At the 2019 Supercomputing Conference in Denver, Colorado, global supercomputer leader Cray, a Hewlett Packard Enterprise company, announced that the King Abdullah University of Science and Technology (KAUST) in Saudi Arabia has selected a Cray CS-Storm 500NX supercomputer to support innovation in the university and the nation through a strategic artificial intelligence (AI) initiative. The added power of the GPU-accelerated CS-Storm system provides KAUST researchers greater computational capabilities to drive positive and significant outcomes in the university's core research areas of global significance: food, water, energy, the environment, and digitalization. From helping to build smart cities to developing AI algorithms that think like scientists, KAUST is the largest research center in the Middle East that brings together faculty, researchers and graduate students to leverage the interconnectedness of science and engineering. With an 8:2 ratio of GPUs to CPUs, the new Cray CS-Storm 500NX supercomputer will meet KAUST's most demanding computing requirements for production scalability, while also delivering a low total cost of ownership. Engineered for the convergence of modeling, simulation and analytics, the CS-Storm fast-started KAUST's AI journey and provides researchers and scientists the highly-advanced supercomputing and software capabilities required to analyze large volumes of data for rapid insight, where simulation alone is unsatisfactory for predicting real-world outcomes.

Artificial intelligence (AI) has the potential to deliver significant social and economic benefits, including reducing accidental deaths and injuries, making new scientific discoveries, and increasing productivity.[1] However, an increasing number of activists, scholars, and pundits see AI as inherently risky, creating substantial negative impacts such as eliminating jobs, eroding personal liberties, and reducing human intelligence.[2] Some even see AI as dehumanizing, dystopian, and a threat to humanity.[3] As such, the world is dividing into two camps regarding AI: those who support the technology and those who oppose it. Unfortunately, the latter camp is increasingly dominating AI discussions, not just in the United States, but in many nations around the world. There should be no doubt that nations that tilt toward fear rather than optimism are more likely to put in place policies and practices that limit AI development and adoption, which will hurt their economic growth, social ...

The Artificial Intelligence in Digital Marketing report additional predicts the dimensions and valuation of the global industry throughout the forecast amount. The Artificial Intelligence in Digital Marketing Market report examines the economic status and prognosis of worldwide and major regions, in the prospect of all players, types and end-user application/industries; this report examines the most notable players in major and global regions, also divides this market by segments and applications/end businesses.

Analytics Insight Magazine, a brand of Stravium Intelligence has named "Top 100 Artificial Intelligence Companies of 2019". The publication recognizes Top 100 Artificial Intelligence Companies leveraging their efficiency to reduce human efforts and introducing innovation across various sectors. The enlisted companies are flourishing across many businesses with varied deployment while showcasing how their technology provides new business opportunities to companies and individuals. Artificial Intelligence and its subsets including Machine Learning, Deep Learning, Computer Vision and others are undoubtedly driving new-wave of innovation across different business operations in current times. With a consistent increase in employee count, the companies are creating effective employment and job opportunities in various parts of the world.

In'The Terminator' series of action films starring Arnold Schwarzenegger, a cybernetic organism (cyborg) is programmed from the future to go back in time and kill the mother of the scientist who leads the fight against Skynet, an artificial intelligence system that will cause a nuclear holocaust. Terrifying and at times comical ("I'll be back", "Make my day") The Terminator cyborg was among the first presentations of artificial intelligence (AI) to a global audience. While numerous facets of AI have been developed over the past couple of decades, all with positive outcomes, the fear of AI being programmed to do something devastating to the human race, of computers "going rogue", continues to persist. On the other hand, AI holds tremendous potential for benefiting humanity in ways we are only just starting to recognize. This article gives an overview of artificial intelligence including some of its most interesting manifestations. The first step is defining what we mean by artificial intelligence. One definition of AI is "the simulation of human intelligence processes by machines, especially computers." Such processes include learning by acquiring information, understanding the rules around using that information, employing reasoning to reach conclusions, and self-correcting.

The global internet of things (IoT) in manufacturing market size was USD 27.76 Billion in 2018 and is projected to reach USD 136.83 billion by 2026, exhibiting a CAGR of 22.1% during the forecast period. The internet of things (IoT) in manufacturing comprises mechanical and electrical parts, advanced sensors, network connectivity architecture, controls, software applications, and smart devices that work together to collect and share real-time information between machines and humans. The internet of things (IoT) in manufacturing industry is gaining robust growth due to the rising adoption of AI (Artificial Intelligence) and other connected devices based on machine learning (M2M, M2P). Implementation of IoT technology in manufacturing industry is providing several organizations with new opportunities including digital transformations techniques and is enabling them to upgrade the current running operations by creating and tracking new business models. Furthermore, IoT solutions help in providing manufacturers a comprehensive vision to monitor complexities keep on arising at every intermediate point in the manufacturing process and assist in developing real-time adjustments.

Today, Mozilla and the German Ministry for Economic Cooperation and Development (BMZ) have announced to join forces in the collection of open speech data in local languages, as well as the development of local innovation ecosystems for voice-enabled products and technologies. The initiative builds on the pilot project, which our Open Innovation team and the Machine Learning Group started together with the organization "Digital Umuganda" earlier this year. The Rwandan start-up collects language data in Kinyarwanda, an African language spoken by over 12 million people. Further languages in Africa and Asia are going to be added. Mozilla's projects Common Voice and Deep Speech will be the heart of the joint initiative, which aims at collecting diverse voice data and opening up a common, public database.

Could the same computer algorithms that teach autonomous cars to drive safely help identify nearby asteroids or discover life in the universe? NASA scientists are trying to figure that out by partnering with pioneers in artificial intelligence (AI)--companies such as Intel, IBM and Google--to apply advanced computer algorithms to problems in space science. Machine learning is a type of AI. It describes the most widely used algorithms and other tools that allow computers to learn from data in order to make predictions and categorize objects much faster and more accurately than a human being can. Consequently, machine learning is widely used to help technology companies recognize faces in photos or predict what movies people would enjoy.

DUBAI: We live in an age in which talking to a machine is no longer a futuristic concept. Many activities and fields are being shaped by innovations in artificial intelligence (AI) and technology, a phenomenon that is fundamentally altering the nature of human interaction, consumption of information and, more controversially, the nature of employment. Adoption of innovations such as voice recognition, virtual agents and biometrics could be the only way for humans to survive in the coming age of automation. Amid the surge in human-machine cooperation, many people nevertheless are eager to find out what the future holds. Will robots outsmart humans and steal their jobs?

Comprehensive decision-strategy space exploration for efficient territorial planning strategies Olivier Billaud, 1, Maxence Soubeyrand, 1, Sandra Luque, 1 and Maxime Lenormand 1, † 1 TETIS, Univ Montpellier, AgroParisTech, Cirad, CNRS, Irstea, Montpellier, France Multi-Criteria Decision Analysis (MCDA) is a well-known decision support tool that can be used in a wide variety of contexts. It is particularly useful for territorial planning in situations where several actors with different, and sometimes contradictory, point of views have to take a decision regarding land use development. While the impact of the weights used to represent the relative importance of criteria has been widely studied in the recent literature, the impact of order weights determination have rarely been investigated. This paper presents a spatial sensitivity analysis to assess the impact of order weights determination in Multi-Criteria Analysis by Ordered Weighted Averaging. We propose a methodology based on an efficient exploration of the decision-strategy space defined by the level of risk and tradeoff in the decision process. We illustrate our approach with a land use planning process in the South of France. The objective is to find suitable areas for urban development while preserving green areas and their associated ecosystem services. The ecosystem service approach has indeed the potential to widen the scope of traditional landscape-ecological planning by including ecosystem-based benefits, including social and economic benefits, green infrastructures and biophysical parameters in urban and territorial planning. We show that in this particular case the decision-strategy space can be divided into four clusters. Each of them is associated with a map summarizing the average spatial suitability distribution used to identify potential areas for urban development.