<span class="vcard">Adam Zewe | MIT News</span>
Adam Zewe | MIT News
Artificial Intelligence Brain and cognitive sciences Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology Electrical engineering and computer science (EECS) Faculty machine learning MIT Schwarzman College of Computing Profile School of Engineering

Understanding the nuances of human-like intelligence

Associate Professor Phillip Isola studies the ways in which intelligent machines “think,” in an effort to safely integrate AI into human society.

Aeronautical and astronautical engineering Algorithms Artificial Intelligence Autonomous vehicles Computer science and technology computer-vision Disaster response Electrical engineering and computer science (EECS) Laboratory for Information and Decision Systems (LIDS) machine learning MIT Schwarzman College of Computing National Science Foundation (NSF) Research Robotics School of Engineering

Teaching robots to map large environments

A new approach developed at MIT could help a search-and-rescue robot navigate an unpredictable environment by rapidly generating an accurate map of its surroundings.

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

A faster problem-solving tool that guarantees feasibility

The FSNet system, developed at MIT, could help power grid operators rapidly find feasible solutions for optimizing the flow of electricity.

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

Method teaches generative AI models to locate personalized objects

After being trained with this technique, vision-language models can better identify a unique item in a new scene.

Algorithms Artificial Intelligence Climate change Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology Electrical engineering and computer science (EECS) Emissions Energy Energy storage Infrastructure Lincoln Laboratory machine learning MIT Energy Initiative MIT Schwarzman College of Computing MIT Sloan School of Management Research School of Engineering Supercomputing Sustainable computing

Responding to the climate impact of generative AI

Explosive growth of AI data centers is expected to increase greenhouse gas emissions. Researchers are now seeking solutions to reduce these environmental harms.

Artificial Intelligence Broad Institute Computer Science and Artificial Intelligence Laboratory (CSAIL) Computer science and technology Electrical engineering and computer science (EECS) health Health Care Imaging machine learning Medicine MIT Schwarzman College of Computing National Institutes of Health (NIH) Research School of Engineering

New AI system could accelerate clinical research

By enabling rapid annotation of areas of interest in medical images, the tool can help scientists study new treatments or map disease progression.

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What does the future hold for generative AI?

At the inaugural MIT Generative AI Impact Consortium Symposium, researchers and business leaders discussed potential advancements centered on this powerful technology.

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

3 Questions: The pros and cons of synthetic data in AI

Artificially created data offer benefits from cost savings to privacy preservation, but their limitations require careful planning and evaluation, Kalyan Veeramachaneni says.

Algorithms Artificial Intelligence Climate change Computer modeling Data EAPS Earth and atmospheric sciences Emissions IDSS machine learning MIT Schwarzman College of Computing Pollution Public health Research School of Science Sustainability

Simpler models can outperform deep learning at climate prediction

New research shows the natural variability in climate data can cause AI models to struggle at predicting local temperature and rainfall.

Algorithms Artificial Intelligence Autonomous vehicles Cities Civil and environmental engineering Climate change Computer modeling Computer science and technology Electrical engineering and computer science (EECS) Emissions IDSS Laboratory for Information and Decision Systems (LIDS) machine learning MIT Schwarzman College of Computing Research School of Engineering Sustainability Transportation

Eco-driving measures could significantly reduce vehicle emissions

New research shows automatically controlling vehicle speeds to mitigate traffic at intersections can cut carbon emissions between 11 and 22 percent.