The robots are coming for your jobs, too. China's state news agency has debuted a virtual anchor designed to be able to deliver the news 24 hours a day. Xinhua unveiled its "artificial intelligence news anchor" this week at an internet conference in the eastern city of Wuzhen. "Hello, you are watching English news program. I am AI news anchor in Beijing," the computer-generated host announced in a robotic voice at that start of its English-language broadcast.

China's state news agency Xinhua this week introduced the newest members of its newsroom: AI anchors who will report "tirelessly" all day every day, from anywhere in the country. Chinese viewers were greeted with a digital version of a regular Xinhua news anchor named Qiu Hao. The anchor, wearing a red tie and pin-striped suit, nods his head in emphasis, blinking and raising his eyebrows slightly. "Not only can I accompany you 24 hours a day, 365 days a year. I can be endlessly copied and present at different scenes to bring you the news," he says.

China's state-owned Xinhua News Agency introduced so-called "composite anchors" on Wednesday, combining the images and voices of human anchors with artificial intelligence (AI) technology. The new AI anchors, launched by Xinhua and Beijing-based search engine operator Sogou during the World Internet Conference in Wuzhen, can deliver the news with "the same effect" as human anchors because the machine learning programme is able to synthesise realistic-looking speech, lip movements and facial expressions, according to a Xinhua news report on Wednesday. "AI anchors have officially become members of the Xinhua News Agency reporting team. They will work with other anchors to bring you authoritative, timely and accurate news information in both Chinese and English," Xinhua said. The AI anchors are now available throughout Xinhua's internet and mobile platforms such as its official Chinese and English apps, WeChat public account, and online TV webpage.

Forget swiping though endless profiles. Dating apps are using artificial intelligence to suggest where to go on a first date, recommend what to say and even find a partner who looks like your favourite celebrity. Until recently smartphone dating apps--such as Tinder which lets you see in real time who is available and "swipe" if you wish to meet someone--left it up to users to ask someone out and then make the date go well. But to fight growing fatigue from searching through profiles in vain, the online dating sector is turning to artificial intelligence (AI) to help arrange meetings in real life and act as a dating coach. These new uses for AI--the science of programming computers to reproduce human processes like thinking and decision making--by dating apps were highlighted at the four-day Web Summit which wraps up Thursday in Lisbon.

China's state-run press agency has welcomed two additions to its team of journalists - two news presenters powered by artificial intelligence (AI). Dressed in suit and tie, the AI newsreaders can'learn from live broadcast videos and read texts as naturally as a professional news anchor', according to Xinhua. The digital doppelgangers were modelled after the agency's journalists, English-language anchor Zhang Zhao and his Chinese-language counterpart Qiu Hao, and were jointly developed by Xinhua and search engine company Sogou.com. Tell the difference: China's state-run press agency Xinhua unveiled its first AI anchor (right) based on one of this human presenters Qiu Hao (left) The AI anchors were jointly developed by Xinhua and search engine company Sogou.com The two AI anchors were first seen in action at the ongoing 2018 World Internet Conference in Wuzhen, Zhejiang.

The media plays a central role in monitoring powerful institutions and identifying any activities harmful to the public interest. In the investing sphere constituted of 46,583 officially listed domestic firms on the stock exchanges worldwide, there is a growing interest `to do the right thing', i.e., to put pressure on companies to improve their environmental, social and government (ESG) practices. However, how to overcome the sparsity of ESG data from non-reporting firms, and how to identify the relevant information in the annual reports of this large universe? Here, we construct a vast heterogeneous information network that covers the necessary information surrounding each firm, which is assembled using seven professionally curated datasets and two open datasets, resulting in about 50 million nodes and 400 million edges in total. Exploiting this heterogeneous information network, we propose a model that can learn from past adverse media coverage patterns and predict the occurrence of future adverse media coverage events on the whole universe of firms. Our approach is tested using the adverse media coverage data of more than 35,000 firms worldwide from January 2012 to May 2018. Comparing with state-of-the-art methods with and without the network, we show that the predictive accuracy is substantially improved when using the heterogeneous information network. This work suggests new ways to consolidate the diffuse information contained in big data in order to monitor dominant institutions on a global scale for more socially responsible investment, better risk management, and the surveillance of powerful institutions.

UNSUPERVISED DEEP CLUSTERING FOR SOURCE SEPARATION: DIRECT LEARNING FROM MIXTURES USING SPATIAL INFORMATION Efthymios Tzinis ] Shrikant Venkataramani ] Paris Smaragdis ][ ] University of Illinois at Urbana-Champaign, Department of Computer Science [ Adobe Research ABSTRACT We present a monophonic source separation system that is trained by only observing mixtures with no ground truth separation information. We use a deep clustering approach which trains on multi-channel mixtures and learns to project spectrogram bins to source clusters that correlate with various spatial features. We show that using such a training process we can obtain separation performance that is as good as making use of ground truth separation information. Once trained, this system is capable of performing sound separation on monophonic inputs, despite having learned how to do so using multi-channel recordings. Index Terms -- Deep clustering, source separation, unsupervised learning 1. INTRODUCTION A central problem when designing source separation systems is that of defining what constitutes a source.

Many people are interested in taking astonishing photos and sharing with others. Emerging hightech hardware and software facilitate ubiquitousness and functionality of digital photography. Because composition matters in photography, researchers have leveraged some common composition techniques to assess the aesthetic quality of photos computationally. However, composition techniques developed by professionals are far more diverse than well-documented techniques can cover. We leverage the vast underexplored innovations in photography for computational composition assistance. We propose a comprehensive framework, named CAPTAIN (Composition Assistance for Photo Taking), containing integrated deep-learned semantic detectors, sub-genre categorization, artistic pose clustering, personalized aesthetics-based image retrieval, and style set matching. The framework is backed by a large dataset crawled from a photo-sharing Website with mostly photography enthusiasts and professionals. The work proposes a sequence of steps that have not been explored in the past by researchers. The work addresses personal preferences for composition through presenting a ranked-list of photographs to the user based on user-specified weights in the similarity measure. The matching algorithm recognizes the best shot among a sequence of shots with respect to the user's preferred style set. We have conducted a number of experiments on the newly proposed components and reported findings. A user study demonstrates that the work is useful to those taking photos.

At their core, internet forums like Reddit work because they are centered around a democratic ideal. The content that makes the front page is whatever is most liked by the community. In theory, each website user has one vote and majority rule decides what content wins and what content loses. However, as malcontents run bots on sites like Reddit (as well as Instagram, Facebook, and Twitter), the process that makes these websites great is being tested. As a single user votes 150 times or automates thousands of comments to shift public opinion, the democratic procedure becomes eroded.

China's state-run news agency Xinhua has unveiled the latest additions to its team of reporters -- two AI anchors. The two anchors, one that speaks in English and another in Chinese, have the likeness of some of Xinhua's human anchors, but their voices, facial expressions and mouth movements are synthesized and animated using deep learning techniques. "AI anchors have officially become members of the Xinhua News Agency reporting team," the agency said. "They will work with other anchors to bring you authoritative, timely and accurate news information in both Chinese and English." China's South China Morning Post reports that the AI anchors are available through Xinhua's English and Chinese apps, its TV webpage and its WeChat public account. The technology behind the anchors is being provided by search engine company Sogou.