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IoT and edge computing in the construction site
Published in Pieter Pauwels, Kris McGlinn, Buildings and Semantics, 2023
If successfully implemented, automation and construction will ultimately shape the future of work in construction. Therefore, it is imperative that the current work force adapt and create a symbiotic relationship with the emerging technology: where the automation and technology could improve productivity while the worker learns the skill sets needed to use and optimise the technology, ultimately ensuring job security. As the many challenges faced by the construction industry continue to grow, technology will play an integral role in supporting the current workforce. The augmentation of technology will continue to educate and inspire the next generation of workers. Technologies can increase worker efficiency and can even preserve jobs by helping companies improve efficiencies, reduce costs, and increase value. As seen in other industries, demand for labour will increase the need for technologies and automation. It is important to not let machines pass up human involvement, but rather take advantage of emergence to enhance the workers, the construction industry, and society as a whole.
Artificial Intelligence for Precision Medicine
Published in Kamal Kumar Sharma, Akhil Gupta, Bandana Sharma, Suman Lata Tripathi, Intelligent Communication and Automation Systems, 2021
Alan Turing defined AI concepts while John McCarthy first used the ‘artificial intelligence’ terminology in 1956 at a Dartmouth conference. In AI, machines could execute jobs that require human intelligence, for example, learning, reasoning and diagnosing diseases. In 1959, while working at IBM, Arthur Samuel first introduced the ‘machine learning’ (ML) terminology. ML and AI are often used alternatively; however, ML is a means of fulfilling AI. With conventional programming, humans compose a particular algorithm to allow machines to carry out a certain job. However, for complicated jobs, such as image recognition, particular transcribed rules might be neither pragmatic nor inclusive enough. ML enables computers to learn such rules instantly with no obvious programming, which renders ML the desirable framework for AI applications [9].
AI—The history and evolution
Published in Saswat Sarangi, Pankaj Sharma, Artificial Intelligence, 2018
AI has several advantages, including using it to reduce the margin of error of its use in the exploration of unsafe, hazardous and hostile environments. AI can also do well in repetitive and monotonous jobs or the areas where analysis of past data can help in arriving at the best possible course of action. For example, there could be numerous applications of AI for medical professionals. AI can help make doctors more efficient by providing them with the information they need in a timely and orderly manner. AI can also make surgeries more precise, less dangerous and less painful so that patients can recover faster.42 In short, AI can help doctors in arriving at the correct diagnosis for a patient and to also recommend the best therapy.
Impact of AI and COVID-19 on manufacturing systems: An Asia Pacific Perspective on the two Competing exigencies
Published in Production & Manufacturing Research, 2023
Malini Mittal Bishnoi, Swamynathan Ramakrishnan, Swathi Suraj, Ashish Dwivedi
AI can become one of the most intelligent tools to address global challenges, especially concerning global warming, sustainability, hunger, sanitation, literacy, etc. by integrating economic growth and technological intervention (Fujii & Managi, 2018). AI can facilitate cost reduction and increase the engagement of a skilled workforce thereby contributing to rural employment in developing nations. Logg et al. (2018) report that AI can be the panacea to solve several problems in the APAC region. Concerning the use of interactive chat-bots, classrooms can become more engaging and create skill-based workforces in the technical sector of Asian countries by improving the teaching-learning scenario. AI can improve the job markets in the field of statistics, logistics, analytics, marketing, pattern recognition, voice recognition, etc. Global dangers like the mass evacuation of people for industrialization, climate prediction, etc. can also be predicted with the adoption of AI technologies and newer job opportunities can be created in the fields of industrial sustainability. Hence, there is a positive correlation between the growth of AI and the economic development of the region Haseeb et al. (2019). AI can create a productive and happy workforce, leading to improvement in living standards in the APAC region. AI can become one of the most efficient tools in providing precise and reliable production solutions in the field of engineering.
Artificial intelligence and the role of researchers: Can it replace us?
Published in Drying Technology, 2020
With the rapid advances in Artificial Intelligence (AI) technology, increasingly more jobs are being automated by AI. AI algorithms (e.g., deep-learning based) operating on big data (e.g., from sensors) are now able to solve a number of real-world problems related to perception, planning, reasoning, motion and natural language processing. Indeed, there are many doomsday predictions in the media that AI will replace jobs and allow computers to take over the world. For example, Kai-Fu Lee, AI expert, has predicted that AI will automate and potentially eliminate 40% of jobs within 15 years.[1] He says that AI will surely replace 'repetitive' jobs, e.g., those tasks that are being automated by robots in factories. Further, he predicts that AI will potentially replace many 'white-collar' tasks in the fields of accounting, healthcare, marketing, law, hospitality and other areas. However, there is little systematic understanding of how this will happen and to what kind of professions to greater or lesser extents. In this regard, a few studies have attempted to research the phenomenon,[e.g.,2] including coming up with AI automation scores for work activities of all major occupations. Much of these studies suggest that highly creative and knowledge-intensive tasks cannot be automated by AI. Yet, there have been examples of creative art pieces generated by AI algorithms that even art critics could not distinguish from human-drawn paintings.[3]
An interactive platform for the analysis of landscape patterns: a cloud-based parallel approach
Published in Annals of GIS, 2019
Jing Deng, Michael R. Desjardins, Eric M. Delmelle
We apply the Windows HPC 2012 Server job manager to orchestra and manage the tasks on the cloud. The scheduling system of Windows HPC includes two major concepts, Job and Task. A job essentially estimates the requirement for computing resources and consists of multiple tasks that define commands, environmental settings, inputs and outputs, and working directories. Users can group jobs based on their running status, check details, modify specific tasks, and edit their execution order. Importantly, jobs can be saved as templates for future modification and replication. We predefine the job template for our metrics calculation task, using the same data input and output folder structures. Therefore, to simply launch a job in the system, we can expect the results to be generated under the same folder.