The use of AI is increasing across a multitude of functionalities, but according to data provided by the Consumer Technology Association, its top use in 2018 was in cybersecurity: specifically in detecting and deterring security intrusions, with 44 percent of all AI applications being used for that purpose. Artificial intelligence is particularly attuned to performing and automating cybersecurity tasks by utilizing deep-learning algorithms to find patterns in data, detect vulnerable user behaviors, and predict security trends. Azure Sentinel, Microsoft's cloud-based SIEM (security information and event management) tool is one such example of an artificial intelligence that doesn't just automate security operations tasks but uses algorithms to learn from its actions. Like any AI, the more data it receives, the more accurate its decisions are in identifying threats and responding to them. Ultimately, the goal is for AI to be fully autonomous in its prediction, detection, and response to malicious threats, working behind the scenes and providing a seamless front-end-user experience.

Intelligence should be "distributed" where "knowledge" is created and "decisions" are made A few years ago, it was hard to find anyone to have a serious discussion about Artificial Intelligence (AI) outside academic institutions. Like any new major technology trend, the new wave of making AI and intelligent systems a reality is creating curiosity and enthusiasm. People are jumping on its bandwagon adding not only great ideas but also in many cases a lot of false promises and sometimes misleading opinions. Built by giant thinkers and academic researchers, AI adoption by industries and further development in academia around the globe is progressing at a faster rate than anyone had excepted. Accelerated by the strong belief that our biological limitations are increasingly becoming a major obstacle towards creating smart systems and machines that work with us to better use our biological cognitive capabilities to achieve higher goals. This is driving an overwhelming wave of demands and investments across industries to apply AI technologies to solve real-world problems and create smarter machines and new businesses.

Recent years have witnessed the world-wide emergence of mega-metropolises with incredibly huge populations. Understanding residents mobility patterns, or urban dynamics, thus becomes crucial for building modern smart cities. In this paper, we propose a Neighbor-Regularized and context-aware Non-negative Tensor Factorization model (NR-cNTF) to discover interpretable urban dynamics from urban heterogeneous data. Different from many existing studies concerned with prediction tasks via tensor completion, NR-cNTF focuses on gaining urban managerial insights from spatial, temporal, and spatio-temporal patterns. This is enabled by high-quality Tucker factorizations regularized by both POI-based urban contexts and geographically neighboring relations. NR-cNTF is also capable of unveiling long-term evolutions of urban dynamics via a pipeline initialization approach. We apply NR-cNTF to a real-life data set containing rich taxi GPS trajectories and POI records of Beijing. The results indicate: 1) NR-cNTF accurately captures four kinds of city rhythms and seventeen spatial communities; 2) the rapid development of Beijing, epitomized by the CBD area, indeed intensifies the job-housing imbalance; 3) the southern areas with recent government investments have shown more healthy development tendency. Finally, NR-cNTF is compared with some baselines on traffic prediction, which further justifies the importance of urban contexts awareness and neighboring regulations.

Generative Adversarial Net (GAN) has been proven to be a powerful machine learning tool in image data analysis and generation [1]. In this paper, we propose to use Conditional Generative Adversarial Net (CGAN) [2] to learn and simulate time series data. The conditions can be both categorical and continuous variables containing different kinds of auxiliary information. Our simulation studies show that CGAN is able to learn different kinds of normal and heavy tail distributions, as well as dependent structures of different time series and it can further generate conditional predictive distributions consistent with the training data distributions. We also provide an in-depth discussion on the rationale of GAN and the neural network as hierarchical splines to draw a clear connection with the existing statistical method for distribution generation. In practice, CGAN has a wide range of applications in the market risk and counterparty risk analysis: it can be applied to learn the historical data and generate scenarios for the calculation of Value-at-Risk (VaR) and Expected Shortfall (ES) and predict the movement of the market risk factors. We present a real data analysis including a backtesting to demonstrate CGAN is able to outperform the Historic Simulation, a popular method in market risk analysis for the calculation of VaR. CGAN can also be applied in the economic time series modeling and forecasting, and an example of hypothetical shock analysis for economic models and the generation of potential CCAR scenarios by CGAN is given at the end of the paper.

In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers. In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods. Finally, the limitations of each technique and their application in the real-world problem are discussed.

Deep neural networks, although shown to be a successful class of machine learning algorithms, are known to be extremely unstable to adversarial perturbations. Improving the robustness of neural networks against these attacks is important, especially for security-critical applications. To defend against such attacks, we propose dividing the input image into multiple patches, denoising each patch independently, and reconstructing the image, without losing significant image content. We call our method D3. This proposed defense mechanism is non-differentiable which makes it non-trivial for an adversary to apply gradient-based attacks. Moreover, we do not fine-tune the network with adversarial examples, making it more robust against unknown attacks. We present an analysis of the tradeoff between accuracy and robustness against adversarial attacks. We evaluate our method under black-box, grey-box, and white-box settings. On the ImageNet dataset, our method outperforms the state-of-the-art by 19.7% under grey-box setting, and performs comparably under black-box setting. For the white-box setting, the proposed method achieves 34.4% accuracy compared to the 0% reported in the recent works.

Three distinct sounds were detected by NASA's Insight Lander while sitting on Mars' surface. The first "Mars quake" has been detected, NASA announced Tuesday. The finding "officially kicks off a new field: Martian seismology!," said Bruce Banerdt of NASA's Jet Propulsion Laboratory. NASA said this is the first trembling that appears to have come from inside the planet, as opposed to being caused by forces above the surface, such as wind. The sound was detected by NASA's Insight Lander, a robot spacecraft that's now sitting on the Martian surface.

The government will set guidelines by the end of March next year for preventing technology leaks from universities that conduct research with foreign firms, sources close to the matter said Wednesday. The move comes as the United States and China grow cautious about advanced technologies such as artificial intelligence being converted for military use. While Japan already regulates the disclosure of sensitive technologies and products by the nation's state organizations and companies to overseas firms under a foreign exchange and foreign trade law, university laboratories have been managing infrequent arrangements on their own, leading some experts to voice concerns about the risk of information leaks. The envisioned guidelines would require universities and other research institutions to set regulations on joint projects involving foreign entities. They will be based on the comprehensive innovation strategy adopted by the Cabinet in 2018 aimed at promoting university research on AI, biotechnology and other leading technologies.

Hitachi Ltd. said Wednesday it has reached a deal to buy U.S. assembly robot-maker JR Automation Technologies LLC for $1.43 billion (¥160 billion) to strengthen its factory automation business in the American market. Hitachi said it agreed Tuesday to buy the manufacturer from private equity firm Crestview Partners, which holds a 93 percent stake in the company. The Japanese manufacturer will acquire all shares in the Michigan-based technology provider by the end of this year. JR Automation Technologies was founded in 1980 and has strengths in automated manufacturing and technology solutions. The company has about 2,000 employees at 23 manufacturing facilities in North America, Europe and Asia, it said.

A Google offshoot will pave the way for commercial drone deliveries in the U.S. after getting the green light from the FAA. The precedent, which was foreshadowed by the FAA's Unmanned Aircraft System Integration Executive Director, Jay Merkle, last month, is a major step for commercial drones in the U.S. where regulators have been slow to allow widespread usage. Approval of the drones, which will be operated by a company called Wing, effectively classifies the company as a small aircraft operator -- a determination that carries a stringent set of mandatory guidelines. Wing has scored another major victory with its recent approval from the FAA in the U.S. Wing, the first commercial drone company approved by the FAA in the U.S. will start delivering in Virginia. The drones is powered entirely by electric and can fly up to 120 km/h (almost 75 mph).