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MIT researchers develop an efficient approach for training more reliable reinforcement learning models, focusing on complex tasks that involve variability. Image credits: MIT News; iStock

MIT researchers develop an efficient way to train more reliable AI agents

December 6, 2024

The technique could make AI systems better at complex tasks that involve variability.

Advancing urban tree monitoring with AI-powered digital twins

December 6, 2024

The Tree-D Fusion system integrates generative AI and genus-conditioned algorithms to create precise simulation-ready models of 600,000 existing urban trees across North America.

Four from MIT named 2025 Rhodes Scholars

December 4, 2024

Yiming Chen ’24, Wilhem Hector, Anushka Nair, and David Oluigbo will start postgraduate studies at Oxford next fall.

A new way to create realistic 3D shapes using generative AI

December 4, 2024

Researchers propose a simple fix to an existing technique that could help artists, designers, and engineers create better 3D models.

Photonic processor could enable ultrafast AI computations with extreme energy efficiency

December 3, 2024

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.

Improving health, one machine learning system at a time

November 26, 2024

Marzyeh Ghassemi works to ensure health-care models are trained to be robust and fair.

Nanoscale transistors could enable more efficient electronics

November 13, 2024

Researchers are leveraging quantum mechanical properties to overcome the limits of silicon semiconductor technology.

A causal theory for studying the cause-and-effect relationships of genes

November 12, 2024

By sidestepping the need for costly interventions, a new method could potentially reveal gene regulatory programs, paving the way for targeted treatments.

3 questions: Leveraging insights to enable clinical outcomes

November 12, 2024

Thomas Heldt, associate director of IMES, describes how he collaborates closely with MIT colleagues and others at Boston-area hospitals.

Caption:The new ICSR Data Hub serves as an evolving, public web depository of datasets gathered by MIT researchers examining racial bias in American society and institutions.

Empowering systemic racism research at MIT and beyond

November 6, 2024

Researchers in the MIT Initiative on Combatting Systemic Racism are building an open data repository to advance research on racial inequity in domains like policing, housing, and health care.