人工智慧與台灣製造業

HT Kung (孔祥重)

Harvard University

台灣人工智慧學校 校長

Presentation at

人工智慧學校交流論壇

Kaohsiung, Taiwan

21 February 2018

Copyright © 2018 H. T. Kung

Message

• AI is bringing a renaissance to manufacturing

• Data is king

• Taiwan has advantages in manufacturing data

• Must move fast to seize the opportunity

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Agenda

Part 1: Preamble

– Rapidly advancing AI

– Automation and AI in manufacturing

Part 2: Deep learning

Part 3: Data is king

Part 4: Challenges in AI transformation of manufacturing in Taiwan

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AI Is Hot, Especially Deep Learning • Deep learning has brought break-through accuracy in

speech recognition, computer vision, and text understanding

• Rapid advances continue; these days research in this area has about one-month turnaround time

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• Many have faith in machine learning, "If the problem looks difficult, let's use deep learning“ – a shallow attitude! Some even joked: "only machines can learn now!“

Yoshua Bengio

AI Transformation of Manufacturing: “Automation” “Automation + AI”

• Traditional focus: Automation – Machines follow orders in performing repetitive

tasks, including data collection

• New opportunity: AI – Machines understand data and come up models

for prediction and decision making in all areas of manufacturing

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AI Transformation: Automation Automation + AI

Automation collects data and AI uses the data

So, What’s the Real Difference Between AI and Automation? 2017

Questions

• Which sectors in the AI ecosystem for manufacturing should Taiwan focus on?

• How?

I hope that this presentation will shed some light on these questions

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Agenda

Part 1: Preamble

– Rapidly advancing AI

– Automation and AI in manufacturing

Part 2: Deep learning

Part 3: Data is king

Part 4: Challenges in AI transformation of manufacturing in Taiwan

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Object Detection in ImageNet Challenge: Some Examples in Bird Category

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Deep Neural Networks (DNNs) for ImageNet Challenge

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Error Rate Curves

What Are DNNs?

• A stack of neural network layers

• At each layer, inner products are performed between input and layer weights, and result is passed into non-linear activation function

10 Alan Aspuru-Guzik 2017

11 Michael Bronstein, 2017

Convolutional Neural Network (CNN): An Efficient Version of DNN with a Reduced

Number of Model Parameters

Input & Results

From Each Layers

CNN

Deep neural nets have gone deeper for ImageNet: • LeNet (1998)

– 5 learned layers • AlexNet (2012)

– 8 learned layers – Error Rate: 11.7%

(for ImageNet) • VGG (2014)

– 19 learned layers – Error Rate: 7.3%

• GoogLeNet (2014) – 22 learned layers – Error Rate: 6.7%

• ResNet (late 2015) – 152 learned layers – Error Rate: 3.57%

AlexNet

(2012)

8 Layers

VGG

(2014)

19 Layers

ResNet

(2015)

152 Layers

LeNet

(1998)

5 Layers

GoogLeNet

(2014)

22 Layers

(A 34-layer

Version)

Deeper and Deeper CNNs

Deep Learning Increasingly Being Ported to Edge Devices

“Soon, our devices won’t have keyboards, touch screens, or other traditional interfaces, … Instead, they will be semi- or fully autonomous devices, such as vehicles, drones, and robots”

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Federated Learning (Google, April 2017)

“My phone isn’t smart until it can read my mind,” May 2017

E.g., Facial authentication is already here

AI-powered Household Devices

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We can now talk to devices when we need to get stuff done

These voice platforms have become technology gateways in households A trade-off between convenience and privacy

Where Does Knowledge in AI Come From?

Most of the knowledge in the world in the future is going to be extracted by machines and will reside in machines – Yann LeCun, Director of AI Research, Facebook

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Pedro Domingos, The

Master Algorithm: How the

Quest for the Ultimate

Learning Machine Will

Remake Our World. New

York: Basic Books, 2015

A History • 1944: Neural networks first proposed by

Warren McCullough and Walter Pitts. They argued that the neural networks could solve equations

• 1950: AI as a discipline

• 1980: Expert systems, genetic algorithms, multi-layer perceptron (MLP), backpropagation

• 2010: Deep learning – Improved algorithm, powerful computing

hardware, and large datasets

– A good name including the word “deep” also helps 16

Agenda

Part 1: Preamble

– Rapidly advancing AI

– Automation and AI in manufacturing

Part 2: Deep learning

Part 3: Data is king

Part 4: Challenges in AI transformation of manufacturing in Taiwan

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PROJECT THETA Started in April 2017 on AI Transformation of Manufacturing in Taiwan

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June 6, 2017

With 陳昇瑋, etc.

我們已看到產業有許多應用的成功案例，其中深度學

習達到無人所及的優異表現

例如，台灣人工智慧學校陳昇瑋執行長的工作隊有 15

個以上產業項目正在進行中，其中有 8 項在導入生產

線中

然而，世界各地也注意到這股人工智慧商機，並且積

極地投入應用開發

台灣必須搶佔先機，發展地更快，而且做

得更好 20

好消息，然而 . . .

An AI Application Example: Automated Optical Inspection (AOI) for Defect Identification

in Display Manufacturing

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陳昇瑋, etc. Training Data

FPR

Data Size Matters: False Positive Rate (FPR) Decreases as Training Data Increases, for a Given False Negative Rate (FNR)

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Data Preparation Represents Significant Efforts

A Typical Project Schedule 4/25 5/5 5/15 5/25 6/4 6/14 6/24 7/4 7/14

公司參訪

討論資料格式

取得第一批資料

資料清理與建立基準模型

人工二篩銅屑影像

調控模型與產生第一批需複判圖像

架設複判測驗 UI Interface

資料複判

取得第二批資料

調控模型與產生第二批需複判圖像

First company visit Data format discussion

First batch of data received Data preprocessing and model building

Defect labeling Model fine-tuning and images for rechecking

Data recheck Web UI Data rechecked

Second batch of data received Model fine-tuning and images for rechecking

Most time spent in data acquisition, preparation, and validation

Collaboration with Domain Experts

Collaboration between AI and domain experts is essential in addressing many domain-specific issues such as:

• Noisy/incorrect sensor measurements

• Incorrect labels (e.g., “defects” vs. “non-defects”) are surprisingly common (can be 5-10% or higher) – How can we mitigate the problem and still train an

accurate classifier?

• Data starvation for defect/failure examples

• Unstable data (e.g., during chemical reactions)

• Inconsistency in environments (e.g., different materials and new machines)

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資料驅動著深度學習發展

依 80/20 定律來規劃工作：

80% 應投入於資料準備，而 20% 才是建構人工智慧演算法

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Therefore, 資料就是王道 Data Is King

台灣獨特的優勢

台灣在某些領域擁有獨特的資料，例如製造業

台灣這些領域的產業，可以利用這獨特的資料優勢

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Agenda

Part 1: Preamble

– Rapidly advancing AI

– Automation and AI in manufacturing

Part 2: Deep learning

Part 3: Data is king

Part 4: Challenges in AI transformation of manufacturing in Taiwan

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Some Challenges • Organization readiness

– Large manufacturers may have IT departs which can be tasked for AI transformation (automation + AI)

– Small manufacturers would likely need help

• Data preparation – Manufacturers often depend on equipment vendors

• Business case – Support from management, return on investment (ROI),

etc. – “People don't want to buy a quarter-inch drill, they want a

quarter-inch hole”---Theodore Levitt 1925～2006

• Success examples – Only a few reference points are available

• Access to talent – There is a severe shortage in AI talent. “Global AI Race”

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台灣人工智慧學校: 盡我們的一部分義務

(Do Our Part)

台灣人工智慧學校將迅速地跟上市場脈動、更全面地回應產業需求

We will help develop talent, tools, new industries (e.g., next-generation AOI equipment), etc.

如同 Nike 的品牌精神，我們也有一個很簡單的理念：「做，就對了！」 ("Just Do It"---Nike's Slogan)

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台灣人工智慧學校是什麼？

對於產業人士，在這裡您可以學習如何使用人工智慧提升您的競爭力.

---台灣人工智慧學校是您的產業學校

("Taiwan AI Academy is your school")

對於老師和學生，在這裡您可以與志同道合夥伴一起為產業升級做出貢獻

---台灣人工智慧學校是您的貢獻平台

("Taiwan AI Academy is your platform")

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Conclusion

• AI is bringing a renaissance to manufacturing

• Data is king

• Taiwan has advantages in manufacturing data

• Must move fast to seize the opportunity

台灣人工智慧學校 is our response to the calling

We look forward to working with you all

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Thank you for your attention

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