The next big thing in the social sector has officially arrived. Machine learning is now at the center of international conferences, $25 million dollar funding competitions, fellowships at prestigious universities, and Davos-launched initiatives. Yet amidst all of the hype, it can be difficult to understand which social sector problems machine learning is best positioned to solve, how organizations can practically use it to enhance their impact, and what kind of sector-wide investments can enable the ambitious use of it for social good in the future. Our work at IDinsight, a nonprofit that uses data and evidence to help leaders in the social sector combat poverty, and the work of other organizations offer some insights into these questions. Machine learning uses data (usually a lot) and statistical algorithms to predict something unknown.

WINTER PARK, Fla.--(BUSINESS WIRE)--Feb 20, 2019--COPC Inc., a global consulting firm that helps companies improve operations to transform the customer experience, and Execs In The Know, a global community of customer experience professionals, have announced the release of the 2018 Corporate Edition of the Customer Experience Management Benchmark (CXMB) Series. The report, The CX Journey: Understanding Corporate Strategies and Best Practices, provides customer experience management insights from the corporate perspective. A key finding is that since 2017, companies have dramatically increased their use of artificial intelligence (AI)-powered solutions for customer service. "Our new corporate report shows that companies see tremendous potential in AI-powered solutions for customer care, both in applications that are customer-facing and in those that assist call center agents with their work. However, we also know from previous research that customers want a quick and easy way out of any AI-powered solution to reach a live person. Our findings overwhelmingly show that companies are keenly aware of this necessity in any customer-facing application. And while customers still want that personal interaction, we think that AI-powered solutions will find their appropriate place in the service journey," said Kyle Kennedy, president and chief operating officer, COPC Inc.

Bayesian optimisation (BO) has been a successful approach to optimise functions which are expensive to evaluate and whose observations are noisy. Classical BO algorithms, however, do not account for errors about the location where observations are taken, which is a common issue in problems with physical components. In these cases, the estimation of the actual query location is also subject to uncertainty. In this context, we propose an upper confidence bound (UCB) algorithm for BO problems where both the outcome of a query and the true query location are uncertain. The algorithm employs a Gaussian process model that takes probability distributions as inputs. Theoretical results are provided for both the proposed algorithm and a conventional UCB approach within the uncertain-inputs setting. Finally, we evaluate each method's performance experimentally, comparing them to other input noise aware BO approaches on simulated scenarios involving synthetic and real data.

Terrorism has become one of the most tedious problems to deal with and a prominent threat to mankind. To enhance counter-terrorism, several research works are developing efficient and precise systems, data mining is not an exception. Immense data is floating in our lives, though the scarce availability of authentic terrorist attack data in the public domain makes it complicated to fight terrorism. This manuscript focuses on data mining classification techniques and discusses the role of United Nations in counter-terrorism. It analyzes the performance of classifiers such as Lazy Tree, Multilayer Perceptron, Multiclass and Na\"ive Bayes classifiers for observing the trends for terrorist attacks around the world. The database for experiment purpose is created from different public and open access sources for years 1970-2015 comprising of 156,772 reported attacks causing massive losses of lives and property. This work enumerates the losses occurred, trends in attack frequency and places more prone to it, by considering the attack responsibilities taken as evaluation class.

We are in the midst of the Artificial Intelligence Revolution (AIR), the next major epoch in the history of technological innovation. Artificial intelligence (AI) is globally gaining momentum not only in scientific research, but also in business, finance, consumer, art, healthcare, esports, pop culture, and geopolitics. As AI becomes increasingly pervasive, it is important to examine at a macro level whether AI gains from disorder. Antifragile is a term and concept put forth by Nassim Nicholas Taleb, a former quantitative trader and self-proclaimed "flâneur" turned author of New York Times bestseller of "The Black Swan: The Impact of the Highly Improbable." Taleb describes antifragile as the "exact opposite of fragile" which is "beyond resilience or robustness" in "Antifragile: Things That Gain From Disorder."

SAO PAULO/DETROIT - Ford Motor Co. said on Tuesday it will close its oldest factory in Brazil and exit its heavy commercial truck business in South America, a move that could cost more than 2,700 jobs as part of a restructuring meant to end losses around the world. Ford previously said the global reorganization, to impact thousands of jobs and possible plant closures in Europe, would result in $11 billion in charges. Following that announcement, analysts and investors had expected a similar restructuring in South America. Ford Chief Executive Jim Hackett said last month that investors would not have to wait long for the South American reorganization plan. The factory slated for closure is in Sao Bernardo do Campo, an industrial suburb of Sao Paulo that has operated since 1967.

Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret data dependencies. CA has found applications in fields ranging from epidemiology to social sciences. However, current methods used to perform CA do not scale to large, high-dimensional datasets. By re-interpreting the objective in CA using an information-theoretic tool called the principal inertia components, we demonstrate that performing CA is equivalent to solving a functional optimization problem over the space of finite variance functions of two random variable. We show that this optimization problem, in turn, can be efficiently approximated by neural networks. The resulting formulation, called the correspondence analysis neural network (CA-NN), enables CA to be performed at an unprecedented scale. We validate the CA-NN on synthetic data, and demonstrate how it can be used to perform CA on a variety of datasets, including food recipes, wine compositions, and images. Our results outperform traditional methods used in CA, indicating that CA-NN can serve as a new, scalable tool for interpretability and visualization of complex dependencies between random variables.

Samsung Electronics will introduce its new products and solutions to its business partners around the world at Samsung Forum 2019. During the two-month event, strategic products including the 2019 QLED TV lineup as well as customized products for regional markets will be showcased. Based on'New Bixby,' Samsung's intelligence platform, Connected Solution will also be exhibited where global business partners can interact with various Samsung products. Starting with the European Forum, Samsung will invite media and partners from Europe, Southwest Asia and Latin America to Porto of Portugal from February 12th to 22nd. From March 7th to 11th, Samsung will host the Middle East and CIS (Commonwealth of Independent States) Forum in Antalya of Turkey.

Machine Learning as a Service Market to reach USD 16.13 billion by 2025 Machine Learning as a Service Market valued approximately USD 0.87 billion in 2017 is anticipated to grow with a healthy growth rate of more than 43.9% over the forecast period 2018-2025. Machine learning as a service is a significant range of solutions and services that are offered by cloud service providers. The tools offered by service providers include APIs, data visualization, natural language processing, face recognition, deep learning, and predictive analytics. The main benefit associated with these services is that the customers are able to quickly start with machine learning with no need to install or download any software on their servers. Enhancements in technology, growth in data volume and rise in IT spending in some of the developing regions are the major factors which are driving the growth in the global market.

Machine learning clustering techniques are not the only way to extract topics from a text data set. Text mining literature has proposed a number of statistical models, known as probabilistic topic models, to detect topics from an unlabeled set of documents. One of the most popular models is the latent Dirichlet allocation (LDA) algorithm developed by Blei, Ng, and Jordan [i]. LDA is a generative unsupervised probabilistic algorithm that isolates the top K topics in a data set as described by the most relevant N keywords. In other words, the documents in the data set are represented as random mixtures of latent topics, where each topic is characterized by a Dirichlet distribution over a fixed vocabulary.