A 3D design environment from CSAIL lets users iterate an object’s shape and electronic function in one cohesive space, to add existing sensors to early-stage prototypes.

As more intelligent, independent machines make their way into the public sphere, engineers Julie Shah and Laura Major are urging designers to rethink not just how robots fit in with society, but also how society can change to accommodate these new, “working” robots.

Researchers at CSAIL recently made a major development in the area of lost languages: a new system that has been shown to be able to automatically decipher a lost language, without needing advanced knowledge of its relation to other languages.

Imagine that one day you’re riding the train and decide to hop the turnstile to avoid paying the fare. It probably won’t have a big impact on the financial well-being of your local transportation system. But now ask yourself, “What if everyone did that?” The outcome is much different — the system would likely go bankrupt and no one would be able to ride the train anymore.

A group led by researchers at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) has developed a machine learning model that can look at an X-ray to quantify how severe the edema is, on a four-level scale ranging from 0 (healthy) to 3 (very, very bad). The system determined the right level more than half of the time, and correctly diagnosed level 3 cases 90 percent of the time.

Most new roboticists want to program their robots to solve interesting, complex tasks — but it turns out that just moving them through space without colliding with objects is more difficult than it sounds.

The national dialogue on race has progressed powerfully and painfully in the past year, and issues of racial bias in the news have become ubiquitous. However, for over a decade, researchers from MIT’s Imagination, Computation, and Expression Laboratory (ICE Lab) have been developing systems to model, simulate, and analyze such issues of identity. 

One reason that cyberattacks have continued to grow in recent years is that we never actually learn all that much about how they happen. Companies fear that reporting attacks will tarnish their public image, and even those who do report them don’t share many details because they worry that their competitors will gain insight into their security practices.

In a new study at the European Conference on Computer Vision last month, researchers unveiled a hybrid language-vision model that can compare and contrast a set of dynamic events captured on video to tease out the high-level concepts connecting them.