scholarly journals Artificial Intelligence Technologies and Related Urban Planning and Development Concepts: How Are They Perceived and Utilized in Australia?

2020 ◽  
Vol 6 (4) ◽  
pp. 187 ◽  
Author(s):  
Tan Yigitcanlar ◽  
Nayomi Kankanamge ◽  
Massimo Regona ◽  
Andres Ruiz Maldonado ◽  
Bridget Rowan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Urban Planning ◽  
Methodological Approach ◽  
Digital Transformation ◽  
Social Media Analytics ◽  
Urban Context ◽  
Banking And Finance ◽  
Urban Planning And Development ◽  
Artificial Creativity

Artificial intelligence (AI) is a powerful technology with an increasing popularity and applications in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing. Although many of these areas closely relate to the urban context, there is limited understanding of the trending AI technologies and their application areas—or concepts—in the urban planning and development fields. Similarly, there is a knowledge gap in how the public perceives AI technologies, their application areas, and the AI-related policies and practices of our cities. This study aims to advance our understanding of the relationship between the key AI technologies (n = 15) and their key application areas (n = 16) in urban planning and development. To this end, this study examines public perceptions of how AI technologies and their application areas in urban planning and development are perceived and utilized in the testbed case study of Australian states and territories. The methodological approach of this study employs the social media analytics method, and conducts sentiment and content analyses of location-based Twitter messages (n = 11,236) from Australia. The results disclose that: (a) digital transformation, innovation, and sustainability are the most popular AI application areas in urban planning and development; (b) drones, automation, robotics, and big data are the most popular AI technologies utilized in urban planning and development, and; (c) achieving the digital transformation and sustainability of cities through the use of AI technologies—such as big data, automation and robotics—is the central community discussion topic.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

scholarly journals Artificial intelligence and machine learning: changing paradigm in diagnostics and imaging

2019 ◽  
Vol 8 (1) ◽  
pp. 382
Author(s):  
Rahul Badwaik
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Setting ◽  
Digital Transformation ◽  
Current Status ◽  
Healthcare Industry ◽  
Review Of The Literature

Healthcare industry is currently undergoing a digital transformation, and Artificial Intelligence (AI) is the latest buzzword in the healthcare domain. The accuracy and efficiency of AI-based decisions are already been heard across countries. Moreover, the increasing availability of electronic clinical data can be combined with big data analytics to harness the power of AI applications in healthcare. Like other countries, the Indian healthcare industry has also witnessed the growth of AI-based applications. A review of the literature for data on AI and machine learning was conducted. In this article, we discuss AI, the need for AI in healthcare, and its current status. An overview of AI in the Indian healthcare setting has also been discussed.

Methodology for cognition of digital society

2020 ◽  
Vol 3 (2) ◽  
pp. 17-26
Author(s):  
N. N. Meshcheryakova
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Research Methods ◽  
Large Volume ◽  
Digital Transformation ◽  
Digital Society ◽  
Modern Information ◽  
Short Time

Digital sociology is a computational social science that uses modern information systems and technologies, has already formed. But the conflict with traditional sociology and its research methods has not yet been resolved. This conflict can be overcome if we remember that there is a common goal – the knowledge of the phenomena and processes of social life, which is primary in relation to the methods to be agreed upon. Digital transformation of sociology is essential, since 1) traditional sociological methods do not solve the problem of providing voluminous, reliable empirical data qualitatively and in a short time; 2) the transition from contact research methods to unobtrusive ones is in demand. The adaptation of four modern information technologies-cloud computing, big data, the Internet of things and artificial intelligence – for the purposes of sociology provides a qualitative transition in the methodology of knowledge of the digital society. Cloud computing provide researchers with tools, big data – research materials, Internet of things technology aimed at collecting indicators (receiving signals) in large volume, in real time, as direct, not indirect evidence of human behavior. The development of “artificial intelligence” technology expands the possibility of receiving processed signals of the quality of the social system without building a preliminary hypothesis, in a short time and on a large volume of processed data. Digital transformation of sociology does not mean abandoning the use of traditional methods of sociological analysis, but it involves expanding the competence of a sociologist, which requires a revision of University curricula. At the same time, combining the functions of an expert on the subject (sociologist) and data analyst in one specialist is assessed as unpromising, it is proposed to combine their professional competencies in working on unified research projects.

An Empirical Analysis of Artificial Intelligence, Big Data and Analytics Applications in Exploration and Production Operations

2021 ◽  
Author(s):  
Armstrong Lee Agbaji
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Empirical Analysis ◽  
Oil And Gas ◽  
Oil Industry ◽  
Digital Transformation ◽  
Deep Dive ◽  
Exploration And Production ◽  
Oil And Gas Operations

Abstract Oil and Gas operations are now being "datafied." Datafication in the oil industry refers to systematically extracting data from the various oilfield activities that are naturally occurring. Successful digital transformation hinges critically on an organization's ability to extract value from data. Extracting and analyzing data is getting harder as the volume, variety, and velocity of data continues to increase. Analytics can help us make better decisions, only if we can trust the integrity of the data going into the system. As digital technology continues to play a pivotal role in the oil industry, the role of reliable data and analytics has never been more consequential. This paper is an empirical analysis of how Artificial Intelligence (AI), big data and analytics has redefined oil and gas operations. It takes a deep dive into various AI and analytics technologies reshaping the industry, specifically as it relates to exploration and production operations, as well as other sectors of the industry. Several illustrative examples of transformative technologies reshaping the oil and gas value chain along with their innovative applications in real-time decision making are highlighted. It also describes the significant challenges that AI presents in the oil industry including algorithmic bias, cybersecurity, and trust. With digital transformation poised to re-invent the oil & gas industry, the paper also discusses energy transition, and makes some bold predictions about the oil industry of the future and the role of AI in that future. Big data lays the foundation for the broad adoption and application of artificial intelligence. Analytics and AI are going to be very powerful tools for making predictions with a precision that was previously impossible. Analysis of some of the AI and analytics tools studied shows that there is a huge gap between the people who use the data and the metadata. AI is as good as the ecosystem that supports it. Trusting AI and feeling confident with its decisions starts with trustworthy data. The data needs to be clean, accurate, devoid of bias, and protected. As the relationship between man and machine continues to evolve, and organizations continue to rely on data analytics to provide decision support services, it is imperative that we safeguard against making important technical and management decisions based on invalid or biased data and algorithm. The variegated outcomes observed from some of the AI and analytics tools studied in this research shows that, when it comes to adopting AI and analytics, the worm remains buried in the apple.

scholarly journals Significance of Technology and Digital Transformation in Shaping the Future of Oil and Gas Industry

2021 ◽  
Vol 12 (3) ◽  
pp. 3345-3352
Author(s):  
Shubham Parsoya Et.al
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Oil And Gas ◽  
Big Data Analytics ◽  
Oil And Gas Industry ◽  
Digital Transformation ◽  
Gas Industry ◽  
The Future

Digital transformation in the field of oil and Gas industry is already a significant impact creator. It is actually act like catalyst through which the overall functionality of the oil and gas industry get enhanced and the overall output with the help of technologically-advanced mechanism, increased up to manifold. In the present scenario, the over-all quest is not just about the volume of the oil and petroleum, but it is also regarding the overall value generated throughout the process. And such enhanced level of value generation is taking place with great pace with the help of enhanced level of implementations of different types of technologies in different type of activities related to the oil and gas industry. In the present scenario, oil and gas industry’s business model is no longer depending upon just the inflated and narrow based value-chain mechanism. It is actually depending upon the almost all modernized and futuristic technologies. The modern technologies include big data analytics, 3D printing technology, cyber security, digital marketing, Artificial Intelligence, Internet of Things, drone technologies, database management system, etc. all these technologies are not only supports in handling the overall business capability of the oil and Gas Industries, but also eliminate the overall negative impact generating elements. With the help of technologies and digital transformation, the overall profitability of the oil and gas industry enhanced. Digital transformation is a prominent and significant impact creator which is not limited to the oil and gas industry, but also reaching up to the all-global level Businesses. It is transforming the overall business operations by enhancing the speed of innovation and making the use of practical knowledge base which ultimately enhance the overall power of operations and increase efficiencies. With the emergence of digital transformation technologies especially with the emergence of big data analytics, the Internet of Things and Artificial Intelligence have supports several types of innovative and new ways of developing and transforming the overall market as well as the customer satisfaction in significant manner. All such innovative technologies and digital transformations are contributing significantly in shaping the future of oil and gas industry

scholarly journals The Digital Transformation and Disruption in Business Models of the Banks under the Impact of FinTech and BigTech

2020 ◽  
Vol 14 (1) ◽  
pp. 294-305
Author(s):  
Virginia Mărăcine ◽  
Oona Voican ◽  
Emil Scarlat
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Business Models ◽  
Business Processes ◽  
Digital Transformation ◽  
Operating Costs ◽  
Continuous Increase ◽  
The Impact

AbstractThe explosive development of artificial intelligence, machine learning and big data methods in the last 10 years has been felt in the financial-banking field which has subjected to profound changes aimed at determining an unprecedented increase in the efficiency and profitability of the businesses they carry out. The tendencies of applying the concepts coming from AI, together with the continuous increase of the volume, complexity and variety of the data that the banks collect, store and process have acquired the generic names of FinTech, respectively BigTech. Five main areas exist where Fintechs and Bigtechs can provide improvements in business models for the banks: introducing specialized platforms, covering neglected customer segments, improving customer selection, reduction of the operating costs of the banks, and optimization of the business processes of the banks. We will present some of these improvements, and then we will show how the business models of the banks dramatically transform under the influence of these changes.

Global digitalization of sociocultural reproduction: “deus ex machina” or the end of the human era?

2021 ◽  
pp. 121-132
Author(s):  
Irina Mikailova
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
Human Nature ◽  
Methodological Approach ◽  
Digital Transformation ◽  
Digital Culture ◽  
Collective Consciousness ◽  
Cultural Ideals ◽  
The Law ◽  
Self Organizing

The article is focused on discussing the new methodological approach from the terms of Synergetic Historicism, to the study on specifics of reproducing digital culture and its influence on individual and collective consciousness. The results of the investigation in question based on the Method of Dual Oppositions and the Law of Self-Organizing Social and Cultural Ideals, showed that the global digital transformation toward substituting the biological human brain for Artificial Intelligence threaten Humanity not only with the irreversible transformation of human nature, but also with the end of Human Era. The results of the analysis as deconstructive implications for the reproduction of digital culture in recent years indicate that the selected path contributes to deepen the divide between digital culture and the subjects of reproduction, as well as the worsening of the problem, which was initially focused digitization process.

Digital Transformation Journey of HR

2022 ◽  
pp. 94-115
Author(s):  
Tuğba Karaboğa ◽  
Hasan Aykut Karaboğa ◽  
Dogan Basar ◽  
Songul Zehir
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Human Resources ◽  
Digital Transformation ◽  
Digital Culture ◽  
Digital Workplace ◽  
Digital Platforms ◽  
Digital Management ◽  
Employee Experience

Big data and artificial intelligence (AI) technologies have changed how we live, how we work, and how we organize businesses. Thus, it is no surprise that it is also changing how we manage human resources (HR). For HR leaders, digital transformation is a very hot topic, having the potential to create high value for businesses. First, HR can transform all functions, processes, and systems by leveraging digital platforms and applications. Second, HR can lead business digitalization, enabling a compelling employee experience where a digital culture, a digital workplace, and digital management are welcomed. To provide a more pragmatic perspective, this chapter discusses digitalization of HR with big data and artificial intelligence (AI) technologies and identifies key digital HR strategies and roles needed to sustain the digital transformation. Also, this chapter presents the advantages of digital HR and the basic pitfalls HR faces in the digital transformation of HR.

scholarly journals The Sustainability of Artificial Intelligence: An Urbanistic Viewpoint from the Lens of Smart and Sustainable Cities

2020 ◽  
Vol 12 (20) ◽  
pp. 8548 ◽  
Author(s):  
Tan Yigitcanlar ◽  
Federico Cugurullo
Keyword(s):  
Artificial Intelligence ◽  
Urban Infrastructure ◽  
Quality Monitoring ◽  
Transport Systems ◽  
Current Status ◽  
Sustainable Cities ◽  
Air Quality Monitoring ◽  
Banking And Finance ◽  
Sustainable Urbanism ◽  
Artificial Creativity

The popularity and application of artificial intelligence (AI) are increasing rapidly all around the world—where, in simple terms, AI is a technology which mimics the behaviors commonly associated with human intelligence. Today, various AI applications are being used in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing. More recently, AI applications have also started to become an integral part of many urban services. Urban artificial intelligences manage the transport systems of cities, run restaurants and shops where every day urbanity is expressed, repair urban infrastructure, and govern multiple urban domains such as traffic, air quality monitoring, garbage collection, and energy. In the age of uncertainty and complexity that is upon us, the increasing adoption of AI is expected to continue, and so its impact on the sustainability of our cities. This viewpoint explores and questions the sustainability of AI from the lens of smart and sustainable cities, and generates insights into emerging urban artificial intelligences and the potential symbiosis between AI and a smart and sustainable urbanism. In terms of methodology, this viewpoint deploys a thorough review of the current status of AI and smart and sustainable cities literature, research, developments, trends, and applications. In so doing, it contributes to existing academic debates in the fields of smart and sustainable cities and AI. In addition, by shedding light on the uptake of AI in cities, the viewpoint seeks to help urban policymakers, planners, and citizens make informed decisions about a sustainable adoption of AI.

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