Planning Technology and society

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.

Algorithms Alzheimer's Artificial Intelligence Data Imaging Lincoln Laboratory machine learning National Institutes of Health (NIH) Neurons Neuroscience open source Research Software

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.

Artificial Intelligence Computer science and technology Data Electrical Engineering & Computer Science (eecs) Human-computer interaction IDSS Laboratory for Information and Decision Systems (LIDS) machine learning MIT Schwarzman College of Computing Research School of Engineering Wind

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.

Algorithms Artificial Intelligence Data Electrical Engineering & Computer Science (eecs) Human-computer interaction machine learning MIT Schwarzman College of Computing MIT-IBM Watson AI Lab Research Research Laboratory of Electronics School of Engineering

Method prevents an AI model from being overconfident about wrong answers

More efficient than other approaches, the “Thermometer” technique could help someone know when they should trust a large language model.

Algorithms Artificial Intelligence Computer science and technology Data Electrical Engineering & Computer Science (eecs) Human-computer interaction Laboratory for Information and Decision Systems (LIDS) machine learning MIT Schwarzman College of Computing Research School of Engineering

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.

Algorithms Artificial Intelligence Computer science and technology Data Human-computer interaction IDSS Laboratory for Information and Decision Systems (LIDS) machine learning Mechanical engineering MIT Schwarzman College of Computing Research School of Engineering

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.