scholarly journals Everything You Always Wanted to Know About AI - Nowcasting Digital Skills With Wikipedia

2019 ◽  
Author(s):  
Fabian Stephany
Keyword(s):  
Artificial Intelligence ◽  
New Technologies ◽  
Data Driven ◽  
Online Data ◽  
Digital Skills ◽  
Online Encyclopedia ◽  
Initial Research

With the use of online data from the tech job platform dice.com and the online encyclopedia Wikipedia, two networks of digital skills are created around the topic of Artificial Intelligence. Initial research indicates that new skill tags first join the Wikipedia network, before they appear in AI-related job announcements on dice.com. The findings of this work could be used in order to create a data-driven strategy for the acquisition and the development of adequate skills needed to implement and leverage new technologies at best.

scholarly journals Here Comes the Hyper-Connected Augmented Consumer

2017 ◽  
Vol 9 (2) ◽  
pp. 10-17 ◽  
Author(s):  
Andrew T. Stephen
Keyword(s):  
Artificial Intelligence ◽  
New Technologies ◽  
Search Costs ◽  
Data Driven ◽  
Smart Sensors ◽  
Market Intelligence ◽  
Consumer Data ◽  
New Type ◽  
To Come ◽  
Iot Devices

Abstract Consumers have become always on and constantly connected. Search costs have plummeted, individuals’ abilities to digitally express themselves and their opinions increased, and the opportunities for superior business and market intelligence for companies have skyrocketed. This has given rise to more, richer, and new sources of consumer data that marketers can leverage, and has fueled the data-driven insights revolution in marketing. But there is more to come very soon. In marketing, we are quickly moving from the age of the connected consumer to the age of the augmented consumer. New technologies like wearable devices, smart sensors, consumer IoT devices, smart homes, and, critically, artificial intelligence ecosystems will not only connect, but will substantially and meaningfully augment the consumer in terms of their thoughts and behaviors. The biggest challenge for marketers will lie in how they approach marketing to this new type of consumer, particularly personal artificial intelligence ecosystems. This means marketing to algorithms, instead of people, and that is very different to how most marketing work is currently done.

scholarly journals DATA AND ARTIFICIAL INTELLIGENCE FOR BETTER AND INTELLIGENT REGULATION

2021 ◽  
Vol 14 (1) ◽  
pp. 7-25
Author(s):  
Manuel Cabugueira
Keyword(s):  
Artificial Intelligence ◽  
Policy Making ◽  
New Technologies ◽  
Evidence Base ◽  
Data Driven ◽  
Evidence Based ◽  
Public Intervention ◽  
Economic Competitiveness ◽  
Better Regulation ◽  
Evidence Base Approach

In this article we bring forward a reflection on how data technology and artificial intelligence can improve the implementation of an evidence-based, data-driven, regulation. We start by arguing in favor of an evidence-base approach to regulation, meaning that policy making should be supported by information on the expected and observed impacts. We reach this position by acknowledging that, on one side, markets fail and public intervention will promote social welfare and economic competitiveness but, on the other, regulation also fails creating implementation and compliance costs. It follows that public intervention has to be supported by a demonstration that benefits will outweigh the costs. In this paper we discuss the challenges presented by this evidence-base regulation and how the new tools from data technologies and artificial intelligence may provide new resources to face those difficulties. We conclude that there is an obvious match between the solutions that these new technologies present and the requirements to “better regulate” and to “regulate better”. In the end, it seems only natural that evidence-base regulation should also be data-driven. Keywords: Regulation; Artificial Intelligence; Better Regulation; evidence-based regulation, data-driven regulation

scholarly journals Modern Business Intelligence: Big Data Analytics and Artificial Intelligence for Creating the Data-Driven Value

2021 ◽  
Author(s):  
Ahmed A.A. Gad-Elrab
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Business Intelligence ◽  
Data Analytics ◽  
New Technologies ◽  
Big Data Analytics ◽  
Data Driven ◽  
A Value ◽  
Challenges And Opportunities ◽  
Modern Business

Currently, business intelligence (BI) systems are used extensively in many business areas that are based on making decisions to create a value. BI is the process on available data to extract, analyze and predict business-critical insights. Traditional BI focuses on collecting, extracting, and organizing data for enabling efficient and professional query processing to get insights from historical data. Due to the existing of big data, Internet of Things (IoT), artificial intelligence (AI), and cloud computing (CC), BI became more critical and important process and received more great interest in both industry and academia fields. The main problem is how to use these new technologies for creating data-driven value for modern BI. In this chapter, to meet this problem, the importance of big data analytics, data mining, AI for building and enhancing modern BI will be introduced and discussed. In addition, challenges and opportunities for creating value of data by establishing modern BI processes.

Artificial Intelligence as a Technological Innovation to Accelerate Economic Development

2019 ◽  
Vol 12 (3) ◽  
pp. 125-133
Author(s):  
S. V. Shchurina ◽  
A. S. Danilov
Keyword(s):  
Artificial Intelligence ◽  
Economic Development ◽  
Technological Innovation ◽  
Manufacturing Industry ◽  
New Technologies ◽  
Retail Trade ◽  
High Tech ◽  
Industrial Complex ◽  
Technological Gap ◽  
The Government

The subject of the research is the introduction of artificial intelligence as a technological innovation into the Russian economic development. The relevance of the problem is due to the fact that the Russian market of artificial intelligence is still in the infancy and the necessity to bridge the current technological gap between Russia and the leading economies of the world is coming to the forefront. The financial sector, the manufacturing industry and the retail trade are the drivers of the artificial intelligence development. However, company managers in Russia are not prepared for the practical application of expensive artificial intelligence technologies. Under these circumstances, the challenge is to develop measures to support high-tech projects of small and medium-sized businesses, given that the technological innovation considered can accelerate the development of the Russian economy in the energy sector fully or partially controlled by the government as well as in the military-industrial complex and the judicial system.The purposes of the research were to examine the current state of technological innovations in the field of artificial intelligence in the leading countries and Russia and develop proposals for improving the AI application in the Russian practices.The paper concludes that the artificial intelligence is a breakthrough technology with a great application potential. Active promotion of the artificial intelligence in companies significantly increases their efficiency, competitiveness, develops industry markets, stimulates introduction of new technologies, improves product quality and scales up manufacturing. In general, the artificial intelligence gives a new impetus to the development of Russia and facilitates its entry into the five largest world’s economies.

The Future of Human Workers

2018 ◽  
Author(s):  
Mahesh K. Joshi ◽  
J.R. Klein
Keyword(s):  
Artificial Intelligence ◽  
New Technologies ◽  
Mental Capacity ◽  
Machine Intelligence ◽  
The Internet ◽  
Traditional Model ◽  
Global Workforce ◽  
Repetitive Tasks ◽  
The Internet Of Things

New technologies like artificial intelligence, robotics, machine intelligence, and the Internet of Things are seeing repetitive tasks move away from humans to machines. Humans cannot become machines, but machines can become more human-like. The traditional model of educating workers for the workforce is fast becoming irrelevant. There is a massive need for the retooling of human workers. Humans need to be trained to remain focused in a society which is constantly getting bombarded with information. The two basic elements of physical and mental capacity are slowly being taken over by machines and artificial intelligence. This changes the fundamental role of the global workforce.

AI Narratives

2020 ◽  
Keyword(s):  
Artificial Intelligence ◽  
New Technologies ◽  
Ancient Greece ◽  
Historical Overview ◽  
Intelligent Machines ◽  
Narrative History ◽  
History Of ◽  
Imaginative Thinking ◽  
The Relationship ◽  
Insight Into

This book is the first to examine the history of imaginative thinking about intelligent machines. As real artificial intelligence (AI) begins to touch on all aspects of our lives, this long narrative history shapes how the technology is developed, deployed, and regulated. It is therefore a crucial social and ethical issue. Part I of this book provides a historical overview from ancient Greece to the start of modernity. These chapters explore the revealing prehistory of key concerns of contemporary AI discourse, from the nature of mind and creativity to issues of power and rights, from the tension between fascination and ambivalence to investigations into artificial voices and technophobia. Part II focuses on the twentieth and twenty-first centuries in which a greater density of narratives emerged alongside rapid developments in AI technology. These chapters reveal not only how AI narratives have consistently been entangled with the emergence of real robotics and AI, but also how they offer a rich source of insight into how we might live with these revolutionary machines. Through their close textual engagements, these chapters explore the relationship between imaginative narratives and contemporary debates about AI’s social, ethical, and philosophical consequences, including questions of dehumanization, automation, anthropomorphization, cybernetics, cyberpunk, immortality, slavery, and governance. The contributions, from leading humanities and social science scholars, show that narratives about AI offer a crucial epistemic site for exploring contemporary debates about these powerful new technologies.

The Oxford Handbook of Ethics of AI

2020 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Ethical Issues ◽  
Rapid Development ◽  
Data Driven ◽  
International Human Rights Law ◽  
Human Knowledge ◽  
Human Rights Law ◽  
Ai Ethics ◽  
And Behavior ◽  
Control Complex

This book explores the intertwining domains of artificial intelligence (AI) and ethics—two highly divergent fields which at first seem to have nothing to do with one another. AI is a collection of computational methods for studying human knowledge, learning, and behavior, including by building agents able to know, learn, and behave. Ethics is a body of human knowledge—far from completely understood—that helps agents (humans today, but perhaps eventually robots and other AIs) decide how they and others should behave. Despite these differences, however, the rapid development in AI technology today has led to a growing number of ethical issues in a multitude of fields, ranging from disciplines as far-reaching as international human rights law to issues as intimate as personal identity and sexuality. In fact, the number and variety of topics in this volume illustrate the width, diversity of content, and at times exasperating vagueness of the boundaries of “AI Ethics” as a domain of inquiry. Within this discourse, the book points to the capacity of sociotechnical systems that utilize data-driven algorithms to classify, to make decisions, and to control complex systems. Given the wide-reaching and often intimate impact these AI systems have on daily human lives, this volume attempts to address the increasingly complicated relations between humanity and artificial intelligence. It considers not only how humanity must conduct themselves toward AI but also how AI must behave toward humanity.

scholarly journals Why a Virtual Assistant for Moral Enhancement When We Could have a Socrates?

2021 ◽  
Vol 27 (4) ◽  
Author(s):  
Francisco Lara
Keyword(s):  
Artificial Intelligence ◽  
Virtual Reality ◽  
New Technologies ◽  
Personal Autonomy ◽  
Moral Enhancement ◽  
Moral Decisions ◽  
Educational Methodology ◽  
Better Than

AbstractCan Artificial Intelligence (AI) be more effective than human instruction for the moral enhancement of people? The author argues that it only would be if the use of this technology were aimed at increasing the individual's capacity to reflectively decide for themselves, rather than at directly influencing behaviour. To support this, it is shown how a disregard for personal autonomy, in particular, invalidates the main proposals for applying new technologies, both biomedical and AI-based, to moral enhancement. As an alternative to these proposals, this article proposes a virtual assistant that, through dialogue, neutrality and virtual reality technologies, can teach users to make better moral decisions on their own. The author concludes that, as long as certain precautions are taken in its design, such an assistant could do this better than a human instructor adopting the same educational methodology.

Impact of Big Data and Artificial Intelligence on Industry: Developing a Workforce Roadmap for a Data Driven Economy

2021 ◽  
Author(s):  
Marina Johnson ◽  
Rashmi Jain ◽  
Peggy Brennan-Tonetta ◽  
Ethne Swartz ◽  
Deborah Silver ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Driven

scholarly journals Out of the loop? On the radical and the routine in urban big data

Urban Studies ◽  
2021 ◽  
pp. 004209802110140
Author(s):  
Sarah Barns
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Computational Models ◽  
Data Driven ◽  
Urban Systems ◽  
Out Of The Loop ◽  
Urban Futures ◽  
Urban Behaviour ◽  
The City

This commentary interrogates what it means for routine urban behaviours to now be replicating themselves computationally. The emergence of autonomous or artificial intelligence points to the powerful role of big data in the city, as increasingly powerful computational models are now capable of replicating and reproducing existing spatial patterns and activities. I discuss these emergent urban systems of learned or trained intelligence as being at once radical and routine. Just as the material and behavioural conditions that give rise to urban big data demand attention, so do the generative design principles of data-driven models of urban behaviour, as they are increasingly put to use in the production of replicable, autonomous urban futures.

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