Artificial Intelligence

AI improves productivity. It benefits mankind in many positive ways, but can also be problematic. Learning what AI it is, and exploring it's applications allows you to use it well.

Course Code: BIT206
Fee Code: S2
Duration (approx) Duration (approx) 100 hours
Qualification Statement of Attainment
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Explore Potential Applications for Artificial Intelligence.

Artificial neural networks are the building blocks of AI. The course investigates and compares human neural network with 
artificial neural networks. The human brain is trained to solve problems by attempting them. Similarly, AI makes use of deep learning and machine learning, while trying to resolve an issue logically in its own programming language. 

Learn how AI is used by businesses in marketing and customer relations. For example, AI can observe a customer’s online shopping patterns and make recommendations. The company can target market their products. Customers receive specific information, and can choose the best option.

AI can be seen in industries such as horticulture and agriculture (e.g. for precision pest control), transport and logistics industry (e.g. implementing self-driven vehicles), healthcare sector (e.g. customising patient's treatment delivery). Education, leisure, environment are some other industry sectors that make use of AI. 

AI is everywhere!

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)

Each lesson culminates in an assignment which is submitted to the school, marked by the school's tutors and returned to you with any relevant suggestions, comments, and if necessary, extra reading.


  • Discuss the scope and nature of artificial intelligence.
  • Evaluate the potential and significance of artificial intelligence now and into the future.
  • Investigate and compare human and artificial neural networks.
  • Explain deep learning and its significance in artificial intelligence.
  • Explain machine learning and its significance in artificial intelligence.
  • Explain different applications for artificial intelligence in business.
  • Explain different applications for artificial intelligence in horticulture and agriculture.
  • Explain how AI can be applied in a variety of other industrial situations to process and manage operations.

What You Will Do

  • Ask 2-3 friends or family members to list 2 artificial intelligent systems they have contact with daily or weekly.
  • Conduct a research on a robot of your preference.
  • Research ethics principles for AI online. Read 1-3 articles and take notes on any listed principles and examples of ethics in use or breaches of ethics.
  • Spend 5-10 minutes writing down your thoughts on ethical frameworks, then think about how your own principles or guidelines could translate to use in AI
  • Evaluate a substantial video which explains how artificial neural networks are formed and operate.
  • Survey your home or workplace and look for devices, appliances or items which use cybernetics in their control.
  • Search online for the phrase “Deep Learning in AI” and watch at least 3 of the videos you find.
  • Make a list of DNNs you use.
  • Using online sites available such as Quick, Draw!* or Autodraw*, or others you may know of, have a try at letting AI ‘guess’ what you’re drawing.
  • Use any video streaming platform and search for "look to speak technology" videos and read any available comments or reviews and note your immediate response to this AI.
  • Talk to someone you know who is very good at mathematics and ask them to explain what algorithms are and how they might function in artificial intelligence.
  • Take notes on how marketing emails in your inbox did or did not influence you to make a purchase.
  • Click around and explore a retail website the site and look at any suggestions the site makes for other purchases and make notes.
  • Look online for examples of AI use in vehicle routing problems.
  • Select an industry such as healthcare and do further research into how AI is used to support industry.
  • Think about how AI could be integrated into your industry.


AI is changing all of the time with new technology, new ideas, new ways of using AI. It's use can be controversial, but ultimately, it is a tool and like any other tool, only good if it is used well.

Some applications may be problematic perhaps ethically or legally. Other applications could be wonderful, increasing productivity with few or no negative consequences. The real challenge is making choices about where and how to use A.I.

Making Predictions

For anyone involved in the use of AI or wanting to implement AI, it is important that they can make predictions on the usefulness of AI to their organisation. This includes –

  • Being able to identify and assess the way AI can be used by an organisation.
  • Being able to put forward a case for the use of AI.
  • Understanding AI and attendant technology, such as deep learning, neural networks, machine learning etc.
  • The social and ethical implications of the use of AI.

 It is also important to recognise that AI does not replace humans. AI can take on more mundane tasks to allow humans to deal with more human related tasks, such as patient care, creative tasks etc.

Do you need AI?

When looking at AI, it is important for any organisation or individual to consider whether AI would benefit them.  AI is a very useful tool, but it is not suitable for everyone. 

  •  It can be costly. The technology can be costly for some businesses, although it is cheaper in some areas.
  • It may not be necessary. 
  • Training staff to use AI can be costly, and if it is not effective or necessary, then you are adding additional costs to the business for no reason.

We also have to consider that in a larger business, AI may be suitable in some areas of the business but not others.

So the first step is to consider –

  • Does the organisation need AI?

If the answer is yes, then the organisation needs to consider how and why they need AI.
Before jumping into AI, it is useful to carry out an analysis and look at what aspects of the business would benefit from the use of AI.

  • Would it reduce costs/waste/time issues?
  • Why would it be beneficial to use AI as opposed to human beings?
  • Would the use of AI enhance human tasks?
  • Would it improve customer service?

Then consider – yes, we need AI. It is then important to consider the ways in which AI could be used and how it could enhance business operations.
For example, in a small business, staff may have multiple roles, such as answering queries, packing orders for transport, ordering stock, even producing stock.  What could be done to reduce their workload?

  • Answering queries might be time consuming and distracting, perhaps a text-generating service to answer initial queries might be useful?
  • A drone to deliver orders?
  • Warehouse robots to pick products for delivery.

These are just simple examples. Every organisation is different and the way AI will benefit them will vary from organisation to organisation, so thinking creatively about AI and its uses in an organisation is essential.

Career Opportunities in Artificial Intelligence 

The applications are endless. Here are just a few possibilities:

Computer Programmer - using AI to assist with writing computer code

Product Manager - Based on data interpretations managers estimate the impacts and make changes or amend systems to increase company growth. Big Data Engineer - Using the big data, they design the system for a business to interact effectively with its customers and the rest of the world. 

Business Intelligence Developer- find out trends in a business and employs the data for the progress of the company. 

Data Scientist - they gather data and based on the past and present data patterns, they make predictions. 

Robotics Scientist – design and develop a variety of robotic systems for different purposes.

Mechanic - training machinery to function better

ACS Distance Education holds an Educational Membership with the ATA.
ACS Distance Education holds an Educational Membership with the ATA.
ACS Global Partner - Affiliated with colleges in seven countries around the world.
ACS Global Partner - Affiliated with colleges in seven countries around the world.
Since 1999 ACS has been a recognised member of IARC (International Approval and Registration Centre). A non-profit quality management organisation servicing education.
Since 1999 ACS has been a recognised member of IARC (International Approval and Registration Centre). A non-profit quality management organisation servicing education.
UK Register of Learning Providers, UK PRN10000112
UK Register of Learning Providers, UK PRN10000112

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Course Contributors

The following academics were involved in the development and/or updating of this course.

Peta Jinnath Abdul

B.Sc., Grad.Dip.Ed., M.Creative Writing

Josiane Joubran

CSC consultant with IBM, Software QA Engineer, Course Writer and Tutor. Josiane is an I.T professional with extensive experience with computer hardware and engineering in Lebanon and Australia. Josiane has a B.Eng., Grad.Dip.I.T., Master Info.Tech., MCP, MCSE.

Rajy Xavier

An Electrical Engineer with 12 years of experience in Engineering and Education. After completing masters in Telecommunication Engineering, she worked as an Assistant Professor in Electrical Engineering for 5 years.

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