Fifteen years ago, cybersecurity could be boiled down to a simple strategy: Secure the perimeter. Experts fought against malware and other nefarious code by implementing firewalls and other point-of-entry defenses. Since then, however, companies have moved their operations online and allowed employees to bring their own devices (BYOD) to work. The so-called perimeter has dissolved in the process, forcing security practitioners to prioritize tracking, understanding and ultimately making judgments about the information flowing both inside and outside of their company. Many businesses use 10, 20 or 30 different security products to protect their systems. They all have advantages, and security practitioners will use different combinations to investigate a potential threat. If a team has access to 15 tools, for instance, one engineer might think to use three of them while another tries a completely different subset. There isn't enough time to try them all, so experts pick products based on their experience and what they believe will be best suited to the task. It's a messy problem that doesn't have a simple answer.

The first time many of us heard about China's use of facial recognition on jaywalkers was just this week when a prominent Chinese businesswoman was publicly "named and shamed" for improper street crossing. Turns out, she wasn't even there: China's terrifyingly over-the-top use of tech for citizen surveillance made a mistake. The AI system identified Dong Mingzhu's face from a bus advertisement for her company's products. "[The] president of China's biggest air conditioning maker," wrote The Telegraph, "had her image flashed up on a public display screen in the city of Ningbo, near Shanghai, with a caption saying she had illegally crossed the street on a red light." Shortly after, Ningbo traffic police admitted the mistake and claimed to have "completely upgraded the system to reduce the false recognition rate."

Cars in China are watching their drivers and reporting where they are to the government. Authorities claim that the data is only used to ensure that the roads, cars and drivers are safe. But privacy experts fear the data could be used for more invasive forms of surveillance. Hundreds of car manufacturers of electric vehicles – including Tesla, as well as more traditional companies like Volkswagen, BMW and Ford – transmit dozens of different kinds of information to the government. The information is sent without the driver or owner of the car even knowing it.

A booking database run by the Marriott hotel chain has been hit by a vast hack that could affect half a billion people. The vast collection of people's personal information, used to book rooms at its Starwood properties, has been accessed by unauthorised people since 2014, it said. The cyberattack included information about those people's credit cards that could be used to steal money, Marriott warned. Uber has halted testing of driverless vehicles after a woman was killed by one of their cars in Tempe, Arizona. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

Understanding and evaluating the robustness of neural networks against adversarial attacks is a subject of growing interest. Attacks proposed in the literature usually work with models that are trained to minimize cross-entropy loss and have softmax activations. In this work, we present interesting experimental results that suggest the importance of considering other loss functions and target representations. Specifically, (1) training on mean-squared error and (2) representing targets as codewords generated from a random codebook show a marked increase in robustness against targeted and untargeted attacks under white-box and black-box settings. Our results show an increase in accuracy against untargeted attacks of up to 98.7\% and a decrease of targeted attack success rates of up to 99.8\%. For our experiments, we use the DenseNet architecture trained on three datasets (CIFAR-10, MNIST, and Fashion-MNIST).

The explosive growth in fake news and its erosion to democracy, justice, and public trust has increased the demand for fake news analysis, detection and intervention. This survey comprehensively and systematically reviews fake news research. The survey identifies and specifies fundamental theories across various disciplines, e.g., psychology and social science, to facilitate and enhance the interdisciplinary research of fake news. Current fake news research is reviewed, summarized and evaluated. These studies focus on fake news from four perspective: (1) the false knowledge it carries, (2) its writing style, (3) its propagation patterns, and (4) the credibility of its creators and spreaders. We characterize each perspective with various analyzable and utilizable information provided by news and its spreaders, various strategies and frameworks that are adaptable, and techniques that are applicable. By reviewing the characteristics of fake news and open issues in fake news studies, we highlight some potential research tasks at the end of this survey.

Technology is shaping our lives in a multitude of ways. This is fuelled by a technology infrastructure, both legacy and state of the art, composed of a heterogeneous group of hardware, software, services and organisations. Such infrastructure faces a diverse range of challenges to its operations that include security, privacy, resilience, and quality of services. Among these, cybersecurity and privacy are taking the centre-stage, especially since the General Data Protection Regulation (GDPR) came into effect. Traditional security and privacy techniques are overstretched and adversarial actors have evolved to design exploitation techniques that circumvent protection. With the ever-increasing complexity of technology infrastructure, security and privacy-preservation specialists have started to look for adaptable and flexible protection methods that can evolve (potentially autonomously) as the adversarial actor changes its techniques. For this, Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) were put forward as saviours. In this paper, we look at the promises of AI, ML, and DL stated in academic and industrial literature and evaluate how realistic they are. We also put forward potential challenges a DL based security and privacy protection technique has to overcome. Finally, we conclude the paper with a discussion on what steps the DL and the security and privacy-preservation community have to take to ensure that DL is not just going to be hype, but an opportunity to build a secure, reliable, and trusted technology infrastructure on which we can rely on for so much in our lives.

In Texas, a company called Alegion is helping disabled veterans take part in the new digital economy. The vets' job: preparing data so that an artificial intelligence (AI) system can learn from it. "There's a whole new industry sprouting on the shoulders of AI," says Alegion CEO Nathaniel Gates in an interview with IEEE Spectrum. When people talk about AI, they're often referring to software that gets very good at a particular task via a technique called deep learning. With this method, AI systems are given vast amounts of labeled data, and as they run through it, they learn to draw conclusions.

Astrobiotics' Peregrine lander is one of the many in the running Astrobotic Technology: The Pittsburgh-based firm was founded in 2008 by Carnegie Mellon professor Red Whittaker. It was among the many teams that participated in Google's $20 million Lunar XPrize, which shut down this year without a winner. Astrobiotic's lunar lander, dubbed Peregrine, stands on four shock-absorbing legs and attaches to the launch vehicle via a standard clamp. 'The Peregrine Lander precisely and safely delivers payloads to lunar orbit and the lunar surface on each mission,' the firm says. 'Payloads can be mounted above or below the decks, and can remain attached or deployed according to their needs.'

Best listening experience is on Chrome, Firefox or Safari. "Ai" used to be a crossword puzzle clue for three-toed sloth, but today it more likely stands for artificial intelligence. In information technology circles, AI is tossed around as a panacea for everything from disease to employee boredom. If you define AI as the application of algorithms that can learn and evolve as they're exposed to new data, the use cases do indeed seem endless. The ultimate goal of AI is machinery that acts autonomously but within bounds -- such as federal policy on this or that.