Probabilistic programming has emerged as a powerful paradigm that integrates uncertainty directly into computational models. By embedding probabilistic constructs into conventional programming ...

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 ...

Probabilistic programming is a recent and extremely dynamic field of research which lies at the intersection of statistical machine learning and programming language theory. Probabilistic programming ...

Statistical packages have been used for decades to analyze large datasets or to perform mathematically intractable statistical methods. These packages are not capable of working with random variables ...

The Pentagon's blue-sky research agency is readying a nearly four-year project to boost artificial intelligence systems by building machines that can teach themselves -- while making it easier for ...

Researchers have developed an easy-to-use tool that enables someone to perform complicated statistical analyses on tabular data using just a few keystrokes. Their method combines probabilistic AI ...

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 ...

Computer vision systems sometimes make inferences about a scene that fly in the face of common sense. For example, if a robot were processing a scene of a dinner table, it might completely ignore a ...

How to make artificial intelligence more approachable for ordinary mortals — that is, people who are neither programmers nor IT admins nor machine learning scientists — is a topic very much in vogue ...