CSAIL joined the launch of a new research initiative aiming to apply advanced machine learning and robotics to listen to and translate the communication of sperm whales. 

Scientists from CSAIL came up with a way to better navigate the complexity of supporting computer program instructions. Their tool “VeGen” (pronounced “vegan”) automatically generates compiler plugins to effectively use more complicated instructions.

CSAIL researchers aim to reduce the amount of wrong or out-of-date information online and also create deep learning models that dynamically adjust to recent changes. 

What if creating a new DSL could be done without coding experts, and without nearly as much as time and effort?

The rise of big data has led to an explosion in visualizations used in news articles and scientific papers. In particular, there’s been growing interest in interactive visualizations that allow readers to dig into the data themselves. 

The Toyota Research Institute (TRI) announced that it will be extending its AI research collaboration with MIT. TRI, which also has existing relationships with Stanford and the University of Michigan, has selected 13 additional academic institutions to participate in the next five-year phase of its research initiative.

While autonomous cars have gained swift momentum since Leonardo da Vinci’s self-propelled cart circa 1500, the thought of going completely hands-free still feels slightly supernatural. These four-wheelers of the future use a combination of GPS for calculating longitude, latitude, speed, and course to navigate, LiDAR technologies, which use laser light pulses that map surroundings, and machine learning to see and understand -- but to what degree depends on the level of autonomy.  

Suppose you're a machine-learning researcher trying to build a model that could help plan for the COVID-19 pandemic. You want to incorporate a disease simulator into the model, but it's written in the C++ programming language, rather than an existing machine-learning workflow like PyTorch or TensorFlow. A team from MIT CSAIL recently developed a clever work-around.

In the quest to capture social intelligence in machines, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Department of Brain and Cognitive Sciences created an algorithm capable of inferring goals and plans, even when those plans might fail.