By Tom Fleischman for the Cornell Chronicle
June 21, 2022
An autonomous vehicle is able to navigate city streets and other less-busy environments by recognizing pedestrians, other vehicles and potential obstacles through artificial intelligence. This is achieved with the help of artificial neural networks, which are trained to “see” the car’s surroundings, mimicking the human visual perception system.
June 14, 2022
By Kate Blackwood for the Cornell Chronicle
Electrons and their behavior pose fascinating questions for quantum physicists, and recent innovations in sources, instruments and facilities allow researchers to potentially access even more of the information encoded in quantum materials.
However, these research innovations are producing unprecedented – and until now, indecipherable – volumes of data.
An interdisciplinary team of Cornell and Harvard University researchers developed a machine learning tool to parse quantum matter and make crucial distinctions in the data, an approach that will help scientists unravel the most confounding phenomena in the subatomic realm.
The Cornell-led project’s paper, “Correlator Convolutional Neural Networks as an Interpretable Architecture for Image-like Quantum Matter Data,” published June 23 in Nature Communications. The lead author is doctoral student Cole Miles.
Showcasing America’s most promising young scientists and engineers, the Blavatnik Family Foundation and the New York Academy of Sciences named 31 finalists for the world’s largest unrestricted prize honoring early-career scientists and engineers.
