The Chinese government has wholeheartedly embraced surveillance technology to exercise control over its citizenry in ways both big and small. It's facial-scanning passers-by to arrest criminals at train stations, gas pumps, and sports stadiums and broadcasting the names of individual jaywalkers. Government-maintained social credit scores affect Chinese citizens' rights and privileges if they associate with dissidents. In Tibet and Xinjiang, the government is using facial recognition and big data to surveil the physical movements of ethnic minorities, individually and collectively, to predict and police demonstrations before they even start. China is even using facial recognition to prevent the overuse of toilet paper in some public bathrooms.

At a glance, a single worker of the genus Macrotermes is not a very complex creature--less than half an inch long, eyeless, wingless, with an abdomen so transparent you can spot the dead grass it ate for lunch. Put it in a group, though, and it may pile up pinhead-sized balls of mud, one after the other, until a complex mound takes shape. By the time that mound is 17 feet tall, it will be equivalent in scale to the Burj Khalifa. In its basement sits a symbiotic fungus, which digests grass for the nest and requires continuous care from the workers. Although termites build without the benefit of architects or engineers, their mounds are ingeniously constructed, using cues known only to the bugs.

Job creation in the Middle East remains paramount as a lever to enable prosperity and sustainability. However, advances in automation, technology and specifically robotics and AI, continue to have a significant impact on jobs and on the workforce. So, how much will be replacement of humans vs. an augmentation of skills? And is it possible that Hollywood-like dystopian scenarios, such as the Matrix or Terminator, could become a reality? The truth is, the threat of jobs being replaced by new technology has always been there.

It's Friday, August 3, and I have hooked a live one. Using StreamingPhish, a tool that identifies potential phishing sites by mining data on newly registered certificates, I've spotted an Apple phishing site before it's even ready for victims. Conveniently, the operator has even left a Web shell wide open for me to watch him at work. As I download the phishing kit, I take a look at the site access logs from within the shell. Evidently, I've caught the site just a few hours after the certificate was registered.

The Artificial Intelligence Market is segmented on the Basis of Technology Type, End-User Type and Regional Analysis. By Technology Type this market is segmented on the basis of Machine learning, Natural language processing, Image processing and Speech recognition. By End-User Type this market is segmented on the basis of Media & advertising, BFSI, IT & telecom, Retail, Healthcare, Automotive & transportation and Others. By Regional Analysis this market is segmented on the basis of North America, Europe, Asia Pacific, Latin America, Middle East and Africa.

Whenever something serious happens, we usually try and determine cause and effect. What was it that caused this thing to unfold the way it did? Whilst the theory is nice, we often employ some rather dubious explanations to try and explain the series of events. There have been attempts in the past to generate mathematical models for general causality, but they haven't been particularly effective, especially for more complex problems. A new study from the University of Johannesburg, South Africa and National Institute of Technology Rourkela, India, has attempted to use AI to do a better job.

The 2020 Summer Olympics in Tokyo will deploy a facial recognition system to identify more than 300,000 athletes, staff, volunteers, and journalists at the games. It's the first time that facial recognition technology will be used for security at the Olympics. The planned system is supposed to address several unique security considerations for the upcoming games. Unlike many previous Olympics, Tokyo will not have a centralized Olympic Park in which athletes and others can travel freely between the main facilities--instead, the 2020 Games will be spread out across the Tokyo metropolitan area. Given that setup, people will need to authenticate their identities at more than 40 venues across the city, which could potentially lead to longer wait times to enter the facilities.

From the discovery of an eighth exoplanet circling distant star Kepler-90 to Microsoft's ambitious new project to map and decode the human immune system, Artificial Intelligence (AI) and especially its subset Machine Learning – a field of Computer Science that gives computers the ability to "learn" based on past data – have seen a promising boom in application across numerous industries over the past decade. The investment banking sector is amongst those. Opportunities abound, from the basics – like relieving employees of time-consuming, menial tasks, such as cleaning their inboxes or resetting passwords – to more consequential services – such as fighting money laundering, rogue trading, and cybercrime. Even more, the technology promises to protect employee rights through unprejudiced recruitment. Over the past few years, banks, from HSBC to Credit Suisse, have been partnering with financial technology companies to integrate AI into a wide range of operations.

Zero-shot learning transfers knowledge from seen classes to novel unseen classes to reduce human labor of labelling data for building new classifiers. Much effort on zero-shot learning however has focused on the standard multi-class setting, the more challenging multi-label zero-shot problem has received limited attention. In this paper we propose a transfer-aware embedding projection approach to tackle multi-label zero-shot learning. The approach projects the label embedding vectors into a low-dimensional space to induce better inter-label relationships and explicitly facilitate information transfer from seen labels to unseen labels, while simultaneously learning a max-margin multi-label classifier with the projected label embeddings. Auxiliary information can be conveniently incorporated to guide the label embedding projection to further improve label relation structures for zero-shot knowledge transfer. We conduct experiments for zero-shot multi-label image classification. The results demonstrate the efficacy of the proposed approach.

Conventional approaches used supervised learning to estimate off-line writer identifications. In this study, we improved the off-line writer identifica- tions by semi-supervised feature learning pipeline, which trained the extra unla- beled data and the original labeled data simultaneously. In specific, we proposed a weighted label smoothing regularization (WLSR) method, which assigned the weighted uniform label distribution to the extra unlabeled data. We regularized the convolutional neural network (CNN) baseline, which allows learning more discriminative features to represent the properties of different writing styles. Based on experiments on ICDAR2013, CVL and IAM benchmark datasets, our results showed that semi-supervised feature learning improved the baseline meas- urement and achieved better performance compared with existing writer identifications approaches.