Machine learning

White blood cells are among the first to mount an attack against sepsis, a deadly complication of Covid-19. In a project led by MIT Professor Daniela Rus, researchers are developing a machine learning system to detect an activated immune response to sepsis which could lead to earlier, more aggressive treatment. Here, a white blood cell attacks malaria.

Marshaling artificial intelligence in the fight against Covid-19

The MIT-IBM Watson AI Lab is funding 10 research projects aimed at addressing the health and economic consequences of the pandemic.

May 19, 2020

Sapple, a system developed at the MIT Computer Science and Artificial Intelligence Laboratory, analyzes in-home appliance usage to better understand health patterns, using just radio signals and a smart electricity meter.

What can your microwave tell you about your health?

An MIT system uses wireless signals to measure in-home appliance usage to better understand health tendencies.

May 18, 2020

Researchers with the IDSS Covid-19 Collaboration (Isolat) are designing a control model for testing and isolating members of communities like MIT’s to reduce Covid-19 infection.

A data-driven response to a pandemic

Isolat, a volunteer collaboration organized by the Institute for Data, Systems, and Society, informs coronavirus policy by analyzing data associated with the pandemic.

May 15, 2020

MIT researchers have developed a kind of creativity test for generative models to see how far they can go in visualizing objects in photos from various angles and in different colors. The tool shows how much the model's imagination depends on the variety of images it has seen.

Visualizing the world beyond the frame

Researchers test how far artificial intelligence models can go in dreaming up varied poses and colors of objects and animals in photos.

May 6, 2020

MIT researchers have proposed a technique for shrinking deep learning models that they say is simpler and produces more accurate results than state-of-the-art methods. It works by retraining the smaller, pruned model at its faster, initial learning rate.

A foolproof way to shrink deep learning models

Researchers unveil a pruning algorithm to make artificial intelligence applications run faster.

April 30, 2020

MIT researchers used machine learning to find entirely new algorithms for encoding exploration. Their machine-designed algorithms outperformed human-designed algorithms on the wide range of simulated tasks and environments shown here.

Automating the search for entirely new “curiosity” algorithms

Researchers show that computers can “write” algorithms that adapt to radically different environments better than algorithms designed by humans.

April 28, 2020

MIT researchers have developed a new automated AI system with improved computational efficiency and a much smaller carbon footprint. The researchers’ system trains one large neural network comprising many pretrained subnetworks of different sizes that can be tailored to diverse hardware platforms without retraining.

Reducing the carbon footprint of artificial intelligence

MIT system cuts the energy required for training and running neural networks.

April 23, 2020

WiDS Cambridge, co-hosted by the Institute for Data, Systems, and Society, recognized and empowered women in STEM across a variety of disciplines.

Annual Women in Data Science conference discusses fake news

WiDS Cambridge, co-hosted by the Institute for Data, Systems, and Society, recognizes and empowers women in STEM across a variety of disciplines.

April 17, 2020

The startup Posh has created chat bots that use conversational memory to hold more natural conversations. Posh’s chat bots are currently used by about a dozen credit unions across a variety of voice and text platforms.

Deploying more conversational chatbots

Startup Posh has created chatbots that use “conversational memory” to have more natural exchanges.

April 17, 2020

This research started as part of a project started in class 2.168 (Learning Machines), which was instructed by Professor George Barbastathis (pictured).

Model quantifies the impact of quarantine measures on Covid-19’s spread

A machine learning algorithm combines data on the disease's spread with a neural network, to help predict when infections will slow down in each country.

April 16, 2020