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.

There are numerous production scenarios in which machine learning and automation can enhance existing processes for the creation, management and distribution of content – however, introducing AI to video production needs careful planning. The contextual power of content should not be lost in the pursuit of efficiency. We can already see the benefits in transforming certain aspects of production, such as the introduction of machine learning into video encoding, which has removed the limitations of hardware-based systems and allows video services to be delivered in high fidelity across the globe. Machine learning is also useful for content creation. Video editors are able to train automated algorithms to recognize certain'triggers' within recorded or live video inputs, and instantly create short clips, sequences or apply overlays and effects without human intervention.

Abstract--Multi-label classification aims to classify instances with discrete non-exclusive labels. Most approaches on multilabel classificationfocus on effective adaptation or transformation of existing binary and multi-class learning approaches but fail in modelling the joint probability of labels or do not preserve generalization abilities for unseen label combinations. To address these issues we propose a new multi-label classification scheme, LNEMLC - Label Network Embedding for Multi-Label Classification, thatembeds the label network and uses it to extend input space in learning and inference of any base multi-label classifier. The approach allows capturing of labels' joint probability at low computational complexity providing results comparable to the best methods reported in the literature. We demonstrate how the method reveals statistically significant improvements over the simple kNN baseline classifier. We also provide hints for selecting the robust configuration that works satisfactory across data domains. I. INTRODUCTION In our daily life, we continuously encounter data classified with multiple categories. Be it youtube videos, Instagram photos, articles in newspapers or more recently even our genome on gene analysis websites; we depend heavily on labels to guide us through various types of objects to find that which is to our liking and we rely on labels to organize our information flow. Labels usually denote the simplest understandable terms, while it is from how they occur together that creates sophisticated concepts and contexts.

Speakers at the event include Israeli pop star and entrepreneur Ivri Lider. Lider is co-owner of MyPart Inc., a Tel Aviv-based startup that lets little-known artists offer their original songs, lyrics, music, translations, and visual art to successful musicians of their choice. One of the key speakers will be Avi Korenblum, a 20-year veteran of Israeli intelligence organizations. In 2012, Korenblum founded New York-headquartered online data analysis company Voyager Labs. Incorporated as Voyager Analytics Inc., the company develops artificial intelligence technology that provides enterprises with real-time actionable insights into their users' on-site activity.

For decades we have overheard these admonitions and reservations about artificial intelligence taking over and ending humankind.

Catanzaro was the first person doing neural network work at Nvidia when he ….