| 週次 |
授課內容 |
| 第1週 |
Chapter 1: Introduction to Intelligent Technology
1.1 Course outline and assessment.
1.2 Intelligence: Human/Animal intelligence vs artificial intelligence.
1.3 Intelligent technology and AI/ML. |
| 第2週 |
Chapter 1: Introduction to Intelligent Technology
1.4 Exemplar intelligent technologies, systems and services.
1.5 Five foundational types of intelligent technology.
1.6 Intelligent infrastructure.
1.7 Issues in intelligent technology. |
| 第3週 |
Chapter 2: History of AI
2.1 Evolution of technology.
2.2 Evolution of AI. |
| 第4週 |
Chapter 3: First Foundational Type of iTech
3.1 National language processing (NLP)
3.2 Voice assistant: Alexa, Google Assistant and Siri.
3.3 Machine translator: Google Translate.
3.4 ChatBot: Google Bard and OpenAI ChatGPT.
3.5 Epilog:
-- 3.5.1 Text-to-text (document summarization, paraphrasing)
-- 3.5.2 Voice-to-text. |
| 第5週 |
Chapter 4: Second Foundational Type of iTech
4.1 Image processing: Pattern/Object recognition.
4.2 Character recognition.
-- 4.2.1 Optical character recognition.
-- 4.2.2 Car plate recognition.
4.3 Object recognition.
-- 4.3.1 Image/Photo tagging for untagged image/photo.
-- 4.3.2 Image/Photo captioning for uncaptioned image/photo.
-- 4.3.3 ImageNet challenge (Boosting the rise of AI research in 2010).
4.4 Multiple-Image processing.
-- 4.4.1 Object and motion detection.
-- 4.4.2 Auto-driving.
-- 4.4.3 Autonomous system.
4.5 Epilog: Image-to-text and video-to-text.
|
| 第6週 |
Chapter 5: Third Foundational Type of iTech
5.1 Robotic.
5.2 Industrial robot.
-- 5.2.1 Manufacturing automation.
-- 5.2.2 Working a some danger zones.
5.3 Humanoid robot for mimicking human body movement.
5.4 Military robot.
5.5 Exploring outer-space.
5.6 Epilog:
-- 5.6.1 Can a robot decide who is enemy?
-- 5.6.2 Can a robot kill the enemy? |
| 第7週 |
Chapter 6: Forth Foundational Type of iTech
6.1 Game playing.
6.2 IBM Deep Blue.
6.3 Alpha Go.
6.4 Poker game.
6.5 Epilog
-- 6.5.1 Would an AI player bluff?
-- 6.5.2 Are they intelligent? |
| 第8週 |
Chapter 7: Fifth Foundational Type of iTech
7.1 Nature inspired optimization algorithms.
7.2 Industrial applications.
-- 7.2.1 Production cost reduction.
-- 7.2.2 Production process scheduling.
-- 7.2.3 Logistics.
-- 7.2.4 Workflow management.
7.3 Key ideas.
-- 7.3.1 Simulation annealing.
-- 7.3.2 Genetic algorithm and evolutionary computing.
-- 7.3.3 Big data analytic.
7.4 Epilog: Are they really intelligent? |
| 第9週 |
Project progress report. |
| 第10週 |
Chapter 8: Key Concepts in Intelligent Technology
8.1 AI model.
-- 8.1.1 Just a mathematical model with lot of parameters.
-- 8.1.2 Capturing the complicated regular patterns from a dataset.
8.2 Evolution.
-- 8.2.1 Simple (classical) models.
-- 8.2.2 Complex (contemporary) models.
-- 8.2.3 Model made up of multiple (thousands of) complex models. |
| 第11週 |
Chapter 8: Key Concepts in Intelligent Technology
8.3 Learning (i.e. model building) from a set of data.
8.4 Epilog
-- 8.4.1 Data collection.
-- 8.4.2 Representation of non-numeric data.
-- 8.4.3 Human-in-a-loop learning.
-- 8.4.4 Model bias and data poisoning.
-- 8.4.5 Technological support: Internet and cloud. |
| 第12週 |
Chapter 9: Research & Development in iTech
9.1 Intelligent infrastructure.
9.2 Foundation models: Academic and industry.
9.3 AI as a service: Industry.
9.4 Intelligent infrastructure: Industry.
9.5 Intelligent services: Industry.
9.6 Epilog
-- 9.6.1 Increasingly demand on the use of cloud XPU (i.e. GPU, TPU and IPU).
-- 9.6.2 AI at the edge: Use of AI without connecting to the Internet? |
| 第13週 |
Chapter 10: XYZ-To-Text and Text-To-XYZ
10.1 XYZ-To-Text.
-- 10.1.1 Everything observable instance in the nature could be converted to text.
-- 10.1.2 All observable instances are encoded and collected for training an AI model.
-- 10.1.3 XYZ = Measurement data, text, photo, voice or video.
-- 10.1.4 Internet-of-Thing == Internet-of-Text.
10.2 Text-To-XYZ.
-- 10.2.1 Text to text: Story writing.
-- 10.2.2 Text to speech.
-- 10.2.3 Text to image.
-- 10.2.4 Text to video.
10.3 Epilog
-- 10.5.1 Fake news.
-- 10.5.2 Fake images.
-- 10.5.3 Fake videos.
-- 10.5.4 Knowledge generation? |
| 第14週 |
Chapter 11: Personal Applications of iTech
11.1 Examples.
-- 11.1.1 Topic survey (informal survey).
-- 11.1.2 Document preparation (spelling check and correction, grammar check, paraphrasing).
-- 11.1.3 Search keywords recommendation (Google search engine and Bing).
-- 11.1.4 Document translation (Goolge Translate).
-- 11.1.5 Image/Photo editing.
-- 11.1.6 Voice command to PC/Phone.
-- 11.1.7 Driving, parking and route recommendation.
-- 11.1.8 Product recommendation.
11.2 Effective use of iTech.
-- 11.2.1 A user should be able to complete the task even if no iTech has been used.
-- 11.2.2 A user should be able to determine in which step which iTech is helpful.
-- 11.2.3 A user should be able to justify if the result is making sense.
-- 11.2.4 A user should be able to identify the limitation of the use of an iTech.
-- 11.2.5 A user should use an iTech as an assistant role but should not survive on the iTech. |
| 第15週 |
Chapter 12: Business & Industrial Applications of iTech
12.1 Administration.
12.2 Marketing.
12.3 Customer support.
12.4 Manufacturing.
12.5 Logistics. |
| 第16週 |
Chapter 13: Societal Issues of AI
13.1 AI safety: About 99.99% recognition rate.
13.3 AI ethic: Ethical use of AI systems. Something AI can do but we cannot let it do.
13.3 AI bias: All about the training dataset and the AI models.
13.4 Augmented reality 2.0: Reals and fakes mix-up.
13.5 Job replacement
-- 13.5.1 Operational (administrative and technical) staff?
-- 13.5.2 Middle/Top management staff?
13.6 Roles of human workers in the AI era: From using AI to serving AI?
-- 13.6.1 Robotic systems maintenance.
-- 13.6.2 Cloud systems maintenance.
-- 13.6.3 Data labeling/tagging.
-- 13.6.4 System testing and user experience testing.
Project final report (Session I)
Project final report (Session II) |
自主學習
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