ICT Introduction to Artificial Intelligence

Hours: 8 / Access Length: 12 Months / Delivery: Online, Self-Paced

Course Overview:

The ICT Introduction to Artificial Intelligence course focuses on providing students with a basic understanding of AI. It allows them to understand the subsets of AI and the different types based on functionality and technology. Students learn about the development and history of AI as well as people who were important pioneers, influencers and creators in this field of computer science. Students will understand the differences between artificial narrow intelligence, artificial general intelligence and artificial super intelligence. They will learn about reactive machines, limited memory, theory of mind and self-awareness. Students will explore the world of machine learning including AI robots and understand how they function and the purposes they serve. The course provides examples of real-world applications of AI and how it solves problems and benefits society. Considerations about ethics, privacy and security will be explored. By learning about many examples of the technology and through experiences students gain a practical understanding. Students will learn 'the implications for the future of AI and how it benefits mankind. They will be exposed to the extensive and innovative careers that are available.

Course Outline:

Lesson 1: Describing the subsets of AI

• 1.1.1 Define Artificial Intelligence and how it relates to problem solving
• 1.1.2 Describe how algorithms are used in AI
• 1.1.3 Explain what an algorithm consists of and how they are used in problem solving
• 1.1.4 Define "Big Data" and examples of it in today's world
• 1.1.5 Describe some everyday examples of AI and their purposes
• 1.1.6 Describe AIs significant impact in different areas

Lesson 2: Subsets and History of AI

• 2.1: DESCRIBING THE SUBSETS OF AI
• 2.1.2 Describe how these subsets are connected
• 2.1.3 Explain why machine learning is the most used area of AI
• 2.1.4 Explain the difference between machine learning and deep learning
• 2.2: Describe how AI has developed over time
• 2.2.1 Create a timeline of the development of AI
• 2.2.2 Identify who the word "Artificial Intelligence" was first coined by and when
• 2.2.3 Identify milestones in the development of AI
• 2.2.4 Describe some examples of how AI has been used over time (Product Examples)
• 2.2.5 What are some international laws and ethics regulations regarding the use of AI

Lesson 3: AI Types Based on Technology

• 3.1: TYPES OF AI ACCORDING TO TECHNOLOGY
• 3.1.1 Identify the three types of AI that are divided by technology
• 3.1.2 Explain why narrow AI is the only one achieved so far
• 3.1.3 Describe some examples of narrow AI
• 3.1.4 Explain what Natural Language Processing is and how it provides a personalized experience
• 3.1.5 Explain how narrow AI can be reactive or have limited memory
• 3.1.6 Describe examples of narrow AI in today's world
• 3.1.7 Define what factors make AI considered to be "Deep AI" type
• 3.1.8 Explain how Deep AI is different from Narrow AI
• 3.1.8 Define Artificial Super Intelligence

Lesson 4: AI Types Based on Functionality

• 4.1.1 IDENTIFY THE FOUR TYPES OF AI THAT ARE DIVIDED BY FUNCTIONALITY
• 4.1.2 Describe what a reactive machine can and cannot do
• 4.1.4 Explain how reactive machines work.
• 4.1.5 Describe some everyday examples reactive machines
• 4.1.6 Define what the limited memory class of machines are.
• 4.1.7 Explain how the "Theory of Mind" machines are for the future and are different from reactive and limited memory
• machines
• 4.1.8 Explain how machines with self-awareness are the final future step of AI

Lesson 5: Machine Learning in AI

• 5.1: HOW DOES MACHINE LEARNING FIT INTO AI
• 5.1.2 Define machine learning
• 5.1.2 Describe how artificial intelligence applies machine learning
• 5.1.3 Identify the four stages of machine learning training
• 5.1.4 Explain how data collection is the first step in ML
• 5.1.5 Identify examples of machine learning
• 5.1.6 Identify examples of machine learning
• 5.1.7 Explain how machine learning works
• 5.2: DESCRIBE THREE CATEGORIES OF MACHINE LEARNING
• 5.2.1 Define supervised learning
• 5.2.2 Define unsupervised learning
• 5.2.3 Define reinforcement learning
• 5.2.4 Describe how machines use data differently in each category of machine learning

Lesson 6: AI and Robotics

• 6.1: AI AND ROBOTICS TOGETHER
• 6.1.1 Explain how is AI and robots work together
• 6.1.2 Identify examples of robots that use AI
• 6.1.3 Describe how robots use AI accomplish tasks
• 6.1.4 Explain how robots help people in different areas of life
• 6.1.5 Identify different types of robots

Lesson 7: The Future of AI and Careers

• 7.1.1 Explain why the "Theory of Mind" AI will be in the future
• 7.1.2 Explain how AI will help solve problems
• 7.1.3 Define deep neural networks
• 7.2 DESCRIBE SOME CAREERS IN AI
• 7.2.1. Identify careers that use AI
• 7.2.2 Explain some soft skills that people in AI careers will need to be successful
• 7.2.3 Explain ways career fields will be impacted by AI
• 7.2.4 Describe the skills and background needed to have a career in AI
• 7.2.5 Describe Career Paths in AI
• 7.2.6 Identify some companies that hire AI Professionals

Lesson 8: Legal and Ethical considerations

• 8.1.1 Identify what ethical considerations will need to continue to be addressed in AI in the future
• 8.1.2 Explain some security issues that arise with AI
• 8.1.3 Explain what "algorithmic bias" means.
• 8.1.4 Describe how training data affects the accuracy of supervised machine learning
• 8.1.5 Identify privacy issues involved with AI
• 8.1.6 Explain how culture, beliefs and religion can create bias/conflict in AI
• 8.1.7 Define what ethical guidelines, organizations and principles that govern them

All necessary course materials are included.

System Requirements:

Internet Connectivity Requirements:

• Cable, Fiber, DSL, or LEO Satellite (i.e. Starlink) internet with speeds of at least 10mb/sec download and 5mb/sec upload are recommended for the best experience.

NOTE: While cellular hotspots may allow access to our courses, users may experience connectivity issues by trying to access our learning management system.  This is due to the potential high download and upload latency of cellular connections.   Therefore, it is not recommended that students use a cellular hotspot as their primary way of accessing their courses.

Hardware Requirements:

• CPU: 1 GHz or higher
• RAM: 4 GB or higher
• Resolution: 1280 x 720 or higher.  1920x1080 resolution is recommended for the best experience.
• Speakers / Headphones
• Microphone for Webinar or Live Online sessions.

Operating System Requirements:

• Windows 7 or higher.
• Mac OSX 10 or higher.
• Latest Chrome OS
• Latest Linux Distributions
  

NOTE: While we understand that our courses can be viewed on Android and iPhone devices, we do not recommend the use of these devices for our courses. The size of these devices do not provide a good learning environment for students taking online or live online based courses.

Web Browser Requirements:

• Latest Google Chrome is recommended for the best experience.
• Latest Mozilla FireFox
• Latest Microsoft Edge
• Latest Apple Safari

Basic Software Requirements (These are recommendations of software to use):

• Office suite software (Microsoft Office, OpenOffice, or LibreOffice)
• PDF reader program (Adobe Reader, FoxIt)
• Courses may require other software that is described in the above course outline.