Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human-computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing.

With the increasing growth of information through smart devices, increasing the quality level of human life requires various computational paradigms presentation including the Internet of Things, fog, and cloud. Between these three paradigms, the cloud computing paradigm as an emerging technology adds cloud layer services to the edge of the network so that resource allocation operations occur close to the end-user to reduce resource processing time and network traffic overhead. Hence, the resource allocation problem for its providers in terms of presenting a suitable platform, by using computational paradigms is considered a challenge. In general, resource allocation approaches are divided into two methods, including auction-based methods(goal, increase profits for service providers-increase user satisfaction and usability) and optimization-based methods(energy, cost, network exploitation, Runtime, reduction of time delay). In this paper, according to the latest scientific achievements, a comprehensive literature study (CLS) on artificial intelligence methods based on resource allocation optimization without considering auction-based methods in various computing environments are provided such as cloud computing, Vehicular Fog Computing, wireless, IoT, vehicular networks, 5G networks, vehicular cloud architecture,machine-to-machine communication(M2M),Train-to-Train(T2T) communication network, Peer-to-Peer(P2P) network. Since deep learning methods based on artificial intelligence are used as the most important methods in resource allocation problems; Therefore, in this paper, resource allocation approaches based on deep learning are also used in the mentioned computational environments such as deep reinforcement learning, Q-learning technique, reinforcement learning, online learning, and also Classical learning methods such as Bayesian learning, Cummins clustering, Markov decision process.

IMAGE: Researchers explore the past, present, and future of smart vehicles and what their integration with smart cities would take. Central to any technological progress is the enrichment of human life. The internet and wireless connectivity have done that by allowing not only virtually anyone anywhere to connect real time, but by making possible connections between humans and a range of intelligent devices both indoors and outdoors, putting smart cities on the horizon. One key aspect of realizing smart cities is "smart vehicles", the latest development in intelligent transportation systems (ITS), which involve the integration of communication, mapping, positioning, network, and sensor technologies to ensure cooperative, efficient, intelligent, safe, and economical transportation. For decades, research on bringing to the streets smart vehicles that operate successfully as part of smart city infrastructure has focused on improving computing paradigms for vehicular network connectivity.

Classical computing using digital symbols--equivalent to a Turing Machine--is reaching its limits. It is undeniable that computing's historic exponential performance increases have improved the human condition. Yet such increases are a thing of the past due in large part to the constraints of physics and how today's systems are constructed. Hardware device designers struggle to eliminate the effects of nanometer-scale thermodynamic fluctuations, and the soaring cost of fabrication plants has eliminated all but a few companies as a source of future chips. Software developers' ability to imagine and program effective computational abstractions and implementations are clearly challenged in complex domains like economic systems, ecological systems, medicine, social systems, warfare, and autonomous vehicles.

The human brain has always been under study for inspiration of computing systems. Although there's a very long way to go until we can achieve a computing system that matches the efficiency of the human brain for cognitive tasks, several brain-inspired computing paradigms are being researched. Convolutional neural networks are a widely used machine learning approach for AI-related applications due to their significant performance relative to rules-based or symbolic approaches. Nonetheless, for many tasks machine learning requires vast amounts of data and training to converge to an acceptable level of performance. A Ph.D. student from Khalifa University, Eman Hasan, is investigating another AI computation methodology called'hyperdimensional computing," which can possibly take AI systems a step closer toward human-like cognition.

Federated learning (FL) is a rapidly growing research field in machine learning. However, existing FL libraries cannot adequately support diverse algorithmic development; inconsistent dataset and model usage make fair algorithm comparison challenging. In this work, we introduce FedML, an open research library and benchmark to facilitate FL algorithm development and fair performance comparison. FedML supports three computing paradigms: on-device training for edge devices, distributed computing, and single-machine simulation. FedML also promotes diverse algorithmic research with flexible and generic API design and comprehensive reference baseline implementations (optimizer, models, and datasets). We hope FedML could provide an efficient and reproducible means for developing and evaluating FL algorithms that would benefit the FL research community. We maintain the source code, documents, and user community at https://fedml.ai.

The ever-increasing number of Internet of Things (IoT) devices has created a new computing paradigm, called edge computing, where most of the computations are performed at the edge devices, rather than on centralized servers. An edge device is an electronic device that provides connections to service providers and other edge devices; typically, such devices have limited resources. Since edge devices are resource-constrained, the task of launching algorithms, methods, and applications onto edge devices is considered to be a significant challenge. In this paper, we discuss one of the most widely used machine learning methods, namely, Deep Learning (DL) and offer a short survey on the recent approaches used to map DL onto the edge computing paradigm. We also provide relevant discussions about selected applications that would greatly benefit from DL at the edge.

Atop those demands sits the ever-present threat of technology disruption, the forces of innovation that will disrupt their business in the coming years or possibly render it obsolete farther down the line. Emerging technologies fill the hearts and minds of technology's talented innovators with inspiration, hope and daring. They turn sci-fi dreams into reality and challenged accepted technology and computing paradigms. Before enterprise dreams become reality, businesses must to invest in the infrastructure and talent that will lay groundwork for future investments. Artificial intelligence-based and enabled technologies will heavily play into the next wave of enterprise computing capabilities, but innovators have to navigate and manage accountability, market support and hype. CIOs will push for emerging technologies and a customer-focused operating model, but they will also be held accountable for innovation that delivers.

Information-rich representations of text often decrease sample complexity when an natural language processing (NLP) system is trained on a task. One effective way of producing such representations is the traditional NLP pipeline: tokenization, tagging, parsing etc. An alternative are so-called embeddings that represent text in a high-dimensional real-valued space that is smooth and thereby supports generalization. Most commonly, words are represented as embeddings, but more recently contextualized embeddings like ELMo have been proposed. I will address two challenges for embeddings in this talk.

You're not the only one nervous about AI -in light of rapid AI growth and adoption, the U.S. Government recently held three Subcommittee Meetings designed to understand the implications posed by the widespread adoption of AI technology in the public and private sectors. So why is the US Government concerned about AI in society, and what role should it be considering in the private sector? Sid Mair, senior vice president of Federal Systems at Penguin Computing, weighs in. Beginning his technology career at NASA, Sid brings more than 30 years of expertise across all aspects of the federal market, including the Department of Defense, Homeland Security, civilian agencies in both classified and unclassified areas, as well the Executive and Congressional branches of government. Penguin Computing most recently built the world's largest AI cluster in the private sector.