<span class="vcard">Adam Zewe | MIT News</span>
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Algorithms Artificial Intelligence Computer modeling Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology Data Electrical engineering and computer science (EECS) Faculty machine learning MIT Schwarzman College of Computing Profile School of Engineering Social networks

Modeling relationships to solve complex problems efficiently

Associate Professor Julian Shun develops high-performance algorithms and frameworks for large-scale graph processing.

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AI simulation gives people a glimpse of their potential future self

By enabling users to chat with an older version of themselves, Future You is aimed at reducing anxiety and guiding young people to make better choices.

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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.

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3 Questions: Should we label AI systems like we do prescription drugs?

Researchers argue that in health care settings, “responsible use” labels could ensure AI systems are deployed appropriately.

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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.

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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.

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3 Questions: How to prove humanity online

AI agents could soon become indistinguishable from humans online. Could “personhood credentials” protect people against digital imposters?

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.

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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.