In partnership with... Driven by the rapid progress in Artificial Intelligence (AI) research, intelligent machines are gaining the ability to learn, improve and make calculated decisions in ways that will enable them to perform tasks previously thought to rely solely on human experience, creativity, and ingenuit y. As a result, we will in the near future see large parts of our lives influenced by AI. AI innovation will also be central to the achievement of the United Nations' Sustainable Development Goals (SDGs) and will help solving humanity's grand challenges by capitalizing on the unprecedented quantities of data now being generated on sentiment behavior, human health, commerce, communications, migration and more. With large parts of our lives being influenced by AI, it is critical that government, industry, academia and civil society work together to evaluate the opportunities presented by AI, ensuring that AI benefits all of humanity. Responding to this critical issue, ITU and the XPRIZE Foundation are organizing AI for Good Global Summit in Geneva, 7-9 June, 2017 in partnership with a number of UN sister agencies.

The buzzwords "Industry 4.0" and "digital business" represent the start of a complex transformational process that will deeply affect industry and society during the next decade. This transformation is based on the convergence of the real (analog) world and the virtual (digital) world by means of machineto- machine (M2M) communication, autonomous systems (for example, robotics) and the Internet of Things (IoT). The German government uses the term "Industry 4.0" as the title of a government project promoting the computerization of traditional industries and the creation of intelligent factories (smart factories) that will be supported by cyberphysical systems and the IoT. The digits "4.0" in Industry 4.0 stand for the fourth industrial revolution: the transition of production from digital processing to fully interconnected processes, products and services. It follows the evolution of production processes for tradable goods from manufacturing to industry production (the first revolution), the move from steam-driven machine production to electricity-driven production (the second revolution) and the shift from analog processing to digital processing and microelectronics (the third revolution). One of the major features of Industry 4.0 is the ability of machines and devices to communicate with each other without a human interface.

Teleperformance (Paris:RCF), the worldwide leader in outsourced omnichannel customer experience management, today announced the launch of its chat bot (artificial intelligence) technology, increasing its portfolio of services. The new solution was leveraged by a strategic partnership with Artificial Solutions, a technology leader in artificial intelligence and Natural Language Interaction (NLI). Teleperformance provides services in 265 languages and will initially provide artificial intelligence services in 35 languages, using sophisticated chat bots with state-of-the-art natural language understanding and intelligence capabilities. The chat bots can assist customers in factual inquiries by offering automated customer services in natural written language. Teleperformance has developed a distinctive capability of blending chat bots with live support to make the process of addressing customer inquiries seamless, personalized and efficient.

Innovation diffusion has been studied extensively in a variety of disciplines, including sociology, economics, marketing, ecology, and computer science. Traditional literature on innovation diffusion has been dominated by models of aggregate behavior and trends. However, the agent-based modeling (ABM) paradigm is gaining popularity as it captures agent heterogeneity and enables fine-grained modeling of interactions mediated by social and geographic networks. While most ABM work on innovation diffusion is theoretical, empirically grounded models are increasingly important, particularly in guiding policy decisions. We present a critical review of empirically grounded agent-based models of innovation diffusion, developing a categorization of this research based on types of agent models as well as applications. By connecting the modeling methodologies in the fields of information and innovation diffusion, we suggest that the maximum likelihood estimation framework widely used in the former is a promising paradigm for calibration of agent-based models for innovation diffusion. Although many advances have been made to standardize ABM methodology, we identify four major issues in model calibration and validation, and suggest potential solutions.

Despite the wide use of machine learning in adversarial settings including computer security, recent studies have demonstrated vulnerabilities to evasion attacks---carefully crafted adversarial samples that closely resemble legitimate instances, but cause misclassification. In this paper, we examine the adequacy of the leading approach to generating adversarial samples---the gradient descent approach. In particular (1) we perform extensive experiments on three datasets, MNIST, USPS and Spambase, in order to analyse the effectiveness of the gradient-descent method against non-linear support vector machines, and conclude that carefully reduced kernel smoothness can significantly increase robustness to the attack; (2) we demonstrate that separated inter-class support vectors lead to more secure models, and propose a quantity similar to margin that can efficiently predict potential susceptibility to gradient-descent attacks, before the attack is launched; and (3) we design a new adversarial sample construction algorithm based on optimising the multiplicative ratio of class decision functions.

Biomedical researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a new recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data. Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies. It also can be customized to fit the needs of different scenarios. Ontology Recommender 2.0 combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available.

The history of battle knows no bounds, with weapons of destruction evolving from prehistoric clubs, axes, and spears to bombs, drones, missiles, landmines, and systems used in biological and nuclear warfare. More recently, lethal autonomous weapon systems (LAWS) powered by artificial intelligence (AI) have begun to surface, raising ethical issues about the use of AI and causing disagreement on whether such weapons should be banned in line with international humanitarian laws under the Geneva Convention. Much of the disagreement around LAWS is based on where the line should be drawn between weapons with limited human control and autonomous weapons, and differences of opinion on whether more or less people will lose their lives as a result of the implementation of LAWS. There are also contrary views on whether autonomous weapons are already in play on the battlefield. Ronald Arkin, Regents' Professor and Director of the Mobile Robot Laboratory in the College of Computing at Georgia Institute of Technology, says limited autonomy is already present in weapon systems such as the U.S. Navy's Phalanx Close-In Weapons System, which is designed to identify and fire at incoming missiles or threatening aircraft, and Israel's Harpy system, a fire-and-forget weapon designed to detect, attack, and destroy radar emitters.

The Donald Trump administration has asked Congress to give the federal government the ability to track and destroy any type of drone flying on domestic soil, a document obtained by the New York Times reveals. Under the proposal, government agencies and law enforcement would have the ability to monitor and take action against any unmanned aircraft system flying over an area designated for protection. The draft legislation would authorize government agencies to track, take control of and destroy any drone that it determines to be a threat to a "covered facility, location, or installation," which could refer to any number of locations. The proposal would call for the government to respect "privacy, civil rights and civil liberties" when exercising its power to take down drones, but courts would be given no jurisdiction to hear lawsuits filed by drone operators who have their vehicles downed. An exception for drones would be created through the proposal in U.S. hacking and surveillance laws and in Federal Aviation Administration (FAA) aircraft regulations, which currently protect the unmanned aircraft.

Drone law is, like the wobbly uncrewed aircraft themselves, hardly settled law. On Friday, the District of Columbia circuit court of appeals overturned an existing rule from the FAA that mandated drone users register in a federal database in an attempt to enforce accountability. According to the decision, the rule did not have the legal standing to apply to anyone flying for hobby or recreational purposes, which is likely most of the over 800,000 people who registered. In the FAA Modernization and Reform Act of 2012, Congress specified special rules for model airplanes, flown for hobby or recreational purposes, and charged the FAA with creating rules to govern the growing field of small unmanned aerial vehicles, specifically ones that fell outside this hobbyist/model airplane exception. In December 2015, the FAA announced that all owners of unmanned vehicles weighing more than 250 grams (or roughly as much as two sticks of butter) had to register as a drone operator, in a national database of drone users.

NASA's Exploration Mission-1 has stepped closer to reality, as the space agency completed the second flight controller tests for the engines that will power its'megarocket.' Engineers conducted a 500-second test on the component said to be the'brain' of the RS-25 engines – the four engines that will simultaneously provide 2 million pounds of thrust for the Space Launch System (SLS). NASA previously tested the first flight controller in March ahead of installation in one of the EM-1 engines, and once they've reviewed the new data, the second controller will be installed. NASA's Exploration Mission-1 has stepped closer to reality, as the space agency completed the second flight controller tests for the engines that will power its'megarocket.' Engineers conducted a 500-second test on the component said to be the'brain' of the RS-25 engines Nasa's Orion, stacked on a Space Launch System rocket capable of lifting 70 metric tons will launch from a newly refurbished Kennedy Space Center in 2019.