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Algorithms Artificial Intelligence Cleaner industry Climate Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology Data DMSE Electrical engineering and computer science (EECS) Emissions Energy Environment Human-computer interaction machine learning MIT Schwarzman College of Computing School of Engineering Sustainability Sustainable computing Water

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.

Algorithms Artificial Intelligence Computer science and technology Data Electrical engineering and computer science (EECS) Faculty Health Care Laboratory for Information and Decision Systems (LIDS) machine learning MIT Schwarzman College of Computing MIT Sloan School of Management Profile School of Engineering social media Technology and society

Algorithms and AI for a better world

Assistant Professor Manish Raghavan wants computational techniques to help solve societal problems.

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

Ecologists find computer vision models’ blind spots in retrieving wildlife images

Biodiversity researchers tested vision systems on how well they could retrieve relevant nature images. More advanced models performed well on simple queries but struggled with more research-specific prompts.

Artificial Intelligence Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology Data Defense Advanced Research Projects Agency (DARPA) Electrical engineering and computer science (EECS) Human-computer interaction Laboratory for Information and Decision Systems (LIDS) machine learning MIT Schwarzman College of Computing National Science Foundation (NSF) Research School of Engineering

Researchers reduce bias in AI models while preserving or improving accuracy

A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.

Artificial Intelligence Computer science and technology Data machine learning Media Lab Research School of Architecture and Planning Technology and society

Study: Some language reward models exhibit political bias

Research from the MIT Center for Constructive Communication finds this effect occurs even when reward models are trained on factual data.

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

Enabling AI to explain its predictions in plain language

Using LLMs to convert machine-learning explanations into readable narratives could help users make better decisions about when to trust a model.

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

Citation tool offers a new approach to trustworthy AI-generated content

Researchers develop “ContextCite,” an innovative method to track AI’s source attribution and detect potential misinformation.

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Want to design the car of the future? Here are 8,000 designs to get you started.

MIT engineers developed the largest open-source dataset of car designs, including their aerodynamics, that could speed design of eco-friendly cars and electric vehicles.

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Photonic processor could enable ultrafast AI computations with extreme energy efficiency

This new device uses light to perform the key operations of a deep neural network on a chip, opening the door to high-speed processors that can learn in real-time.

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A data designer driven to collaborate with communities

Associate Professor Catherine D’Ignazio thinks carefully about how we acquire and display data — and why we lack it for many things.