從 project theta 到台灣人工智慧學校
TRANSCRIPT
From Project θ to Taiwan AI Academy
Sheng-Wei ChenResearch Fellow, Academia Sinica
Chairman, Taiwan Data Science Foundation
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
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陳昇瑋 / 從大數據走向人工智慧
McKinsey’s Four Dimensions in AI Value Chain
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Smart R&D and
forecasting
ProjectOptimized
production with
lower cost and
higher efficiency
ProduceProducts and
services at the
right price, time,
and targets
PromoteEnriched and
tailored user
experience
Provide
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
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Provide
Project
Promote
Produce
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
February – November in 2017
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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
Unmet “Soft” Needs for Nurturing Next-Generation Industries in the AI Era
Human resource development Machine learning experts with hands-on experiences
Problem/opportunity identificationProblem identification is the biggest challenge for newcomers
Business transformationProblem identification and solving strategiesSpin-offs/R&D initiatives
Shared technology infrastructureKnowledge base, datasets and baseline practices
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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
…
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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/
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/
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
…
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https://www.researchmfg.com/2011/02/soldering-defect-symptom/
Deep Neural Networks
Deep Convolution Neural Networks
Transfer learningPre-trained using 14-million image dataset
Resnet with > 8-million parameters
Input images Model training / inference
OK
OK
Case study –Human vs. Neural Inspection
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4 human inspectors for 23 product linesThroughput: 300K patches per human per day = 1.2M patches per dayLeakage rate between 5% to 10% while False alarm rate > 10%
Human
AI
Equipment: A PC with NVIDIA GeForce 1080 Ti(4,000 USD)Throughput: 167 patches per second = 10 K patches per minute = 14M patches per dayLeakage rate < 0.01% while False alarm rate < 5%
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
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Residual networks
Very similar to Residual network in Image classification
main stream + residuals
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Residual network reference
Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks, Pranav et.al., 2017
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)
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Model & workflow
Pigment selection
model31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
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Input Output
Challenges for AI Development in Taiwan
Wide gap between academia and industry
Lack of experienced talents Used to adopt rather than develop
technology
http://aiacademy.tw/
Address the “lack of AI talents” problem
Offer short, intensive and scalable training courses
Aim to train >= 1500 talents each year
http://aiacademy.tw/
Domain experts + AI
Strong linkage with the academy
Real-life problems from industry as exercises and term projects
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Corporate Partner ProgramCorporates provide real-life problems (and datasets)Students tackle these problems as term projectsCorporates may recruit students after they finish the training courses
Current class design
Elite Engineer Class (技術領袖培訓班)12 weeks9am to 6pm on Monday to FridayLectures + hands-on sessions + term projectsMid-term and final exams
Manager Class (經理人周末研修班)12 weeks9am to 9pm each Saturday Lectures only
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Elite Engineer Class
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Applications due on Dec 4, 2017. Nearly 500 applicants registered while we can only accept 208 students.
Two-step filtering:1. Document review2. Entrance exam: calculus, linear
algebra, probability, statistics, programming