We report a data mining pipeline and subsequent analysis to understand the core periphery power structure created in three national newspapers in Bangladesh, as depicted by statements made by people appearing in news. Statements made by one actor about another actor can be considered a form of public conversation. Named entity recognition techniques can be used to create a temporal actor network from such conversations, which shows some unique structure, and reveals much room for improvement in news reporting and also the top actors' conversation preferences. Our results indicate there is a presence of cliquishness between powerful political leaders when it comes to their appearance in news. We also show how these cohesive cores form through the news articles, and how, over a decade, news cycles change the actors belonging in these groups.

Believe it or not- we all are leveraging artificial intelligence like technology in our everyday life.

Whether an artificial intelligence could replace a medical doctor is both a matter for future conjecture and a hotly contested subject.

Along with the usual highlights from this week, we have some space exploration highlights, including your first chance to hear the wind on Mars. Also, Ghost in the Shell: Stand Alone Complex_2045 is coming to Netflix, and you need to know about the Audi E-Tron. NASA's InSight lander picked up something that other Mars missions never have -- audio of the planet's winds. "Capturing this audio was an unplanned treat," Bruce Banerdt, InSight's principal investigator, said in a statement. "But one of the things our mission is dedicated to is measuring motion on Mars, and naturally that includes motion caused by sound waves."

Methods proposed in the literature towards continual deep learning typically operate in a task-based sequential learning setup. A sequence of tasks is learned, one at a time, with all data of current task available but not of previous or future tasks. Task boundaries and identities are known at all times. This setup, however, is rarely encountered in practical applications. Therefore we investigate how to transform continual learning to an online setup. We develop a system that keeps on learning over time in a streaming fashion, with data distributions gradually changing and without the notion of separate tasks. To this end, we build on the work on Memory Aware Synapses, and show how this method can be made online by providing a protocol to decide i) when to update the importance weights, ii) which data to use to update them, and iii) how to accumulate the importance weights at each update step. Experimental results show the validity of the approach in the context of two applications: (self-)supervised learning of a face recognition model by watching soap series and learning a robot to avoid collisions.

Earlier this week, the rapper 2 Milly filed a lawsuit claiming that Fortnite--the most popular video game in the world--swiped his dance move. The case fuels ongoing controversy over video game companies' unlicensed use of existing hip-hop dances, which players can purchase to customize their in-game avatars. But can these companies freely copy and sell dance moves without payment to the artist? The groove in question is the "Milly Rock," a simple, two-step dance in which the arms circle while the hips swing from side to side. Popularized by 2 Milly in 2014, it became a viral hip-hop phenomenon.

Continuing with a stream of anime announcements that started at least a month ago with Pacific Rim and Altered Carbon, Netflix has revealed it's working on a continuation of yet another classic series with Ghost in the Shell: SAC_2045, which is due in 2020. The director of the original series, Kenji Kamiyama is teaming up to co-direct with Shinji Aramaki (Appleseed) on this one, after the two were already paired on the Ultraman movie that's coming to Netflix next year and a Blade Runner series that's in the works for Cartoon Network and Crunchyroll. YES, A NEW GHOST IN THE SHELL ANIME IS COMING. Ghost in the Shell: SAC_2045 will be directed by Appleseed's Shinji Aramaki and Ghost in the Shell: Stand Alone Complex's Kenji Kamiyama. On @Netflix in 2020! *desire to explore the true meaning of human consciousness intensifies* pic.twitter.com/MgKzX2KydQ

Google is working to reduce gender bias in its Google Translate tool after it was accused of sexism for automatically translating sentences to include masculine pronouns. Translations from English into French, Italian, Portuguese or Spanish will also now provide a feminine alternative as well as a masculine one for gendered words such as "strong" or "beautiful." In the past, Google's algorithm had to choose between masculine or feminine when translating a word - automatically defaulting to masculine in many instances. Additionally, the tool will offer gender-specific translations for phrases and sentences from Turkish to English. The update comes after two Stanford University professors pointed out that the artificial intelligence used by Google Translate was converting news articles written in Spanish to English by changing phrases referring to women into "he said" or "he wrote."

Faces play a magnificent role in human robot interaction, as they do in our daily life. The inherent ability of the human mind facilitates us to recognize a person by exploiting various challenges such as bad illumination, occlusions, pose variation etc. which are involved in face recognition. But it is a very complex task in nature to identify a human face by humanoid robots. The recent literatures on face biometric recognition are extremely rich in its application on structured environment for solving human identification problem. But the application of face biometric on mobile robotics is limited for its inability to produce accurate identification in uneven circumstances. The existing face recognition problem has been tackled with our proposed component based fragmented face recognition framework. The proposed framework uses only a subset of the full face such as eyes, nose and mouth to recognize a person. It's less searching cost, encouraging accuracy and ability to handle various challenges of face recognition offers its applicability on humanoid robots. The second problem in face recognition is the face spoofing, in which a face recognition system is not able to distinguish between a person and an imposter (photo/video of the genuine user). The problem will become more detrimental when robots are used as an authenticator. A depth analysis method has been investigated in our research work to test the liveness of imposters to discriminate them from the legitimate users. The implication of the previous earned techniques has been used with respect to criminal identification with NAO robot. An eyewitness can interact with NAO through a user interface. NAO asks several questions about the suspect, such as age, height, her/his facial shape and size etc., and then making a guess about her/his face.

An accident in a swimming pool left Chieko Asakawa blind at the age of 14. For the past three decades she's worked to create technology - now with a big focus on artificial intelligence (AI) - to transform life for the visually impaired. "When I started out there was no assistive technology," Japanese-born Dr Asakawa says. "I couldn't read any information by myself. Those "painful experiences" set her on a path of learning that began with a computer science course for blind people, and a job at IBM soon followed. She started her pioneering work on accessibility at the firm, while also earning her doctorate. Dr Asakawa is behind early digital Braille innovations and created the world's first practical web-to-speech browser. Those browsers are commonplace these days, but 20 years ago, she gave blind internet users in Japan access to more information than they'd ever had before. Now she and other technologists are looking to use AI to create tools for visually impaired people.