MOUNTAIN VIEW, CA--(Marketwired - Nov 1, 2016) - Today, Redis Labs, the home of Redis, introduced an open source project Redis-ML, the Redis Module for Machine Learning that accelerates the delivery of real-time recommendations and predictions for interactive apps, in combination with Spark Machine Learning (Spark ML). Machine learning is fast becoming a critical requirement for modern smart applications. Redis-ML accelerates the delivery of real-time predictive analytics for use cases such as fraud detection and risk evaluation in financial products, product or content recommendations for e-commerce applications, demand forecasting for manufacturing applications or sentiment analyses of customer engagements. Spark ML (previously MLlib) delivers proven machine learning libraries for classification and regression tasks. Combined with Redis-ML, applications can now deliver precise, re-usable machine learning models, faster and with lower execution latencies.

Ars is excited to be hosting this online debut of Sunspring, a short science fiction film that's not entirely what it seems. You know it's the future because H (played with neurotic gravity by Silicon Valley's Thomas Middleditch) is wearing a shiny gold jacket, H2 (Elisabeth Gray) is playing with computers, and C (Humphrey Ker) announces that he has to "go to the skull" before sticking his face into a bunch of green lights. It sounds like your typical sci-fi B-movie, complete with an incoherent plot. Except Sunspring isn't the product of Hollywood hacks--it was written entirely by an AI. To be specific, it was authored by a recurrent neural network called long short-term memory, or LSTM for short. The AI named itself Benjamin. Knowing that an AI wrote Sunspring makes the movie more fun to watch, especially once you know how the cast and crew put it together. Director Oscar Sharp made the movie for Sci-Fi London, an annual film festival that includes the 48-Hour Film Challenge, where contestants are given a set of prompts (mostly props and lines) that have to appear in a movie they make over the next two days. Sharp's longtime collaborator, Ross Goodwin, is an AI researcher at New York University, and he supplied the movie's AI writer, initially called Jetson. As the cast gathered around a tiny printer, Benjamin spat out the screenplay, complete with almost impossible stage directions like "He is standing in the stars and sitting on the floor."

Some foundations of statistical science have been questioned recently, especially the use and abuse of p-values. See also this article published in FiveThirtyEight.com. Statistical tests of hypotheses rely on p-values and other mysterious parameters and concepts that only the initiated can understand: power, type I error, type II error, or UMP tests, just to name a few. Pretty much all of us have had to learn this old stuff (pre-dating the existence of computers) in some college classes. Sometimes results from a statistical test will be published in a mainstream journal - for instance about whether or not global warming is accelerating - using the same jargon that few understand, and accompanied by misinterpretations and flaws in the use of the test itself. Especially when tests are repeated over and over (or data adulterated or wrongly collected to start with) until they deliver the answer that we want.

I keep hearing Data Scientists say that'Statistics is Dead', and they even have big debates about it attended by the good and great of Data Science. Interestingly, there seem to be very few actual statisticians at these debates. So why do Data Scientists think that stats is dead? Where does the notion that there is no longer any need for statistical analysis come from? I guess that really we should start at the beginning by asking the question'What Is Statistics?'.

In the next five years, every important decision, whether it's business or personal, will be made with the assistance of IBM Watson. Watson, the company's artificial intelligence-fueled system, is working in fields like health care, finance, entertainment and retail, connecting businesses more easily with their customers, making sense of big data and helping doctors find treatments for cancer patients. The Watson system is set to transform how businesses function and how people live their lives. "Our goal is augmenting intelligence," Rometty said. "It is man and machine. This is all about extending your expertise. It doesn't matter what you do. IBM's conference this week, which the company said drew 17,000 attendees, explored how companies, including retailers, educators, human resources departments and financial institutions, amon others, can use Watson. "The challenge IBM has right now is to define the marketplace," said Jeff Kagan, an independent industry analyst, who attended the conference. "Ten years from now, will IBM be the leader?

If you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential. This book examines machine learning models including logistic regression, decision trees, and support vector machines, and applies them to common problems such as categorizing documents and classifying images. It begins with the fundamentals of machine learning, introducing you to the supervised-unsupervised spectrum, the uses of training and test data, and evaluating models. You will learn how to use generalized linear models in regression problems, as well as solve problems with text and categorical features. You will be acquainted with the use of logistic regression, regularization, and the various loss functions that are used by generalized linear models.

As technologist Joi Ito, director of the MIT Media Lab and a board member at the The New York Times and at Sony, recently predicted, "This is the year artificial intelligence becomes more than just a computer science problem." This week, the White House issued a formal position paper with 23 recommendations on artificial intelligence. Let me cut to the chase: they don't think superhuman A.I. is imminent. While you're relaxing in the good news, take in the pleasant surprise that the National Science and Technology Council (NSTC) and the Office of Science and Technology Policy (OSTP), which advises the President in policy and budget development, coordinated efforts to deliver this cohesive position paper. The outcome is a chunky, committee-clarified read, but for all that, it cuts to the chase on big issues in nontechnical language.

Move over desktop: Mobile is the future of online purchases and commerce, thanks to its ability to connect with location-based and personalized data. Forrester's new report "Predictions 2017: Mobile is the Face of Digital," scheduled to be publicly released Tuesday, explains that mobile has become the new pathway for consumers find brands -- and it is moving past the traditional social media app. Advertisers will increasingly use mobile to connect next year using chatbots, other artificial intelligence-enabled platforms like Apple's Siri or Amazon's Alexa and messaging apps, the report said. The company previously said that people spend more than two hours a day on mobile, and that by 2019 the majority of our billion websites will be on mobile. "The magic of mobile is the immediacy," said Julie Ask, principal analyst at Forrester and co-author of the report.

Neural networks (NN) have achieved state-of-the-art performance in various applications. Unfortunately in applications where training data is insufficient, they are often prone to overfitting. One effective way to alleviate this problem is to exploit the Bayesian approach by using Bayesian neural networks (BNN). Another shortcoming of NN is the lack of flexibility to customize different distributions for the weights and neurons according to the data, as is often done in probabilistic graphical models. To address these problems, we propose a class of probabilistic neural networks, dubbed natural-parameter networks (NPN), as a novel and lightweight Bayesian treatment of NN. NPN allows the usage of arbitrary exponential-family distributions to model the weights and neurons. Different from traditional NN and BNN, NPN takes distributions as input and goes through layers of transformation before producing distributions to match the target output distributions. As a Bayesian treatment, efficient backpropagation (BP) is performed to learn the natural parameters for the distributions over both the weights and neurons. The output distributions of each layer, as byproducts, may be used as second-order representations for the associated tasks such as link prediction. Experiments on real-world datasets show that NPN can achieve state-of-the-art performance.

Hybrid methods that utilize both content and rating information are commonly used in many recommender systems. However, most of them use either handcrafted features or the bag-of-words representation as a surrogate for the content information but they are neither effective nor natural enough. To address this problem, we develop a collaborative recurrent autoencoder (CRAE) which is a denoising recurrent autoencoder (DRAE) that models the generation of content sequences in the collaborative filtering (CF) setting. The model generalizes recent advances in recurrent deep learning from i.i.d. input to non-i.i.d. (CF-based) input and provides a new denoising scheme along with a novel learnable pooling scheme for the recurrent autoencoder. To do this, we first develop a hierarchical Bayesian model for the DRAE and then generalize it to the CF setting. The synergy between denoising and CF enables CRAE to make accurate recommendations while learning to fill in the blanks in sequences. Experiments on real-world datasets from different domains (CiteULike and Netflix) show that, by jointly modeling the order-aware generation of sequences for the content information and performing CF for the ratings, CRAE is able to significantly outperform the state of the art on both the recommendation task based on ratings and the sequence generation task based on content information.