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Record Nr. |
UNINA9910367255803321 |
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Autore |
Kreutzer Ralf |
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Titolo |
Understanding artificial intelligence : fundamentals, use cases and methods for a corporate AI journey / / by Ralf T. Kreutzer, Marie Sirrenberg |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (XVII, 313 p.) |
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Collana |
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Management for Professionals, , 2192-8096 |
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Disciplina |
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Soggetti |
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Information technology |
Business - Data processing |
Artificial intelligence |
Knowledge management |
Data mining |
IT in Business |
Artificial Intelligence |
Knowledge Management |
Data Mining and Knowledge Discovery |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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What is Artificial Intelligence and how to exploit it? -- Basics and drivers of Artificial Intelligence -- Fields of application of Artificial Intelligence – production area -- Fields of application of Artificial Intelligence – customer service, marketing and sales -- Fields of application of Artificial Intelligence – retail, service and maintenance sector -- Fields of application of Artificial Intelligence – health care, education and human resource management -- Fields of application of Artificial Intelligence – energy sector, smart home, mobility and transport -- Fields of application of Artificial Intelligence – financial services and creative sector -- Fields of application of Artificial Intelligence – security sector and military sector -- AI challenge – how |
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Artificial Intelligence can be anchored in a company -- Outlook. |
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Sommario/riassunto |
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Artificial Intelligence (AI) will change the lives of people and businesses more fundamentally than many people can even imagine today. This book illustrates the importance of AI in an era of digitalization. It introduces the foundations of AI and explains its benefits and challenges for companies and entire industries. In this regard, AI is approached not just as yet another technology, but as a fundamental innovation, which will spread into all areas of the economy and life, and will disrupt business processes and business models in the years to come. In turn, the book assesses the potential that AI holds, and clarifies the framework that is necessary for pursuing a responsible approach to AI. In a series of best-practice cases, the book subsequently highlights a broad range of sectors and industries, from production to services; from customer service to marketing and sales; and in industries like retail, health care, energy, transportation and many more. In closing, a dedicated chapter outlines a roadmap for a specific corporate AI journey. No one can ignore intensive work with AI today - neither as a private person, let alone as a top performer in companies. This book offers a thorough, carefully crafted, and easy to understand entry into the field of AI. The central terms used in the AI context are given a very good explanation. In addition, a number of cases show what AI can do today and where the journey is heading. An important book that you should not miss! Professor Dr. Harley Krohmer University of Bern "Inspiring, thought provoking and comprehensive, this book is wittingly designed to be a catalyst for your individual and corporate AI journey.” Avo Schönbohm, Professor at the Berlin School of Economics and Law, Enterprise Game Designer at LUDEO and Business Punk. |
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2. |
Record Nr. |
UNINA9911056729403321 |
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Autore |
Garcia Quevedo Diana |
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Titolo |
AI for Qualitative Research : A Hands-On Guide for Management Scholars / / by Diana Garcia Quevedo, Josue Kuri |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Palgrave Macmillan, , 2026 |
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ISBN |
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Edizione |
[1st ed. 2026.] |
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Descrizione fisica |
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1 online resource (XII, 174 p. 16 illus., 7 illus. in color.) |
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Disciplina |
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Soggetti |
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Executives - Training of |
Technological innovations |
Management Education |
Innovation and Technology Management |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Chapter 1 Introduction -- Chapter 2 Overview of Artificial Intelligence, Machine Learning, Natural Language Processing, and Large Language Models -- Chapter 3 Natural Language Processing in Management Research -- Chapter 4 Ethical Considerations -- Chapter 5 Systems and Tools to Use NLP and LLMs: Getting Started -- Chapter 6 Using LLMs in Qualitative Analysis -- Chapter 7 Data Evaluation and Validation -- Chapter 8 Classification -- Chapter 9 Clustering and Topic Modeling -- Chapter 10 Information Retrieval (IR) and Retrieval-Augmented Generation (RAG) -- Chapter 11 Perspectives on LLMs in Management and Qualitative Research. |
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Sommario/riassunto |
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This open access book will guide qualitative researchers in the social sciences with little to no coding experience in leveraging large language models (LLMs). Responding to a lack of instructional materials that recognize the need to equip qualitative researchers with the most advanced tools, this book offers a research-focused guide to harness the power of LLMs. The content is divided into two parts, beginning with an introduction to LLMs, natural language processing, and machine learning, as well as a historical and ethical perspective on the use of AI in research. The second part of the book serves as a hands-on guide, providing step-by-step instructions for the use of LLMs to |
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analyze large datasets. It is written with practical cases, taken from management sciences, and emphasizes maintaining a close connection to the data throughout the process. It will be highly valuable to researchers in management studies, as well as in the wider social sciences. Diana Garcia Quevedo is a Visiting Professor at Clemson University, South Carolina, US, and a recipient of the Stand Up for Science fund at ESCP Business School, where she studies innovation in entrepreneurship and green venturing. Her research focuses on the impact of women entrepreneurs on the economy and society. She also works on new methods for qualitative research, particularly large language models, to analyze large amounts of online data inductively. Josue Kuri is a Principal Scientist at Amazon Web Services (AWS), where he leads cloud infrastructure planning automation efforts. At AWS, he pioneered the use of machine learning for network forecasting and the development of a digital twin platform to optimize large-scale digital infrastructure. Prior to AWS, he worked at Google and Facebook (now Meta) on the operational and strategic planning of network infrastructure, including investments in submarine cables. Additionally, he pursues an interest in expanding the use of AI in research and education. |
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