When I was a child, each year without exception our family would drive to Cornwall in a wheezing Ford Sierra for the summer holidays. We'd stay with my great-uncle, a retired army major (gruff bachelor, suspected womaniser, borderline alcoholic), who was perhaps the last person I'd ever meet who earnestly deployed the phrase: "Children should be seen and not heard." In order to preserve quiet in the house, we went out a lot. We'd eat the same sandy ham sandwiches and shoo the same crabs from under the same rocks. Familiarity might have bred contempt, were it not for the Game Boy my brother and I brought along for the ride.

Every industry is inching towards automation and the insurance industry is no exception. According to a white paper titled "2017 Future of Claims Study" published by the legal research firm Lexis Nexis, insurance claims are being increasingly processed using drones, artificial intelligence and app-based interfaces as opposed to sending field agents to examine such claims. The study was conducted using a sample size of 24 insurance executives and their opinions on automation in insurance. The push towards automation is largely driven by customers' need for faster and more convenient processing of claims. " While there hasn't yet been a complete shift to Virtual Claims handling, carriers who want to remain competitive will need to make the move to virtual and consider touchless processing if customer preferences are any indication," the study says.

Artificial intelligence and increasing automation is going to decimate middle class jobs, worsening inequality and risking significant political upheaval, Stephen Hawking has warned. In a column in The Guardian, the world-famous physicist wrote that "the automation of factories has already decimated jobs in traditional manufacturing, and the rise of artificial intelligence is likely to extend this job destruction deep into the middle classes, with only the most caring, creative or supervisory roles remaining." He adds his voice to a growing chorus of experts concerned about the effects that technology will have on workforce in the coming years and decades. The fear is that while artificial intelligence will bring radical increases in efficiency in industry, for ordinary people this will translate into unemployment and uncertainty, as their human jobs are replaced by machines. Technology has already gutted many traditional manufacturing and working class jobs -- but now it may be poised to wreak similar havoc with the middle classes.

BEIJING--China's leading smartphone maker and one of its biggest internet companies are in a showdown over user data, the big prize in the emerging era of artificial intelligence. To build its AI capability--so that its phones can, say, make restaurant suggestions based on a user's text messages--Huawei Technologies Co. is collecting user-activity information on its advanced Honor Magic smartphone. Among the information captured: text messages sent using the popular WeChat TCEHY 1.05% social-media app. WeChat owner Tencent Holdings Ltd. contends that Huawei is effectively taking Tencent's data and violating the privacy of WeChat users, according to people familiar with the situation. It has asked the Chinese government to intervene, these people say.

The following essay is reprinted with permission from The Conversation, an online publication covering the latest research. As an Artificial Intelligence researcher, I often come across the idea that many people are afraid of what AI might bring. It's perhaps unsurprising, given both history and the entertainment industry, that we might be afraid of a cybernetic takeover that forces us to live locked away, "Matrix"-like, as some sort of human battery. And yet it is hard for me to look up from the evolutionary computer models I use to develop AI, to think about how the innocent virtual creatures on my screen might become the monsters of the future. Might I become "the destroyer of worlds," as Oppenheimer lamented after spearheading the construction of the first nuclear bomb?

Authorities in China are exploring predictive analytics, facial recognition, and other artificial intelligence (AI) technologies to help prevent crime in advance. Based on behavior patterns, authorities will notify local police about potential offenders. Cloud Walk, a company headquartered in Guangzhou, has been training its facial recognition and big data rating systems to track movements based on risk levels. Those who are frequent visitors to weapons shops or transportation hubs are likely to be flagged in the system, and even places like hardware stores have been deemed "high risk" by authorities. A Cloud Walk spokesman told The Financial Times, "Of course, if someone buys a kitchen knife that's OK, but if the person also buys a sack and a hammer later, that person is becoming suspicious." Cloud Walk's software is connected to the police database across more than 50 cities and provinces, and can flag suspicious characters in real time.

Distributed learning is an effective way to analyze big data. In distributed regression, a typical approach is to divide the big data into multiple blocks, apply a base regression algorithm on each of them, and then simply average the output functions learnt from these blocks. Since the average process will decrease the variance, not the bias, bias correction is expected to improve the learning performance if the base regression algorithm is a biased one. Regularization kernel network is an effective and widely used method for nonlinear regression analysis. In this paper we will investigate a bias corrected version of regularization kernel network. We derive the error bounds when it is applied to a single data set and when it is applied as a base algorithm in distributed regression. We show that, under certain appropriate conditions, the optimal learning rates can be reached in both situations.

In this paper, we suggest a framework to make use of mutual information as a regularization criterion to train Auto-Encoders (AEs). In the proposed framework, AEs are regularized by minimization of the mutual information between input and encoding variables of AEs during the training phase. In order to estimate the entropy of the encoding variables and the mutual information, we propose a non-parametric method. We also give an information theoretic view of Variational AEs (VAEs), which suggests that VAEs can be considered as parametric methods that estimate entropy. Experimental results show that the proposed non-parametric models have more degree of freedom in terms of representation learning of features drawn from complex distributions such as Mixture of Gaussians, compared to methods which estimate entropy using parametric approaches, such as Variational AEs.

In the enterprise today, connected IoT devices are everywhere - both inside and outside corporate environments. The need to identify, manage, control and secure a quickly growing web of connections and outside devices is making the already challenging task of security even more important, and onerous. In his session at @ThingsExpo, Rich Boyer, CISO and Chief Architect for Security at NTT i3, discussed new ways of thinking and the approaches needed to address the emerging challenges of security in the enterprise. With a focus on the challenges and specific technical solutions possible using distributed trust, mutability, autonomy, and disposability, he showed how a single cohesive security management infrastructure can be created for the enterprise while still allowing the distributed value of IoT to exist anywhere. Speaker Bio At NTT Innovation Institute, Inc. (NTT i3) Rich Boyer is the Chief Information Security Officer and Chief Architect for Security working on novel solutions to secure the emerging IOT infrastructure.

An official from a suburban Minneapolis mosque where an early morning explosion occurred says the blast happened in the imam's office during the first prayer of the day. The Star Tribune reports that Mohamed Omar, executive director of the Dar Al-Farooq Islamic Center in Bloomington, says the center occasionally receives threatening calls and emails. Bloomington police Chief Jeff Potts said Saturday that investigators are trying to determine the cause of the blast. Authorities say the explosion damaged one room but it didn't hurt anyone. Asad Zaman, director of the Muslim-American Association of Minnesota, says the organization is offering a $10,000 reward for information leading to an arrest and conviction.