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AI and Big Data in Tourism

Definitions, Areas, and Approaches

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Applied Data Science in Tourism

Part of the book series: Tourism on the Verge ((TV))

Abstract

Classical definitions of Artificial Intelligence (AI) date back to the 1950s, all including the concept that AI can enable computers to accomplish intelligent tasks and activities, i.e., requiring human-level intelligence. Given the difficulties in defining human intelligence, a more operational definition refers to the abilities and capabilities AI aims to automatize: communication, in all forms and including all types of media (text, picture, audio); perception, which has attracted a considerable amount of attention with recent developments in new input/output devices (sensors, the Internet of Things); knowledge, making it storable, retrievable, and processable for a variety of applications; planning, as a backup for decision-making and responding (robotics, autonomous driving); and reasoning, simulating human thinking and learning processes. As all these are interconnected, so are the corresponding subfields of AI research: problem-solving, intelligent agents, natural language processing (NLP), speech recognition, computer vision, robotics, knowledge representation, and machine learning. Despite its ups and downs, the use of AI technologies and systems has become so widespread that discussions about their applications, performances, and impact are quotidian. As such, this chapter aims to discuss and explore why and how investing in AI and big data can be considerably beneficial for the tourism sector.

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Correspondence to Luisa Mich .

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Appendices

Further Readings

  • Alpaydin, E. (2021). Machine learning (Revised and Updated Edition). MIT Press Essential Knowledge Series.

  • Baggio, J. A., & Baggio, R. (2020). Modelling and simulations for tourism and hospitality: An introduction. Channel View Publications.

  • Balakrishnan, T., Chui, M., Hall, B., & Henke, N. (2020). The state of AI in 2020. McKinsey. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/global-survey-the-state-of-ai-in-2020

  • Iafrate, F., & ISTE (Londyn), & John Wiley & Sons. (2018). Artificial intelligence and big data: The birth of a new intelligence. ISTE.

  • Ilyas, I. F., & Chu, X. (2019). Data cleaning. ACM Books https://doi.org/10.1145/3310205

  • Laudon, K. C., & Laudon, J. P. (2020). Managing information systems: Managing the digital firm (16th ed.) Pearson.

  • O’Neil, C., & Schutt, R. (2013). Doing data science: Straight talk from the frontline. O’Reilly Media.

  • Russell, S. J., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.

  • Sigala, M., Rahimi, R., & Thelwall, M. (Eds.). (2019). Big data and innovation in tourism, travel, and hospitality: Managerial approaches, techniques, and applications (1st ed.). Springer Publishing.

  • Voulgaris, Z., & Bulut, Y. E. (2018). AI for data science: Artificial intelligence frameworks and functionality for deep learning, optimization, and beyond. Basking Ridge.

  • Zhang, D., Mishra, S., Brynjolfsson, E., Etchemendy, J., Ganguli, D., Grosz, B., Lyons, T., Manyika, J., Niebles, J. C., Sellitto, M., Shoham, Y., Clark, J., & Perrault, R. (2021). The AI index 2021 annual report. AI Index Steering Committee Human-Centered AI Institute, Stanford University https://hai.stanford.edu/research/ai-index-2021

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Mich, L. (2022). AI and Big Data in Tourism. In: Egger, R. (eds) Applied Data Science in Tourism. Tourism on the Verge. Springer, Cham. https://doi.org/10.1007/978-3-030-88389-8_1

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