HONG KONG – SoftBank Group Corp. is in talks to invest in Makeblock Co., a Chinese maker of DIY robotics, people familiar with the matter said. Makeblock is seeking to raise about $60 million in a series C round by the end of this year as it targets a doubling of its valuation to $400 million, the people said, asking not to be identified talking about private plans. The figures are preliminary and could vary depending on negotiations, the people said. SoftBank and Makeblock declined to comment. Makeblock is competing with a coterie of Chinese startups, as well as Pearson PLC, Lego Group and Sony Corp., who all want a slice of a learning industry that focuses on science, technology, engineering and math.

It's starting to seem like the KFC restaurants in China aren't really interested in serving chicken. Following the chain's earlier experiments with robot-staffed restaurants and facial-recognition technology, KFC China has teamed up with Huawei -- the country's largest smartphone company -- to release a Colonel-themed smartphone in honor of the KFC's 30th anniversary in China. "Both brands have witnessed the rise of China in these 30 years, and both embody the spirit of the times. Our collaboration is a homage to the era," said Steven Li, the senior vice president of KFC marketing for Yum China, in a statement to Campaign Asia-Pacific. The phone, named the KFC Huawei 7 Plus, comes with red casing and features an outline of the Colonel's face on the back.

Telecom operators have started using artificial intelligence (AI) technology capabilities that process and analyze real-time customer data to recommend and automatically trigger the next best action. Ooredoo, a top telecom operator in the Middle East, is investing in artificial intelligence technology to ensure next-generation customer experience to its mobile phone users in Qatar – as part of its digital strategy. Vodafone India faced 4.8 percent churn in fiscal 2017 as compared with 4.3 percent n fiscal 2016 due to competition. Churn of post-paid customers was 1.9 percent against 1.8 percent. Churn of pre-paid users was 5 percent against 4.5 percent.

For years Dish Network Corp. DISH 1.00% Chief Executive Charlie Ergen has sought out deals and partnerships with just about every major telecom company, from Sprint Corp. S -2.31% to T-Mobile US Inc. TMUS -0.83% to AT&T Inc. --so far, to no avail. Now, the satellite-television mogul is turning his attention to the technology world and a new--and somewhat surprising--potential partner has emerged: Amazon.com The two men--eccentric billionaires with geek tendencies and shared interest in space and robotics--have gotten to know each other better over the past year and have discussed a partnership to enter the wireless business, according to people familiar with the matter. Among the ideas: Amazon could help finance a network Dish is building focused on the "Internet of Things"--the idea that everything from bikes to Amazon's drones can have web connectivity everywhere. Another idea is that Amazon, as a founding partner of Dish's new wireless network, could offer an option for Prime members to pay a little more a month for a connectivity or phone plan, one of the people said.

Overview: Artificial Intelligence (AI) and Cognitive Computing are increasingly integrated in many areas including Internet search, entertainment, commerce applications, content optimization, and robotics. The long-term prospect for these technologies is that they will become embedded in many different other technologies and provide autonomous decision making on behalf of humans, both directly, and indirectly through many processes, products, and services. AI will anticipated to have an ever increasing role in ICT including both traditional telecommunications as well as many communications enabled applications and digital commerce. Fast growing AI technologies for consumer facing industries include chat bots and Virtual Personal Assistants (VPA) and smart advisors. These technologies leverage autonomous agents to enable an ambient user experience for applications, services, and enhanced commerce.

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included.

Of all the fields in the chatbot-crazed world, customer service is one of the prime targeting areas for automation. Virtual Customer Agents (customer service focused bots or VCAs for short) are intelligent systems that are able to understand what users ask via chat and provide them with adequate answers to solve users' issues. In the context of this article when we talk about VCAs we mean systems that are able to understand natural language and texting and do not just operate in a rule based multiple-choice environment. In short, these VCAs compete directly with humans to resolve customer service issues. The current reality of chatbots nicely counterbalances all the hype that AI is getting and also offers guidance as to where things need to develop.

I attended the Huawei European Innovation Day recently, and was enthralled by how the new technology is giving rise to industrial revolutions. These revolutions are what will eventually unlock the development potential around the world. It is important to leverage the emerging technologies, since they are the resources which will lead us to innovation and progress. Huawei is innovative in its partnerships and collaboration to define the future, and the event was a huge success. For many people, the concept of Artificial Intelligence (AI) is a thing of the future. It is the technology that has yet to be introduced.

The security startup Jask launched today with $12 million in Series A funding, led by Dell Technologies Capital. The company uses artificial intelligence (AI) to monitor and secure networks. Battery Ventures, which led the firm's $2.5 million seed funding round, also contributed to the series A round. This brings the startup's total funding to $14.5 million. The company plans to use the money to expand its data science and machine learning teams, accelerate product development, and focus on machine learning and AI research.

In this paper we investigate the behavioural differences between mobile phone customers with prepaid and postpaid subscriptions. Our study reveals that (a) postpaid customers are more active in terms of service usage and (b) there are strong structural correlations in the mobile phone call network as connections between customers of the same subscription type are much more frequent than those between customers of different subscription types. Based on these observations we provide methods to detect the subscription type of customers by using information about their personal call statistics, and also their egocentric networks simultaneously. The key of our first approach is to cast this classification problem as a problem of graph labelling, which can be solved by max-flow min-cut algorithms. Our experiments show that, by using both user attributes and relationships, the proposed graph labelling approach is able to achieve a classification accuracy of $\sim 87\%$, which outperforms by $\sim 7\%$ supervised learning methods using only user attributes. In our second problem we aim to infer the subscription type of customers of external operators. We propose via approximate methods to solve this problem by using node attributes, and a two-ways indirect inference method based on observed homophilic structural correlations. Our results have straightforward applications in behavioural prediction and personal marketing.