We propose new metrics on sets, ontologies, and functions that can be used in various stages of probabilistic modeling, including exploratory data analysis, learning, inference, and result interpretation. These new functions unify and generalize some of the popular metrics on sets and functions, such as the Jaccard and bag distances on sets and Marczewski-Steinhaus distance on functions. We then introduce information-theoretic metrics on directed acyclic graphs drawn independently according to a fixed probability distribution and show how they can be used to calculate similarity between class labels for the objects with hierarchical output spaces (e.g., protein function). Finally, we provide evidence that the proposed metrics are useful by clustering species based solely on functional annotations available for subsets of their genes. The functional trees resemble evolutionary trees obtained by the phylogenetic analysis of their genomes.

Investigations have been performed into using clustering methods in data mining time-series data from smart meters. The problem is to identify patterns and trends in energy usage profiles of commercial and industrial customers over 24-hour periods, and group similar profiles. We tested our method on energy usage data provided by several U.S. power utilities. The results show accurate grouping of accounts similar in their energy usage patterns, and potential for the method to be utilized in energy efficiency programs.

This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. ISBN 9781627058414, 189 pages.

Using machine learning and traditional geological methods to make targets at Integra Gold's (TSXV: ICG) Lamaque gold project, the Quebec-based team from SGS Geostat took home the top prize in Integra's 1-million Gold Rush Challenge. The winner was determined on...

Google and Facebook aren't the only ones vying to be the standard bearer for the hottest AI technique around. China's leading Internet search company, Baidu, which is also investing heavily in a popular and powerful machine-learning technology called deep learning, today released some key code that it uses to make this AI software run very efficiently. Baidu's code was recently used to build an impressive speech-recognition system called Deep Speech 2. For some short sentences, this system is better than most humans at recognizing speech correctly (see "Baidu's Deep-Learning System Rivals People at Speech Recognition"). This is an especially useful technology for Baidu, because it offers a better way for the company's many millions of users to access its services, especially on mobile. Typing Chinese characters on a smartphone is tricky and complex, and many people in China already prefer to use their voice to send short messages or to search the Web for information.

When researchers Frey and Osborne predicted in 2013 that 47% of US jobs were susceptible to automation by 2050, they set off a wave of dystopian concern. But the key word is "susceptible". The automation revolution is possible, but without a radical change in the social conventions surrounding work it will not happen. The real dystopia is that, fearing the mass unemployment and psychological aimlessness it might bring, we stall the third industrial revolution. Instead we end up creating millions of low skilled jobs that do not need to exist.

There are few things more frustrating than a slow-loading Web page. For companies, what's even worse is what comes after: users abandoning their site in droves. Amazon, for example, estimates that every 100-millisecond delay cuts its profits by 1 percent. To help combat this problem, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Harvard University have developed a system that decreases page-load times by 34 percent. Dubbed "Polaris," the framework determines how to overlap the downloading of a page's objects, such that the overall page requires less time to load.

A good friend recently told me that it takes a special kind of nerd to appreciate what Google's AlphaGo did to international Go champion Lee Sedol: a nerd that is both a Go nerd and a computer nerd. For Go nerdiness, I am recently enamored with the massively complex game that has exponentially more outcomes and dimensions than chess. As for the tech nerdiness, many of us assumed that after DeepBlue beat Kasparov in chess, any other game was a foregone conclusion. But actually, it's taken twenty years for a computer to rise to the level of top-ranked Go players, because high-level Go incorporates less calculation of a limited set of future outcomes and far more intuition. Challenges like this are not just an interesting competition.

The concept of inhuman intelligence goes back to the deep prehistory of mankind. At first the province of gods, demons, and spirits, it transferred seamlessly into the interlinked worlds of magic and technology. Ancient Greek myths had numerous robots, made variously by gods or human inventors, while extant artefacts like the Antikythera calendrical computer show that even in 200 BCE we could build machinery that usefully mimicked human intellectual abilities. There has been no age or civilisation without a popular concept of artificial intelligence (AI). Ours, however, is the first where the genuine article--machinery that comfortably exceeds our own thinking skills--is not only possible but achievable.

Countless science fiction novels and films have delved into what could happen if humans created robots or other machines with artificial intelligence. Usually they either become almost-human best friends and companions, or they turn into megalomaniacs with the goal of turning us into their slaves. The reality of artificial intelligence, or AI, is that scientists have developed robots that can beat us at chess, but there are significant nuances that are still missing. Rao Kambhampati, a professor of computer science and engineering at Arizona State University, spoke about these developments.