從 Project Theta 到台灣人工智慧學校
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鑑於人工智慧的重要性，以及台灣產業長久被缺少高階人才的問題所困擾，甚至成為產業發展及升級的障礙，台灣人工智慧學校為此而成立。希望很快地讓「找不到人才」不再成為企業發展人工智慧的障礙，同時建立「自己的問題自己解決」的文化，打破被技術殖民的慣性，重建社會自信。 台灣人工智慧學校將以最好的師資及與產學界的密切合作，進行人工智慧技術人才的密集培訓。讓不同專業領域的學員都能如虎添翼，以人工智慧加上原本的領域知識，具備協助各企業解決問題，以及帶領人工智慧團隊的能力。他們將擔任技術種子角色，深入台灣產業的各個層面，以其本身專業及人工智慧技術協助企業解決在邁向智慧化的過程中所面臨的難題。 我將在這場演講中，分享台灣人工智慧學校的來龍去脈，如何從一個稱為 Project Theta 的計畫開始，慢慢確認人才培育的需求，以及形塑台灣人工智慧學校的雛型。這個故事從 2017 年三月的某個早晨開始...
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從 Project Theta 到台灣人工智慧學校
- 1. From Project θ to Taiwan AI Academy Sheng-Wei Chen Research Fellow, Academia Sinica Chairman, Taiwan Data Science Foundation
- 2. Change is the only constant - Heraclitus (535 BC - 475 BC)
- 3. 陳昇瑋 / 從大數據走向人工智慧 6
- 4. 陳昇瑋 / 從大數據走向人工智慧 7 Mastering Chess and Shogi by self play with reinforcement learning
- 5. 陳昇瑋 / 從大數據走向人工智慧 AI IN MANUFACTURING 8
- 6. 陳昇瑋 / 從大數據走向人工智慧 9 2016 Global manufacturing competitiveness index rankings
- 7. 陳昇瑋 / 從大數據走向人工智慧 1/3 of the GDP Manufacturing GDP of $178B, almost 1/3 of total GDP 30% of the employment are in the manufacturing sector Cheap labor cost of $9.42/hr with average labor productivity of almost $60k in GDP/person 17% corporate tax rate 10
- 8. 陳昇瑋 / 從大數據走向人工智慧 McKinsey’s Four Dimensions in AI Value Chain 11 Smart R&D and forecasting Project Optimized production with lower cost and higher efficiency Produce Products and services at the right price, time, and targets Promote Enriched and tailored user experience Provide
- 9. 陳昇瑋 / 從大數據走向人工智慧 The Four-P Dimensions in Manufacturing Improve product design Automate supplier assessment and price negotiation Anticipate parts requirements Improve manufacturing processes Automate assembly lines limit product rework Optimize pricing Predict sales of maintenance services Refine sales-leads prioritization Optimize flight/fleet planning and route Enhance maintenance engineering Enhance pilot training 12 Provide Project Promote Produce
- 10. 14
- 11. Professor • Prior to joining Harvard in 1992, Dr. Kung taught at Carnegie Mellon University for 19 years. • In 1999 he started a joint Ph.D. program with colleagues at the Harvard Business School on information, technology, and management, and co-chaired this Harvard program from 1999 to 2006. • Member of National Academy of Engineering • Guggenheim Fellowship • IEEE Computer Society Charles Babbage Award HT Kung • Academician, Academia • William H. Gates Professor, Harvard John A. Paulson School of Engineering and Applied Sciences Current Past Experiences
- 12. 16 To empowerTaiwan (manufacturing) industries with Artificial Intelligence
- 13. February – November in 2017 17 台塑石化 長春石化 奇美實業 英業達 欣興電子 敬鵬工業 可成科技 致茂電子 永進機械 研華科技 農科院 紡織所 聯發科技 台積電 宏遠紡織 台元紡織 佳和紡織 強盛染整 臺灣塑膠 龍鼎蘭花 經緯航太科技
- 14. Unmet “Soft” Needs for Nurturing Next-Generation Industries in the AI Era Human resource development Machine learning experts with hands-on experiences Problem/opportunity identification Problem identification is the biggest challenge for newcomers Business transformation Problem identification and solving strategies Spin-offs/R&D initiatives Shared technology infrastructure Knowledge base, datasets and baseline practices 18
- 15. 人工智慧發展策略建議 Project θ Weekly Meetings at NCTU and Also Online with Academia Sinica June 6, 2017
- 16. 人工智慧發展策略建議 Why it’s called Project θ? 20
- 17. 人工智慧發展策略建議 Industry-wide Problems Automated Optical Inspection (AOI) systems Adaptive process control Predictive maintenance Component selection optimization … 21
- 18. 人工智慧發展策略建議 A sad story that AI-assisted AOI can help avoid 14 suicide events in 2010 at Foxconn China factories Only 2 of the suicides survived Industry-wide Problem #1: Human Operators for Optical Inspection https://theinitium.com/article/20170802-mainland-Foxconn-factorygirl/
- 19. 人工智慧發展策略建議 Human Operators for Optical Inspection The factories recruit only workers under 29 years old Their work involve checking scratches on consumer products (likely Apple iPhone) for 2,880 times a day This means 4 times per minute assuming 12 working hours per day https://theinitium.com/article/20170802-mainland-Foxconn-factorygirl/
- 21. 人工智慧發展策略建議 25 Typical PCB defects
- 22. 人工智慧發展策略建議 Typical defects after SMT (Surface-Mount Technology) process 短路 空焊 極反 缺件 浮高 跪腳 撞件 錫球 墓碑 … 26 https://www.researchmfg.com/2011/02/soldering-defect-symptom/
- 23. More SMT/DIP Defect Examples 27
- 24. Typical Deep-Learning based AOI Systems 30 AI … OK
- 25. Deep Neural Networks Deep Convolution Neural Networks Transfer learning Pre-trained using 14-million image dataset Resnet with > 8-million parameters Input images Model training / inference OK OK
- 26. Case study – Human vs. Neural Inspection 33 4 human inspectors for 23 product lines Throughput: 300K patches per human per day = 1.2M patches per day Leakage rate between 5% to 10% while False alarm rate > 10% Human AI Equipment: A PC with NVIDIA GeForce 1080Ti (4,000 USD) Throughput: 167 patches per second = 10 K patches per minute = 14M patches per day Leakage rate < 0.01% while False alarm rate < 5%
- 27. 人工智慧發展策略建議 Industry-wide Problem #2: Adaptive process control 35
- 28. 人工智慧發展策略建議 Case Study: A Chemical Process 12 parameters Hydrogen (H) Catalyst Ethylene (C2H4), Ethane (C2H6), Butene (C4H8) Pressure, temperature, fluid level, and so on Output A quality index of a certain chemical product 36
- 29. Acceptable range Yield rate: 61% QualityIndex
- 30. Residual networks Very similar to Residual network in Image classification main stream + residuals 38 Residual network reference Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks, Pranav et.al., 2017
- 31. 39 Preliminary Control Results Human yield rate: 61% CNN yield rate: 98% QualityIndex
- 32. 人工智慧發展策略建議 Industry-wide Problem #3: Predictive maintenance Especially important for equipment with high failure cost (such as motors in machine tools) Also important for expensive consumables (such as blades used in precision cutting machines) 40
- 33. 人工智慧發展策略建議 Industry-wide Problem #4: Component selection optimization 41
- 34. 人工智慧發展策略建議 te 42
- 35. Model & workflow Pigment selection model 31 點反射率 32 個染料 (0, 1) •Multi-classification (每筆 observation 最多 3 個 1) Pigment concentration model 31 點反射率 ＊ 4 組 , 共 124 dim •一組是該顏色的反射率 •其他 3 組是染料對應的反射率 (固定一種濃度, 1.5%) 3 個染料濃度 (0 – 5 %) • Multiple output regression Reflectance prediction model 32 個染料濃度 (沒有用則 0) 31 點反射率 (0 – 1) • Multiple output regression 43 Input Output
- 36. 人工智慧發展策略建議 44 Pigment 1 Pigment 2 Pigment 3
- 37. 人工智慧發展策略建議 PROJECT Θ TEAM HAS SOLVED 10+ PROBLEMS FROM 10+ COMPANIES WITHIN 6 MONTHS… 45
- 38. 人工智慧發展策略建議 LOOKS IT WORKS OUT, BUT … 46
- 39. 人工智慧發展策略建議 47
- 40. Challenges for AI Development in Taiwan Wide gap between academia and industry Lack of experienced talents Used to adopt rather than develop technology
- 41. TAIWAN AI ACADEMY－ A SOLUTION TO SCALE OUT PROJECT Θ 68
- 42. http://aiacademy.tw/ Address the “lack of AI talents” problem Offer short, intensive and scalable training courses Aim to train >= 1500 talents each year
- 43. http://aiacademy.tw/ Domain experts + AI Strong linkage with the academy Real-life problems from industry as exercises and term projects
- 44. 72 Corporate Partner Program Corporates provide real-life problems (and datasets) Students tackle these problems as term projects Corporates may recruit students after they finish the training courses
- 45. Current class design Elite Engineer Class (技術領袖培訓班) 12 weeks 9am to 6pm on Monday to Friday Lectures + hands-on sessions + term projects Mid-term and final exams Manager Class (經理人周末研修班) 12 weeks 9am to 9pm each Saturday Lectures only 73
- 46. Elite Engineer Class 74 Applications due on Dec 4, 2017. Nearly 500 applicants registered while we can only accept 208 students. Two-step filtering: 1. Document review 2. Entrance exam: calculus, linear algebra, probability, statistics, programming
- 47. EmpoweringTaiwan in the AI Era! Sheng-Wei Chen Academia Sinica