Organizations have generated unprecedented amounts of employee feedback through weekly or monthly pulse surveys, annual engagement surveys, and internal social networks and collaboration platforms. But many still struggle with how to efficiently comb through that mountain of information to identify actionable insights leaders can use to improve employee engagement and retention. Some companies are now turning to artificial intelligence (AI) tools to conduct sentiment analysis on employee feedback, gauge how employees feel and address their concerns. While text analysis of survey responses isn't new, the emergence of smarter algorithms enables faster and more precise search and categorization of unstructured data, such as open-ended comments, said Alan Lepofsky, vice president and principal analyst with Constellation Research, a technology research firm in Silicon Valley. Lepofsky, author of the recent report Why Artificial Intelligence Will Power the Future of Work, said vendors have made advances in sentiment analysis technology.

In partnership with... Driven by the rapid progress in Artificial Intelligence (AI) research, intelligent machines are gaining the ability to learn, improve and make calculated decisions in ways that will enable them to perform tasks previously thought to rely solely on human experience, creativity, and ingenuit y. As a result, we will in the near future see large parts of our lives influenced by AI. AI innovation will also be central to the achievement of the United Nations' Sustainable Development Goals (SDGs) and will help solving humanity's grand challenges by capitalizing on the unprecedented quantities of data now being generated on sentiment behavior, human health, commerce, communications, migration and more. With large parts of our lives being influenced by AI, it is critical that government, industry, academia and civil society work together to evaluate the opportunities presented by AI, ensuring that AI benefits all of humanity. Responding to this critical issue, ITU and the XPRIZE Foundation are organizing AI for Good Global Summit in Geneva, 7-9 June, 2017 in partnership with a number of UN sister agencies.

In the latest round of man versus machine, machine has come out on top. Google's AlphaGo beat Go world champion Ke Jie for a second time in as many days, taking an unassailable lead in the three-part series. By rights, Deepmind's AI can now be considered the world's best Go player, having beaten the game's two biggest names in a little under a year. Although today's result wasn't as "close" as the first match, where AlphaGo won by just half a point, Deepmind CEO Demis Hassabis said that Ke Jie played "perfectly" for much of the contest before he resigned, at least according to AlphaGo's evaluations. "For the first 100 moves it was the closest we've ever seen anyone play against the Master version of AlphaGo," Hassabis confirmed in the post-game press conference. "Today's game was different from the first," Ke said, reported by The Verge.

Summary: In this last article in our series on recommenders we look to the future to see how the rapidly emerging capabilities of Deep Learning can be used to enhance recommender performance. In our first article, "Understanding and Selecting Recommenders" we talked about the broader business considerations and issues for recommenders as a group. In our second article, "5 Types of Recommenders" we attempted to detail the most dominant styles of Recommenders. Our third article, "Recommenders: Packaged Solutions or Home Grown" focused on how to acquire different types of recommenders and how those sources differ. In this last article in our series on recommenders we look to the future to see how the rapidly emerging capabilities of Deep Learning can be used to enhance performance.

This article was posted by Arpan Gupta (Indian Institute of Technology). Let's learn from a precise demo on Fitting Logistic Regression on Titanic Data Set for Machine Learning Description:On April 15, 1912, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This tragedy has led to better safety regulations for ships. To see the algorithms, click here.

Boston-based Partners HealthCare on Wednesday said it plans to integrate deep learning technology from GE Healthcare across its network. The 10-year collaboration will involve Massachusetts General Hospital and Brigham and Women's Hospital Center for Clinical Data Science. The initiative will feature co-located, multidisciplinary teams with broad access to data, computational infrastructure and clinical expertise. The initial focus will be on the development of applications aimed at improving clinician productivity and patient outcomes in diagnostic imaging. Eventually, the groups will create new business models for applying AI to healthcare, and they will develop products for additional medical specialties, such as molecular pathology, genomics and population health.

Teleperformance (Paris:RCF), the worldwide leader in outsourced omnichannel customer experience management, today announced the launch of its chat bot (artificial intelligence) technology, increasing its portfolio of services. The new solution was leveraged by a strategic partnership with Artificial Solutions, a technology leader in artificial intelligence and Natural Language Interaction (NLI). Teleperformance provides services in 265 languages and will initially provide artificial intelligence services in 35 languages, using sophisticated chat bots with state-of-the-art natural language understanding and intelligence capabilities. The chat bots can assist customers in factual inquiries by offering automated customer services in natural written language. Teleperformance has developed a distinctive capability of blending chat bots with live support to make the process of addressing customer inquiries seamless, personalized and efficient.

We study implicit regularization when optimizing an underdetermined quadratic objective over a matrix $X$ with gradient descent on a factorization of $X$. We conjecture and provide empirical and theoretical evidence that with small enough step sizes and initialization close enough to the origin, gradient descent on a full dimensional factorization converges to the minimum nuclear norm solution.

It can be difficult to tell whether a trained generative model has learned to generate novel examples or has simply memorized a specific set of outputs. In published work, it is common to attempt to address this visually, for example by displaying a generated example and its nearest neighbor(s) in the training set (in, for example, the L2 metric). As any generative model induces a probability density on its output domain, we propose studying this density directly. We first study the geometry of the latent representation and generator, relate this to the output density, and then develop techniques to compute and inspect the output density. As an application, we demonstrate that "memorization" tends to a density made of delta functions concentrated on the memorized examples. We note that without first understanding the geometry, the measurement would be essentially impossible to make.

The majority of medical documents and electronic health records (EHRs) are in text format that poses a challenge for data processing and finding relevant documents. Looking for ways to automatically retrieve the enormous amount of health and medical knowledge has always been an intriguing topic. Powerful methods have been developed in recent years to make the text processing automatic. One of the popular approaches to retrieve information based on discovering the themes in health & medical corpora is topic modeling, however, this approach still needs new perspectives. In this research we describe fuzzy latent semantic analysis (FLSA), a novel approach in topic modeling using fuzzy perspective. FLSA can handle health & medical corpora redundancy issue and provides a new method to estimate the number of topics. The quantitative evaluations show that FLSA produces superior performance and features to latent Dirichlet allocation (LDA), the most popular topic model.