| dc.contributor.advisor | 于卓民<br>郭曉玲 | zh_TW |
| dc.contributor.author (Authors) | 盧葦寧 | zh_TW |
| dc.contributor.author (Authors) | Lu, Wei-Ning | en_US |
| dc.creator (作者) | 盧葦寧 | zh_TW |
| dc.creator (作者) | Lu, Wei-Ning | en_US |
| dc.date (日期) | 2019 | en_US |
| dc.date.accessioned | 7-Aug-2019 17:09:51 (UTC+8) | - |
| dc.date.available | 7-Aug-2019 17:09:51 (UTC+8) | - |
| dc.date.issued (上傳時間) | 7-Aug-2019 17:09:51 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0105363060 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/125048 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 企業管理研究所(MBA學位學程) | zh_TW |
| dc.description (描述) | 105363060 | zh_TW |
| dc.description.abstract (摘要) | 人工智慧(Artificial Intelligence)的研究，自1950至今從未停歇，電腦「深度學習」的技術在2012科學家發現其結果精準度大幅超越傳統演算法，開啟了各項智慧應用的未來。電腦視覺是深度學習應用最廣泛的例子，自網際網路及各式行動裝置普及後，每天都有超過100萬TB的數位影像產生，若能透過自動化處理、抽取影像內容資訊，就能發展出人性化的服務。現行電腦視覺技術雖然在導入人工智慧後精確度大幅提昇，但作法會耗費大量的時間等待演算法開發，且需投入許多專業人力。在成本過高與專業人力不足的情況下，台灣雖有強大的硬體市場及技術優勢，但在軟硬體結合上尚無法作適宜的整合。本研究藉由商業模式的研究，以Osterwalder & Pigneur（2010）的商業模式藍圖（Business Model Canvas）九大要素中的價值主張、目標客層、收益流、關鍵伙伴等四項內容為思考架構，透過分析人工智慧的外部環境，結合個案公司的競爭優勢與能耐，提出一套全新的人工智慧終端應用平台商業模式。在產品技術面向上，相較於傳統僅提供預先訓練好的人工智慧演算法開發，能夠降低人工智慧應用開發門檻。人工智慧終端應用開發平台不需要擁有技術背景也能簡易操作，能縮短開發時程、縮小人工智慧應用的知識門檻，個案公司期望結合台灣的硬體能量，讓客戶能專注在垂直商品開發，在人工智慧的時代為硬體帶來更多新價值。而在商業面上，透過進一步研究此平台的目標客群，個案公司鎖定原先即擁有豐厚業務能力與管道的系統整合大廠與國際級晶片製造商，能讓人工智慧被加速導入到商品，被客戶所接受，真正普及人工智慧的應用。 | zh_TW |
| dc.description.abstract (摘要) | In the last 3 years, machine learning and deep learning have gained a lot of popularity and the momentum has continued to build. Artificial Intelligence (AI) is getting smarter every day. It is so smart that Google’s AlphaGo has even beaten some of the world’s top players in the game of Go in some devastating matches. Despite how smart AI is today, it’s still not broadly used in our daily lives yet. There remains a wide gap between the research developed within the labs and real-life applications. There are two main reasons for this 1) technology-wise the AI computation is costly, both in money and computing resources and 2) building an AI team is time-consuming. Ultimately, the gap of AI knowledge slows down the creation of new AI products.By using Business Model Canvas, a methodology proposed by Osterwalder & Pigneur（2010）, this research focuses on four components (i.e., customer segments, value proposition, revenue stream, and key partnerships) of the methodology to construct an expansion plan for the case company. The case company has brought out a new business platform - Artificial Intelligence Application Developing Platform. It is an end-to-end platform which supports customers without AI knowledge to build their own AI applications without any coding experience. The platform is designed to ease the process of building and deploying AI applications with data preparation capabilities, advancing machine learning algorithms, and offering options to build and deploy models in a variety of environments. The customers don`t need to have AI expertise, but can still build their own AI applications through the platform easily. The key to bring AI to life is the combination of technology energy from the case company and the resources from the major players in the industry. According to Tractica, the market size of visual intelligence will achieve $48.6 billion by 2022. The case company’s target market is security and surveillance industry. The strategy is to partner with chip vendors like Intel and Nvidia to leverage their resources for the sales channel. As a Taiwan-based team, the case company also has the great opportunities to work closely with system integrators to deploy solutions for hardware and to provide good software solutions to add values to hardware. | en_US |
| dc.description.tableofcontents | 第一章、 緒論 1第一節、 研究背景與動機 1第二節、 研究問題與目的 3第三節、 研究流程與章節簡介 5第二章、 環境分析 7第一節、 市場分析與現況 8第二節、 競爭者分析 14第三章、 個案公司介紹 20第一節、 創始團隊與核心能力 20第二節、 主要服務 22第三節、 公司現況概觀 27第四章、 商業模式 31第一節、 價值主張 33第二節、 目標客層 38第三節、 關鍵合作夥伴 47第四節、 收益流 50第五章、 結論與建議 55第一節、 研究結論 55第二節、 研究限制與未來研究建議 59附錄 60參考文獻 65 | zh_TW |
| dc.format.extent | 2119704 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0105363060 | en_US |
| dc.subject (關鍵詞) | 新創 | zh_TW |
| dc.subject (關鍵詞) | 商業模式 | zh_TW |
| dc.subject (關鍵詞) | 人工智慧 | zh_TW |
| dc.title (題名) | 人工智慧終端應用開發平台商業模式之研究 | zh_TW |
| dc.title (題名) | Business Model for An Artificial Intelligence Application Developing Platform | en_US |
| dc.type (資料類型) | thesis | en_US |
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| dc.identifier.doi (DOI) | 10.6814/NCCU201900376 | en_US |
