Sinopsis
Large Language Models (LLMs) represent a groundbreaking evolution in the field of artificial intelligence, blending theory, practice, and future potential to reshape industries and human-computer interaction. At the theoretical level, LLMs are rooted in deep learning architectures, which allow them to process and generate natural language with fluency. In practice, they transform sectors like healthcare, education, customer service, and content creation, by automating tasks, providing insights, and enhancing user experiences. However, the use of LLMs also brings challenges, like ethical concerns regarding bias, misinformation, and data privacy, alongside technical issues like computational costs. The future of LLMs may focus on improving their efficiency, addressing ethical dilemmas, and integrating them into specialized applications. As research progresses, LLMs may become more adaptable, intelligent, and integrated into daily life, with the potential to redefine human-machine collaboration. Theory, Practice, and Future Direction of Large Language Models explores the transformative power of large language models (LLMs) by delving into their theoretical foundations, practical applications, and future potential. It showcases an array of applications across industries, demonstrating how LLMs solve real-world problems, enhance productivity, and drive innovation in fields like healthcare, finance, education, and entertainment. This book covers topics such as ethics and bias, robotics, and task automation, and is a useful resource for business owners, computer engineers, academicians, researchers, and data scientists.
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Acerca de los autores
Dr. Ismail Lamaakal (Member, IEEE) received a Master of Science degree in Computer Science from the Multidisciplinary Faculty of Nador, University Mohammed Premier, Oujda, Morocco. He is currently advancing towards his Ph.D. in Computer Science at the same esteemed institution. As an Artificial Intelligence Scientist, his research primarily focuses on the innovative integration of Tiny Machine Learning, the Internet of Things (IoT), Human-computer interaction, and Embedded Systems. His work is characterized by its pioneering approach in the field, emphasizing practical applications and advancements in these interconnected domains. His contributions are marked by a commitment to pushing the boundaries of AI and its applications in the modern technological landscape.
Prof. Yassine Maleh is a PhD of the University Hassan 1st in Morocco in the field of Internet of Things Security and privacy, since 2013. He is Senior Member of IEEE, Member of the International Association of Engineers IAENG and The Machine Intelligence Research Labs. Dr Maleh has made contributions in the fields of information security and privacy, Internet of Things Security, Wireless and Constrained Networks Security. His research interests include Information Security and Privacy, Internet of Things, Networks Security, Information system and IT Governance. He has published over than 50 papers (Book chapters, international journals and conferences/workshops), and 4 edited books and 1 authored book. He is the editor in chief of the International Journal of Smart Security Technologies (IJSST). He serves as an Associate Editor for IEEE Access (2019 Impact Factor 4.098), the International Journal of Digital Crime and Forensics (IJDCF) and the International Journal of Information Security and Privacy (IJISP). He was also a Guest Editor of a special issue on Recent Advances on Cyber Security and Privacy for Cloud-of-Things of the International Journal of Digital Crime and Forensics (IJDCF), Volume 10, Issue 3, July-September 2019. He has served and continues to serve on executive and technical program committees and as a reviewer of numerous international conference and journals such as Elsevier Ad Hoc Networks, IEEE Network Magazine, IEEE Sensor Journal, ICT Express, and Springer Cluster Computing. He was the Publicity chair of BCCA 2019 and the General Chair of the MLBDACP 19 symposium.
Prof. Khalid El Makkaoui received his master’s degree in networks and systems and a Ph.D. in computer science from Hassan 1st University, Settat, Morocco, in 2014 and 2018. Since 2019, he has been an Associate Professor with the Department of Computer Science at the Multidisciplinary Faculty of Nador, University Mohammed Premier, Oujda, Morocco. His research interests focus on cybersecurity and artificial intelligence. He has published over 40 papers (book chapters, international journals, and conferences).
Prof. Ibrahim Ouahbi is a professor of computer science at the Multidisciplinary Faculty of Nador, University Mohammed Premier, Oujda, Morocco. He received his Ph.D. in didactics of informatics in 2018 from Sidi Mohammed Ben Abdellah University, Fez, Morocco. He was a professor of educational technologies at the Faculty of Educational Sciences, Mohammed V University of Rabat, in 2019. His research interests include artificial intelligence, cybersecurity, and ICT integration in science education and learning.
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Paperback. Condición: new. Paperback. Large Language Models (LLMs) represent a groundbreaking evolution in the field of artificial intelligence, blending theory, practice, and future potential to reshape industries and human-computer interaction. At the theoretical level, LLMs are rooted in deep learning architectures, which allow them to process and generate natural language with fluency. In practice, they transform sectors like healthcare, education, customer service, and content creation, by automating tasks, providing insights, and enhancing user experiences. However, the use of LLMs also brings challenges, like ethical concerns regarding bias, misinformation, and data privacy, alongside technical issues like computational costs. The future of LLMs may focus on improving their efficiency, addressing ethical dilemmas, and integrating them into specialized applications. As research progresses, LLMs may become more adaptable, intelligent, and integrated into daily life, with the potential to redefine human-machine collaboration. Theory, Practice, and Future Direction of Large Language Models explores the transformative power of large language models (LLMs) by delving into their theoretical foundations, practical applications, and future potential. It showcases an array of applications across industries, demonstrating how LLMs solve real-world problems, enhance productivity, and drive innovation in fields like healthcare, finance, education, and entertainment. This book covers topics such as ethics and bias, robotics, and task automation, and is a useful resource for business owners, computer engineers, academicians, researchers, and data scientists. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9798369383889
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Paperback. Condición: new. Paperback. Large Language Models (LLMs) represent a groundbreaking evolution in the field of artificial intelligence, blending theory, practice, and future potential to reshape industries and human-computer interaction. At the theoretical level, LLMs are rooted in deep learning architectures, which allow them to process and generate natural language with fluency. In practice, they transform sectors like healthcare, education, customer service, and content creation, by automating tasks, providing insights, and enhancing user experiences. However, the use of LLMs also brings challenges, like ethical concerns regarding bias, misinformation, and data privacy, alongside technical issues like computational costs. The future of LLMs may focus on improving their efficiency, addressing ethical dilemmas, and integrating them into specialized applications. As research progresses, LLMs may become more adaptable, intelligent, and integrated into daily life, with the potential to redefine human-machine collaboration. Theory, Practice, and Future Direction of Large Language Models explores the transformative power of large language models (LLMs) by delving into their theoretical foundations, practical applications, and future potential. It showcases an array of applications across industries, demonstrating how LLMs solve real-world problems, enhance productivity, and drive innovation in fields like healthcare, finance, education, and entertainment. This book covers topics such as ethics and bias, robotics, and task automation, and is a useful resource for business owners, computer engineers, academicians, researchers, and data scientists. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9798369383889
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