| Week |
Course Content |
| Week 1 |
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. |
| Week 2 |
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. |
| Week 3 |
Chapter 2: History of AI
2.1 Evolution of technology.
2.2 Evolution of AI. |
| Week 4 |
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. |
| Week 5 |
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.
|
| Week 6 |
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? |
| Week 7 |
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? |
| Week 8 |
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? |
| Week 9 |
Project progress report. |
| Week 10 |
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. |
| Week 11 |
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. |
| Week 12 |
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? |
| Week 13 |
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? |
| Week 14 |
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. |
| Week 15 |
Chapter 12: Business & Industrial Applications of iTech
12.1 Administration.
12.2 Marketing.
12.3 Customer support.
12.4 Manufacturing.
12.5 Logistics. |
| Week 16 |
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) |
self-directed
learning |
03.Preparing presentations or reports related to industry and academia.
(1) Self-learn the use of at least one AI tool to solve a practical problem.
(2) Compile written report, i.e. formal report, which is conformed with a mater thesis format.
(3) Prepare presentation slides, in which the content is in consistent with the content in the written report.
(4) Rehearse the oral presentation to ensure the timing is not over-run. |