Google co-founder Sergey Brin has warned that technology companies must take greater responsibility for the impact of their work on society, particularly AI. Addressing investors, the entrepreneur welcomed the'technology renaissance' we are experiencing but said he recognises the many potential threats it brings. Brin promised he would work to protect humanity from the potentially negative impacts of AI, including manipulation and'sci-fi style sentience'. Google has become a top target in Silicon Valley for regulators, authorities and critics in advertising and media concerned about its growing influence. His comments follow a period of heightened global awareness about the misuse of digital services and dark predictions over the future of intelligent machines.

From video games such as Civilization to board games such as Risk, gaming offers more opportunities for world domination than you can rattle a sabre at. If you're a would-be Caesar, Napoleon or Vladimir Putin, there are countless releases that let you rule the globe with an iron fist. These games of conquest and diplomacy tend to be long and complicated, though, and while they make for some deep, absorbing contests, they can consume entire evenings in mammoth, multi-hour play sessions. Enter CIV: Carta Impera Victoria – an empire-building card game that aims to squeeze centuries of war, trade and politics into just 20 minutes. Unlike most releases in the genre, it comes without a giant board or masses of plastic soldiers.

Jeremy Hunt has called for an independent review to explore how innovation and technology can be used to transform health care. The review, led by American cardiology, genetics and digital medicine expert Dr Eric Topol, will consider ways to train existing staff and the implications on the skills required of future healthcare professional. Hunt said, on Friday last week: "Every week we hear about exciting new developments surfacing in the NHS which could help provide answers to some of our greatest challenges, such as cancer or chronic illness. "These give us a glimpse of what the future of the whole NHS could be, which is why in the year of the NHS' 70th birthday I want to empower staff to offer patients modern healthcare more widely and more quickly." Topol's previous work has focused on the relationship between technology and health care. He said: "While it's hard to predict the future, we know artificial intelligence, digital medicine and genomics will have an enormous impact for improving the efficiency and precision in healthcare.

Jeremy Hunt has called for an independent review to explore how innovation and technology can be used to transform health care. The review, led by American cardiology, genetics and digital medicine expert Dr Eric Topol, will consider ways to train existing staff and the implications on the skills required of future healthcare professional. Hunt said, on Friday last week: "Every week we hear about exciting new developments surfacing in the NHS which could help provide answers to some of our greatest challenges, such as cancer or chronic illness. "These give us a glimpse of what the future of the whole NHS could be, which is why in the year of the NHS' 70th birthday I want to empower staff to offer patients modern healthcare more widely and more quickly." Topol's previous work has focused on the relationship between technology and health care. He said: "While it's hard to predict the future, we know artificial intelligence, digital medicine and genomics will have an enormous impact for improving the efficiency and precision in healthcare.

A fully automated artificial intelligence (AI)-based multispectral absorbance imaging system effectively classified function and potency of induced pluripotent stem cell derived retinal pigment epithelial cells (iPSC-RPE) from patients with age-related macular degeneration (AMD). The finding from the system could be applied to assessing future cellular therapies, according to research presented at the 2018 ARVO annual meeting. The software, which uses convolutional neural network (CNN) deep learning algorithms, effectively evaluated release criterion for the iPSC-RPE cell-based therapy in a standard, reproducible, and cost-effective fashion. The AI-based analysis was as specific and sensitive as traditional molecular and physiological assays, without the need for human intervention. "Cells can be classified with high accuracy using nothing but absorbance images," wrote lead investigator Nathan Hotaling and colleagues from the National Institutes of Health in their poster.

The company rolling out Australia's National Broadband Network (NBN) has announced entering three-year research and development (R&D) partnerships with the University of Technology Sydney (UTS) and the University of Melbourne. Under what it called "major collaborative relationships", NBN said it would work with the two universities on Internet of Things (IoT), robotics, artificial intelligence (AI), smart cities, programmable networks, data analytics and visualisation, wireless technologies, and "technology for social good" R&D projects. "These two new relationships will help NBN Co double down on our strong focus on technology innovation for customer experience and operational excellence," NBN CTO Ray Owen explained. "With these innovative institutions -- UoM and UTS -- we saw a natural fit in helping NBN Co further enable the digital economy." NBN added that the agreements are also expected to cover opportunities such as "student exchanges" and post-doctoral research collaboration by giving the universities "access to real-world telecoms network operational data".

Artificial intelligence brings tremendous opportunity for business and society. But it has also created fear that letting computers make decisions could cause serious problems that might need to be addressed sooner rather than later. Broadly speaking, AI refers to computers mimicking intelligent behavior, crunching big data to make judgments on everything from how to avoid car accidents to where the next crime might happen. If a computer consistently denies a loan to members of a certain sex or race, is that discrimination? Will regulators have the right to examine the algorithm that made the decision? Some big technology companies are seeking to set ethical standards through alliances with futurists, civil-rights activists and social scientists--which critics see as an effort to prevent regulation by government.

The growing race for military superiority between Washington and Beijing is entering a new phase, with both world powers preparing to square off in the cutting-edge realm of artificial intelligence. A cadre of tech gurus at the Defense Department and in the intelligence community are working to develop an interagency center designed to position the United States as the dominant force in the emerging technology subsector. Michael Griffin, the Pentagon's chief of research and engineering, has been making the rounds on Capitol Hill and in national security circles in Washington to extol the necessity and opportunity posed by the organization, dubbed the Joint Artificial Intelligence Center. Artificial intelligence technologies, which leverage various binary computations and algorithms to replicate human decision-making and risk assessments, has revolutionized the commercial and defense sectors. On the military side, automation fueled by artificial intelligence has assisted the U.S. and allied forces in areas such as combat logistics, resupply and analysis of raw intelligence collected by the Pentagon, the CIA and other agencies.

Journal of Parallel and Distributed Computing 104, 19-35.

Convolutional and Recurrent, deep neural networks have been successful in machine learning systems for computer vision, reinforcement learning, and other allied fields. However, the robustness of such neural networks is seldom apprised, especially after high classification accuracy has been attained. In this paper, we evaluate the robustness of three recurrent neural networks to tiny perturbations, on three widely used datasets, to argue that high accuracy does not always mean a stable and a robust (to bounded perturbations, adversarial attacks, etc.) system. Especially, normalizing the spectrum of the discrete recurrent network to bound the spectrum (using power method, Rayleigh quotient, etc.) on a unit disk produces stable, albeit highly non-robust neural networks. Furthermore, using the $\epsilon$-pseudo-spectrum, we show that training of recurrent networks, say using gradient-based methods, often result in non-normal matrices that may or may not be diagonalizable. Therefore, the open problem lies in constructing methods that optimize not only for accuracy but also for the stability and the robustness of the underlying neural network, a criterion that is distinct from the other.