Q&A: Chris Rackauckas on the equations at the heart of practically everything
Have a question about numerical differential equations? Odds are this CSAIL research affiliate has already addressed it.
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Computer Science and Artificial Intelligence Laboratory (CSAIL)
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Unpacking black-box models
Researchers create a mathematical framework to evaluate explanations of machine-learning models and quantify how well people understand them.
Artificial intelligence system learns concepts shared across video, audio, and text
A machine-learning model can identify the action in a video clip and label it, without the help of humans.
What words can convey
Natural language processing models capture rich knowledge of words’ meanings through statistics.
Soft assistive robotic wearables get a boost from rapid design tool
Scientists have created a design and fabrication tool for soft pneumatic actuators for integrated sensing, which can power personalized health care, smart homes, and gaming.
A one-up on motion capture
A new neural network approach captures the characteristics of a physical system’s dynamic motion from video, regardless of rendering configuration or image differences.
Seven from MIT elected to American Academy of Arts and Sciences for 2022
Prestigious honor society announces more than 250 new members.
3 Questions: Designing software for research ethics
PhD candidate Jonathan Zong found a lack of systems that earn and maintain public trust in large-scale online research — so he made one himself.
Estimating the informativeness of data
MIT researchers can now estimate how much information data are likely to contain, in a more accurate and scalable way than previous methods.
An easier way to teach robots new skills
Researchers have developed a technique that enables a robot to learn a new pick-and-place task with only a handful of human demonstrations.
A new state of the art for unsupervised computer vision
MIT CSAIL scientists created an algorithm to solve one of the hardest tasks in computer vision: assigning a label to every pixel in the world, without human supervision.
Anticipating others’ behavior on the road
A new machine-learning system may someday help driverless cars predict the next moves of nearby drivers, cyclists, and pedestrians in real-time.
A flexible way to grab items with feeling
MIT engineers Edward Adelson and Sandra Liu duo develop a robotic gripper with rich sensory capabilities.
Learning to think critically about machine learning
A multidisciplinary team of graduate students helps infuse ethical computing content into MIT’s largest machine learning course.
Does this artificial intelligence think like a human?
A new technique compares the reasoning of a machine-learning model to that of a human, so the user can see patterns in the model’s behavior.