AI that can learn the patterns of human language
On its own, a new machine-learning model discovers linguistic rules that often match up with those created by human experts.
On its own, a new machine-learning model discovers linguistic rules that often match up with those created by human experts.
Lincoln Laboratory Supercomputing Center dataset aims to accelerate AI research into managing and optimizing high-performance computing systems.
New research showcases a pilot application using seismometers to monitor groundwater aquifers in California.
Algorithms designed to ensure multiple users share a network fairly can’t prevent some users from hogging all the bandwidth.
Methods that make a machine-learning model’s predictions more accurate overall can reduce accuracy for underrepresented subgroups. A new approach can help.
Researchers have made strides toward machine-learning models that can help doctors more efficiently find information in a patient’s health record.
Failing to consider neighborhood texture in hurricane-related wind loss models may undervalue stronger construction by over 80 percent.
Christoph Paus, the MIT physicist who co-led the effort to detect the particle, looks ahead to the next 10 years.
Researchers develop tools to help data scientists make the features used in machine-learning models more understandable for end users.
The second AI Policy Forum Symposium convened global stakeholders across sectors to discuss critical policy questions in artificial intelligence.
A new system lets robots manipulate soft, deformable material into various shapes from visual inputs, which could one day enable better home assistants.
Erin Walk, a PhD student in social and engineering systems, studies the impact of social media on the Syrian conflict.
MIT scientists unveil the first open-source simulation engine capable of constructing realistic environments for deployable training and testing of autonomous vehicles.
Pinpointing risks can also help businesses save money as they become more resilient.
Jonathan Weissman and collaborators used their single-cell sequencing tool Perturb-seq on every expressed gene in the human genome, linking each to its job in the cell.