Artificial Intelligence Revolution Research Articles

Understanding hierarchical structural evolution in a scientific discipline: A case study of artificial intelligence

Detecting what type of knowledge constitutes a discipline, tracking how the knowledge changes, and understanding why the changes are triggered are the key issues in analyzing scientific development from a macro perspective, which is usually analyzed by the topic of evolution. However, traditional methods assume that the disciplinary structure is flat with only one-layer topics, rather than a tree-like structure with hierarchical topics, which leads to the inability of existing methods to effectively examine the details of the evolution, such as the interactions between different research directions. In this paper, we take artificial intelligence (AI) as a case in which we study its hierarchical structural evolution, more precisely inspecting disciplinary development, by analyzing 65,887 AI-related research papers published during a 10-year period from 2009 to 2018. From a hierarchical topic model that can construct a topic-tree with multi-layer organizations, we design a visual analysis model for the topic-tree to systematically and visually investigate how knowledge transfers along the topic-tree and how the topic-tree changes over time. Moreover, some assistant indicators are employed to help in the exploration of the complicated structural evolution. Then, we discover the latent relationship between the sub-structures within a topic as well as the triggering reason for the knowledge migration. Based on these results, we conclude that different topics have different development patterns and that the recent artificial intelligence revolution stems from the interactions among the different topics.

VPLIV RAZVOJA UMETNE INTELIGENCE NA NACIONALNO VARNOST

The development of artificial intelligence will have a significant impact on international security and the use of a military instrument of power. One of the most important tasks for national security professionals and decision makers is thus to prepare for the repercussions of artificial intelligence development. In the development of military capabilities, artificial intelligence is integrated into intelligence, observation, control and reconnaissance applications, as well as into logistics, cyber operations, information operations, command and control systems, semi-autonomous and autonomous vehicles, and lethal autonomous weapon systems. The artificial intelligence revolution is not going to happen tomorrow. Therefore, pre-prepared policies and the knowledge shared by policy- and decision makers can help us manage the unknowns ahead. Ključne besede Artificial intelligence, national security, military instrument of power, military capabilities, decision-makers

EXplainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research.

Until date, several machine learning approaches have been proposed for the dynamic modeling of temporal omics data. Although they have yielded impressive results in terms of model accuracy and predictive ability, most of these applications are based on "Black-box" algorithms and more interpretable models have been claimed by the research community. The recent eXplainable Artificial Intelligence (XAI) revolution offers a solution for this issue, were rule-based approaches are highly suitable for explanatory purposes. The further integration of the data mining process along with functional-annotation and pathway analyses is an additional way towards more explanatory and biologically soundness models. In this paper, we present a novel rule-based XAI strategy (including pre-processing, knowledge-extraction and functional validation) for finding biologically relevant sequential patterns from longitudinal human gene expression data (GED). To illustrate the performance of our pipeline, we work on in vivo temporal GED collected within the course of a long-term dietary intervention in 57 subjects with obesity (GSE77962). As validation populations, we employ three independent datasets following the same experimental design. As a result, we validate primarily extracted gene patterns and prove the goodness of our strategy for the mining of biologically relevant gene-gene temporal relations. Our whole pipeline has been gathered under open-source software and could be easily extended to other human temporal GED applications.

Found in Translation: Unpacking the Artificial Intelligence Revolution That Has Already Arrived.

Found in Translation: Unpacking the Artificial Intelligence Revolution That Has Already Arrived.

The Fourth-Revolution in the Water Sector Encounters the Digital Revolution.

The so-called fourth revolution in the water sector will encounter the Big data and Artificial Intelligence (AI) revolution. The current data surplus stemming from all types of devices together with the relentless increase in computer capacity is revolutionizing almost all existing sectors, and the water sector will not be an exception. Combining the power of Big data analytics (including AI) with existing and future urban water infrastructure represents a significant untapped opportunity for the operation, maintenance, and rehabilitation of urban water infrastructure to achieve economic and environmental sustainability. However, such progress may catalyze socio-economic changes and cross sector boundaries (e.g., water service, health, business) as the appearance of new needs and business models will influence the job market. Such progress will impact the academic sector as new forms of research based on large amounts of data will be possible, and new research needs will be requested by the technology industrial sector. Research and development enabling new technological approaches and more effective management strategies are needed to ensure that the emerging framework for the water sector will meet future societal needs. The feature further elucidates the complexities and possibilities associated with such collaborations.

Artificial Intelligence & Machine Learning in Computer Vision Applications

Deep learning and machine learning innovations are at the core of the ongoing revolution in Artificial Intelligence for the interpretation and analysis of multimedia data. The convergence of large-scale datasets and more affordable Graphics Processing Unit (GPU) hardware has enabled the development of neural networks for data analysis problems that were previously handled by traditional handcrafted features. Several deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTM)/Gated Recurrent Unit (GRU), Deep Believe Networks (DBN), and Deep Stacking Networks (DSNs) have been used with new open source software and libraries options to shape an entirely new scenario in computer vision processing.

Technology Focus: Drilling Innovation (February 2020)

Technology Focus Summer jobs working on drilling rigs in western Oklahoma and California in 1980 and ’81 were great adventures. High oil and gas demand meant major and independent operators used some of the latest, state-of-the-art rigs to probe deep into high-pressure reservoirs. Planning and executing wells back then was a manual process with experience, horsepower, pencil, and calculator. My adventures continued after I graduated from college in 1982. Ever-evolving drilling, completion, and subsurface technology characterized the years that followed. Fast forward to February 2020. “The more things change, the more they stay the same,” the old-timers might say. In many field locations today, old-school machinery and processes remain in place. However, we see artificial intelligence (AI), data analytics, machine learning, and related advancements transforming our industry. These developments have been headlines for several years now but will take more time to reach full fruition. Experts agree this is just the beginning. Jensen Huang, chief executive officer and founder of Nvidia, highlighted this during a CNBC interview in November. “We are at the beginning of the AI revolution, one of the most powerful technological forces of all time,” he said. “We’ve created the ability for computers to understand the code of human knowledge. All industries will be transformed.” Huang, an AI visionary, showed a 4D simulation of an unmanned Mars landing craft on final approach going from 12,000 mph to zero in 6 minutes. Such visionaries are also working in upstream exploration and production to achieve remarkable advancements. A focus on drilling innovation reveals no shortage of SPE papers that exemplify these exciting trends. Without exception, the featured articles reflect increased volumes of surface and downhole data that are being captured and analyzed to enable deep learning, improved efficiency, safety, and value. As noted in paper OTC 29487, oil and gas companies are creating layers of digital knowledge enabling a new generation of AI-driven applications. New insights into mitigation of drillstring vibration, well-control events, and rate-of-penetration optimization follow. Managed-pressure drilling remains a persistent trend that continues to evolve across onshore, offshore, and geographical boundaries, opening new frontiers once seen as out of reach. One need not look to outer space to imagine the extent of breakthroughs to come in the not-too-distant future. Recommended additional reading at OnePetro: www.onepetro.org. SPE/IADC 194535 Nitrogen-Cap Drilling: A Managed-Pressure-Drilling Alternative for Highly Fractured Carbonate Reservoirs by Alexey Podust, Tengizchevroil, et al. SPE 196166 Standardization: Successful Implementation of Driving Consistency in Deepwater Operations by Daniel A. Durey, Shell, et al. SPE 197942 Innovative Digital Well-Construction-Planning Solution Enhances Coherency and Efficiency of the Drilling-Design Process by Waldemar Szemat-Vielma, Schlumberger, et al.

Thinking on Qualification of Civil Subject of Robot in Age of Artificial Intelligence

Artificial intelligence revolution has undoubtedly brought us a more intelligent, refined and humane society. However, the corresponding social problems are constantly appearing, supporting legal provisions that keeping up with the times should be the first, and the determination of the legal status of intelligent robot has undoubtedly become the premise of some specific regulatory measures. Based on the application of intelligent robot in medical field, providing electronic personality to intelligent robots would be an effective resolution, in this foundation, problems such as the right ability and legal responsibility, the tort liability would correspondingly get resolved. Intelligent robots undoubtedly help pushed advancement of society, so the development of law can’t just give up eating for fear of choking, it also should not be impeded the social progress. In a word, law is more and more important in intelligent society.

TD‐PSO: Task distribution approach based on particle swarm optimization for vehicular ad hoc network

AbstractThe rapid advancement of the artificial intelligence revolution during the past decade has significantly affected vehicular ad hoc networks (VANETs). Several applications have been introduced that must meet the requirements of a VANET, including automatic driving and preaccident alerts and broadcasting of video. The customization of vehicles for implementation of these applications is costly and might not be possible due to many constraints, particularly resource limitations. In order to achieve compliance with the VANET framework within the resource limitations, this article proposes limiting the time frame related with the individual parts of the process. To this end, this article recommends that the resource‐intensive ciphertext‐policy attribute‐based encryption (CP‐ABE) task be simplified by virtue of partitioning it into subtasks. This can be achieved by a machine‐learning technique (decision tree) in a manner that significantly influences the completion times of all subtasks. An approach based on particle swarm optimization (PSO), called task‐distribution PSO (TD‐PSO) , is proposed to perform the CP‐ABE task distribution on a VANET. The performance of this approach is evaluated by comparison with a genetic algorithm (GA), followed by comparison of these two solutions with the optimal solution proposed by the linear programming (LP) method. Results show that the TD‐PSO approach consumes less overhead than the GA. Moreover, comparison with the optimal solution proposed by LP shows that the near‐optimal solution obtained using TD‐PSO is more accurate than that obtained using the GA in most scenarios.

Machine Learning Building Blocks for Real-Time Emulation of Advanced Transport Power Systems

The revolution of artificial intelligence (AI) is transforming major industries worldwide. With accurate inferencing, AI has caught the attention of many engineers and scientists. Promisingly, hardware-in-the-loop (HIL) emulation can adopt this type of modeling method as one of the alternatives after comprehensive investigation. This paper proposes an approach for emulating power electronic motor drive transients for advanced transportation applications (ATAs) using machine learning building blocks (MLBBs) without any traditional circuit-oriented transient solver. The more electric aircraft (MEA) power system is chosen as a case study to validate the real-time emulation performance of MLBBs. Inside MLBBs, neural networks (NNs) have been applied to build component-level, device-level, and system-level models for various equipment. These models are well trained in a cluster and transplanted into the field-programmable gate array (FPGA) based hardware platform. Finally, MLBB emulation results are compared with PSCAD/EMTDC for system-level and SaberRD for device-level, which showed high consistency for model accuracy and high speed-up for hardware execution.

Understanding the Benefits, Demerits and Criticisms of the Revolution of Computational Analysis and Artificial Intelligence in Law

Computational law is an emerging branch of legal studies and information concerned with the mechanization of legal reasoning. Machine learning can be described a technique by which an automated response is generated based on input data. Legal experts spend lot of time dealing with complicated legal tasks that require effective analysis, reasoning and decision making. Machine learning could be said to replicate human behavior in this sense. This paper debates the benefits, limitations and implications of computational analysis and artificial intelligence in representations of the law.

CPU-Accelerator Co-Scheduling for CNN Acceleration at the Edge

Convolutional neural networks (CNNs) are widely deployed for many artificial intelligence (AI) applications, such as object detection and image classification. Due to the burgeoning revolution in edge AI, CNN hardware accelerators are also being employed in resource-constrained edge devices for achieving better performance and energy efficiency at the edge. Although CNN accelerators enable fast and energy-efficient CNN inference at the edge, the remaining hardware resources on the edge devices except for the CNN accelerator remain idle, which could otherwise be utilized for attaining even better performance and energy efficiency for CNN inferences. In this paper, we propose a CPU-accelerator co-scheduling technique for convolution (CONV) layer operations of CNN inferences in resource-constrained edge devices. Our proposed co-scheduling technique exploits an inherent parallelism in CNN output channels, that is, the operations for generating different output channels in a CONV layer can be executed in parallel. For load balancing between the CPU and the CNN accelerator, we also propose a simple, yet accurate latency model for CONV layer operations in the CPU and the accelerator. Based on the latency estimation of CONV layer operations provided by our proposed model, we distribute the tasks to the CPU and the CNN accelerator in a load-balance manner to minimize the idle period during the CONV layer operations in both the CPU and the CNN accelerator. We implement our proposed hardware/software (HW/SW) co-scheduling technique in various field-programmable gate array system-on-chip (FPGA-SoC) platforms as a proof-of-concept. Experimental results indicate that our proposed co-scheduling technique improves system performance by $1.18\times -2.00\times $ with energy reduction of 14.9% – 49.7% as compared to the accelerator-only execution.

Cloud, Fog, and Mist Computing: Advanced Robot Applications

As we are witnessing the dawn of the artificial intelligence revolution, it is a good time to discuss the system-architecture challenges of the new age of robotics. Most of the disputes are related to the wide variety of cloud-computing arrangements along the questions of why, with what, and how to exploit existing information technologies to cope effectively with the increasing demands. Traditional industrial robot makers are very conservative, relying exclusively on onboard execution. However, the rising user demand for advanced robot skills, such as dexterous manipulation and free navigation in an unstructured environment, has inspired newcomers to break with tradition and move robot software architecture to the next level. Inspired by the talks of Prof. Imre Rudas on cloud robotics, this article highlights some substantial factors of system design and discusses a real-world industrial example from my RaD practice.

The Revolution of Artificial Intelligence and the Significance of Adopting AI in different Industries

Artificial Intelligence (AI) is one of the most widely inflated technologies in several industries today. With the emergence of IoT, Big data and Digitalization, many industries produce large sets of data and AI begins to be the prominence for solving the increasing number of complications in this relevance. Artificial Intelligence (AI) and Machine Learning (ML) applications, spectacle substantial guarantee in gaining commercial traction in several businesses as AI brings with a probable of genuine human-to-machine interaction. When machines become intelligent, they can understand needs, connect with data points and arrive at better decisions. Therefore, Artificial Intelligence (AI) and Machine Learning technologies are being quickly adopted in wide range of applications in several industries. In this paper, we epitomize the fundamentals and the significance of adopting of Artificial Intelligence technologies in different industries.

Introduction to the Special Issue on the 1st IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS 2019)

This issue of the IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS) includes the highlighted papers from the 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS 2020), which was originally planned to be held at the Centro Congressi Porto Antico in Genoa, Italy, on March 23–25, 2020. However, due to the difficult pandemic situation that the world was living during those days, the conference was postponed to August 31–September 4, 2020, and was finally held as a fully virtual event. Despite those difficulties, the conference was a great success in terms of participation which demonstrated the consolidation of the IEEE Circuits and Systems Society (CASS) community interested in the research and advances of new circuits and systems paradigms and hardware platforms that can boost a new generation of more efficient Artificial Intelligence (AI) systems and may enlarge the AI application scenarios and performances. The AI revolution is promising to incorporate the AI computing power to all industrial and social sectors. To make this trend possible, new computing platforms suited to efficiently support AI emerging algorithms, systems, and applications in different scenarios (from cloud servers to edge and autonomous devices) should be developed at a parallel pace. The IEEE AICAS conference has appeared and it is being consolidated as an international forum to communicate state-of-the-art research, innovation, and development results and advances in AI circuits and systems. It is an ideal forum to join efforts and exchange experiences between different international communities from academia and industry boosting the progress of AI circuits and systems.

From the National Corpus of Polish to the Polish Corpus Infrastructure

AbstractThe National Corpus of Polish emerged as a cumulative result of many years of work on large reference corpora by computer scientists and linguists in Poland. While its impact on research in linguistics, humanities and language technology is unquestionable and highly significant, the construction of the national corpus was halted in 2011. In the paper we call for activating the research community and funding institutions around the construction of a corpus infrastructure with the national corpus at its heart. It is claimed that on the verge of an artificial intelligence revolution the envisaged Polish Corpus Infrastructure would provide reliable language data, combine available resources and allow easy integration of new ones.

The data explosion: Is your business ready for the AI revolution?

This paper discusses the explosion of artificial intelligence (AI) and what this means for data centres and the broader industry, as well as how businesses can adequately prepare for the transformation that it will bring.

Artificial intelligence and sustainable development

Artificial intelligence (AI) is rapidly opening up a new frontier in the fields of business, corporate practices, and governmental policy. The intelligence of machines and robotics with deep learning capabilities have created profound disrupting and enabling impacts on business, governments, and society. They are also influencing the larger trends in global sustainability. As the AI revolution transforms our world, it could herald a utopian future where humanity co-exists harmoniously with machines, or portend a dystopian world filled with conflict, poverty and suffering. More immediately, would AI accelerate our progress on the United Nations (UN) Sustainable Development Goals (SDGs) or bring us further down the path toward greater economic uncertainty, environmental collapse, and social upheaval? What are some of the implications for business leadership and the education of future business leaders? This article aims to address these questions by analyzing the impacts of AI in three case studies. It draws some preliminary inferences for management education and the business of leading corporations in the midst of rapid technological and social change. This study combines the perspectives of business strategy and public policy to analyze the impacts of AI on sustainable development with a specific focus on the advancement of the SDGs. It also draws some lessons on managerial learning and leadership development for global sustainability.

In AI We Trust Incrementally: a Multi-layer Model of Trust to Analyze Human-Artificial Intelligence Interactions

Real engines of the artificial intelligence (AI) revolution, machine learning (ML) models, and algorithms are embedded nowadays in many services and products around us. As a society, we argue it is now necessary to transition into a phronetic paradigm focused on the ethical dilemmas stemming from the conception and application of AIs to define actionable recommendations as well as normative solutions. However, both academic research and society-driven initiatives are still quite far from clearly defining a solid program of study and intervention. In this contribution, we will focus on selected ethical investigations around AI by proposing an incremental model of trust that can be applied to both human-human and human-AI interactions. Starting with a quick overview of the existing accounts of trust, with special attention to Taddeo’s concept of “e-trust,” we will discuss all the components of the proposed model and the reasons to trust in human-AI interactions in an example of relevance for business organizations. We end this contribution with an analysis of the epistemic and pragmatic reasons of trust in human-AI interactions and with a discussion of kinds of normativity in trustworthiness of AIs.

The AI Advantage:How to Put the Artificial Intelligence Revolution to Work

AI depends on mathematics, algorithms, computers. Knowledge creation depends on making conjectures and refutations and serendipity. Human intelligence is neither ultimate nor absolute. Homo sapiens are not the ultimate manifestation of the genus Homo. Homo sapiens are still evolving and are likely to speciate into a more powerful life form in the future. The best of human knowledge is captured in human designed axiomatic systems. These systems are not perfect as we learn from Godel Incompleteness Theorem, Turing’s halting theorem and Chaitin’s Algorithmic Information Theory. All future AI systems will need to account for these and many more yet undiscovered aspects of human intelligence derived knowledge. These aspects are not touched upon Thomas Davenport’s The AI Advantage . This is not a criticism of the book because Davenport has sought to address a very different, down-to-earth audience of business entrepreneurs who are unlikely to possess deep knowledge of the functioning of the human brain, its intellectual abilities (and disabilities), its innovative ability to create artificial intelligence, etc. What AI does today, even half-century ago would have appeared superhuman and miraculous. Then, intelligent activity was assumed to be unmechanizable and therefore not a threat to ‘intelligent’ humans. It all changed when IBM’s Watson beat human experts in the TV game Jeopardy in 2011 and Google’s AlphaGo beat world champions in the game Go in 2017.