2020/12/15

1. 數位轉型與產業 AI 化
薛良斌
COO, InfuseAI ⼯合

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. 1 數位轉型

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. 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. “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. 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. 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. 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. 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. 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. 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. 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. 2 數位轉型與 AI

15. 破壞性創新
• Artificial Intelligence (AI)
• Big Data
• Cloud Computing
• Internet of Things (IoT)

16. 問題：什麼是 AI？

17. 什麼是 AI？

18. 什麼是 AI？

19. data → value

20. y=F(x)給很多 (x, y)

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. 案例分享

23. 問題討論

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

26. 3 產業 AI 化

27. 產業 AI 化
• 台灣⼈⼯智慧學校
• The problem was the problem
• MLOps & PrimeHub

28. A. 台灣⼈⼯智慧學校

30. March – November in 2017
19

31. 1,000x

32. 01
02
03
04
AI

33. http://aiacademy.tw/
01
02
03
AI
+

34. •
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•
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35. 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。

36. 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。

37. B. The problem was the problem

38. 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.

39. C. MLOps & PrimeHub

40. PrimeHub
Make AI workflow 10x faster
• 模型開發
• 模型部署
• ⾃動化

41. 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%

42. 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

43. 理想：我們來做 AI
準備資料 模型訓練 部署上線

44. 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

45. 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

46. MLOps
1 → N

47. 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

48. 暸解更多
• 雲端試⽤環境
• 焦點⼩組⼯作坊
• 需求討論規劃

49. 結語

50. 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

51. 聯絡⽅式
hlb@infuseai.io
hlb iamhlb iamhlb

52. 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