Algorithms

Robert Hallowell (left) and Michael Matthews developed the Visibility Estimation through Image Analytics algorithm.

An algorithm with an eye for visibility helps pilots in Alaska

Real-time visibility estimates are a critical need in remote areas where visual flight is common and automated weather stations are scarce.

January 6, 2020

MIT researchers have built a new benchmark tool that can accurately predict how long it takes given code to execute on a computer chip, which can help programmers tweak the code for better performance.

Tool predicts how fast code will run on a chip

Machine-learning system should enable developers to improve computing efficiency in a range of applications.

January 6, 2020

In a new study, researchers at MIT and IBM combine three popular text-analysis tools — topic modeling, word embeddings, and optimal transport — to compare thousands of documents per second. Here, they show that their method (left) clusters newsgroup posts by category more tightly than a competing method.

Finding a good read among billions of choices

As natural language processing techniques improve, suggestions are getting speedier and more relevant.

December 20, 2019

An automated machine-learning model developed by MIT researchers significantly outperforms human Wall Street analysts in predicting quarterly business sales.

Model beats Wall Street analysts in forecasting business financials

Using limited data, this automated system predicts a company’s quarterly sales.

December 19, 2019

ObjectNet, a dataset of photos created by MIT and IBM researchers, shows objects from odd angles, in multiple orientations, and against varied backgrounds to better represent the complexity of 3D objects. The researchers hope the dataset will lead to new computer vision techniques that perform better in real life.

This object-recognition dataset stumped the world’s best computer vision models

Objects are posed in varied positions and shot at odd angles to spur new AI techniques.

December 10, 2019

Dixia Fan stands in front of the ITT holding some of the structures towed in the tank to study VIVs, which are important for engineering offshore ocean structures like marine drilling risers that connect underwater oil wells to the surface.

Intelligent Towing Tank propels human-robot-computer research

A novel experimental facility integrates automation and active learning, illuminating a path to accelerated scientific discovery.

December 9, 2019

Based on shadows that an out-of-view video casts on nearby objects, MIT researchers can estimate the contents of the unseen video. In the top row, researchers used this method to recreate visual elements in an out-of-view video; the original elements are shown in the bottom row.

Using computers to view the unseen

A new computational imaging method could change how we view hidden information in scenes.

December 6, 2019

An MIT-invented model demonstrates an understanding of some basic “intuitive physics” by registering “surprise” when objects in simulations move in unexpected ways, such as
rolling behind a wall and not reappearing on the other side.

Helping machines perceive some laws of physics

Model registers “surprise” when objects in a scene do something unexpected, which could be used to build smarter AI.

December 2, 2019

With their model, researchers were able to generate on-demand brain scan templates of various ages (pictured) that can be used in medical-image analysis to guide disease diagnosis.

Producing better guides for medical-image analysis

Model quickly generates brain scan templates that represent a given patient population.

November 26, 2019

Daniela Rus (far right), director of the Computer Science and Artificial Intelligence Laboratory (CSAIL), moderated a panel on dispelling the myths of AI technologies in the workplace. The AI and the Work of the Future Congress was co-organized by CSAIL, the MIT Initiative on the Digital Economy, and the MIT Work of the Future Task Force.

MIT conference focuses on preparing workers for the era of artificial intelligence

As automation rises in the workplace, speakers explore ways to train students and reskill workers.

November 22, 2019

An MIT-invented model efficiently and simultaneously optimizes control and design of soft robots for target tasks, which has traditionally been a monumental undertaking in computation. The model, for instance, was significantly faster and more accurate than state-of-the-art methods at simulating how quadrupedal robots (pictured) should move to reach target destinations.

How to design and control robots with stretchy, flexible bodies

Optimizing soft robots to perform specific tasks is a huge computational problem, but a new model can help.

November 22, 2019

DeepRole, an MIT-invented gaming bot equipped with “deductive reasoning,” can beat human players in tricky online multiplayer games where player roles and motives are kept secret.

Bot can beat humans in multiplayer hidden-role games

Using deductive reasoning, the bot identifies friend or foe to ensure victory over humans in certain online games.

November 19, 2019

“We believe that AI plays a key role now, and in the future development of the region, if it’s used in the right way,” says Omar Costilla Reyes, one of four MIT graduate students working to help Latin America adopt artificial intelligence technologies. Pictured here (left to right) are Costilla Reyes, Emilia Simison, Pedro Antonio Colon-Hernandez, and Guillermo Bernal.

Students push to speed up artificial intelligence adoption in Latin America

To help the region catch up, students organize summit to bring Latin policymakers and researchers to MIT.

November 19, 2019

Two permanent magnets are tracked with magnetic field sensors. MIT engineers have devised an algorithm for high-speed tracking of any number of magnets, with significant implications for augmented reality and prosthesis control.

Algorithm may improve brain-controlled prostheses and exoskeletons

An improved method for magnet tracking enables high-speed wireless tracking through various materials.

November 18, 2019

A new tool reveals what AI models leave out in recreating a scene. Here, a GAN, or generative adversarial network, has dropped the pair of newlyweds from its reconstruction (right) of the photo it was asked to draw (left).

Visualizing an AI model’s blind spots

New tool highlights what generative models leave out when reconstructing a scene.

November 8, 2019

MIT News | Massachusetts Institute of Technology

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