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61 articles

Cracking open the black box of automated machine learning

2019-05-31 872 MachineLearning AutoML

ATMSeer is an interactive visualization tool for users to see and control algorithms and hyperparameters of machine-learning (AutoML) systems. The work was developed by researchers in the MIT Laboratory for Information and Decision Systems (LIDS), Hong Kong University of Science and Technology (HKUST), and Zhejiang University.
New AI programming language goes beyond deep learning

2019-06-26 774 artificialintelligence fintech MachineLearning Gen Iot

MIT researchers’ probabilistic programming system, Gen, is making it easier for novices to get their feet wet with artificial intelligence, while also helping experts advance the field.
Artificial intelligence model detects asymptomatic Covid-19 infections through cellphone-recorded coughs

2020-10-29 670 artificialintelligence MachineLearning COVID19 covid Healthcare artificalintelligence DigitalHealth

An artificial intelligence model can detect people who are asymptomatic with Covid-19, through cellphone-recorded coughs. The work was led by Brian Subirana and colleagues at the MIT Auto-ID Lab.
Using machine learning to track the pandemic’s impact on mental health

2020-11-05 555 MachineLearning healthcare covid19 mentalhealth DigitalHealth

Researchers have found an increase in anxiety and in thoughts about suicide in response to Covid-19 after analyzing Reddit posts. They used machine learning to study hundreds of thousands of posts, allowing them to identify changes in the tone and content of language that people used as the pandemic progressed.
Using machine learning to hunt down cybercriminals

2019-10-09 546 artificialintelligence MachineLearning cybersecurity

A new machine-learning system developed by researchers at MIT CSAIL and UCSD identifies “serial hijackers” of internet IP addresses.
Machine-learning system tackles speech and object recognition, all at once

2018-09-18 512 MachineLearning

MIT researchers have developed a machine-learning model that highlights objects in an image as their described in audio captions in real-time. The work is out of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).
Translating lost languages using machine learning

2020-10-21 435 MachineLearning

MIT researchers have created a machine learning system that aims to help linguists decipher lost languages.
Revolutionizing everyday products with artificial intelligence

2018-06-01 428 MachineLearning NeuralNetwork

MIT researchers in mechanical engineering are using AI and machine learning technologies to enhance the products we use in everyday life.
Machine learning uncovers potential new TB drugs

2020-10-15 426 AI MachineLearning

Using a machine-learning approach that incorporates uncertainty, MIT researchers identified several promising compounds that target a protein required for the survival of the bacteria that cause tuberculosis.
System brings deep learning to “internet of things” devices

2020-11-13 327 artificialintelligence AI DeepLearning iot InternetOfThings MachineLearning datasecurity

MCUNet is a new MIT system that brings machine learning to microcontrollers. The advance could enhance the function and security of devices connected to the Internet of Things (IoT).
“Doing machine learning the right way”

2020-03-08 306 bigdata MachineLearning

MIT computer scientist Aleksander Madry of CSAIL and EECS wants to do machine learning “the right way” by making models more accurate, efficient, and robust against errors caused by adversarial examples, and by addressing ethical artificial intelligence for society.
Anticipating heart failure with machine learning

2020-10-01 303 machinelearning

A new algorithm developed at MIT CSAIL aims to distinguish between different pulmonary edema severity levels automatically by looking at a single X-ray image.
Deep-learning technique reveals “invisible” objects in the dark

2018-12-12 274 machinelearning bigdata DeepLearning

Revealing the invisible: An MIT deep learning technique reveals transparent objects in the dark.
Bringing human-like reasoning to driverless car navigation

2019-05-23 250 artificialintelligence machinelearning

To bring more human-like reasoning to autonomous vehicles, MIT CSAIL researchers created an end-to-end autonomous navigation system that uses only simple maps, like Google Maps, and visual data to enable driverless cars to navigate routes in new, complex environments.
Artificial intelligence system uses transparent, human-like reasoning to solve problems

2018-09-11 239 ai artificialintelligence MachineLearning DeepLearning

Artificial intelligence model from the MIT Lincoln Laboratory's Intelligence and Decision Technologies Group sets a new standard for understanding how a neural network makes decisions.
How to control robots with brainwaves and hand gestures

2018-06-20 235 MachineLearning

A system from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) allows users to instantly correct robot mistakes with nothing more than brain signals and hand gestures such as the flick of a finger.
Machine learning picks out hidden vibrations from earthquake data

2020-02-28 217 artificialintelligence datascience machinelearning

An MIT machine-learning technique picks out hidden vibrations from earthquake data, which may help scientists more accurately map vast underground geologic structures.
Drag-and-drop data analytics

2019-06-27 187 artificialintelligence MachineLearning

Northstar, an interactive data-science system developed by MIT and Brown University researchers, lets users drag-and-drop and manipulate data, and use a virtual data scientist tool to generate machine-learning models that run prediction tasks on datasets, on a user-friendly touchscreen interface.
Identifying artificial intelligence “blind spots”

2019-01-24 185 machinelearning

A new model identifies AI “blind spots” — instances where autonomous robots and driverless vehicles and have “learned” from training examples that don’t match what’s actually happening in the real world. The work as developed by MIT and Microsoft researchers.
Machine learning moves popular data elements into a bucket of their own

2019-04-03 174 ArtificialIntelligence MachineLearning

LearnedSketch from MIT CSAIL uses a neural network to automatically predict if a specific element will appear frequently in a data stream. If it does, it’s placed in a separate bucket of so-called “heavy hitters,” making frequency-estimation much more efficient and with much less error.