Almost a quarter century ago, a book was written about how organizations would focus on share of customer as opposed to share of market, building a personalized collaboration driven by big data. With advanced analytics, banking may finally getting close to realizing this vision. In 1993, a then revolutionary book, "The One to One Future: Building Relationships One Customer at a Time" was published, proposing the idea that as technology makes it affordable to track individual customers, marketing shifts from finding customers for products to finding products for customers. According to the authors, Don Peppers and Martha Rogers, Ph.D., a company could use technology to gather information about, and to communicate directly with, individuals to form a commercial bond. The book became a bestseller, and was on every marketer's bookshelf … almost a quarter century ago.

Samsung has agreed to acquire Viv, the next-gen AI assistant that was developed by Siri co-founders Dag Kittlaus and Adam Cheyer. TechCrunch reports that it will continue to operate as an independent company, providing services to Samsung and its platforms. Viv made numerous headlines earlier this year, when the team demonstrated the power of its assistant. In one scenario, it was able to successfully order a pizza over the Internet customized with toppings from several employees with a few simple voice commands. Here are some excerpts from Kittlaus' Medium post regarding the acquisition: Samsung, as you surely know, is the market leader in smart devices across a wide range of use cases and industries.

COLORADO SPRINGS, CO--(Marketwired - October 05, 2016) - Cherwell Software, a global leader in IT service management (ITSM) solutions, announced today to customers at its annual conference that the Cherwell Service Management platform is utilizing Microsoft Azure Machine Learning to integrate predictive analytics into its ITSM solution. Azure Machine Learning, part of the Microsoft Cortana Intelligence Suite, provides a cloud-based service that enables companies to apply statistical techniques to large amounts of data and leverage analytics to solve problems and create smarter applications. Cherwell customers can now harness the power of Azure Machine Learning to analyze incident and ticket data stored within Cherwell Service Management, and utilize pattern recognition to create algorithms that assist with incident prioritization, triage, and resolution. "Azure Machine Learning and the Cortana Intelligence Suite offer an easy way to add powerful intelligence to existing applications. By integrating Azure Machine Learning with Cherwell Service Management, Cherwell has introduced the next generation of service management," commented Dawson Stoops, VP of Technical Alliances at Cherwell.

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

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

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

Samsung has agreed to acquire Viv, an AI and assistant system co-founded by Dag Kittlaus, Adam Cheyer and Chris Brigham -- who created Siri, which was acquired by Apple in 2010. The three left Apple in the years after the acquisition and founded Viv in 2012. Pricing information was not available, but we'll check around. Viv has been billed as a more extensible, powerful version of Siri. Viv will continue to operate as an independent company that will provide services to Samsung and its platforms.

In artificial neural networks, learning from data is a computationally demanding task in which a large number of connection weights are iteratively tuned through stochastic-gradient-based heuristic processes over a cost-function. It is not well understood how learning occurs in these systems, in particular how they avoid getting trapped in configurations with poor computational performance. Here we study the difficult case of networks with discrete weights, where the optimization landscape is very rough even for simple architectures, and provide theoretical and numerical evidence of the existence of rare - but extremely dense and accessible - regions of configurations in the network weight space. We define a novel measure, which we call the "robust ensemble" (RE), which suppresses trapping by isolated configurations and amplifies the role of these dense regions. We analytically compute the RE in some exactly solvable models, and also provide a general algorithmic scheme which is straightforward to implement: define a cost-function given by a sum of a finite number of replicas of the original cost-function, with a constraint centering the replicas around a driving assignment. To illustrate this, we derive several powerful new algorithms, ranging from Markov Chains to message passing to gradient descent processes, where the algorithms target the robust dense states, resulting in substantial improvements in performance. The weak dependence on the number of precision bits of the weights leads us to conjecture that very similar reasoning applies to more conventional neural networks. Analogous algorithmic schemes can also be applied to other optimization problems.

To infer multilayer deep representations of high-dimensional discrete and nonnegative real vectors, we propose an augmentable gamma belief network (GBN) that factorizes each of its hidden layers into the product of a sparse connection weight matrix and the nonnegative real hidden units of the next layer. The GBN's hidden layers are jointly trained with an upward-downward Gibbs sampler that solves each layer with the same subroutine. The gamma-negative binomial process combined with a layer-wise training strategy allows inferring the width of each layer given a fixed budget on the width of the first layer. Example results illustrate interesting relationships between the width of the first layer and the inferred network structure, and demonstrate that the GBN can add more layers to improve its performance in both unsupervisedly extracting features and predicting heldout data. For exploratory data analysis, we extract trees and subnetworks from the learned deep network to visualize how the very specific factors discovered at the first hidden layer and the increasingly more general factors discovered at deeper hidden layers are related to each other, and we generate synthetic data by propagating random variables through the deep network from the top hidden layer back to the bottom data layer.

Modern data is messy and high-dimensional, and it is often not clear a priori what are the right questions to ask. Instead, the analyst typically needs to use the data to search for interesting analyses to perform and hypotheses to test. This is an adaptive process, where the choice of analysis to be performed next depends on the results of the previous analyses on the same data. Ultimately, which results are reported can be heavily influenced by the data. It is widely recognized that this process, even if well-intentioned, can lead to biases and false discoveries, contributing to the crisis of reproducibility in science. But while %the adaptive nature of exploration any data-exploration renders standard statistical theory invalid, experience suggests that different types of exploratory analysis can lead to disparate levels of bias, and the degree of bias also depends on the particulars of the data set. In this paper, we propose a general information usage framework to quantify and provably bound the bias and other error metrics of an arbitrary exploratory analysis. We prove that our mutual information based bound is tight in natural settings, and then use it to give rigorous insights into when commonly used procedures do or do not lead to substantially biased estimation. Through the lens of information usage, we analyze the bias of specific exploration procedures such as filtering, rank selection and clustering. Our general framework also naturally motivates randomization techniques that provably reduces exploration bias while preserving the utility of the data analysis. We discuss the connections between our approach and related ideas from differential privacy and blinded data analysis, and supplement our results with illustrative simulations.