The key idea behind the probabilistic framework to machine learning is that learning can be thought of as inferring plausible models to explain observed data. A machine can use such models to make ...

Researchers can demonstrate that on some standard computer-vision tasks, short programs -- less than 50 lines long -- written in a probabilistic programming language are competitive with conventional ...

An app developed by Gamalon recognizes objects after seeing a few examples. A learning program recognizes simpler concepts such as lines and rectangles. Machine learning is becoming extremely powerful ...

Scientists have built simulations to help explain behavior in the real world, including modeling for disease transmission and prevention, autonomous vehicles, climate science, and in the search for ...

A collaboration including the University of Oxford, University of British Columbia, Intel, New York University, CERN, and the National Energy Research Scientific Computing Center is working to make it ...