COIT20249 Professional skills for Information and Communication Technology

Assessment Task

Students are required to write an academic report as per the format outlined in chapter 5 of the textbook. The report must follow the CQU APA referencing style. See the American Psychological Association (APA) abridged guide updated Term 1 2019 available from: CQU APA referencing style. Please note that the prescribed textbook uses APA referencing guidelines. See also the Referencing Style subsection below.

The report is to be based on the following case study.

Background context:

Without referring to a rigorous definition of intelligence, which one of the following is more intuitively intelligent? An iPhone or a 5-year-old child? One would reasonably think that a 5-year-old is more intelligent not because they can perform complicated calculations at their age but because they have the potential to learn to perform well in a variety of settings.

The ability to learn is an integral part of intelligence. Thanks to the ever-increasing computational power, much advancement has been possible in the field of machine learning, which is fundamentally concerned about how we can build computer systems and algorithms that can automatically improve with experience.
Some machine learning algorithms mimic the way humans learn. Whenever they make a mistake, they receive a punishment; whenever they perform well, they receive a reward. Assuming machines are programmed to maximise the total reward, over time they will learn to choose actions that lead to reward rather than punishment. This particular type of machine learning is called reinforcement learning.

Some machine learning algorithms are based on a large training dataset. For example, spam filtering algorithms can be trained with a large set of emails labelled “spam” and “not spam”. Those algorithms are intelligent enough to extract the “features” in spam emails that can be used to discern spam from normal emails. This type of machine learning algorithm is commonly called statistical machine learning.

Machine learning has been adopted in an increasing number of applications – it is the backbone of many well-known applications that we use every day, for example, face recognition, natural language processing, fraud detection and personalised recommendations on Netflix, Amazon and Youtube.

Case study situation:

You are an IT consultant of a consulting company. Your company has an excellent track record for applying innovation to unlock trapped value within their clients’ organisation and helping them to3 embrace IT innovation. One of your clients, JD has recently contacted you to prepare a document on the use of machine learning in their company.

Here are the details of your client company:

JD is an Australian online retailer that sells a large range of merchandise, including consumer electronics, apparel/accessories and books to customers worldwide. With the mass adoption of e-commerce, JD saw a robust increase in sales over the past decade. However, as the big players in this industry continued their quest to capture market share, shoppers around the world only spent A$200 million on retail goods purchased on JD’s online store over the past 2018/19 fiscal year. This is 20% down from the A$250 million of the previous year.

The Chief Technology Officer (CTO) of JD believes the application of machine learning in their company equates with future business success and is keen to increase the role that machine learning plays in their customer’s experience. “Consumers expected personalised recommendations tailored to their individual tastes and preferences. Many online retailer platforms have embedded machine learning algorithms to entice customers and to make sure they keep coming back to their online retail stores”, said the CTO of JD, “machine learning can also be applied in other business functional areas, for example, automated resume screening in HR, to increase the overall business efficiency to gain a competitive edge in this industry”.

Before JD shifts their IT strategy to embrace the power of machine learning, they want your organisation to prepare a document addressing the following tasks:

(1) Explain the definition of machine learning, and the difference and relationship between artificial intelligence and machine learning;
(2) Survey the application of machine learning in three different industries other than online retailer industry/ecommerce;
(3) Investigate how machine learning can be adopted in JD. Discuss its application to at least two different business functional areas of JD; and the advantages and disadvantages of its application.
(4) Discuss the ethical, legal and social issues about the application of machine learning on online retailer platforms;
(5) Make three recommendations as to how JD can adopt machine learning in their business.
You have to complete this investigation and write a report for your team leader in the next three weeks. Since this is an initial investigation the report should not contain in-depth technical details.