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.

Artificial intelligence: criminal and procedural aspects

2021 ◽  
pp. 75-89
Author(s):  
Igor I. Kartashov ◽  
Ivan I. Kartashov
Keyword(s):  
Artificial Intelligence ◽  
New Technologies ◽  
Human Beings ◽  
Criminal Proceedings ◽  
Legal Advice ◽  
Judicial Practice ◽  
Legal Expert ◽  
To Come ◽  
Definition Of ◽  
Self Learning

For millennia, mankind has dreamed of creating an artificial creature capable of thinking and acting “like human beings”. These dreams are gradually starting to come true. The trends in the development of modern so-ciety, taking into account the increasing level of its informatization, require the use of new technologies for information processing and assistance in de-cision-making. Expanding the boundaries of the use of artificial intelligence requires not only the establishment of ethical restrictions, but also gives rise to the need to promptly resolve legal problems, including criminal and proce-dural ones. This is primarily due to the emergence and spread of legal expert systems that predict the decision on a particular case, based on a variety of parameters. Based on a comprehensive study, we formulate a definition of artificial intelligence suitable for use in law. It is proposed to understand artificial intelligence as systems capable of interpreting the received data, making optimal decisions on their basis using self-learning (adaptation). The main directions of using artificial intelligence in criminal proceedings are: search and generalization of judicial practice; legal advice; preparation of formalized documents or statistical reports; forecasting court decisions; predictive jurisprudence. Despite the promise of using artificial intelligence, there are a number of problems associated with a low level of reliability in predicting rare events, self-excitation of the system, opacity of the algorithms and architecture used, etc.

scholarly journals "Did you know that David Beckham speaks nine languages?”: AI-supported production process for enhanced personalization of audio-visual content

2021 ◽  
Author(s):  
Izabela Derda
Keyword(s):  
Artificial Intelligence ◽  
Production Process ◽  
Stage Model ◽  
Visual Content ◽  
Consumer Evaluation ◽  
Consumer Data ◽  
New Type ◽  
The Media ◽  
Media Experience ◽  
New Phase

The introduction of artificial intelligence (AI) into the media production process has contributed to the automation of selected tasks and stronger hybridization of man and machine in the process; however, the AI-supported production process has expanded from the traditional, three-stage model by a new phase of consumer evaluation and feedback collection, analysis, and application. This has opened a way for far-reaching content personalization and thus offers a new type of media experience. Powering the production process with a constant stream of consumer data has also affected the process itself and changed its nature from linear to cyclical.

scholarly journals A Fog Computing Architecture with Multi-Layer for Computing-Intensive IoT Applications

2021 ◽  
Vol 11 (24) ◽  
pp. 11585
Author(s):  
Muhammad Muneeb ◽  
Kwang-Man Ko ◽  
Young-Hoon Park
Keyword(s):  
Artificial Intelligence ◽  
Data Analysis ◽  
New Technologies ◽  
Fog Computing ◽  
Layered Architecture ◽  
Computing Platform ◽  
Computing Paradigm ◽  
Iot Devices ◽  
Analysis System ◽  
Data Analysis System

The emergence of new technologies and the era of IoT which will be based on compute-intensive applications. These applications will increase the traffic volume of today’s network infrastructure and will impact more on emerging Fifth Generation (5G) system. Research is going in many details, such as how to provide automation in managing and configuring data analysis tasks over cloud and edges, and to achieve minimum latency and bandwidth consumption with optimizing task allocation. The major challenge for researchers is to push the artificial intelligence to the edge to fully discover the potential of the fog computing paradigm. There are existing intelligence-based fog computing frameworks for IoT based applications, but research on Edge-Artificial Intelligence (Edge-AI) is still in its initial stage. Therefore, we chose to focus on data analytics and offloading in our proposed architecture. To address these problems, we have proposed a prototype of our architecture, which is a multi-layered architecture for data analysis between cloud and fog computing layers to perform latency- sensitive analysis with low latency. The main goal of this research is to use this multi-layer fog computing platform for enhancement of data analysis system based on IoT devices in real-time. Our research based on the policy of the OpenFog Consortium which will offer the good outcomes, but also surveillance and data analysis functionalities. We presented through case studies that our proposed prototype architecture outperformed the cloud-only environment in delay-time, network usage, and energy consumption.

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.

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.

Artificial intelligence as a moving force of improvement and innovative development in education and pedagogic

2019 ◽  
pp. 21-30
Author(s):  
M. G. Koliada ◽  
T. I. Bugayova
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Interaction Analysis ◽  
Educational Data Mining ◽  
Main Task ◽  
Intellectual System ◽  
Will Force ◽  
To Come ◽  
Artificial Intelligence Systems

The article discusses the history of the development of the problem of using artificial intelligence systems in education and pedagogic. Two directions of its development are shown: “Computational Pedagogic” and “Educational Data Mining”, in which poorly studied aspects of the internal mechanisms of functioning of artificial intelligence systems in this field of activity are revealed. The main task is a problem of interface of a kernel of the system with blocks of pedagogical and thematic databases, as well as with the blocks of pedagogical diagnostics of a student and a teacher. The role of the pedagogical diagnosis as evident reflection of the complex influence of factors and reasons is shown. It provides the intelligent system with operative and reliable information on how various reasons intertwine in the interaction, which of them are dangerous at present, where recession of characteristics of efficiency is planned. All components of the teaching and educational system are subject to diagnosis; without it, it is impossible to own any pedagogical situation optimum. The means in obtaining information about students, as well as the “mechanisms” of work of intelligent systems based on innovative ideas of advanced pedagogical experience in diagnostics of the professionalism of a teacher, are considered. Ways of realization of skill of the teacher on the basis of the ideas developed by the American scientists are shown. Among them, the approaches of researchers D. Rajonz and U. Bronfenbrenner who put at the forefront the teacher’s attitude towards students, their views, intellectual and emotional characteristics are allocated. An assessment of the teacher’s work according to N. Flanders’s system, in the form of the so-called “The Interaction Analysis”, through the mechanism of fixing such elements as: the verbal behavior of the teacher, events at the lesson and their sequence is also proposed. A system for assessing the professionalism of a teacher according to B. O. Smith and M. O. Meux is examined — through the study of the logic of teaching, using logical operations at the lesson. Samples of forms of external communication of the intellectual system with the learning environment are given. It is indicated that the conclusion of the found productive solutions can have the most acceptable and comfortable form both for students and for the teacher in the form of three approaches. The first shows that artificial intelligence in this area can be represented in the form of robotized being in the shape of a person; the second indicates that it is enough to confine oneself only to specially organized input-output systems for targeted transmission of effective methodological recommendations and instructions to both students and teachers; the third demonstrates that life will force one to come up with completely new hybrid forms of interaction between both sides in the form of interactive educational environments, to some extent resembling the educational spaces of virtual reality.

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.

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