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數位轉型與產業 AI 化

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數位轉型與產業 AI 化

  1. 1. 數位轉型與產業 AI 化 薛良斌 COO, InfuseAI ⼯合
  2. 2. 講者介紹 薛良斌 aka 布丁 / hlb • 2018 - now, 共同創辦⼈ & 營運長, InfuseAI • 2016 - 2018, 產品總監, cacaFly • 2013 - 2015, KKTIX 總監, KKBOX • 2006 - 2013, 共同創辦⼈, Handlino • 產品規劃設計 • 線上活動售票平台 • 數位廣告與 AI 推薦引擎
  3. 3. 1 數位轉型
  4. 4. Digital Transformation Software is eating the world • Amazon, bookseller • Netflix, video service • iTunes, music site • Pixar, movie production • Google, marketing platform • Skype, telecom • LinkedIn, recruiting firm • Airbnb, hotel • Uber, taxi  • ??? after COVID-19: Food delivery, Fintech, healthcare, ...
  5. 5. Buzzwords 智慧____ • 資訊化 • ⾃動化 • 智慧化 • 數字化 • 數位化 • 智慧製造 • 智慧⼯廠 • 智慧城市 • 智慧醫療 • 智慧零售 • 智慧運輸 • 智慧學習 • ⼈⼯智慧 • ⼤數據 • 雲端運算 • 物聯網 • ⼯業4.0
  6. 6. “Digitization was using digital tools to automate and improve the existing way of working without really altering it fundamentally or playing the new rules of the game,” says technology strategist and veteran industry analyst Dion Hinchcliffe. Digital transformation “is a more caterpillar-to-butterfly process, moving gracefully from one way of working to an entirely new one, replacing corporate body parts and ways of functioning completely in some cases to capture far more value than was possible using low-scale, low-leverage legacy business.” Siebel, Thomas M.. Digital Transformation: Survive and Thrive in an Era of Mass Extinction
  7. 7. Digital 數位化 Rapid Business Innovation Empowering people to experiment, release, and constantly enhance digital offerings Digitized 數字化 Operational Excellence Instilling discipline around core transaction and back office processes
  8. 8. Digital business transformation: Becoming Future Ready Source: MIT CISR 2015 CIO Digital Disruption Survey (N=413) and a series of executive interviews conducted between 2015 and 2017. CustomerExperience Increasingcustomerdelight Increasing automation, standardization, reuse, and productivity Operational Efficiency TRADITIONALTRANSFORMED TRADITIONAL TRANSFORMED Future Ready • Both innovative and low cost • Great customer experience • Modular and agile • Data is a strategic asset • Ecosystems ready Integrated Experience • Customer gets an (simulated) integrated experience despite complex operations • Strong design and UX • Rich mobile experience including purchasing products Industrialized • Plug and play products/services • Service enabled ‘crown jewels’ • One best way to do each key task • Single source of truth Silos and Spaghetti • Product driven • Complex landscape of processes, systems and data • Perform via heroics Future Ready • Both innovative and low cost • Great customer experience • Modular and agile • Digital partnering • Data is a strategic asset • Ecosystems ready Integrated Experience • Customer gets an (simulated) integrated experience despite complex operations • Strong design and UX • Rich mobile experience including purchasing products Industrialized • Plug and play products/services • Service enabled ‘crown jewels’ • One best way to do each key task • Single source of truth Silos and Spaghetti • Product driven • Complex landscape of processes, systems and data • Perform via heroics Digitized 數字化 Digital數位化
  9. 9. Choose your pathway to Future Ready Choose a Pathway Where are you today? What is your digital disruption threat level? 1. Follow Pathway 1 if your Customer Experience is ok and threat is not high. 2. Can’t wait to improve your Customer Experience or facing the threat of new competitors? Follow Pathway 2. 3. Can’t wait to improve customer experience but a few initiatives will make a big difference (e.g., a great app)? Start with those and then focus on operations and repeat in small steps on Pathway 3. 4. High threat and can’t see a way to change the organization fast enough? Follow Pathway 4.Source: MIT CISR TMT and Transformation Survey (N=1311). Note: Not included are companies on multiple pathways or those that have not yet started transforming.
  10. 10. All pathways pay off Source: MIT CISR 2019 Top Management Teams and Transformation Survey (N=1311). Circles represent (industry-corrected) average performance differences when comparing companies where the digital transformation was ≥50% complete with those where it was <50% complete. Not included in this analysis are combined pathways options. Average net margin 14% higher and revenue growth 26% higher when comparing firms above and below 50% complete on transformation PATHWAY MARGIN REVENUE
  11. 11. The four pathways to Future Ready Note: Pathway lines are based on a series of informal interviews (conducted between 2015 and 2017) on digital transformation with senior executives globally. The lines were confirmed via the MIT CISR 2017 Pathways to Digital Business Transformation survey (N=400). Explosions represent significant organizational changes. CustomerExperience Increasingcustomerfocus Increasing automation, standardization, reuse, and productivity Operational Efficiency TRADITIONALTRANSFORMED TRADITIONAL TRANSFORMED Integrated Experience Future Ready Silos and Spaghetti Industrialized
  12. 12. Becoming Future Ready requires explosions Source: MIT CISR 2017 Pathways to Digital Business Transformation Survey (N=400). EXPLOSION MOST IMPORTANT CHALLENGE Decision Rights Getting the right people to lead key decisions, providing decision-making authority to teams e.g., What vs how, off-path spend, “we are different” New Ways of Working Changing collective work habits and the culture e.g., Bringing in the customer voice, agile, evidence-based Organizational Surgery Removing organizational complexity e.g., Reducing alignment challenges Platform Mindset Connecting organizational silos e.g., Reuse, APIs, data sharing, decreasing spaghetti
  13. 13. The challenge: Get to 50% as quickly as possible Source: MIT CISR 2019 Top Management Teams and Transformation Survey (N=1311) and MIT CISR 2017 Pathways to Digital Business Transformation Survey (N=400). Mechanism importance is relative to the particular pathway. PATHWAY AVERAGE PROGRESS IMPORTANT PROGRESS MECHANISM 50% Reuse of existing processes, data, and technology 56% Minimum viable product approach 53% Dashboards 50% Agile methodology
  14. 14. 2 數位轉型與 AI
  15. 15. 破壞性創新 • Artificial Intelligence (AI) • Big Data • Cloud Computing • Internet of Things (IoT)
  16. 16. 問題:什麼是 AI?
  17. 17. 什麼是 AI?
  18. 18. 什麼是 AI?
  19. 19. data → value
  20. 20. y=F(x)給很多 (x, y)
  21. 21. AI 應⽤分類 • Supervised Learning • Classification • Regression • Unsupervised Learning • Clustering • Anomaly Detection • Natural Language Processing (NLP) • Association Rule Mining • Recommendation • Reinforcement Learning • AlphaGo • Autonomous driving • Recommendation
  22. 22. 案例分享
  23. 23. 問題討論
  24. 24. 問題 • AI 不能做什麼? • y = F(x), F 隨著 x, y 改變 • 訓練好⼀個模型,事情才剛開始 • 資料越多越聰明? • 精度:⼯業 vs 商業
  25. 25. 3 產業 AI 化
  26. 26. 產業 AI 化 • 台灣⼈⼯智慧學校 • The problem was the problem • MLOps & PrimeHub
  27. 27. A. 台灣⼈⼯智慧學校
  28. 28. March – November in 2017 19
  29. 29. 1,000x
  30. 30. 01 02 03 04 AI
  31. 31. http://aiacademy.tw/ 01 02 03 AI +
  32. 32. • • • • • • • • • • • • • • •
  33. 33. 台灣⼈⼯智慧學校⾃ 2018 年成立以來,透過系統化的教 學與做中學的⽅式,成功且⼤量的為台灣產業孕育出 7,000 位 AI ⼈才。
  34. 34. InfuseAI 與台灣⼈⼯智慧學校密切合作,解決各種教學⽅⾯的 需求。各分校的助教只需要在 PrimeHub 平台簡單地操作,所 有管理⼯作就⾃動完成。學員們可以立刻開始使⽤,把他們的 長才發揮在學習新技術上。
  35. 35. B. The problem was the problem
  36. 36. Kremer Prize Human-powered aircraft • £50,000 for first Human-powered aircraft to complete figure-8 for 1.5miles (1959) • 18 years later, MacCready redefined and problem and won it • The second Kremer prize of £100,000 was won on 12 June 1979, again by Paul MacCready https://www.fastcompany.com/1663488/wanna-solve-impossible-problems-find-ways-to-fail-quicker He came up with a new problem that he set out to solve: How can you build a plane that could be rebuilt in hours, not months? And he did. He built a plane with Mylar, aluminum tubing, and wire.
  37. 37. C. MLOps & PrimeHub
  38. 38. PrimeHub Make AI workflow 10x faster • 模型開發 • 模型部署 • ⾃動化
  39. 39. 投入 AI 的公司
 還沒有部署過⼀個 AI 模型 Source: 2020 state of enterprise machine learning, Algorithmia 55%
  40. 40. 42% 參與調查的公司 可以在 30 天內部署⼀個 AI 模型 Source: 2020 state of enterprise machine learning, Algorithmia
  41. 41. 理想:我們來做 AI 準備資料 模型訓練 部署上線
  42. 42. 現實:AI 需要⼤量 DevOps 資料來源: 機器學習系統的隱藏技術債, Sculley et al. 準備 模型訓練 部署上線 設定 Configuration 數據收集 Data Collection 數據驗證 Data Verification 撰寫 ML Code 機器資源管理 Machine Resource Management 分析⼯具 Analytics Tools 程序管理⼯具 Process Management Tools特徵提取 Feature Extraction 服務基礎建設 Serving Infrastructure 部署管理 Deployment Management 監控 Monitoring
  43. 43. MLOps 0 → 1 👩🔧 Data Engineer Extract Data Prepare Data Extract Data Prepare Data 👨💻 DevOps Deploy 👨🔬 Data Scientist Build Models Train Models 👩💼 Biz Analyst Validate 👩🚀 App Engineer 🦸 SRE Use Models Monitor
  44. 44. MLOps 1 → N
  45. 45. Running on any infrastructure IDE Data Source Workflow Integration Algorithm & Library Programming Language Model Operations SQL • Infrastructure Orchestration • Collaboration & Reproducibility • Model Operations • Governance & Management Enterprise ML platform
  46. 46. 暸解更多 • 雲端試⽤環境 • 焦點⼩組⼯作坊 • 需求討論規劃
  47. 47. 結語
  48. 48. 與成功有約:⾼效能⼈⼠的七個習慣, Stephen R. Covey 以終為始 Begin with the end in mind
  49. 49. 聯絡⽅式 hlb@infuseai.io hlb iamhlb iamhlb
  50. 50. 延伸資料 • Digitization, Digitalization, and Digital Transformation: What’s the Difference? • Four Pathways to Digital Business Transformation, Dr. Stephanie L. Woerner, MIT • 谁卡住了⼯业AI的脖⼦?, 機械之⼼ • 【講講科普】 當你有了三個孩⼦他們分別叫監督式學習、非監督式學習與強化式學習 • [台灣⼈⼯智慧學校] 執⾏長報告, 2019 • How nature and naiveté helped Paul MacCready build a human-powered airplane in only six months • Flight of the Gossamer Condor • 2020 state of enterprise machine learning, Algorithmia

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