Artificial Intelligence

Course CodeBIT206
Fee CodeS2
Duration (approx)100 hours
QualificationStatement of Attainment

What is Artificial Intelligence?

  • How does it work
  • How is it getting better all the time
  • How do we use it

What this Course Can teach you

You will learn to appreciate the nature and scope of A.I. - what it is, how and where it is feasible and appropriate to apply this technology, and what it's potential is to help mankind.

There may be applications you discover through these studies, which you can utilise in your work, but you will discover through these studies that the skills required to create and implement an AI solution are usually going to require a team of highly skilled experts, bringing together a variety of high level skills. This course will give you a very good overview of the subject and in that way lay a foundation for making decisions about what AI solutions may be feasible in any given situation, and how you might proceed to plan, identify required inputs and bring together appropriate resources to take action in any given situation. 

Lesson Structure

There are 8 lessons in this course:

  1. Introduction to Artificial Intelligence
    • A Brief History of A.I.
    • Theory of Mind
    • Types of Artificial Intelligence
    • Three Branches of A.I.
    • Strong A.I.
    • Applied A.I.
    • Cognitive Simulation
    • Reasons AI Matters
    • Introduction to Natural Language Processing
  2. Ethical, Economic, and other Concerns
    • Ethics and A.I.
    • AI Ethics Principles and Guidelines
    • Ethical Perspectives
    • Human Rights
    • Privacy Breach
  3. Neural Networks
    • Structure of the Human Nervous System
    • Cells of the Nervous Tissue
    • Main anatomical features of Neurons
    • Neural Circuits
    • Intelligence and Adaptability
    • Artificial Neurons
    • Input
    • Weight
    • Bias
    • Summation Function
    • Activation Function
    • Artificial Neural Networks
    • Applying Artificial Neural Networks
    • Digital Worm Brains
    • Control system
    • Cybernetics
    • Artificial brain cells
    • Enhanced mobility
  4. Deep Learning
    • Size
    • Width
    • Depth
    • Capacity
    • Architecture
    • Universal Approximators
    • Width Case
    • Depth Case
    • Abstraction and Function
    • Training a deep neural network
    • ReCaptcha
    • Pitfalls of Deep Neural Networks
  5. Machine Learning
    • Algorithms
    • Structured Data
    • Practical Applications
    • Unstructured Data
    • Semi-Structured Data
    • Applying Algorithms
    • Three Types of Machine Learning
    • Supervised Machine Learning
    • Unsupervised Machine Learning
    • Reinforcement Machine Learning
  6. Business Applications
    • A.I. Applications
    • Marketing
    • Customer Relationship Management
    • Social Media
    • Machine Learning and Social Media Ads
    • Customer Service
  7. Application in Environmental & Primary Industries
    • Agriculture and Horticulture
    • Pest Control
    • Precision Agriculture
    • Harvesting robots
    • Robot Tractors
    • Environmental Industries and Climate Change
    • Poaching
    • Air Quality
    • Problem Based Learning (PBL) Project
  8. Industrial and Other Applications
    • Introduction
    • Transport
    • Travel and Transportation
    • Self-driving cars or automated driving systems (ADS)
    • Transport, Freight, and Logistics
    • Factories
    • Health Sector
    • Individual Health and Personalised Health Care
    • General Healthcare
    • Modelling
    • Education Sector
    • Training and education
    • Tutoring and Coaching
    • Plagiarism Detection and Authorship
    • Leisure Industry
    • Entertainment
    • Environment Monitoring and Management
    • Predicting Fire and Analysis (emergency management)


One way is to think in terms of AI can be classified, and the following is just one of several ways experts use. The following considers types of  AI based on similarities to the human mind and an ability to respond to stimuli.

Reactive AI

These are the simplest machines; they have no memory, they simply react (respond) to stimuli. Consequently, they have no ability to "learn" since they do not use previous experiences (which would be stored in its memory) to predict future actions. A well-known example of this type of AI is IBM's "Deep Blue" the computer that beat the world chess champion chess in 1997, by researching all the millions of possible future moves without "understanding" the game. 

Limited Memory AI

This type of AI is built to have time constraints. Limited memory AI are machines with limited memory, where data is stored; in other words, they can be "trained" by historical data, past experiences stored in the memory that forms a reference model to predict future situations.  Most of the applications we use in our daily lives fall into the category "limited memory AI". Here is a list with a few examples: self-driving vehicles, virtual assistants, smart maps, chatbots, face or image recognition, translators, voice recognition, etc.  This type of AI is heavily dependent on pattern recognition.

Theory of Mind AI

“Theory of mind” is how scientists refer to the varying ways in which the human mind perceives and thinks. In terms of AI, this concept involves complex systems perceiving both actions and feelings of people (including human emotions), also known as artificial emotional intelligence, then changing actions or behaviours accordingly. Facial expressions or movements of eyes or hands for instance, may be able to be detected and correlated to various emotions. It is the next level of AI in which research is being done alongside advances in other branches of AI and neuroscience data. 

Self-Aware AI

This type of AI would, in theory, learn and internalise emotions and other human traits in a way that is largely indistinguishable from human self-awareness. Whether or not this type of AI is a realistic or desirable reality is very much debateable. This category of AI may be decades or centuries away from coming into fruition and raises a series of ethical questions. The consequences of the evolution of self-aware machines can be positive or extremely negative, culminating with the end of humanity. 



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