Business and industry have only just scratched the surface when it comes to realizing the potential value from analytics. Consulting firm McKinsey predicted huge value to come in its 2011 report, Big Data: The Next Frontier for Innovation, Competition, and Productivity. The firm released a new report, The Age of Analytics: Competing In a Data-Driven World, to update its previous research, look at some of the new trends, and provide a picture of the current state of analytics in organizations today. InformationWeek spoke with one of the new report's lead researchers and authors, McKinsey Global Institute (MGI) partner Michael Chui in an interview. Overall, MGI still believes there is huge potential for data and analytics to provide value -- even more than anticipated five years ago when the first report was release.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

December 27, 2016 --With excitement swirling around the possibility of a ninth planet, a rebound in the global tiger population for the first time in a century, and the DNA sequenced in space for the first time, 2016 has been a year full of scientific wonder. But as the year comes to a close, there are some breakthroughs particularly worth highlighting. In February, a century after Albert Einstein predicted their existence, an international team of researchers confirmed that they had actually detected a ripple in the fabric of spacetime for the first time. The detection of gravitational waves came across as a "chirp" across the detectors that make up the Laser Interferometer Gravitational-wave Observatory (LIGO), but the researchers say it was the result of two large celestial bodies, possibly black holes, colliding some 1.3 billion years ago. Then, in June, the scientists announced that the cosmos had chirped again.

This year has seen the birth of the first three-person baby, a dangerous Zika epidemic and a huge injustice overturned by medical science. There were also breakthroughs in a range of deadly diseases. A year ago hardly anyone had heard of Zika virus. Now the birth of babies with underdeveloped brains - known as microcephaly - is all too familiar. The World Health Organization declared the disease, which is spread by mosquitoes, a public health emergency.

A new artificial intelligence system can take still images and generate short videos that simulate what happens next similar to how humans can visually imagine how a scene will evolve, according to a new study. Humans intuitively understand how the world works, which makes it easier for people, as opposed to machines, to envision how a scene will play out. But objects in a still image could move and interact in a multitude of different ways, making it very hard for machines to accomplish this feat, the researchers said. But a new, so-called deep-learning system was able to trick humans 20 per cent of the time when compared to real footage. Researchers at the Massachusetts Institute of Technology (MIT) pitted twoneural networks against each other, with one trying to distinguish real videos from machine-generated ones, and the other trying to create videos that were realistic enough to trick the first system.

Expect the Internet of Things (IoT) and artificial intelligence (AI) to have a bigger impact on the workplace in the next few years than 3D printers and virtual reality solutions. After quizzing 566 IT professionals Spiceworks today released its latest study today, Future of IT: Hype vs. Reality. Many expect IoT devices (80 percent) and AI (60 percent) to have a beneficial effect on their business processes. Meanwhile, VR technologies are used by 7 percent of organizations and only 13 percent or survey respondents signaled that they intend to adopt it. "IT professionals are rightly concerned with the practicality of integrating emerging technology in the workplace, particularly when it comes to VR and 3D printing," said Peter Tsai, IT analyst at Spiceworks, in a statement.

A plethora of networks is being collected in a growing number of fields, including disease transmission, international relations, social interactions, and others. As data streams continue to grow, the complexity associated with these highly multidimensional connectivity data presents novel challenges. In this paper, we focus on the time-varying interconnections among a set of actors in multiple contexts, called layers. Current literature lacks flexible statistical models for dynamic multilayer networks, which can enhance quality in inference and prediction by efficiently borrowing information within each network, across time, and between layers. Motivated by this gap, we develop a Bayesian nonparametric model leveraging latent space representations. Our formulation characterizes the edge probabilities as a function of shared and layer-specific actors positions in a latent space, with these positions changing in time via Gaussian processes. This representation facilitates dimensionality reduction and incorporates different sources of information in the observed data. In addition, we obtain tractable procedures for posterior computation, inference, and prediction. We provide theoretical results on the flexibility of our model. Our methods are tested on simulations and infection studies monitoring dynamic face-to-face contacts among individuals in multiple days, where we perform better than current methods in inference and prediction.

Non-negative matrix factorization (NMF) is a prob- lem with many applications, ranging from facial recognition to document clustering. However, due to the variety of algorithms that solve NMF, the randomness involved in these algorithms, and the somewhat subjective nature of the problem, there is no clear "correct answer" to any particular NMF problem, and as a result, it can be hard to test new algorithms. This paper suggests some test cases for NMF algorithms derived from matrices with enumerable exact non-negative factorizations and perturbations of these matrices. Three algorithms using widely divergent approaches to NMF all give similar solutions over these test cases, suggesting that these test cases could be used as test cases for implementations of these existing NMF algorithms as well as potentially new NMF algorithms. This paper also describes how the proposed test cases could be used in practice.

We are in the middle of a remarkable rise in the use and capability of artificial intelligence. Much of this growth has been fueled by the success of deep learning architectures: models that map from observables to outputs via multiple layers of latent representations. These deep learning algorithms are effective tools for unstructured prediction, and they can be combined in AI systems to solve complex automated reasoning problems. This paper provides a recipe for combining ML algorithms to solve for causal effects in the presence of instrumental variables - sources of treatment randomization that are conditionally independent from the response. We show that a flexible IV specification resolves into two prediction tasks that can be solved with deep neural nets: a first-stage network for treatment prediction and a second-stage network whose loss function involves integration over the conditional treatment distribution. This Deep IV framework imposes some specific structure on the stochastic gradient descent routine used for training, but it is general enough that we can take advantage of off-the-shelf ML capabilities and avoid extensive algorithm customization. We outline how to obtain out-of-sample causal validation in order to avoid over-fit. We also introduce schemes for both Bayesian and frequentist inference: the former via a novel adaptation of dropout training, and the latter via a data splitting routine. 1 Introduction Supervised machine learning (ML) provides a myriad of effective methods for solving prediction tasks. In these tasks, the learning algorithm is trained and validated to do a good job predicting the outcome for future examples from the same data generating process (DGP).

Temporal Difference learning or TD($\lambda$) is a fundamental algorithm in the field of reinforcement learning. However, setting TD's $\lambda$ parameter, which controls the timescale of TD updates, is generally left up to the practitioner. We formalize the $\lambda$ selection problem as a bias-variance trade-off where the solution is the value of $\lambda$ that leads to the smallest Mean Squared Value Error (MSVE). To solve this trade-off we suggest applying Leave-One-Trajectory-Out Cross-Validation (LOTO-CV) to search the space of $\lambda$ values. Unfortunately, this approach is too computationally expensive for most practical applications. For Least Squares TD (LSTD) we show that LOTO-CV can be implemented efficiently to automatically tune $\lambda$ and apply function optimization methods to efficiently search the space of $\lambda$ values. The resulting algorithm, ALLSTD, is parameter free and our experiments demonstrate that ALLSTD is significantly computationally faster than the na\"{i}ve LOTO-CV implementation while achieving similar performance.