Nonsense can make sense to machine-learning models
Deep-learning methods confidently recognize images that are nonsense, a potential problem for medical and autonomous-driving decisions.
Deep-learning methods confidently recognize images that are nonsense, a potential problem for medical and autonomous-driving decisions.
Strategy accelerates the best algorithmic solvers for large sets of cities.
The system could help physicians select the least risky treatments in urgent situations, such as treating sepsis.
New technique applied to small computer chips enables efficient vision and detection algorithms without internet connectivity.
“Evolution Gym” is a large-scale benchmark for co-optimizing the design and control of soft robots that takes inspiration from nature and evolutionary processes.
A new AI-powered, virtual platform uses real-world physics to simulate a rich and interactive audio-visual environment, enabling human and robotic learning, training, and experimental studies.
The Raman spectroscopy-based method enables early detection and quantification of pathogens in plants, to enhance plant disease management.
Mechanical engineers are using cutting-edge computing techniques to re-imagine how the products, systems, and infrastructures we use are designed.
New work on linear-probing hash tables from MIT CSAIL could lead to more efficient data storage and retrieval in computers.
A new machine-learning system helps robots understand and perform certain social interactions.
Reducing the complexity of a powerful machine-learning model may help level the playing field for automatic speech-recognition around the world.
A new method forces a machine learning model to focus on more data when learning a task, which leads to more reliable predictions.
A National Science Foundation-funded team will use artificial intelligence to speed up discoveries in physics, astronomy, and neuroscience.
Graduate student Nicholas Kamp describes the MicroBooNE experiment and its implications for our understanding of fundamental particles.
A visual analytics tool helps child welfare specialists understand machine learning predictions that can assist them in screening cases.