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. Japan's On-Art Corp's CEO Kazuya Kanemaru poses with his company's eight metre tall dinosaur-shaped mechanical suit robot'TRX03' and other robots during a demonstration in Tokyo, Japan Japan's On-Art ...

Donald Trump's mobile phone only has one app installed on it, according to a new report. The President current uses an iPhone, despite calling for a boycott of the company after it refused to unlock the San Bernardino shooter's handset last year. His iPhone only has Twitter running on it, Axios reporter Mike Allen has claimed. 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.

He rode in the Beast, the president's armored Cadillac limousine, which carried the American and Belgian flags. The poolers note that there was a heavy security presence on the streets: the city was largely empty, large trucks blocked many intersections and scores of armed police lined the route. The two leaders sat across from each other at the center of a long, rectangular table. The conference room had EU and US flags along the wall, and small flower arrangements at the center of the table. Trump then told the leaders about his foreign trip.

Researchers have developed an AI algorithm to accurately predict the gender of pre-paid mobile phone users, which could be useful in crises. Phone tracking technology is already used to locate those in need of aid in humanitarian crises; but the latest development could help further, for example by identifying vulnerable groups such as women with potentially young children. Mobile phones are one of the fastest growing technologies in the developing world with global penetration rates reaching 90 per cent. However, the fact that most phones in developing countries are pre-paid means that the data lacks key information about the person carrying the phone, including gender and other demographic data, which could be useful in a crisis. Now, a team of researchers from the Data Science Institute and Department of Computing at Imperial College London have developed a machine learning algorithm, which is a form of AI.

I don't need to know exactly why Netflix recommends certain movies to me -- if it looks like a fit, I'm happy to take their recommendation. On the other hand, if your AI tells me that I should undergo an invasive medical treatment because a deep neural network (DNN) recommends it -- well, I'm going to want to understand why before I take your recommendation. AI deployed in military tools, financial tools such as loan assessments, or self-driving cars may use DNNs without being able to establishing culpability -- if we can't understand how an algorithm works, who's responsible when something goes wrong? As long as breakthroughs in artificial intelligence (AI) are common, researchers and startups will probably focus most of their effort into making new, flexible AI models. Maybe we can't explain how these models work, but if x.ai's Amy or Andrew can miraculously figure out how and when to schedule meetings for me, do I even care?

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.