Algorithms
Algorithms

Algorithms

Aeronautical and astronautical engineering Algorithms Artificial Intelligence Computer science and technology computer-vision Laboratory for Information and Decision Systems (LIDS) machine learning MIT Schwarzman College of Computing MIT-IBM Watson AI Lab Research Robotics School of Engineering

A better method for planning complex visual tasks

A new hybrid system could help robots navigate in changing environments or increase the efficiency of multirobot assembly teams.

Algorithms Artificial Intelligence Computer science and technology Design machine learning Mechanical engineering MIT Schwarzman College of Computing Research School of Engineering Systems design

A “ChatGPT for spreadsheets” helps solve difficult engineering challenges faster

The approach could help engineers tackle extremely complex design problems, from power grid optimization to vehicle design.

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Featured video: Coding for underwater robotics

Lincoln Laboratory intern Ivy Mahncke developed and tested algorithms to help human divers and robots navigate underwater.

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

New method could increase LLM training efficiency

By leveraging idle computing time, researchers can double the speed of model training while preserving accuracy.

Algorithms Artificial Intelligence Biological engineering Biology Broad Institute Cells Computer science and technology Diagnostics Electrical engineering and computer science (EECS) Genetics health IDSS Laboratory for Information and Decision Systems (LIDS) machine learning Medicine MIT Schwarzman College of Computing National Institutes of Health (NIH) Research School of Engineering

AI to help researchers see the bigger picture in cell biology

By providing holistic information on a cell, an AI-driven method could help scientists better understand disease mechanisms and plan experiments.

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Exposing biases, moods, personalities, and abstract concepts hidden in large language models

A new method developed at MIT could root out vulnerabilities and improve LLM safety and performance.

Algorithms Artificial Intelligence 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 Energy Initiative MIT Schwarzman College of Computing National Science Foundation (NSF) Research School of Engineering Sustainability Transportation

Parking-aware navigation system could prevent frustration and emissions

By minimizing the need to drive around looking for a parking spot, this technique can save drivers up to 35 minutes — and give them a realistic estimate of total travel time.

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AI algorithm enables tracking of vital white matter pathways

Opening a new window on the brainstem, a new tool reliably and finely resolves distinct nerve bundles in live diffusion MRI scans, revealing signs of injury or disease.

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

Helping AI agents search to get the best results out of large language models

EnCompass executes AI agent programs by backtracking and making multiple attempts, finding the best set of outputs generated by an LLM. It could help coders work with AI agents more efficiently.

Algorithms Alternative energy Artificial Intelligence Batteries Climate change Computer science and technology Electrical engineering and computer science (EECS) Energy Energy storage Faculty Interview Laboratory for Information and Decision Systems (LIDS) machine learning MIT Schwarzman College of Computing Pollution Research School of Engineering Sustainability Sustainable computing

3 Questions: How AI could optimize the power grid

While the growing energy demands of AI are worrying, some techniques can also help make power grids cleaner and more efficient.