Computational system streamlines the design of fluidic devices
This computational tool can generate an optimal design for a complex fluidic device such as a combustion engine or a hydraulic pump.
Computer Science and Artificial Intelligence Laboratory (CSAIL)
An automated way to assemble thousands of objects
A new algorithm for automatic assembly of products is accurate, efficient, and generalizable to a wide range of complex real-world assemblies.
A faster way to preserve privacy online
New research enables users to search for information without revealing their queries, based on a method that is 30 times faster than comparable prior techniques.
Large language models help decipher clinical notes
Researchers used a powerful deep-learning model to extract important data from electronic health records that could assist with personalized medicine.
Ushering in a new era of computing
Dan Huttenlocher is a professor of electrical engineering and computer science and the inaugural dean at MIT Schwarzman College of Computing.
Busy GPUs: Sampling and pipelining method speeds up deep learning on large graphs
New technique significantly reduces training and inference time on extensive datasets to keep pace with fast-moving data in finance, social networks, and fraud detection in cryptocurrency.
A simpler path to better computer vision
New research reveals a scalable technique that uses synthetic data to improve the accuracy of AI models that recognize images.
Study: Zebrafish are smarter than we thought
Researchers have discovered that the brains of these simple fish can create three-dimensional maps of their surroundings.
Empowering social media users to assess content helps fight misinformation
An experimental platform that puts moderation in the hands of its users shows that people do evaluate posts effectively and share their assessments with others.
Solving brain dynamics gives rise to flexible machine-learning models
MIT CSAIL researchers solve a differential equation behind the interaction of two neurons through synapses to unlock a new type of speedy and efficient AI algorithm.
Ensuring AI works with the right dose of curiosity
Researchers make headway in solving a longstanding problem of balancing curious “exploration” versus “exploitation” of known pathways in reinforcement learning.
In machine learning, synthetic data can offer real performance improvements
Models trained on synthetic data can be more accurate than other models in some cases, which could eliminate some privacy, copyright, and ethical concerns from using real data.
Machine learning facilitates “turbulence tracking” in fusion reactors
A new approach sheds light on the behavior of turbulent structures that can affect the energy generated during fusion reactions, with implications for reactor design.
Using sound to model the world
This machine-learning system can simulate how a listener would hear a sound from any point in a room.
Helping blockchain communities fix bugs
MIT alumnus-founded Metrika has developed a suite of analytics tools giving blockchain communities visibility into their networks.