Electrical Engineering & Computer Science (eecs)
Electrical Engineering & Computer Science (eecs)

A more effective way to train machines for uncertain, real-world situations

Researchers develop an algorithm that decides when a “student” machine should follow its teacher, and when it should learn on its own.

New tool helps people choose the right method for evaluating AI models

Selecting the right method gives users a more accurate picture of how their model is behaving, so they are better equipped to correctly interpret its predictions.

Celebrating the impact of IDSS

A two-day conference at MIT reflected on the impact of the Institute for Data, Systems, and Society since its launch, as founding Director Munther Dahleh prepares to step down.

Probabilistic AI that knows how well it’s working

It’s more important than ever for artificial intelligence to estimate how accurately it is explaining data.

Researchers use AI to identify similar materials in images

This machine-learning method could assist with robotic scene understanding, image editing, or online recommendation systems.

Using data to write songs for progress

Senior Ananya Gurumurthy adds her musical talents to her math and computer science studies to advocate using data for social change.

Is medicine ready for AI? Doctors, computer scientists, and policymakers are cautiously optimistic

With the artificial intelligence conversation now mainstream, the 2023 MIT-MGB AI Cures conference saw attendance double from previous years.

A better way to study ocean currents

A new machine-learning model makes more accurate predictions about ocean currents, which could help with tracking plastic pollution and oil spills, and aid in search and rescue.

3 Questions: Jacob Andreas on large language models

The CSAIL scientist describes natural language processing research through state-of-the-art machine-learning models and investigation of how language can enhance other types of artificial intelligence.

Study: AI models fail to reproduce human judgements about rule violations

Models trained using common data-collection techniques judge rule violations more harshly than humans would, researchers report.