If you're looking for the future of mobile, it may be time to stop staring at the palm of your hand and look instead toward the open roads and sky. On Monday, the ginormous trade show Mobile World Congress will open its 2017 edition in Barcelona where about 100,000 people are expected. While there will be the usual unveiling of select smartphones and tablets, MWC has dramatically increased the presence of drones and autonomous vehicles as it seeks to expand the concept of what we mean by the word "mobile." The event is dedicating almost an entire day each to vehicles -- both connected and autonomous -- and drones. In addition, the conference has added multiple sessions on artificial intelligence, both as it relates to vehicles and drones, as well as other mobile uses.

A thought occurs to me oftentimes in the quiet hours of the morning. What if I had more time like this through the day? How will I spend it? What will it be in pursuit of? Will it be in the pursuit of money?

Samsung SDS has unveiled an artificial intelligence (AI)-based chatbot for training retail personnel. Nexshop Training, which was unveiled at the Mobile World Congress (MWC) in Barcelona, is a virtual assistant that helps teach retail personnel how to manage customers and retail space, the South Korean IT solution giant said. It can answer voice and text questions like "What are the new features for the upcoming tablet PC," or "What promotions will be available next week?," The company also showed off Nexshop Sales, which allows store managers to check stock, sales, and consumer purchase history via their smartphones. Nexshop Marketing also allows managers to know what products customers in the shop will prefer; while Nexsign, a biometric security solution, will also be displayed, which features fingerprint and voice security for both mobile and desktop use.

Ahmad Hasan Abu Khayr al-Masri, al Qaeda's second in command, reportedly was killed Sunday in a drone strike in Syria. Israeli broadcaster Arutz Sheva cited unconfirmed reports saying a U.S. drone strike near al-Mastoumeh in Idlib province killed al-Masri, who has been described as the general deputy to al Qaeda leader Ayman al-Zawahiri. Video of the aftermath was posted on YouTube by the Smart News Agency. Al-Masri, 59, was in Iranian custody for a dozen years until 2015 when he was released and moved to Syria. Pictures of the car in which al-Masri reportedly was traveling were posted on Twitter.

Brakes squeal, hard drives crunch, air conditioners rattle, and their owners know it's time for a service call. But some of the most valuable machinery in the world often operates with nobody around to hear the mechanical breakdowns, from the chillers and pumps that drive big-building climate control systems to the massive turbines at hydroelectric power plants. That's why a number of startups are working to train computers to pick up on changes in the sounds, vibrations, heat emissions, and other signals that machines give off as they're working or failing. The hope is that the computers can catch mechanical failures before they happen, saving on repair costs and reducing downtime. "We're developing an expert mechanic's brain that identifies exactly what is happening to a machine by the way that it sounds," says Amnon Shenfeld, founder and CEO of 3DSignals, a startup based in Kfar Saba, Israel, that is using machine learning to train computers to listen to machinery and diagnose problems at facilities like hydroelectric plants and steel mills.

Federico Antoni is managing partner at ALLVP, an early-stage VC based in Mexico. He is a lecturer in management at the Stanford Graduate School of Business. "Over the last couple of years, a billion new people have joined the super-connected world. Billions more around the developing world, now, walk with a high-speed computer in their pockets. And yet, they don't have a bank account, a formal education or access to most of the services we take for granted in the U.S. Imagine the possibilities… imagine how you can change the lives of billions of people."

These days, even when I read news about India's state owned air carrier, as "New AI connectivity between cities X & Y", the first connection my (poor human) brain makes is to Artificial Intelligence. Only then it connects to Air India. This is because everyone (in my extended professional circle) is talking of Artificial Intelligence. Though I had done a short elective on AI in my computer engineering days (16 years back), done some basic LISP programming as part of it, had presented a paper on'Genetic Algorithms' in a seminar (again that long ago), and am an avid watcher of Sci-Fi movies around AI (and claim to have understood Matrix the first time I watched it - with subtitles though, ha!), I realize that my knowledge of what AI is, is no better than a layperson (or worse, since half knowledge is more dangerous). This article is an endeavor to sort of unpack AI (and the associated words like ML - machine learning, DL - deep learning) for myself. And publishing it around to benefit others that are in a similar boat. And also to get feedback from others to correct my understanding. Without much dumbing down (or may be with), Artificial Intelligence is fundamentally anything that a computing machine does. Even the basic accountant calculator that does 2 2 4 is'artificial intelligence'. However, we do not consider them as'AI' because of what is called'AI effect' - When we know'how' a machine does something'intelligent,' it ceases to be regarded as intelligent. Tools we take for granted (pick any of your favorite app) are all'AI'. Anything that is'eaten by software' can be considered as Artificial Intelligence. And why is everyone talking about it?

Abstract: "Tangible heritage, such as temples and statues, is disappearing day-by-day due to human and natural disaster. In e-tangible heritage, such as folk dances, local songs, and dialects, has the same story due to lack of inheritors and mixing cultures. We have been developing methods to preserve such tangible and in-tangible heritage in the digital form. This project, which we refer to as e-Heritage, aims not only record heritage, but also analyzes those recorded data for better understanding as well as displays those data in new forms for promotion and education. This talk consists of three parts. The first part briefly covers e-Tangible heritage, in particular, our projects in Cambodia and Kyushu. Here I emphasize not only challenge in data acquisition but also the importance to create the new aspect of science, Cyber-archaeology, which allows us to have new findings in archaeology, based on obtained digital data. The second part covers how to display a Japanese folk dance by the performance of a humanoid robot. Here, we follow the paradigm, learning-from-observation, in which a robot learns how to perform a dance from observing a human dance performance. Due to the physical difference between a human and a robot, the robot cannot exactly mimic the human actions. Instead, the robot first extracts important actions of the dance, referred to key poses, and then symbolically describes them using Labanotation, which the dance community has been using for recording dances. Finally, this labanotation is mapped to each different robot hardware for reconstructing the original dance performance. The third part tries to answer the question, what is the merit to preserve folk dances by using robot performance by the answer that such symbolic representations for robot performance provide new understandings of those dances. In order to demonstrate this point, we focus on folk dances of native Taiwanese, which consists of 14 different tribes. We have converted those folk dances into Labanotation for robot performance. Further, by analyzing these Labanotations obtained, we can clarify the social relations among these 14 tribes."

Bike Sharing Systems (BSSs) are widely adopted in major cities of the world due to concerns associated with extensive private vehicle usage, namely, increased carbon emissions, traffic congestion and usage of nonrenewable resources. In a BSS, base stations are strategically placed throughout a city and each station is stocked with a pre-determined number of bikes at the beginning of the day. Customers hire the bikes from one station and return them at another station. Due to unpredictable movements of customers hiring bikes, there is either congestion (more than required) or starvation (fewer than required) of bikes at base stations. Existing data has shown that congestion/starvation is a common phenomenon that leads to a large number of unsatisfied customers resulting in a significant loss in customer demand. In order to tackle this problem, we propose an optimisation formulation to reposition bikes using vehicles while also considering the routes for vehicles and future expected demand. Furthermore, we contribute two approaches that rely on decomposability in the problem (bike repositioning and vehicle routing) and aggregation of base stations to reduce the computation time significantly. Finally, we demonstrate the utility of our approach by comparing against two benchmark approaches on two real-world data sets of bike sharing systems. These approaches are evaluated using a simulation where the movements of customers are generated from real-world data sets.

Multimodal clustering is an unsupervised technique for mining interesting patterns in $n$-adic binary relations or $n$-mode networks. Among different types of such generalized patterns one can find biclusters and formal concepts (maximal bicliques) for 2-mode case, triclusters and triconcepts for 3-mode case, closed $n$-sets for $n$-mode case, etc. Object-attribute biclustering (OA-biclustering) for mining large binary datatables (formal contexts or 2-mode networks) arose by the end of the last decade due to intractability of computation problems related to formal concepts; this type of patterns was proposed as a meaningful and scalable approximation of formal concepts. In this paper, our aim is to present recent advance in OA-biclustering and its extensions to mining multi-mode communities in SNA setting. We also discuss connection between clustering coefficients known in SNA community for 1-mode and 2-mode networks and OA-bicluster density, the main quality measure of an OA-bicluster. Our experiments with 2-, 3-, and 4-mode large real-world networks show that this type of patterns is suitable for community detection in multi-mode cases within reasonable time even though the number of corresponding $n$-cliques is still unknown due to computation difficulties. An interpretation of OA-biclusters for 1-mode networks is provided as well.