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.

Traditionally undervalued in the tech industry, empathy -- which is the ability to read and respond to another person's feelings, thoughts and experiences -- is a trait hiring managers and C-level executives can no longer ignore. After all, in a world where artificial intelligence will take up to 5 million jobs away from humans by 2020, the McKinsey Global Institute predicts that up to 14% of human workers will need to adapt to new occupations to secure our future in the workforce. In other words, as we start sharing the workforce with more machines, human soft skills such as empathy will be at a premium. And, that premium is justified. Hiring employees who are empathetic helps companies increase productivity, develop strong leadership and retain high-performing talent.

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.

Artificial intelligence (AI) is a rapidly growing industry that's perpetually impressing people with what's possible. Those advancements wouldn't happen without the people working tirelessly to research innovations. Many of the people pushing artificial intelligence forward are male, and that's evidence of a known gender gap associated with the industry. Concentrated efforts are needed to tackle the problem, but it's a situation that could change. The five women here are among those leading the way in AI research and inspiring everyone by their dedication.

I have worked on the problem of open-sourcing Machine Learning versus sensitivity for a long time, especially in disaster response contexts: when is it right/wrong to release data or a model publicly? This article is a list of frequently asked questions, the answers that are best practice today, and some examples of where I have encountered them. The criticism of OpenAI's decision included how it limits the research community's ability to replicate the results, and how the action in itself contributes to media fear of AI that is hyperbolic right now. It was this tweet that first caught my eye. Anima Anandkumar has a lot of experience bridging the gap between research and practical applications of Machine Learning.

University of Johannesburg and CIRANO; 4 / 46 AEA 2019 - Atlanta SKEMA Introduction Introduction China's rapid development have spurred large migration from rural areas to urban areas Between 1990 and the end of 2015 the proportion of China's population living in urban areas jumped from 26% to 56% Currently estimated by census, there are more than 240 million rural-to-urban migrants and more than 160 million working in cities outside of their hukou.

In the age of data driven solution, the customer demographic attributes, such as gender and age, play a core role that may enable companies to enhance the offers of their services and target the right customer in the right time and place. In the marketing campaign, the companies want to target the real user of the GSM (global system for mobile communications), not the line owner. Where sometimes they may not be the same. This work proposes a method that predicts users' gender and age based on their behavior, services and contract information. We used call detail records (CDRs), customer relationship management (CRM) and billing information as a data source to analyze telecom customer behavior, and applied different types of machine learning algorithms to provide marketing campaigns with more accurate information about customer demographic attributes. This model is built using reliable data set of 18,000 users provided by SyriaTel Telecom Company, for training and testing. The model applied by using big data technology and achieved 85.6% accuracy in terms of user gender prediction and 65.5% of user age prediction. The main contribution of this work is the improvement in the accuracy in terms of user gender prediction and user age prediction based on mobile phone data and end-to-end solution that approaches customer data from multiple aspects in the telecom domain.

Automatically reasoning with conflicting generic clinical guidelines is a burning issue in patient-centric medical reasoning where patient-specific conditions and goals need to be taken into account. It is even more challenging in the presence of preferences such as patient's wishes and clinician's priorities over goals. We advance a structured argumentation formalism for reasoning with conflicting clinical guidelines, patient-specific information and preferences. Our formalism integrates assumption-based reasoning and goal-driven selection among reasoning outcomes. Specifically, we assume applicability of guideline recommendations concerning the generic goal of patient well-being, resolve conflicts among recommendations using patient's conditions and preferences, and then consider prioritised patient-centered goals to yield non-conflicting, goal-maximising and preference-respecting recommendations. We rely on the state-of-the-art Transition-based Medical Recommendation model for representing guideline recommendations and augment it with context given by the patient's conditions, goals, as well as preferences over recommendations and goals. We establish desirable properties of our approach in terms of sensitivity to recommendation conflicts and patient context.

Talking about Intel, one immediately thinks of computer science, since this American company manufactures world-renowned microprocessors for computers. It now aims to use artificial intelligence to protect elephants, the largest terrestrial mammals threatened with extinction due to poaching. These pachyderms are killed for their ivory tusks. It is a software that works with "intelligent" cameras. The project is supported by the National Geographic Society and the Leonardo DiCaprio Foundation.