JX Press has designed a newsroom staffed by engineers and artificial intelligence instead of employing journalists. According to Bloomberg, once the Japenese media firm finds news, it uses algorithms to create stories and filters out 99% of fake news stories. The firm has developed a tool called NewsDigest – a free mobile news app which generates the firm's advertising revenue, as well as filters fake news and finds breaking stories on social media programmes. Fast Alert is another one of its products which analyses social media posts and photos to find Japan's breaking news and reports major international developments that it deems as trustworthy. The team consists of 24 people, of which 17 are engineers and the others are in relevant business functions, not journalists or reporters.

In announcing the investigation, China's Culture and Tourism Ministry said Jinri Toutiao has allowed a martyr to be portrayed in a negative light. It didn't make any statement on potential penalties. The incident caught the public's attention two weeks ago, leading the comic's producer to apologize on his Twitter-like Sina Weibo account. Jinri Toutiao issued an apology on its own platform and removed the offending content. In April, a Bytedance comedy app was shut down by Chinese authorities on the grounds it had hosted lewd content.

Fusing high performance computing and AI 2. Find your next binge-worthy show with AI 3. The connection between self-driving vehicles and radiology 4. Robots are learning new tasks by mimicking humans 5. How AI could spot a silent cancer in time to save lives 5. FUSING HIGH PERFORMANCE COMPUTING AND AI During GTC Taiwan 2018, NVIDIA CEO Jensen Huang announced HGX-2: a "building block" cloud-server platform that will let server manufacturers create more powerful systems around NVIDIA GPUs for high performance computing and AI. TechCrunch's Ron Miller sums it up best, saying that: "It's the stuff that geek dreams are made of. READ ARTICLE 6. FIND YOUR NEXT BINGE-WORTHY SHOW WITH AI While AI may play a leading role in the entertainment industry's depictions of the future on screen, it's already starring in entertainment behind the scenes, thanks to Netflix. Our latest AI Podcast features the company's research and engineering director, Justin Basilico. LISTEN HERE 7. CONNECTING SELF-DRIVING VEHICLES AND RADIOLOGY According to new commentary published in the Journal of American College of Radiology, AI implementation may not be as far as people believe, as seen in self- driving vehicles. "It is important to realize that many of these features are not far-future applications in radiology but will be incorporated into routine clinical practice over the next few years." "In radiology, having AI perform the mundane tasks that humans may struggle with or find interminable…frees us to interact with the images in ways that can push the boundaries of diagnostic science.

In his recent May Day speech, Prime Minister Lee Hsien Loong pointed out that new technology in banking is transforming the financial sector. Among the new services, he mentioned robo-advisers, which like our own CONNECT by Crossbridge, use technology to offer investors financial advice suited to their needs and circumstances. As the first robo-adviser in Singapore, our motivations for launching CONNECT by Crossbridge were simple. We found that affluent investors were not offered the investment options that are made available to wealthier clients through the private banks, and we knew that technology could open up those options to a wider audience. We launched a little under two years ago with personalised, multi-asset, goals-based portfolios meeting the risk profile and objectives of customers utilising the platform.

At the DefCon2018 conference held in China on May 12, hackers and data scientists raised vivid discussions on cyberattacks with the use and abuse of machine learning and possible solutions. It goes without saying that artificial intelligence is now actively used in most security technologies as well as in a wide range of attacks. Attack vectors have become more advanced and sophisticated. If you are curious, there is a remarkable series of posts related to AI and cybersecurity on Forbes, revealing how AI-driven system can be hacked, detailing seven ways cybercriminals can use ML, and uncovering the truth about ML in defense. Today cyberattackers are less interested in traditional platforms but target self-driving cars, human-voice-imitation and image-recognition systems.

Healthcare Artificial Intelligence Market 2018 Research Report implements an exhaustive study on Market Research Future. And also cover the other information such as Healthcare Artificial Intelligence Market trends, Prominent players, chapter-wise Description followed by various user perceptions and Forecast till 2023. Artificial intelligence (AI) or machine intelligence technology using complex algorithms which enables machines to sense, comprehend, and learn tasks requiring general or human intelligence. Artificial intelligence mimics human intelligence capabilities including learning, reasoning, pattern recognition and others to drive machine decisions in applications ranging from financial, diagnosis, marketing and others. The rapid adoption of artificial intelligence in healthcare duplicating the retail industry is another positive sign of the market.

According to Accenture's survey, UAE consumers are ahead of global averages in readiness to adopt AI powered devices and services. Half of them (51%) think they are "cool". An impressive 73 % of consumers currently use or are interested in using the voice-enabled digital assistant in their smartphone, PC or other devices. Such usage is a positive signal for this category as it represents a much more enthusiastic adoption pattern than many new product categories recently released.

Reinforcement learning methods require careful design involving a reward function to obtain the desired action policy for a given task. In the absence of hand-crafted reward functions, prior work on the topic has proposed several methods for reward estimation by using expert state trajectories and action pairs. However, there are cases where complete or good action information cannot be obtained from expert demonstrations. We propose a novel reinforcement learning method in which the agent learns an internal model of observation on the basis of expert-demonstrated state trajectories to estimate rewards without completely learning the dynamics of the external environment from state-action pairs. The internal model is obtained in the form of a predictive model for the given expert state distribution. During reinforcement learning, the agent predicts the reward as a function of the difference between the actual state and the state predicted by the internal model. We conducted multiple experiments in environments of varying complexity, including the Super Mario Bros and Flappy Bird games. We show our method successfully trains good policies directly from expert game-play videos.

Considering the driving habits which are learned from the naturalistic driving data in the path-tracking system can significantly improve the acceptance of intelligent vehicles. Therefore, the goal of this paper is to generate the prediction results of lateral commands with confidence regions according to the reference based on the learned motion primitives. We present a two-level structure for learning and generalizing motion primitives through demonstrations. The lower-level motion primitives are generated under the path segmentation and clustering layer in the upper-level. The Gaussian Mixture Model(GMM) is utilized to represent the primitives and Gaussian Mixture Regression (GMR) is selected to generalize the motion primitives. We show how the upper-level can help to improve the prediction accuracy and evaluate the influence of different time scales and the number of Gaussian components. The model is trained and validated by using the driving data collected from the Beijing Institute of Technology (BIT) intelligent vehicle platform. Experiment results show that the proposed method can extract the motion primitives from the driving data and predict the future lateral control commands with high accuracy.

Time series prediction has been studied in a variety of domains. However, it is still challenging to predict future series given historical observations and past exogenous data. Existing methods either fail to consider the interactions among different components of exogenous variables which may affect the prediction accuracy, or cannot model the correlations between exogenous data and target data. Besides, the inherent temporal dynamics of exogenous data are also related to the target series prediction, and thus should be considered as well. To address these issues, we propose an end-to-end deep learning model, i.e., Hierarchical attention-based Recurrent Highway Network (HRHN), which incorporates spatio-temporal feature extraction of exogenous variables and temporal dynamics modeling of target variables into a single framework. Moreover, by introducing the hierarchical attention mechanism, HRHN can adaptively select the relevant exogenous features in different semantic levels. We carry out comprehensive empirical evaluations with various methods over several datasets, and show that HRHN outperforms the state of the arts in time series prediction, especially in capturing sudden changes and sudden oscillations of time series.