We present a new notion of probabilistic duality for random variables involving mixture distributions. Using this notion, we show how to implement a highly-parallelizable Gibbs sampler for weakly coupled discrete pairwise graphical models with strictly positive factors that requires almost no preprocessing and is easy to implement. Moreover, we show how our method can be combined with blocking to improve mixing. Even though our method leads to inferior mixing times compared to a sequential Gibbs sampler, we argue that our method is still very useful for large dynamic networks, where factors are added and removed on a continuous basis, as it is hard to maintain a graph coloring in this setup. Similarly, our method is useful for parallelizing Gibbs sampling in graphical models that do not allow for graph colorings with a small number of colors such as densely connected graphs.

A growing body of recent research is shedding new light on the role of nonconvex optimization for tackling large scale problems in machine learning, signal processing, and convex programming. This work is developing techniques that help to explain the surprising effectiveness of relatively simple first-order algorithms for certain nonconvex optimizations. When applied to problems that can be formulated as semidefinite programs, these techniques can often be viewed as part of a framework proposed by Burer and Monteiro [4]. The Burer-Monteiro technique is based on factoring the semidefinite variable, and applying classical optimization techniques to the resulting nonconvex objective over the factor. While worst-case complexity considerations imply that such an approach cannot succeed in general, a series of recent papers [11, 40, 35, 13, 1] has shown the strategy to be remarkably effective for a number of problems of practical interest, with analytical convergence guarantees and strong empirical performance. In this paper, we enlarge the collection of problems to which the Burer-Monteiro technique can be successfully applied, by analyzing the convergence properties of gradient descent applied to the problem of rectangular matrix completion from incomplete measurements.

You must understand algorithms to get good at machine learning. The problem is that they are only ever explained using Math. In this mega Ebook written in the friendly Machine Learning Mastery style that you're used to, finally cut through the math and learn exactly how machine learning algorithms work. Using clear explanations, simple pure Python code (no libraries!) and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement a suite of linear, nonlinear and ensemble machine learning algorithms from scratch. I live in Australia with my wife and son and love to write and code.

NEW YORK & LAS VEGAS--(BUSINESS WIRE)--Today at Money 20/20, Mastercard announced its plans to launch artificial intelligence (AI) bots that allow consumers to transact, manage finances, and shop via messaging platforms. According to research firm Gartner, nearly $2 billion in online sales will be performed exclusively through mobile digital assistants by the end of 2016.1 Mastercard is developing bots for both its merchant and bank partners, which will use chat, messaging and natural language interfaces to communicate with consumers. With the Mastercard bots, partners can have a true dialogue with consumers and provide personalized service, seamless user experience and contextual offers and rewards. Mastercard KAI, the Mastercard bot for banks, will seamlessly extend Mastercard services to customers on messaging platforms and make financial information and decisions part of consumers' everyday lives. In this testing phase on Messenger, Mastercard is partnering with Kasisto, the company that created KAI Banking, the conversational artificial intelligence (AI) platform, to power branded virtual assistants and smart bots for financial services and is a current participant in the Mastercard Start Path Global program.

As the EMR "space race" peaks, clinical and health leaders are coming to understand that digitizing data does not, on its own, drive innovation or transformation. Many are wondering what's next. Looking ahead, the next wave in our journey towards digital transformation is Artificial Intelligence (AI). Simply put, Artificial Intelligence is a collection of systems that sense, comprehend, act and learn. The goal of AI in health is to drive greater "data dividends" than what we are getting from investments already made in EMRs and other systems.

Of the vast wealth of information unlocked by the Internet, most is plain text. The data necessary to answer myriad questions--about, say, the correlations between the industrial use of certain chemicals and incidents of disease, or between patterns of news coverage and voter-poll results--may all be online. But extracting it from plain text and organizing it for quantitative analysis may be prohibitively time consuming. Information extraction--or automatically classifying data items stored as plain text--is thus a major topic of artificial-intelligence research. Last week, at the Association for Computational Linguistics' Conference on Empirical Methods on Natural Language Processing, researchers from MIT's Computer Science and Artificial Intelligence Laboratory won a best-paper award for a new approach to information extraction that turns conventional machine learning on its head.

This article originally appeared on the International Business Times. Machines are capable of understanding speech, recognizing faces and driving cars safely, making recent technological advancements seem impressively powerful. But if the field of artificial intelligence is going to make the transformative leap into building human-like machines, it'll first have to master the way babies learn. "Relatively recently in AI there's been a shift from thinking about designing systems that can do the sort of things that adults can do, to realizing if you want to have systems that are as flexible and powerful and do the kinds of things that adults do, you need to have systems that can learn the way babies and children do," developmental psychologist Alison Gopnik, a researcher at the University of California at Berkeley, told International Business Times. "If you compare what computers can do now to what they could do 10 years ago, they've certainly made a lot of progress, but if you compare them to what a 4-year-old can do, there's still a pretty enormous gap."

"Intel sees AI transforming the way businesses operate and how people engage with the world," the company said in a statement yesterday. "Intel is assembling the broadest set of technology options to drive AI capabilities in everything from smart factories and drones to sports, fraud detection and autonomous cars." Dubbed Intel Nervana, the new platform comes courtesy of the company's acquisition of the two-year-old Nervana Systems announced three months ago. The platform will be optimized for AI workloads with an emphasis on both speed and ease of use. The first product in the platform, a chip codenamed "Lake Crest," will begin testing in the first half of next year and will eventually be available to key customers later that year, according to Intel.

Bad hair days may soon be prevented by just looking at your smart phone. Parham Aarabi and Wenzhi Guo, researchers at the University of Toronto Faculty of Applied Science & Engineering, have developed a machine learning algorithm that learns directly from human instructions rather than an existing set of examples. The algorithm outperformed conventional methods of training neural networks by 160 percent and outperformed its own training by 9 percent. The algorithm is seen as a significant leap forward for artificial intelligence as it learned to recognize hair in pictures with greater reliability than that enabled by the training. The two researchers were able to train the algorithm to identify people's hair in photographs--a much more difficult task for computers than it is for humans.

Intel has flexed its AI muscles and beefed up its services with a bunch of new products and collaborations, in an effort to adapt to the technological upheaval of intelligent software. At Intel's first "AI Day" in San Francisco, Brian Krzanich, CEO, said the company is "continuing to evolve" and working to provide an "end-to-end AI solution" to allow companies to easily integrate intelligence into their infrastructures. As data generated by companies continues to pile up, the interest in analyzing that data using machine learning and AI has been piqued. The largest technology companies are all making big investments and staking their claims in AI. But while companies such as Google and Microsoft have developed libraries of machine learning tools such as TensorFlow and Cognitive Toolkit, Intel is more focused on updating servers to cope with the intense computation required to process and train AI systems.