The multifaceted challenge of powering AI
Providing electricity to power-hungry data centers is stressing grids, raising prices for consumers, and slowing the transition to clean energy.
Data
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 and AI for a better world
Assistant Professor Manish Raghavan wants computational techniques to help solve societal problems.
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.
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.
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.
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.
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.
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.
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.