Publications-Theses
Article View/Open
Publication Export
-
題名 AI 技術堆疊架構模型
The AI Tech-Stack Model作者 徐志鈞
Hsu, Chih-Chun貢獻者 蔡瑞煌<br>張欣綠
Tsaih, Rua-Huan<br>Chang, Hsin-Lu
徐志鈞
Hsu, Chih-Chun關鍵詞 AI 技術堆疊架構
綜合 IT/IS 框架
人工智慧
人工智慧暨服務
智慧旅遊
AI tech-stack
Synthesizing IT/IS framework
AI
AIaaS
Smart tourism日期 2022 上傳時間 2-Dec-2022 15:21:01 (UTC+8) 摘要 先進的 AI 技術已被企業應用於支持業務流程自動化，提供數據洞察力，並促進員工和客戶的溝通。數位原生科技巨頭 FAAMG 也應用 AI技術創造獨特的競爭優勢，並同時整合雲堆疊與 AI 核心技術提供 AIaaS 服務，於新冠疫情期間表現優異。然而根據研究，大多數企業在開發部署 AI 項目時仍然面臨策略、管理和運營等不同程度的挑戰，針對上述挑戰問題在過去的文獻中尚未得到很好的解決。基於上述議題，本研究提出了 AI 技術堆疊架構及綜合 IT/IS 框架以傳遞 AI 支持的價值主張。提議的綜合 IT/IS 框架整合了企業現有的 IT/IS 系統、協作數位業務平台與期望 AI 系統的系統分析框架，讓主管能分析自身所需的內/外連接、現有 IT/IS 系統及 AI 管理/分析等三種能力，達到 AI 時代競爭所需具備的網路、學習及聚合的整合綜效。同時本研究提議的 AI 技術堆疊架構歸納出 AI解決方案層、AI服務層、AI數據管道層、AI演算法層、AI框架層、AI平台層和 AI基礎設施層等七層架構，幫助高階主管參與開發部署 AI 系統時做出自行設計、選擇 AIaaS 或第三方開放軟體的方案，企業依此析能擬定 AI 項目開發及部署時程的技術分析架構，以協助企業解決 AI 策略、管理和運營的挑戰。本研究採用專家訪談方法驗證 AI 技術堆疊架構和綜合 IT/IS 框架，經訪談四家旅遊公司高階主管，直接參與分析綜合 IT/IS 框架所需的三種能力來開發部署智慧推薦系統，依序探討 AI 技術堆疊分層架構選擇內部自行開發、或委外 AIaaS 提供工具的最適決策。過程顯示高階主管的全程參與確認 AI 支持的價值主張並擬定投資計畫對 AI 項目的成功起到至關重要的因素，訪談結果顯示提議的清晰易懂、鬆耦合及模塊化的 AI 技術堆疊架構及綜合 IT/IS 框架，提供 AI 項目主管評估 AI 項目專案生命週期管理是不可或缺的系統及技術參考架構。最後本研究也針對研究限制及後續研究提出建言。
Emerging AI technologies have been adopted by enterprises to support business process automation, provide data insights, and facilitate employee and customer engagement. The digital native technology giant FAAMG has also deployed AI technology to create a unique competitive advantage, and at the same time integrated cloud stack and AI core technology to provide AIaaS services, thus performing well during the pandemic period. However, according to research, most enterprises still encounter different levels of challenges in strategy, management, and operation when developing and deploying AI projects. Unfortunately, the above challenges have not been well addressed in the research literature.Based on the above challenges’ topics, this study proposes an AI tech-stack model and a synthesizing IT/IS framework to deliver the AI-enabled value proposition. The proposed synthesizing IT/IS framework integrates a company`s incumbent IT/IS systems, collaborating digital business platforms, and desired AI systems, which allows executives to analyze their own required internal/external connectivity, incumbent IT/IS systems, and AI project management/analysis capabilities. The framework will help the company to achieve the integrated synergy of network, learning, and aggregation effect to compete in the age of AI. At the same time, the proposed AI tech-stack architecture summarizes the AI solution layer, AI service layer, AI data pipeline layer, AI algorithm layer, AI framework layer, AI platform layer, and AI infrastructure layer. When participating in the development and deployment of AI systems, make in-house designs, choose AIaaS or third-party open software solutions, and assist enterprises to formulate technical analysis frameworks for AI project development and deployment schedules to help enterprises solve AI strategy, management, and operation challenges.This research conducted the focus group interview method to verify the AI tech-stack model and synthesizing IT/IS framework. After interviewing senior executives of four local travel companies, they directly participate discuss in the analysis of the three capabilities required by the synthesizing IT/IS framework to develop and deploy a smart recommendation system. The AI tech-stack layered architecture chooses the optimal decision of internal development, AIaaS, or third-party open software. The process shows that the senior executives fully participate in confirming the AI-enabled value proposition and formulating investment plans is a critical factor for the success of AI projects, and the interview results show that the proposed comprehensive, loosely coupled, and modular of AI tech-stack architecture and synthesizing IT/IS framework to provide AI project managers with an indispensable system and technical reference framework for evaluating AI project life cycle management. Finally, this study also highlights research limitations and makes suggestions for future research.參考文獻 Agrawal, A., Gans, J. S., and Goldfarb, A. (2019). Artificial intelligence: the ambiguous labor market impact of automating prediction. Journal of Economic Perspectives, 33(2):31–50.Altexsoft. (25 Apr, 2021). Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI, IBM Watson. available at https://www.altexsoft.com/blog/datascience/comparing-machine-learning-as-a-service-amazon-microsoft-azure-google-cloud-ai-ibm-watson/Amazon Web Services Inc. (2021). Machine Learning Stack, available at, https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/machine-learning-stack.htmlAswani, R. (2020) Rise of AI-as-a-services – Cloud services driving AI adoption, available at https://community.nasscom.in/communities/iot-ai/rise-of-ai-as-a-service---cloud-services-driving-ai-adoption.htmlBala, R., Gill, B. and 3 more (2021). Magic quadrant for cloud infrastructure and platform services. Gartner report, July, 27. - ID G00736363BasuMallick, C., (February 10, 2022), Top 10 Open Source Artificial Intelligence Software in 2021, available at https://www.toolbox.com/tech/innovation/articles/top-open-source-artificial-intelligence-software/Brock, J. K. U., & Von Wangenheim, F. (2019). Demystifying AI: What digital transformation leaders can teach you about realistic artificial intelligence. California Management Review, 61(4), 110-134.Brownlee, J., (November 11, 2019). 14 Different Types of Learning in Machine Learning. available at https://machinelearningmastery.com/types-of-learning-in-machine-learning/Brynjolfsson, E. & McAfee, A. (2017). What`s driving the machine learning explosion? available at https://hbr.org/2017/07/whats-driving-the-machine-learning-explosionBughin, J., McCarthy, B., & Chui, M. (August 28, 2017). A survey of 3,000 executives reveals how businesses succeed with AI. Harvard Business Review. available at https://hbr.org/2017/08/a-survey-of-3000-executives-reveals-how-businesses-succeed-with-aiBuhalis, D., & Amaranggana, A. (2015). Smart Tourism Destinations: Enhancing Tourism Experience Through Personalisation of Services. In Tussyadiah, I. & Inversini, A., (Eds.), Information and Communication Technologies in Tourism 2015, pp. 377-389. Heidelberg, Germany: Springer.Chui, M. (2017). Artificial intelligence the next digital frontier? McKinsey and Company Global Institute, 47, 3-6.Chui, M., Manyika, J., Miremadi, M., Henke, N., Chung, R., Nel, P., & Malhotra, S. (2018). Notes from the AI frontier: Insights from hundreds of use cases. McKinsey Global Institute.Cockburn, I. M., Henderson, R., and Stern, S. (2018). The impact of artificial intelligence on innovation. Technical report, National bureau of economic research.Colak A., (April, 8, 2020). A Guide to 27+ Salesforce Einstein AI Products and Tools, available at https://www.salesforceben.com/guide-to-einstein-ai-products-and-tools/Cusumano, M. A., Yoffie, D. B. & Gawer, A. (2020). The Future of Platforms. MIT Sloan Management Review, 61(3), 46-54.Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard business review, 96(1), 108-116.Das, S. C., Panigrahi, J. K., & Mallik, S. K. (2018). Technological innovation and integration of enterprise applications (ea) for achieving operational excellence: An empirical analysis of trends in erp systems. International Journal of Mechanical Engineering and Technology, 9(5), 733.Deloitte Insight. (2019). Artificial Intelligence: From Expert-Only to Everywhere (2019), TMT Prediction 2019. available at https://www2.deloitte.com/xe/en/insights/industry/technology/technology-media-and-telecom-predictions/cloud-based-artificial-intelligence.htmlEvans PC, Gawer A (2016) The rise of the platform enterprise: a global survey. Center for Global Enterprise, New York. available at http://www.thecge.net/wp-content/uploads/2016/01/PDF-WEB-Platform-Survey_01_12.pdf (Accessed February 16, 2016)Gao, W., Tang, F., Zhan, J., Lan, C., Luo, C., Wang, L., ... & Hao, T. (2020). AIBench: An Agile Domain-specific Benchmarking Methodology and an AI Benchmark Suite. arXiv preprint arXiv:2002.07162.Gambardella, A. and McGahan, A. M. (2010). Business-model innovation: General purpose technologies and their implications for industry structure. Long range planning, 43(2-3):262–271.Geeksforgeeks. (14 Oct, 2020). Top Cloud Computing Platforms for Machine Learning, https://www.geeksforgeeks.org/top-cloud-computing-platforms-for-machine-learning/#:~:text=There%20are%20many%20cloud%20computing,Google%20Cloud%2C%20and%20IBM%20CloudGodse, M., & Mulik, S. (2009, September). An approach for selecting software-as-a-service (SaaS) product. In 2009 IEEE International Conference on Cloud Computing (pp. 155-158). IEEE.Gretzel U, Sigala M, Xiang Z, Koo C (2015). Smart tourism: foundations and developments. Electron Mark 25(3):179–188Hasan, M., (April 17, 2021). The 20 Best AI and Machine Learning Software and Frameworks. available at https://www.ubuntupit.com/best-ai-and-machine-learning-software-and-frameworks/Hofstee , E. (July 4, 2019). What are the infrastructure requirements for Artificial Intelligence?. available at https://blog.leaseweb.com/2019/07/04/infrastructure-requirements-ai/Huckabee, T. (July 2, 2020), Digital Transformations: the COVID-19 crisis is speeding up the need for business to compete in the digital economy. available at https://tehcpa.net/digital-transformation/digital-transformations-the-covid-19-crisis-is-speeding-up-the-need-for-business-to-compete-in-the-digital-economy/Iansiti, M., & Lakhani, K. R. (2020a). Competing in the Age of AI: How machine intelligence changes the rules of business, Harvard Business Review, 98(1) January–February, 60–67.Iansiti, M., & Lakhani, K. R. (2020b). From Disruption to Collision: The New Competitive Dynamics. MIT Sloan Management Review, 61(3), 34-39.Iansiti, M., & Lakhani, K. R. (2020c). Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World. Boston: Harvard Business Review Press.Kakatkar, C., Bilgram, V., & Füller, J. (2020). Innovation analytics: Leveraging artificial intelligence in the innovation process. Business Horizons, 63(2), 171-181.Khalid, S., Jarek, K., & Donna, S., (May, 2021). Practitioners guide to MLOps: A framework for continuous delivery and automation of machine learning. Google, available at https://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdfKrensky, P., Idoine, C., and 5 more (2021). Magic Quadrant for Data Science and Machine Learning Platforms. Gartner report, Updated November 2. - ID G00467320.Magic Quadrant for Data Science and Machine Learning Platforms. Updated 2 November 2021, Published 1 March 2021 - ID G00467320.Magic Quadrant for Cloud Infrastructure and Platform Services. Published 27 July 2021 - ID G00736363.Magoulas, R.and Swoyerh, S. (18. March 2020), AI adoption in the enterprise 2020, O’Reilly. available at https://www.oreilly.com/radar/ai-adoption-in-the-enterprise-2020/McAfee, A. (2011). What every CEO needs to know about the cloud. Harvard business review, 89(11), 124-132.Michel, S. (2014). Capture more value. Harvard business review, 92(10), 20.Microsoft Corp. (May 1, 2018). Microsoft Artificial Intelligence: A platform for all information worker skill set levels, available at https://www.microsoft.com/en-us/us-partner-blog/2018/05/01/microsoft-artificial-intelligence-a-platform-for-all-information-worker-skill-set-levels/Niknejad, N., Ismail, W., Ghani, I., Nazari, B., & Bahari, M. (2020). Understanding Service-Oriented Architecture (SOA): A systematic literature review and directions for further investigation. Information Systems, 91, 101491.PAT Research, Top 18 Artificial Intelligence Platforms. available at https://www.predictiveanalyticstoday.com/artificial-intelligence-platforms/Pandl, K. D., Teigeler, H., Lins, S., Thiebes, S., & Sunyaev, A. (2021). Drivers and Inhibitors for Organizations’ Intention to Adopt Artificial Intelligence as a Service. In Proceedings of the 54th Hawaii International Conference on System Sciences (p. 1769).Panwar, R. (2020). It`s time to develop local production and supply networks. Strategy Insight Note. California Management Review. April 28. Available at: https://cmr.berkeley.edu/2020/04/local-production-supply- networks/Plastino, E. and Purdy, M. (2018). Game changing value from artificial intelligence: eight strategies. Strategy & Leadership.Porter, M. E., & Millar, V. E. (1985). How information gives you competitive advantage, Harvard Business Review, 63(4) July–August, 149–160.Reixa, M., Costa, C., & Aparicio, M. (2012, June). Cloud services evaluation framework. In Proceedings of the Workshop on Open Source and Design of Communication (pp. 61-69).Saripalli, P., & Pingali, G. (2011, July). Madmac: Multiple attribute decision methodology for adoption of clouds. In 2011 IEEE 4th international conference on cloud computing (pp. 316-323). IEEE.Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., ... & Dennison, D. (2015). Hidden technical debt in machine learning systems. Advances in neural information processing systems, 28, 2503-2511.Shivraj, P., (2021). "RISE OF FACEBOOK, AMAZON, APPLE, NETFLIX, GOOGLE DURING COVID-19 PANDEMIC". Electronic Theses, Projects, and Dissertations. 1311. available at https://scholarworks.lib.csusb.edu/etd/1311Sharma A., (Aug. 23, 2020). AWS v/s Google v/s Azure: Who will win the Cloud War, available at https://www.upgrad.com/blog/aws-vs-google-vs-azure/Teece, D. J., & Linden, G. (2017). Business models, value capture, and the digital enterprise. Journal of organization design, 6(1), 1-14.Todd, C., Vazquez Pena, R., & Srinivas, R. (2018). Evaluation of Artificial Intelligence Frameworks. SMU Data Science Review, 1(1), 10.Truck, M (2021). Red Hot: The 2021 Machine Learning, AI and Data (MAD) Landscape, available at https://mattturck.com/data2021/Tsai, C. F., Chen, K., Hu, Y. H., & Chen, W. K. (2020). Improving text summarization of online hotel reviews with review helpfulness and sentiment. Tourism Management, 80, 104122.Tsaih, R. H., & Hsu, C. C. (2018). Artificial intelligence in smart tourism: A conceptual framework. Proceedings of the 18th International Conference on Electronic Business:AI and Smart Tourism, ICEB 2018, 77-78. available at https://iceb.johogo.com/EiCompendex/Ei%20Compendex%20ICEB%202018%20list.pdfTsaih, R. H., Chang, H. L., Hsu, C. C., & Chang, Y. C. (2022). AI Tech-Stack Model. Communications of the ACM, Accepted August 16 2022.Tsaih, R. H., Yen, D. C., & Chang, Y. C. (2015). Challenges deploying complex technologies in a traditional organization. Communications of the ACM, 58(8), 70-75.Tussyadiah, I., & Miller, G. (2019). Perceived impacts of artificial intelligence and responses to positive behaviour change intervention. In Information and communication technologies in tourism 2019 (pp. 359-370). Springer, Cham. available at http://doi.org/10.1007/978-3-030-05940-8_28Xu, M., Liu, J., Liu, Y., Lin, F. X., Liu, Y., & Liu, X. (2019, May). A first look at deep learning apps on smartphones. In The World Wide Web Conference (pp. 2125-2136).Yao, Q., Wang, M., Chen, Y., Dai, W., Li, Y. F., Tu, W. W., ... & Yu, Y. (2018). Taking human out of learning applications: A survey on automated machine learning. arXiv preprint arXiv:1810.13306.Zimmermann, H. (1980). OSI reference model-the ISO model of architecture for open systems interconnection. IEEE Transactions on communications, 28(4), 425-432. 描述 博士
國立政治大學
資訊管理學系
105356507資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105356507 資料類型 thesis dc.contributor.advisor 蔡瑞煌<br>張欣綠 zh_TW dc.contributor.advisor Tsaih, Rua-Huan<br>Chang, Hsin-Lu en_US dc.contributor.author (Authors) 徐志鈞 zh_TW dc.contributor.author (Authors) Hsu, Chih-Chun en_US dc.creator (作者) 徐志鈞 zh_TW dc.creator (作者) Hsu, Chih-Chun en_US dc.date (日期) 2022 en_US dc.date.accessioned 2-Dec-2022 15:21:01 (UTC+8) - dc.date.available 2-Dec-2022 15:21:01 (UTC+8) - dc.date.issued (上傳時間) 2-Dec-2022 15:21:01 (UTC+8) - dc.identifier (Other Identifiers) G0105356507 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/142643 - dc.description (描述) 博士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 105356507 zh_TW dc.description.abstract (摘要) 先進的 AI 技術已被企業應用於支持業務流程自動化，提供數據洞察力，並促進員工和客戶的溝通。數位原生科技巨頭 FAAMG 也應用 AI技術創造獨特的競爭優勢，並同時整合雲堆疊與 AI 核心技術提供 AIaaS 服務，於新冠疫情期間表現優異。然而根據研究，大多數企業在開發部署 AI 項目時仍然面臨策略、管理和運營等不同程度的挑戰，針對上述挑戰問題在過去的文獻中尚未得到很好的解決。基於上述議題，本研究提出了 AI 技術堆疊架構及綜合 IT/IS 框架以傳遞 AI 支持的價值主張。提議的綜合 IT/IS 框架整合了企業現有的 IT/IS 系統、協作數位業務平台與期望 AI 系統的系統分析框架，讓主管能分析自身所需的內/外連接、現有 IT/IS 系統及 AI 管理/分析等三種能力，達到 AI 時代競爭所需具備的網路、學習及聚合的整合綜效。同時本研究提議的 AI 技術堆疊架構歸納出 AI解決方案層、AI服務層、AI數據管道層、AI演算法層、AI框架層、AI平台層和 AI基礎設施層等七層架構，幫助高階主管參與開發部署 AI 系統時做出自行設計、選擇 AIaaS 或第三方開放軟體的方案，企業依此析能擬定 AI 項目開發及部署時程的技術分析架構，以協助企業解決 AI 策略、管理和運營的挑戰。本研究採用專家訪談方法驗證 AI 技術堆疊架構和綜合 IT/IS 框架，經訪談四家旅遊公司高階主管，直接參與分析綜合 IT/IS 框架所需的三種能力來開發部署智慧推薦系統，依序探討 AI 技術堆疊分層架構選擇內部自行開發、或委外 AIaaS 提供工具的最適決策。過程顯示高階主管的全程參與確認 AI 支持的價值主張並擬定投資計畫對 AI 項目的成功起到至關重要的因素，訪談結果顯示提議的清晰易懂、鬆耦合及模塊化的 AI 技術堆疊架構及綜合 IT/IS 框架，提供 AI 項目主管評估 AI 項目專案生命週期管理是不可或缺的系統及技術參考架構。最後本研究也針對研究限制及後續研究提出建言。 zh_TW dc.description.abstract (摘要) Emerging AI technologies have been adopted by enterprises to support business process automation, provide data insights, and facilitate employee and customer engagement. The digital native technology giant FAAMG has also deployed AI technology to create a unique competitive advantage, and at the same time integrated cloud stack and AI core technology to provide AIaaS services, thus performing well during the pandemic period. However, according to research, most enterprises still encounter different levels of challenges in strategy, management, and operation when developing and deploying AI projects. Unfortunately, the above challenges have not been well addressed in the research literature.Based on the above challenges’ topics, this study proposes an AI tech-stack model and a synthesizing IT/IS framework to deliver the AI-enabled value proposition. The proposed synthesizing IT/IS framework integrates a company`s incumbent IT/IS systems, collaborating digital business platforms, and desired AI systems, which allows executives to analyze their own required internal/external connectivity, incumbent IT/IS systems, and AI project management/analysis capabilities. The framework will help the company to achieve the integrated synergy of network, learning, and aggregation effect to compete in the age of AI. At the same time, the proposed AI tech-stack architecture summarizes the AI solution layer, AI service layer, AI data pipeline layer, AI algorithm layer, AI framework layer, AI platform layer, and AI infrastructure layer. When participating in the development and deployment of AI systems, make in-house designs, choose AIaaS or third-party open software solutions, and assist enterprises to formulate technical analysis frameworks for AI project development and deployment schedules to help enterprises solve AI strategy, management, and operation challenges.This research conducted the focus group interview method to verify the AI tech-stack model and synthesizing IT/IS framework. After interviewing senior executives of four local travel companies, they directly participate discuss in the analysis of the three capabilities required by the synthesizing IT/IS framework to develop and deploy a smart recommendation system. The AI tech-stack layered architecture chooses the optimal decision of internal development, AIaaS, or third-party open software. The process shows that the senior executives fully participate in confirming the AI-enabled value proposition and formulating investment plans is a critical factor for the success of AI projects, and the interview results show that the proposed comprehensive, loosely coupled, and modular of AI tech-stack architecture and synthesizing IT/IS framework to provide AI project managers with an indispensable system and technical reference framework for evaluating AI project life cycle management. Finally, this study also highlights research limitations and makes suggestions for future research. en_US dc.description.tableofcontents 目次摘要 3第 1 章 人工智慧、平台經濟與雲服務 9第 2 章 文獻探討 132.1 AI時代競爭的綜合 IT/IS 框架 132.2 AI即服務 (AIaaS) 和 AI 技術堆疊架構 172.3 智慧旅遊定義及價值主張 24第 3 章 AI技術堆疊架構與綜合 IT/IS 框架 283.1 AI 技術堆疊架構 (AI tech-stack) 293.1.1 AI基礎設施層 (AI Infrastructure layer) 333.1.2 AI平台層 (AI Platform layer) 353.1.3 AI框架層 (AI Framework layer) 363.1.4 AI演算法層 (AI Algorithm layer) 393.1.5 AI數據管道層 (AI Data Pipeline layer) 413.1.6 AI服務層 (AI Services layer) 423.1.7 AI解決方案層 (AI Solution layer) 463.1.8 AI技術堆疊架構應用互操作性 (AI Tech-Stack Interoperability) 473.2 綜合 IT/IS 框架 (Synthesizing IT/IS framework) 503.2.1 綜合 IT/IS 框架組成單元 513.2.2 綜合 IT/IS 框架能力分析 52第 4 專家訪談實證結果 554.1 專家訪談實證設計 554.2 AI技術堆疊架構影響專家訪談 - 智慧旅遊研究案例 554.2.1 智慧推薦系統綜合 IT/IS 框架系統分析 574.2.2 智慧推薦系統綜合 IT/IS 框架能力分析 584.2.3 智慧推薦系統 AI 技術堆疊架構分析 61第 5 章 討論與結論 64參考文獻 68附件一、AI人工智慧於智慧旅遊應用訪談紀錄 72 zh_TW dc.format.extent 4507612 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105356507 en_US dc.subject (關鍵詞) AI 技術堆疊架構 zh_TW dc.subject (關鍵詞) 綜合 IT/IS 框架 zh_TW dc.subject (關鍵詞) 人工智慧 zh_TW dc.subject (關鍵詞) 人工智慧暨服務 zh_TW dc.subject (關鍵詞) 智慧旅遊 zh_TW dc.subject (關鍵詞) AI tech-stack en_US dc.subject (關鍵詞) Synthesizing IT/IS framework en_US dc.subject (關鍵詞) AI en_US dc.subject (關鍵詞) AIaaS en_US dc.subject (關鍵詞) Smart tourism en_US dc.title (題名) AI 技術堆疊架構模型 zh_TW dc.title (題名) The AI Tech-Stack Model en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Agrawal, A., Gans, J. S., and Goldfarb, A. (2019). Artificial intelligence: the ambiguous labor market impact of automating prediction. Journal of Economic Perspectives, 33(2):31–50.Altexsoft. (25 Apr, 2021). Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI, IBM Watson. available at https://www.altexsoft.com/blog/datascience/comparing-machine-learning-as-a-service-amazon-microsoft-azure-google-cloud-ai-ibm-watson/Amazon Web Services Inc. (2021). Machine Learning Stack, available at, https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/machine-learning-stack.htmlAswani, R. (2020) Rise of AI-as-a-services – Cloud services driving AI adoption, available at https://community.nasscom.in/communities/iot-ai/rise-of-ai-as-a-service---cloud-services-driving-ai-adoption.htmlBala, R., Gill, B. and 3 more (2021). Magic quadrant for cloud infrastructure and platform services. Gartner report, July, 27. - ID G00736363BasuMallick, C., (February 10, 2022), Top 10 Open Source Artificial Intelligence Software in 2021, available at https://www.toolbox.com/tech/innovation/articles/top-open-source-artificial-intelligence-software/Brock, J. K. U., & Von Wangenheim, F. (2019). Demystifying AI: What digital transformation leaders can teach you about realistic artificial intelligence. California Management Review, 61(4), 110-134.Brownlee, J., (November 11, 2019). 14 Different Types of Learning in Machine Learning. available at https://machinelearningmastery.com/types-of-learning-in-machine-learning/Brynjolfsson, E. & McAfee, A. (2017). What`s driving the machine learning explosion? available at https://hbr.org/2017/07/whats-driving-the-machine-learning-explosionBughin, J., McCarthy, B., & Chui, M. (August 28, 2017). A survey of 3,000 executives reveals how businesses succeed with AI. Harvard Business Review. available at https://hbr.org/2017/08/a-survey-of-3000-executives-reveals-how-businesses-succeed-with-aiBuhalis, D., & Amaranggana, A. (2015). Smart Tourism Destinations: Enhancing Tourism Experience Through Personalisation of Services. In Tussyadiah, I. & Inversini, A., (Eds.), Information and Communication Technologies in Tourism 2015, pp. 377-389. Heidelberg, Germany: Springer.Chui, M. (2017). Artificial intelligence the next digital frontier? McKinsey and Company Global Institute, 47, 3-6.Chui, M., Manyika, J., Miremadi, M., Henke, N., Chung, R., Nel, P., & Malhotra, S. (2018). Notes from the AI frontier: Insights from hundreds of use cases. McKinsey Global Institute.Cockburn, I. M., Henderson, R., and Stern, S. (2018). The impact of artificial intelligence on innovation. Technical report, National bureau of economic research.Colak A., (April, 8, 2020). A Guide to 27+ Salesforce Einstein AI Products and Tools, available at https://www.salesforceben.com/guide-to-einstein-ai-products-and-tools/Cusumano, M. A., Yoffie, D. B. & Gawer, A. (2020). The Future of Platforms. MIT Sloan Management Review, 61(3), 46-54.Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard business review, 96(1), 108-116.Das, S. C., Panigrahi, J. K., & Mallik, S. K. (2018). Technological innovation and integration of enterprise applications (ea) for achieving operational excellence: An empirical analysis of trends in erp systems. International Journal of Mechanical Engineering and Technology, 9(5), 733.Deloitte Insight. (2019). Artificial Intelligence: From Expert-Only to Everywhere (2019), TMT Prediction 2019. available at https://www2.deloitte.com/xe/en/insights/industry/technology/technology-media-and-telecom-predictions/cloud-based-artificial-intelligence.htmlEvans PC, Gawer A (2016) The rise of the platform enterprise: a global survey. Center for Global Enterprise, New York. available at http://www.thecge.net/wp-content/uploads/2016/01/PDF-WEB-Platform-Survey_01_12.pdf (Accessed February 16, 2016)Gao, W., Tang, F., Zhan, J., Lan, C., Luo, C., Wang, L., ... & Hao, T. (2020). AIBench: An Agile Domain-specific Benchmarking Methodology and an AI Benchmark Suite. arXiv preprint arXiv:2002.07162.Gambardella, A. and McGahan, A. M. (2010). Business-model innovation: General purpose technologies and their implications for industry structure. Long range planning, 43(2-3):262–271.Geeksforgeeks. (14 Oct, 2020). Top Cloud Computing Platforms for Machine Learning, https://www.geeksforgeeks.org/top-cloud-computing-platforms-for-machine-learning/#:~:text=There%20are%20many%20cloud%20computing,Google%20Cloud%2C%20and%20IBM%20CloudGodse, M., & Mulik, S. (2009, September). An approach for selecting software-as-a-service (SaaS) product. In 2009 IEEE International Conference on Cloud Computing (pp. 155-158). IEEE.Gretzel U, Sigala M, Xiang Z, Koo C (2015). Smart tourism: foundations and developments. Electron Mark 25(3):179–188Hasan, M., (April 17, 2021). The 20 Best AI and Machine Learning Software and Frameworks. available at https://www.ubuntupit.com/best-ai-and-machine-learning-software-and-frameworks/Hofstee , E. (July 4, 2019). What are the infrastructure requirements for Artificial Intelligence?. available at https://blog.leaseweb.com/2019/07/04/infrastructure-requirements-ai/Huckabee, T. (July 2, 2020), Digital Transformations: the COVID-19 crisis is speeding up the need for business to compete in the digital economy. available at https://tehcpa.net/digital-transformation/digital-transformations-the-covid-19-crisis-is-speeding-up-the-need-for-business-to-compete-in-the-digital-economy/Iansiti, M., & Lakhani, K. R. (2020a). Competing in the Age of AI: How machine intelligence changes the rules of business, Harvard Business Review, 98(1) January–February, 60–67.Iansiti, M., & Lakhani, K. R. (2020b). From Disruption to Collision: The New Competitive Dynamics. MIT Sloan Management Review, 61(3), 34-39.Iansiti, M., & Lakhani, K. R. (2020c). Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World. Boston: Harvard Business Review Press.Kakatkar, C., Bilgram, V., & Füller, J. (2020). Innovation analytics: Leveraging artificial intelligence in the innovation process. Business Horizons, 63(2), 171-181.Khalid, S., Jarek, K., & Donna, S., (May, 2021). Practitioners guide to MLOps: A framework for continuous delivery and automation of machine learning. Google, available at https://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdfKrensky, P., Idoine, C., and 5 more (2021). Magic Quadrant for Data Science and Machine Learning Platforms. Gartner report, Updated November 2. - ID G00467320.Magic Quadrant for Data Science and Machine Learning Platforms. Updated 2 November 2021, Published 1 March 2021 - ID G00467320.Magic Quadrant for Cloud Infrastructure and Platform Services. Published 27 July 2021 - ID G00736363.Magoulas, R.and Swoyerh, S. (18. March 2020), AI adoption in the enterprise 2020, O’Reilly. available at https://www.oreilly.com/radar/ai-adoption-in-the-enterprise-2020/McAfee, A. (2011). What every CEO needs to know about the cloud. Harvard business review, 89(11), 124-132.Michel, S. (2014). Capture more value. Harvard business review, 92(10), 20.Microsoft Corp. (May 1, 2018). Microsoft Artificial Intelligence: A platform for all information worker skill set levels, available at https://www.microsoft.com/en-us/us-partner-blog/2018/05/01/microsoft-artificial-intelligence-a-platform-for-all-information-worker-skill-set-levels/Niknejad, N., Ismail, W., Ghani, I., Nazari, B., & Bahari, M. (2020). Understanding Service-Oriented Architecture (SOA): A systematic literature review and directions for further investigation. Information Systems, 91, 101491.PAT Research, Top 18 Artificial Intelligence Platforms. available at https://www.predictiveanalyticstoday.com/artificial-intelligence-platforms/Pandl, K. D., Teigeler, H., Lins, S., Thiebes, S., & Sunyaev, A. (2021). Drivers and Inhibitors for Organizations’ Intention to Adopt Artificial Intelligence as a Service. In Proceedings of the 54th Hawaii International Conference on System Sciences (p. 1769).Panwar, R. (2020). It`s time to develop local production and supply networks. Strategy Insight Note. California Management Review. April 28. Available at: https://cmr.berkeley.edu/2020/04/local-production-supply- networks/Plastino, E. and Purdy, M. (2018). Game changing value from artificial intelligence: eight strategies. Strategy & Leadership.Porter, M. E., & Millar, V. E. (1985). How information gives you competitive advantage, Harvard Business Review, 63(4) July–August, 149–160.Reixa, M., Costa, C., & Aparicio, M. (2012, June). Cloud services evaluation framework. In Proceedings of the Workshop on Open Source and Design of Communication (pp. 61-69).Saripalli, P., & Pingali, G. (2011, July). Madmac: Multiple attribute decision methodology for adoption of clouds. In 2011 IEEE 4th international conference on cloud computing (pp. 316-323). IEEE.Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., ... & Dennison, D. (2015). Hidden technical debt in machine learning systems. Advances in neural information processing systems, 28, 2503-2511.Shivraj, P., (2021). "RISE OF FACEBOOK, AMAZON, APPLE, NETFLIX, GOOGLE DURING COVID-19 PANDEMIC". Electronic Theses, Projects, and Dissertations. 1311. available at https://scholarworks.lib.csusb.edu/etd/1311Sharma A., (Aug. 23, 2020). AWS v/s Google v/s Azure: Who will win the Cloud War, available at https://www.upgrad.com/blog/aws-vs-google-vs-azure/Teece, D. J., & Linden, G. (2017). Business models, value capture, and the digital enterprise. Journal of organization design, 6(1), 1-14.Todd, C., Vazquez Pena, R., & Srinivas, R. (2018). Evaluation of Artificial Intelligence Frameworks. SMU Data Science Review, 1(1), 10.Truck, M (2021). Red Hot: The 2021 Machine Learning, AI and Data (MAD) Landscape, available at https://mattturck.com/data2021/Tsai, C. F., Chen, K., Hu, Y. H., & Chen, W. K. (2020). Improving text summarization of online hotel reviews with review helpfulness and sentiment. Tourism Management, 80, 104122.Tsaih, R. H., & Hsu, C. C. (2018). Artificial intelligence in smart tourism: A conceptual framework. Proceedings of the 18th International Conference on Electronic Business:AI and Smart Tourism, ICEB 2018, 77-78. available at https://iceb.johogo.com/EiCompendex/Ei%20Compendex%20ICEB%202018%20list.pdfTsaih, R. H., Chang, H. L., Hsu, C. C., & Chang, Y. C. (2022). AI Tech-Stack Model. Communications of the ACM, Accepted August 16 2022.Tsaih, R. H., Yen, D. C., & Chang, Y. C. (2015). Challenges deploying complex technologies in a traditional organization. Communications of the ACM, 58(8), 70-75.Tussyadiah, I., & Miller, G. (2019). Perceived impacts of artificial intelligence and responses to positive behaviour change intervention. In Information and communication technologies in tourism 2019 (pp. 359-370). Springer, Cham. available at http://doi.org/10.1007/978-3-030-05940-8_28Xu, M., Liu, J., Liu, Y., Lin, F. X., Liu, Y., & Liu, X. (2019, May). A first look at deep learning apps on smartphones. In The World Wide Web Conference (pp. 2125-2136).Yao, Q., Wang, M., Chen, Y., Dai, W., Li, Y. F., Tu, W. W., ... & Yu, Y. (2018). Taking human out of learning applications: A survey on automated machine learning. arXiv preprint arXiv:1810.13306.Zimmermann, H. (1980). OSI reference model-the ISO model of architecture for open systems interconnection. IEEE Transactions on communications, 28(4), 425-432. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202201709 en_US
