SK Telecom announced on Saturday that it has switched on its commercial 5G network, marking the occasion with a call from CEO Park Jung-ho in Bundang to Myeondong, using a Samsung 5G smartphone prototype. SK Telecom's 5G network currently covers main areas of 13 cities and counties nationwide. Mischievously, we would like to think the call went something like "Hello, can anyone hear me? There's no-one there…" That's the problem with launching a brand new network technology – penetration levels are somewhat low. Actually, that first call over its commercial 5G network was made between CEO Park located in Bundang, Gyeonggi-do and SK Telecom Manager Park Sook-hee located in Myeongdong, Seoul.

Internet of Things (IoT) and Automation are disrupting and revolutionizing the retail Industry. CES 2018 unveiled significant advancements in Artificial Intelligence and Robotics. These groundbreaking transformations will create a paradigm shift in the world of retail and how we shop. The concept of conventional grocery stores and supermarkets have taken a big hit with the advent of IoT, and online retail is continuing to thrive in the golden era of digitization. The burgeoning of the Intelligent technologies is leading the world towards Automation. The Internet of Shopping (IoS) is the future, where the supermarkets will need no human intervention in managing it.

The rate of progress in the field of artificial intelligence is one of the most hotly contested aspects of the ongoing boom in teaching computers and robots how to see the world, make sense of it, and eventually perform complex tasks both in the physical realm and the virtual one. And just how fast the industry is moving, and to what end, is typically measured not just by actual product advancements and research milestones, but also by the prognostications and voiced concerns of AI leaders, futurists, academics, economists, and policymakers. AI is going to change the world -- but how and when are still open questions. Today, findings from a group of experts were published in an ongoing effort to help answer those questions. The experts include members of Harvard, MIT, Stanford, the nonprofit OpenAI, and the Partnership on AI industry consortium, among others, and they were put together as part of the second annual AI Index.

Artificial Intelligence is taking over retail and has been used across the entire product and service cycle. Right from pre production to post sale, retail players are leveraging AI in different forms in order to bring automation. The following article sheds light on different examples where AI is successfully integrated into key retail functions. Let's go through them one by one. It is a quintessential area where AI can transform efficiency.

A new report released Monday reveals that the Internet Research Agency, the troll farm linked to Russian intelligence, actually increased its social media activity after the 2016 election. The report, which took seven months to complete and is the most comprehensive of its kind to date, comes from researchers at Oxford University and analytics firm Graphika. Their data shows the volume of IRA activity doubling between 2016 and 2017 on Facebook, Instagram, and Twitter, even as the number of ads purchased by the agency decreased. The amount of activity increased the most on Facebook-owned Instagram, where it more than doubled from 2,611 posts in 2016 to 5,956 posts in 2017. The research is based on Facebook data from 2015-2017, Twitter data from 2009-2018, and YouTube data from 2014-2018 that was provided by the companies to the Senate Intelligence Committee and relayed to the researchers.

While Japan is already a leading manufacturer of industrial robots, Groove X is trying to expand the fledgling market for household robots. It has raised 8 billion yen ($71.1 million) from investors including a Toyota Motor Corp-backed fund, chat app operator Line Corp and the Japanese government.

Tokyo is one of the biggest cities in the world, but it also might be the loneliest. So much so that its people may need to be taught how to love by a robot. On Tuesday, robotics venture company Groove X Inc. unveiled a cute knee-high robot called Lovot. The name is a combination of the words "love" and "robot," which befits a device whose primary function is to provide people with comfort and companionship. "Lovot does not have life, but being with one is comforting and warm," said Kaname Hayashi, president of Groove X and leader of the development team that created Pepper, the semi-humanoid robot from SoftBank Robotics.

Recommendation systems are widely used by different user service providers specially those who have interactions with the large community of users. This paper introduces a recommender system based on community detection. The recommendation is provided using the local and global similarities between users. The local information is obtained from communities, and the global ones are based on the ratings. Here, a new fuzzy community detection using the personalized PageRank metaphor is introduced. The fuzzy membership values of the users to the communities are utilized to define a similarity measure. The method is evaluated by using two well-known datasets: MovieLens and FilmTrust. The results show that our method outperforms recent recommender systems.

In a Massive Open Online Course (MOOC), predictive models of student behavior can support multiple aspects of learning, including instructor feedback and timely intervention. Ongoing courses, when the student outcomes are yet unknown, must rely on models trained from the historical data of previously offered courses. It is possible to transfer models, but they often have poor prediction performance. One reason is features that inadequately represent predictive attributes common to both courses. We present an automated transductive transfer learning approach that addresses this issue. It relies on problem-agnostic, temporal organization of the MOOC clickstream data, where, for each student, for multiple courses, a set of specific MOOC event types is expressed for each time unit. It consists of two alternative transfer methods based on representation learning with auto-encoders: a passive approach using transductive principal component analysis and an active approach that uses a correlation alignment loss term. With these methods, we investigate the transferability of dropout prediction across similar and dissimilar MOOCs and compare with known methods. Results show improved model transferability and suggest that the methods are capable of automatically learning a feature representation that expresses common predictive characteristics of MOOCs.

Continual learning is the ability of agents to improve their capacities throughout multiple tasks continually. While recent works in the literature of continual learning mostly focused on developing either particular loss functions or specialized structures of neural network explaining the episodic memory or neural plasticity, we study continual learning from the perspective of the training mechanism. Specifically, we propose a COnitnual Match BAsed Training (COMBAT) framework for training a population of advantage-actor-critic (A2C) agents in Pommerman, a partially observable multi-agent environment with no communication. Following the COMBAT framework, we trained an agent, namely, Navocado, that won the title of the top 1 learning agent in the NeurIPS 2018 Pommerman Competition. Two critical features of our agent are worth mentioning. Firstly, our agent did not learn from any demonstrations. Secondly, our agent is highly reproducible. As a technical report, we articulate the design of state space, action space, reward, and most importantly, the COMBAT framework for our Pommerman agent. We show in the experiments that Pommerman is a perfect environment for studying continual learning, and the agent can improve its performance by continually learning new skills without forgetting the old ones. Finally, the result in the Pommerman Competition verifies the robustness of our agent when competing with various opponents.