Artificial intelligence has made its way into the recruiting industry and brings a new dawn to the way we recruit and hire candidates. AI has completely altered the recruitment landscape from using software to reducing the amount of time spent on administrative tasks by establishing analytics that predicts a candidate's projected fit and quality. Arnold Schwarzenegger hasn't been turned into a giant, laser-eyed robot and AI hasn't reached catastrophic levels of intelligence... For now. helps talent acquisition teams streamline hiring. So what is the next attempt at an AI recruiting game changer?

The Science & Technology Policy Research and Information Center (STPI), established in 1974, aims to support the government's technology policy-making and addressing social needs for globalization and the coming era of knowledge economy. STPI has utilized over 30 years experience in collecting, collating and disseminating science & technology information for the purposes of innovation, competitiveness, sustainable development and social well-being and has integrated and provided with several nationwide technology related information services in improving the efficiency of scientific research. TAIWAN BIG DEMO – A Technology Investment Exhibition is an international event sponsored by Ministry of Science and Technology. The exhibition presents and discusses topics in areas of biomedical, innovation tech, AI, IoT, ICT and FinTech; which aims to strengthen the linkage between the domestic academia and industry around the globe. The event will consist of angel investors, venture capital representatives, startup teams and fellow entrepreneurs etc.

"Better Than Us," Netflix's first original streaming series from Russia, transports audiences to a city only 10 years in the future, where robots serve the population in a variety of positions, some have even replaced humans for certain jobs. They look like the humans they serve, though there is a detached air to their presence and slightly stilted way of moving that make them visibly different. Netflix describes the series as "cyberpunk." Popular culture entertainment is often a way for us to imagine a scenario or event that may never occur in our individual lifetimes. Imagining a future, say, where artificial intelligence (AI) has been integrated into society not simply as a complex web of computational frameworks as we acknowledge and accept it currently, but in the form of robotics that look, and act similar to humans.

Fremont, CA: Cybersecurity, otherwise known as information technology security, refers to the act of protecting the data, systems, networks, and programs from digital attacks. In the existing connected world, the necessity to secure systems is rising. It is evident from the prevailing cyberattacks in different industry sectors like health care, government agencies, education institutions, and energy. According to the UK government's Cyber Security Breaches Survey 2019, 32% of businesses have identified cybersecurity breaches or attacks in the past 12 months. Besides, with the advent of new devices that overhaul people, security risks are evolving, and cyberattackers are becoming more innovative in implementing these attacks.

Judges, prosecutors and defendants sit around a table during a trial at the Kunming Intermediate People's Court in Southwest China's Yunnan Province.

February 13, 2019, The Hague, Netherlands ‒ HealthcareIT solution provider, First Line Software has announced that it joined the International ICanFunction (mICF) Partnership during eHealth week 2019, which was held January 21 – 26, 2019 in the Netherlands. First Line Software actively uses and implements machine learning and artificial intelligence to solve complex challenges for its global HealthcareIT clients. The expertise of First Line technology experts will be applied to improving applications and platforms for personalized health and social care being developed by mICF. "Joining the mICF during e-health 2019 week was significant as First Line was participating in the conference to make connections for collaborating on making e-health applicable to everyone," says Vladimir Litoschenko, VP Business Development for First Line Software. "We look forward to using our healthcareIT expertise to support the achievement of mICF's initiative to build solutions that improve the integration of healthcare practices around the world."

Digital transformation is redefining banking sector. The industry is adopting artificial intelligence and other disruptive technology to create value for their tech-savvy customers. Adoption of Artificial intelligence in banking sector enabling to deliver a seamless experience. But expectations are high and challenges are higher. We spoke to Raj Nair, President of IMC Chamber of Commerce and Industry to understand the development in the segment and the impact of AI in banking.

Despite recent advances of deep Convolutional Neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applications of deep CNNs using optical remote sensing data have focused on the classification of very high-resolution aerial and satellite data, owing to the similarity of these data to the large datasets in computer vision. Accordingly, this study presents a detailed investigation of state-of-the-art deep learning tools for classification of complex wetland classes using multispectral RapidEye optical imagery. Specifically, we examine the capacity of seven well-known deep convnets, namely DenseNet121, InceptionV3, VGG16, VGG19, Xception, ResNet50, and InceptionResNetV2, for wetland mapping in Canada. In addition, the classification results obtained from deep CNNs are compared with those based on conventional machine learning tools, including Random Forest and Support Vector Machine, to further evaluate the efficiency of the former to classify wetlands. The results illustrate that the full-training of convnets using five spectral bands outperforms the other strategies for all convnets. InceptionResNetV2, ResNet50, and Xception are distinguished as the top three convnets, providing state-of-the-art classification accuracies of 96.17%, 94.81%, and 93.57%, respectively. The classification accuracies obtained using Support Vector Machine (SVM) and Random Forest (RF) are 74.89% and 76.08%, respectively, considerably inferior relative to CNNs. Importantly, InceptionResNetV2 is consistently found to be superior compared to all other convnets, suggesting the integration of Inception and ResNet modules is an efficient architecture for classifying complex remote sensing scenes such as wetlands.

The Philips Ultra Fast Scanner includes automatic tissue detection and allows for continuous loading without interrupting the scanning process.