Revolution of Artificial Intelligence and the Internet of Objects in the Customer Journey and the Air Sector

Artificial Intelligence (AI) revolution has recorded more impact than any other revolutions in human history. AI systems have been more far ahead with the recent advances particularity with deep learning that attained state-of-the-art results in almost all Machine Learning (ML) tasks. However, AI systems are venerable but are highly vulnerable which demands timely human intervention. Further, AI systems can easily be tweaked and misguided to produce misleading results that are far from the reality. It is high time to address this susceptibility of AI since reliance on AI systems is keep growing exponentially. AI lacks the key aspect of human intelligence—common sense that guides humans to take better action and decision based on consequences and makes humans more adaptable. This missing aspect of AI inspired the researchers to work toward Artificial general Intelligence (AGI). AGI research involves developing AI systems with human-like consequential and conscious learning. This paper presents the theoretical and practical vulnerability of AI through literature, examples, experiments. The literature and examples concentrates on famous and popular AI systems like deep learning, Google Translate, visual cognition, etc. The experiments are carried out using two datasets; a gene expression dataset for prediction and image dataset for object detection and scene recognition. The experiment results reassert the weakness of AI and the requirement of AGI.

Artificial intelligence (AI) is gaining a strong foothold across numerous applications, lowering the barriers to the use and availability of data. The development of AI cannot be separated from the Industrial Revolution 4.0. AI itself is the senses cognitive and machine learning process in problem- solving. AI systems modeled after the way that human brains operate; taking in information and activating neurons that themselves are not smart, but combine to create probabilistic models that mimic our pattern-recognizing intelligence. Considering this notion of similarity, it subsequently seems abundantly evident that problems facing human-created artificial intelligence will be those that plague human intelligence. The Cyber-Physical System (CPS), together with the Internet of Things, Big Data, Cloud Computing, and Industrial Wireless Networks, are the core technologies related to Industry 4.0. It is crucial to identify the specificities of AI software in software engineering to cope with the Industrial Revolution 4.0. The software used in AI-related with industrial revolution 4.0 needs to follow the rules of software engineering as the AI used is a part of the application used in industrial revolutions 4.0. The challenges in the software related AI can be solved using the PDCA approach, SDLC model, and DevOps practices.

Artificial intelligence (AI) is not some elusive, mystical technology that humanity is chasing, especially in regard to its usage in digital subsurface workflows. Artificial intelligence has been complementing human intelligence since the 1960s, and AI was deeply integrated into our personal and professional lives long before the technological revolutions of the 21st century. However, we tend to not realize how intrinsic AI is to our lives already. We are constantly moving the goalposts for defining AI as it solves more and more problems. This is known as the AI effect, where people tend to only think of AI as ‘whatever hasn’t been done yet’ (Hofstadter, 1979). This article attempts to review the historical melding of human and artificial intelligence in digital subsurface workflows, with some extra focus on the field of geophysics.

Artificial intelligence（AI）, a new type of intellectual capital, has emerged in the Industry 4.0 era, which not only promotes the emergence of new dominant technology paradigm, but also brings great challenges to the creation behavior of new ventures in various countries. Although scholars have noticed that AI, as an important part of digital technology, may have an impact on entrepreneurship theory and practice, AI enabling entrepreneurship has not been considered as an independent research field of entrepreneurship yet, and the theoretical research on AI enabling entrepreneurship also lags behind the development of AI enabling entrepreneurship practice.Different from the traditional entrepreneurial process, AI enabling entrepreneurship refers to the process in which entrepreneurs actively utilize or collaborate with AI to jointly exploit entrepreneurial opportunities. AI enabling entrepreneurship not only gives birth to new entrepreneurial practice problems, but also fundamentally challenges the classical entrepreneurial theory. Firstly, this article analyzes the impact of AI on entrepreneurial intention, entrepreneurial opportunity, business model and entrepreneurial team through the systematic review, and on this basis summarizes the first generation model of AI enabling entrepreneurship, that is, the existing research integration framework. Secondly, the research framework of AI-driven entrepreneurship from artificial narrow intelligence（ANI）to artificial general intelligence（AGI）is constructed based on the new perspective of AI internalization. According to the framework, AI realizes the integration of entrepreneurial elements such as entrepreneurial preference, entrepreneurial resource, entrepreneurial team and business model based on the man-machine coordination. At this stage, AGI is not only an entrepreneurial tool, but also an entrepreneurial partner, which can accelerate the entrepreneurial process and improve the quality of entrepreneurship. However, AI enabling entrepreneurship may also bring potential damage, so it is necessary to introduce regulatory barriers to prevent. Finally, combined with the existing literature, the possible research topics in the future are proposed.Compared with the previous literature, this paper makes innovative contributions in the following three aspects: Firstly, this paper integrates the scattered research at this stage, and builds the early research framework of AI enabling entrepreneurship, which is of great theoretical significance for promoting the systematic research in the AI enabling entrepreneurship field. Secondly, breaking through the previous view that AI is only regarded as the main body of entrepreneurial activities, this paper emphasizes that AI should become an entrepreneurial partner, and AGI can internally accelerate the realization of the integration of entrepreneurial preference, entrepreneurial resource, entrepreneurial team and business model, which is also the innovative significance of the traditional entrepreneurial factor theory of the new paradigm of entrepreneurship in the era of AI. Finally, two important practical values of cultural norms and government supervision in the application process of AI entrepreneurship are proposed.

Artificial Intelligence(AI) and Internet of Things(IoT) are popular domains in Computer Science. AIoT converges AI and IoT, thereby applying AI into IoT. When ‘things’ are programmed and connected to the Internet, IoT comes into place. But when these IoT systems, can analyze data and have decision-making potential without human intervention, AIoT is achieved. AI powers IoT through Decision-Making and Machine Learning, IoT powers AI through data exchange and connectivity. With the AI’s brain and IoT’s body, the systems can have shot-up efficiency, performance and learning from user interactions. Some studies show that, by 2022, AIoT devices such as drones to save rainforests or fully automated cars, would be ruling the computer industries. The paper discusses AIoT at a greater depth, focuses on few case studies of AIoT for better understanding on practical levels, and lastly, proposes an idea for a model which suggests food through emotion analysis.

Artificial intelligence (AI) approaches have recently made major impacts in the healthcare field, igniting a heated discussion over whether AI physicians would eventually replace human doctors. Human doctors are unlikely to be replaced by machines anytime soon, but AI may assist physicians make better clinical decisions or even replace human judgment in certain areas of healthcare (e.g., radiology). The increased availability of healthcare data and the rapid development of big data analysis tools have made recent productive applications of AI in healthcare possible. When driven by appropriate clinical queries, powerful AI systems may find clinically valuable information hidden in enormous volumes of data, which can help clinical decision making. The internet of things (IoT) is a network of many interconnected things that may communicate with one another across a computer network. We may get information from this global network by connecting sensors to it. Thanks to the computer network, we can obtain this information from anywhere on the planet. The internet of things (IoT) enables physical objects to connect to the internet and create systems using various technologies such as near-field communication (NFC) and wireless sensor networks (WSN).

Artificial intelligence (AI) is a human intelligence in machine encountered daily and impacts our lives. It is expected that the use of such technology in the livestock industry will automate the livestock processes and easy to manage. Biometric identification plays a key role in artificial intelligence which shows the individual identity, helps in the process of insurance and claim leakages, continue monitoring of farm animal is essential can be done with new technologies. Infra red temperature measurement camera is the newly added technology with sensor system in (AI). It is a temperature measuring device in the form of electromagnetic waves and the infrared radiation intensity. AI system consists of agent, sensor, actuators and effectors which are connected to cloud. It helps in the detection of estrus, animal diseases, body condition score and various physiological parameters using video surveillance data collection method. Artificial neural network is a branch of artificial intelligence (AI) which is based on a collection of connected units or nodes called artificial neurons and stored in a central database system. Sustainable economic future of dairy farms and to achieve 100% compliance rate. Modern dairy farms uses robotic system to deliver vaccines, machine milking and measurement of feed as per individual performance of the animal. AI analyzes the animal origin food quality traceability method from farm to fork. AI helps in the complete mechanized animal husbandry right from the birth of animal to production and food product. The future of AI in animal sector is not predictable, but advantages and daily increasing demand of AI over other sector will ensure future in animal sector as well.

Artificial Intelligence (AI) is expected to be characterised by wide applicability; for this reason, it has been quickly labelled a General Purpose Technology (GPT). In this paper, we critically assess whether AI is really a GPT. Provided that the answer is "not exactly", we suggest that an alternative framework drawn from the literature on large technical systems (LTS) could be useful to understand the nature of AI. AI, in its current understanding, is a "system technology"- a collection of techniques built and enabled by the conjunction of many sub-systems. From this premise, we try the fundamental building blocks of LTS on AI to provide new insights on its nature, goal orientation, and the actors and factors playing a role in enabling or constraining its development. Thinking in terms of AI LTS can help researchers to identify how control is distributed, coordination is achieved, and where decisions take place, or which levers actors (among which policy makers) can pull to relax constraints or steer the evolution of AI.

Artificial intelligence (AI) is defined as the power of intellectual human mind that designs an intelligent working system which proves in terms of computational power designed by the human intelligence. AI is designed in such a way that it commences to simulate the thought of brain, their thinking pattern, analysing approach and way of computing the problem. AI is one of the most notable fields in the current scenario of Fourth Industrial Revolution. AI is not a concept which finds its application in single field, but it can be used in many domains such as health care, medicines, evolutionary computation, security purposes, diagnosis and evaluation, image classification, accounting databases, transportation and smart cities. AI explores new routes of computation and follows heuristic approaches in order to solve biological problems. The present paper defines applications and current role of AI in health care and how it relates in studies including diagnosis process, image classification for diagnostic sciences, measuring the tendency of congestive heart failure, genetic analysis, drug discovery and much more. New techniques and methods are always a great tool to learn more, analyse more, as well as extract some useful information. Finally, a broad perception of this emerging topic is mentioned here to prove the positive role of AI with biotechnology and health care.

Artificial Intelligence (AI) is a branch of Computer Science whose aim is to make computers intelligent. These “intelligent” activities, include thinking, reasoning, receiving stimuli from the environment and responding to them, solving puzzles, speaking and understanding language, etc. It was John McCarthy who coined the word, “Artificial Intelligence” at the conference on computers in Dartmouth in 1954 indicating that its goal was to achieve a digital equivalent of human level intelligence. In 1970s, AI entered a low-productive period known as the AI winter. During this period, scientific and notably commercial activities in AI dropped dramatically. The victory of IBM's Deep Blue AI program over the reigning world chess champion in 1997 is probably hailed as the biggest achievement of AI. Yet another great AI achievement is the victory of IBM's Watson over the world Jeopardy champions in 2011. This chapter is a brief outline of how through numerous ups and downs AI has come to be where it currently is, and where we might expect it to be heading in the next couple of decades.

The Role of Artificial Intelligence in Revolutionizing Frailty Diagnosis and Patient Care

Artificial Intelligence (AI) is moving so rapidly policy makers, regulators, governments and the legal profession are struggling to keep up. However, AI is not new and it has been used for more than two decades. Coupled with AI, personal data, along with cyber security law, and the challenges posed by the current legal frameworks are nothing short of immense. They are, in part, at odds with each other, and are doing very different things. This paper explores some of the challenges emerging in Australia, Europe and Singapore. The challenge of the interrelationship between personal data and AI arguably begins with who has manufactured the AI. Secondly, who owns the AI. Another challenge that has also emerged is defining AI. Most people are able to understand what AI is and how it is beginning to impact the economy and our daily lives. However, there is no clear legal definition of AI, because AI is so nebulous. This burgeoning area of law is going to challenge society, privacy and economic experts, regulators, innovators of technology, as there continues to be a collision between them. Furthermore, the collection of personal data by AI challenges the notion of where responsibility lies. That is, AI may collect, use and disclose personal data at different points along the technology chain. It will be highlighted how the current data protection laws rather than promote AI projects, largely inhibit its development. This paper identifies some of the tensions between data protection law and AI. This paper argues that there is a need for an urgent and detailed understanding of the opportunities, legal and ethical issues associated with data protection and AI. Doing so will ensure an ongoing balance between the economic and social issues that are attached to the two areas of the law.

Artificial Intelligence (AI) could be a game-changer for business generally and professional services in particular. With the rapid developments in machine learning, data mining and cognitive computing, the next decade promises to see huge leaps forward. While the excitement over the potential applications of AI is understandable, there are issues related to adaptation and application in developing countries, particularly Africa and indeed, Nigeria. This paper reviews the nature of accounting and auditing problems and the need for application of artificial intelligence (AI) technologies to the discipline. The discussion includes current accounting issues for which new AI development should be fruitful, particularly auditing. This paper concludes with future roles of banks going forward and the impacts AI could have on auditing systems.

Artificial intelligence (AI) is a branch of science that creates and studies machines aimed at stimulating human intelligence processes. The primary goal of AI is to improve the speed and efficiency of routine processes As a result, the number of businesses adopting AI continues to rise around the world. The widespread use of technology in education is changing the way we teach and learn. Artificial intelligence is one of the disruptive techniques used to personalize the learning experiences of various learning groups, teachers, and tutors. This Paper explains how Artificial Intelligence can and is being used in the educational sector & current Role of AI in Indian Education Sector. Key Words : Role of AI in Education System – Steps to Implement in AI – Emerging Technology

Artificial Intelligence (AI) poses historically unique challenges for organizational settings. In a world, where there is a currently ongoing entrance of AI and algorithms into the workforce, the emerging autonomy and superiority of AI holds unique potentials but also economic and ethical challenges in the organizational context. With AI being endowed with quasi-human rights and citizenship in the Western and Arabic worlds, the question arises how to handle overpopulation but also misbehavior of AI? Should AI become eternal or is there a virtue in switching off AI at a certain point? If so, we may have to redefine laws around killing, define a virtue of killing and draw on philosophy to answer the question how to handle the abyss of killing AI with ethical grace, rational efficiency and fair style. Further, market distruptions in the wake of AI may already have begun, pressing for a demand to monitor and alleviate potential downfalls of AI. Lastly, with eternal AI overpopulation problems and resource consumption demand for attention of the impact of AI on sustainable development. The presented theoretical results will set the ground for a controlled AI-evolution in the 21st century and guide on the entrance of AI into our contemporary workforce in the organizational world.