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Artificial Intelligence and Applications
Abstract
Artificial Intelligent Technologies ease human life and in the coming future, Artificial Intelligent Technologies can provide a more competitive advantage. In the end, we have been during this research through the AI definitions, brief history, applications of AI
publicly, applications of AI in the military, ethics of AI, and therefore the three rules of robotics. This is not the top of AI, there's more to return from it, who knows what AI can do for us within the future, maybe it'll be a whole society of robots.
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In contemporary time, the production arena has become an interesting scene with introduction of innovations that is changing the climate of production technology. The innovation comes in the form of smart technique of production for an enhanced productivity. Which is termed intelligence manufacturing or production. Intelligence manufacturing has led to enhance productivity in manufacturing sector in recent time on account of 4 th industrial revolution. Intelligent manufacturing (IM) involves the use of sophisticated and advanced analytics, sensor application in robotics and internet based connections to things popularly known as IoT. This study was centered on application of intelligent manufacturing in industrial productivity and cost/time wastage. Purposive sampling method was adopted in this study, random sampling survey methods was used to pick samples for collation of responses from production managers of manufacturing companies at the study area (Lagos state, Nigeria). Population frame of 100 product manufacturing companies was adopted, out of which 73 respondents that constitute production managers and supervisors were selected using random sampling technique. The study censored the opinion and view of professionals such as managers (production), production supervisors on calibrated. The study highlighted emerging areas of application of quality monitoring system in intelligent manufacturing to include advanced analytical tools and censored based applications such as robotics applications used in design and product calibration, virtual and augmented reality application that simulates real situation using virtual approach, machine learning, expert systems (AI), block chain technology, drones for real time supervision of production process.
Artificial intelligence is revolutionizing-and strengthening-modern healthcare through technologies that can predict, grasp, learn, and act, whether it's employed to identify new relationships between genetic codes or to control surgery-assisting robots. It can detect minor patterns that humans would completely overlook. This study explores and discusses the various modern applications of AI in the health sector. Particularly, the study focuses on three most emerging areas of AI-powered healthcare: AI-led drug discovery, clinical trials, and patient care. The findings suggest that pharmaceutical firms have benefited from AI in healthcare by speeding up their drug discovery process and automating target identification. Artificial Intelligence (AI) can help also to eliminate time-consuming data monitoring methods. The findings also indicate that AI-assisted clinical trials are capable of handling massive volumes of data and producing highly accurate results. Medical AI companies develop systems that assist patients at every level. Patients' medical data is also analyzed by clinical intelligence, which provides insights to assist them improve their quality of life.
Artificial intelligence (AI) can assist business leaders in automating time-consuming and labor-intensive operations such as data collection, aggregation, and purification. This enables them to devote more time to high-value tasks and make more informed business decisions. While AI has been shown to offer a number of advantages when it comes to data analysis and delivering insight for investment plan creation, it lacks the emotional intelligence required to meet more complicated investing needs. The privacy and security of clients is another issue that has developed as a result of the usage of artificial intelligence in financial planning.Because AI is still a relatively new breakthrough in an industry with millions of dollars invested, some industry insiders are concerned that cyber-security may not be as advanced as the technology itself, putting the company at risk of hacking. This research outlined several advantages that AI offer during financial planning, followed by limitations and challenges that come in decision making process with AI. Last, this article discusses how humans and AI might cooperate to provide a more strategic and realistic perspective when corporate decision-making processes are complex, unpredictable, and ambiguous.
Introduction:
Artificial intelligence (AI) and machine learning (ML) are rapidly evolving fields in various sectors, including healthcare. This article reviews AI's present applications in healthcare, including its benefits, limitations and future scope.
Sources of data:
A review of the English literature was conducted with search terms 'AI' or 'ML' or 'deep learning' and 'healthcare' or 'medicine' using PubMED and Google Scholar from 2000-2021.
Areas of agreement:
AI could transform physician workflow and patient care through its applications, from assisting physicians and replacing administrative tasks to augmenting medical knowledge.
Areas of controversy:
From challenges training ML systems to unclear accountability, AI's implementation is difficult and incremental at best. Physicians also lack understanding of what AI implementation could represent.
Growing points:
AI can ultimately prove beneficial in healthcare, but requires meticulous governance similar to the governance of physician conduct.
Areas timely for developing research:
Regulatory guidelines are needed on how to safely implement and assess AI technology, alongside further research into the specific capabilities and limitations of its medical use.
Artificial Intelligence (AI) technologies have been researched in educational contexts for more than 30 years (Woolf 1988; Cumming and McDougall 2000; du Boulay 2016). More recently, commercial AI products have also entered the classroom. However, while many assume that Artificial Intelligence in Education (AIED) means students taught by robot teachers, the reality is more prosaic yet still has the potential to be transformative (Holmes et al. 2019). This chapter introduces AIED, an approach that has so far received little mainstream attention, both as a set of technologies and as a field of inquiry. It discusses AIED’s AI foundations, its use of models, its possible future, and the human context. It begins with some brief examples of AIED technologies.
Health care Industries as well as clinical and public health sectors has seen a paradigm shift from a long-established approach to an assimilated advancements in gathering, investigating and testing of ordered and shapeless healthcare data for overall health defensive and safeguarding goals with application of computing technologies. Artificial intelligence (AI) with its enabled cognitive knowledge has proved itself a powerful assisting tool with progressive analysis techniques to the existing healthcare domain. A vast development of AI in the healthcare sphere has become an active assisting instrument for the clinical assistants to derive a better conclusion in many functional medicinal areas like cancer, neurology and cardiology. The powerful techniques of AI include machine learning and deep learning to handle prearranged healthcare data whereas unstructured healthcare datasets are managed by text mining and Natural Language Processing. Also rapid increase of healthcare data leading in the formation of outsized data as well as automated healthcare tools are easily managed by AI-supervised Big data and IoT-based methods. We have made a systematic review of AI application for various medicinal areas keeping an eye on its early hour’s assessment of detection, prediction, diagnosis, treatment, as well as prognosis based on data, information, and knowledge retrieved from evidence-based research analysis of healthcare tools, publicly available medicinal data and biomedical databases. The chapter also progresses with an acknowledgement of challenges and opportunities in using such tools to map, merge and integrate clinical and public health domain dataset to deal with COVID-19 pandemic. We propose that the clinical, public health community as well as computing technology research should come together and gradually evolve and progress from data and information to translational knowledge so that prevailing AI-based techniques can be a powerful assisting tool for the human physicians to unchain the relevant information of the current pandemic.
Artificial intelligence (AI) is one of the most challenging advancements in recent times. Enthusiasm for the utilization of AI for urban development keeps on developing day to day. Smart city areas are expected to proficiently oversee developing urbanization, vitality utilization, and keep up green condition to improve the monetary and expectations. In the smart city community's idea, Information and Communication Technology (ICT) is assuming an essential job in arrangement plan, choice, execution, and extreme profitable administrations. The indispensable target of this chapter is to investigate the job of AI and machine learning (ML) in the development of a brilliant green city such that everyday comforts of its residents is utmost and raise the individuals' capacities to proficiently utilize and embrace the cutting-edge ICT. Transportation in the smart cities are providing an enhanced mechanism that brings various facilities to pop up with the existing standard. The proposed method uses sensors to count the number of passengers who were entering and leaving the bus. The ticketing system is also advanced to electronic ticketing that made the system more automatic. The driver part consists of an automatic system that have some monitoring sensors which is used for the detection of driver's condition and reduce the risk of accidents. The traffic prediction is the additional facility included here. This determines the traffic flow from the data obtained from real-time information. Blockchain technology integrated with AI and ML is a decentralized network that helps in eradicating security issues in future transportation system, paving for green and safe transport with surveillance system in a smart city, thereby enlightening the eminence of human life.