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 ...

Even in this day and age, computer learning is far behind the learning capability of humans. A team of researchers seek to shrink the gap, however, developing a technique called “Bayesian Program ...

CAMBRIDGE, Mass.--(BUSINESS WIRE)--Today, Gamalon, Inc. emerged from stealth mode to announce that it has developed a game-changing new approach to artificial intelligence/machine learning called ...

This illustration gives a sense of how characters from alphabets around the world were replicated through human vs. machine learning. (Credit: Danqing Wang) Researchers say they’ve developed an ...

The entire tech industry has fallen hard for a branch of artificial intelligence called deep learning. Also known as deep neural networks, the AI involves throwing massive amounts of data at a neural ...

The old adage that practice makes perfect applies to machines as well, as many of today’s artificially intelligent devices rely on repetition to learn. Deep-learning algorithms are designed to allow ...

We adapt a semi-Bayesian hierarchical modeling framework to jointly characterize the space–time variability of seasonal precipitation totals and precipitation extremes across the Northern Great Plains ...