Algorithms
Algorithms

Algorithms

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

Robotic helper making mistakes? Just nudge it in the right direction

New research could allow a person to correct a robot’s actions in real-time, using the kind of feedback they’d give another human.

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MIT spinout maps the body’s metabolites to uncover the hidden drivers of disease

ReviveMed uses AI to gather large-scale data on metabolites — molecules like lipids, cholesterol, and sugar — to match patients with therapeutics.

Algorithms Artificial Intelligence Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology Data Electrical engineering and computer science (EECS) Language machine learning MIT Schwarzman College of Computing MIT-IBM Watson AI Lab Research School of Engineering

Like human brains, large language models reason about diverse data in a general way

A new study shows LLMs represent different data types based on their underlying meaning and reason about data in their dominant language.

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AI model deciphers the code in proteins that tells them where to go

Whitehead Institute and CSAIL researchers created a machine-learning model to predict and generate protein localization, with implications for understanding and remedying disease.

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Puzzling out climate change

Accenture Fellow Shreyaa Raghavan applies machine learning and optimization methods to explore ways to reduce transportation sector emissions.

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Streamlining data collection for improved salmon population management

Assistant Professor Sara Beery is using automation to improve monitoring of migrating salmon in the Pacific Northwest.

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3 Questions: Modeling adversarial intelligence to exploit AI’s security vulnerabilities

MIT CSAIL Principal Research Scientist Una-May O’Reilly discusses how she develops agents that reveal AI models’ security weaknesses before hackers do.

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New training approach could help AI agents perform better in uncertain conditions

Sometimes, it might be better to train a robot in an environment that’s different from the one where it will be deployed.

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Expanding robot perception

Associate Professor Luca Carlone is working to give robots a more human-like awareness of their environment.

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Explained: Generative AI’s environmental impact

Rapid development and deployment of powerful generative AI models comes with environmental consequences, including increased electricity demand and water consumption.