Artificial Intelligence: The Future of Technology – EZE Australia

Jun 7, 2023

There are several misconceptions surrounding Artificial Intelligence. These misconceptions can both promote and also tarnish the face of AI. In this article, we will discuss the must-know facts about AI, its advantages and disadvantages as well as its reliability and efficiency.

Artificial Intelligence (AI) is one of the most rapidly developing technologies in the world today. It has the potential to revolutionise many aspects of our lives, from the way we work to the way we interact with the world around us.

At EZE Australia, we’re committed to exploring the potential of AI and developing innovative solutions that can benefit our clients. We believe that AI has the potential to make a positive impact on the world, and we’re excited to be a part of its development.

AI is revolutionising the fields of healthcare, finance, transportation, manufacturing, customer service and many more. As AI continues to develop, it is likely that we will see even more innovative and groundbreaking applications for this technology.

What is AI?

Artificial Intelligence has multiple definitions. According to one definition, it refers to the development of computer systems and machines that can perform tasks that would typically require human intelligence. However, this definition alludes to AI being intelligence that’s demonstrated by machines. Such a loose definition may lead to confusion, misunderstandings, abuses, and hence – unproductivity.

A better definition would be: AI enables machines to simulate human intelligence, such as learning, reasoning, problem-solving, perception, and language understanding. It involves creating algorithms and models that allow computers to analyse large amounts of data, recognise patterns, make decisions, and adapt to changing circumstances. 

AI can be categorised into two types: Narrow AI, which is designed to perform specific tasks, and General AI, which would possess human-like intelligence and the ability to perform any intellectual task that a human being can do. 

Now this is a definition that clearly sets the characteristics and the limitations of an AI, creating the right amount of expectation from users in the market.

Subfields of Artificial Intelligence

Artificial Intelligence encompasses various subfields that focus on different aspects of intelligent systems. Some of the prominent subfields of AI include:

Machine Learning

It involves developing algorithms and techniques that enable machines to learn from data and improve their performance over time without explicit programming. This subfield includes areas such as supervised learning, unsupervised learning, reinforcement learning, and deep learning.

Natural Language Processing (NLP) 

NLP deals with enabling computers to understand, interpret, and generate human language. It involves tasks such as speech recognition, language translation, sentiment analysis, text generation, and question-answering systems.

Computer Vision

This subfield focuses on giving machines the ability to interpret and understand visual information. It includes tasks like object recognition, image classification, object tracking, image generation, and scene understanding.


Robotics combines AI and engineering to design and build machines that can interact with the physical world. It involves creating intelligent robots capable of sensing, perceiving, and manipulating objects in their environment.

Expert Systems

Expert systems are designed to mimic the decision-making capabilities of human experts in specific domains. They use knowledge bases and rule-based reasoning to provide expert-level advice and solutions.

Knowledge Representation and Reasoning

This subfield deals with representing knowledge in a structured and logical manner, allowing AI systems to reason and draw conclusions from the available information. It includes areas such as ontologies, semantic networks, and logical reasoning.

Neural Networks

Neural networks are computational models inspired by the structure and functioning of the human brain. They are used in many AI applications, particularly in deep learning, to recognise patterns, process complex data, and make predictions.

Planning and Scheduling

This subfield focuses on developing algorithms that enable AI systems to plan and schedule actions to achieve specific goals in dynamic environments. It includes task planning, resource allocation, and automated scheduling.

These are just a few examples of the diverse subfields within Artificial Intelligence. AI is a rapidly evolving field, and new subfields and research areas continue to emerge as technology advances.

How do AIs learn?

Artificial Intelligence consumes knowledge and information which allows it to be called “intelligent”. Just like humans, AIs have various learning strategies and systems. 

First, we have Supervised Learning. In this type of learning, neural networks learn from a set of examples mostly from training data where both inputs or the predictors and outputs or the target variables are known to the analyst. Most of the time, the data in supervised learning applications needs to be manually labelled first which also poses the hassle of being a laborious endeavour. 

Next on the list is Unsupervised Learning which in comparison to Supervised Learning, can help find patterns in data without pre-existing labels. Unsupervised learning applications are considered to be an essential component of many successful AI applications.

The last learning paradigm is called Reinforcement Learning where an agent learns to take action in an environment to maximise rewards and minimise penalties over time. Reinforcement Learning sparks exciting challenges such as the need to balance exploration and exploitation or the challenge posed by delayed rewards. 

This type of learning also has the ability to deploy deep artificial neural networks, absorb large amounts of information, and also learn to discover complex relationships between any environment-action pair and future rewards. Reinforcement learning also fares well in the marketing world. For instance, multi-arm bandit problems where the objective is to “earn while learning,” which effectively balances exploitation and exploration.

How Can Artificial Intelligence Help You in Marketing Your Website?

Artificial Intelligence (AI) has become increasingly important in the field of branding. It offers various opportunities and applications that can enhance the effectiveness and efficiency of branding strategies.

Personalised Marketing: AI algorithms can analyse vast amounts of data to identify patterns and preferences of individual consumers. This allows brands to create highly targeted and personalised marketing campaigns, tailoring their messaging and offers to specific customer segments.

Customer Experience and Engagement: AI-powered chatbots and virtual assistants can provide real-time customer support and engagement. They can answer customer queries, recommend products or services, and even simulate human-like conversations. This enhances the overall customer experience and helps build stronger connections with the brand.

Content Creation and Curation: AI algorithms can generate content based on specific guidelines and parameters. This includes writing blog posts, social media updates, and even creating videos. AI can also curate content by analysing user preferences and recommending relevant content to the target audience.

Social Media Management: AI tools can monitor social media platforms, online forums, and other channels to track brand mentions and sentiment. This helps brands understand how they are perceived and allows them to address any negative feedback or issues promptly.

Website Design and Graphic Design: AI can analyse visual content, such as logos, images, and videos, to identify patterns and generate insights. This helps brands understand how their visual identity is perceived by consumers and can inform design decisions.

Market Research and Data Analysis: AI algorithms can analyse large volumes of data to identify trends, consumer behaviour patterns, and market insights. This helps brands make data-driven decisions and develop effective branding strategies.

Predictive Analytics and Forecasting: AI can analyse historical data and use machine learning algorithms to predict future trends, consumer demand, and market fluctuations. This helps brands anticipate changes and proactively adapt their branding strategies.

It’s important to note that while AI offers significant benefits to branding, it should be used in conjunction with human creativity and strategic thinking. AI is a tool that can augment human efforts in developing and executing branding strategies effectively.

What Could Possibly Go Wrong With AI?

Just like any machine known to man, AI also has pitfalls and disadvantages that balance out 

its positive features and advantages. Here are some of them:

  • Lack of Common Sense: AI may have the ability to recognise emotions, however, it cannot understand what these emotions mean and what they imply in different contexts. In line with this is its lack of common sense. Not being able to understand human emotions and empathise, disregards any unsaid rules that humans have. It doesn’t grasp the concept of implicit agreements and general consensus which then affects how they interact with humans and the quality of their service.
  • Objective Functions: AIs have difficulties understanding objective functions in environments where some human objectives are implicitly understood. Despite being able to tackle extremely complex and dimensional problems where the analyst does not fully understand casualties, which is both their strength and major weakness. AI is most likely to generate unexpected, delayed, and hard-to-quantify consequences that do not align with the designer’s objective function.
  • Biassed Artificial Intelligence: AI-generated results or data may be subject to bias. One particular example is racial bias which happens due to the information it learned being highly dependent and influenced by its origin. For instance, an AI application that aims to present criminal offences of a person may show bias depending on the race of the offender.
  • Controllable Artificial Intelligence: AI developers and business users must always prioritise and remember that AIs should be controllable to avoid algorithmic mistakes that may affect their effectiveness and, worst – harm human lives because of their lack of understanding of the concept of emotions or even danger.
  • Paradox of Automation: Businesses should avoid automating mundane tasks assigned initially to humans as this may affect their future abilities to perform more complex tasks if they do not hone their skills and accumulate experience on routine tasks first. 

ChatGPT – Revolutionising the Business World

After exploring what Artificial Intelligence is, its subfields, and disadvantages, let us delve into one of the most popular AI models today – ChatGPT. Chat Generative Pre-trained Transformer or more commonly known as ChatGPT, is a state-of-the-art AI language model developed by OpenAI and has various promising qualities that can boost businesses.

Cuantum Technologies reveals that ChatGPT has a vast knowledge base which enables it to generate more sophisticated and coherent responses compared to earlier models. This results in ChatGPT providing more personalised and accurate answers, making it a valuable tool for a wide range of applications, from customer service to language translation and more. 

ChatGPT is also adaptable. It can be fine-tuned for specific tasks or industries, allowing businesses to customise the AI model to suit their unique needs and requirements better. ChatGPT also has an improved contextual understanding. It takes into account the full context of the conversation allowing it to respond more accurately to user inputs resulting in more natural and fluid conversations between users and the AI.

ChatGPT Strengths

ChatGPT has unparalleled adaptability. Its fine-tuning process makes it highly adaptable to specific tasks, industries or applications. This adaptability allows businesses to customise the model to their specific needs, which in turn allows ChatGPT to provide better results and improve overall performance.

ChatGPT has the ability to understand complex linguistic patterns allowing the model to generate responses that are not only coherent but also contextually accurate. This results in it being able to outperform many other language models when it comes to conversational applications.

ChatGPT has the ability to handle large volumes of concurrent interactions making it an excellent solution for businesses looking to scale their operations. This allows businesses to handle customer support requests, generate more content, and increase sales without sacrificing quality or speed.

ChatGPT is also able to understand and generate text in multiple languages allowing businesses to cater to a diverse customer base and expand their global reach.

ChatGPT Limitations

ChatGPT lacks common sense reasoning, meaning that while the model can technically provide accurate information, it may not always be logically sound. ChatGPT may have outdated and incomplete knowledge bases resulting in poor service or customer support.

ChatGPT is sensitive to input phrasing which means that the users may receive inconsistent responses from ChatGPT depending on the way their queries are phrased. ChatGPT also poses the danger of being biassed or giving out harmful output depending on the data that they are trained on. If the data itself contains harmful content, the model may inadvertently generate similar content.

Lastly, ChatGPT has limited common sense reasoning which can result in poor outputs and customer dissatisfaction.


Artificial intelligence (AI) is a rapidly developing technology that has the potential to revolutionise many aspects of our lives. In the business world, AI is already being used to automate tasks, improve decision-making, and develop new products and services. As AI continues to develop, it’s likely to have an even greater impact on businesses and society as a whole.

At EZE Australia, we are excited about the potential of AI to improve our lives and make the world a better place. We are committed to developing and using AI in a responsible and ethical way, and we believe that this technology has the potential to make a positive impact on the world.

Despite the challenges, the potential benefits of AI for businesses are significant. Businesses that are able to successfully adopt AI are likely to gain a competitive advantage in the years to come.

In addition to the benefits and challenges mentioned above, it’s important to consider the ethical implications of AI. AI has the potential to be used for both good and bad purposes. It’s important to ensure that AI is used in a way that benefits society as a whole and that it does not lead to discrimination or other forms of harm.

As AI continues to develop, it’s important to have a conversation about its potential benefits and risks. By working together, we can ensure that AI is used for good and that it helps to create a better future for everyone.

Reference List


Agrawal, Ajay, Joshua Gans, and Avi Goldfarb (2019), Prediction Machines: the simple
economics of artificial intelligence, Harvard Business Review Press.

Ansari, A., & Riasi, A. (2016). Modeling and evaluating customer loyalty using neural networks:
Evidence from startup insurance companies. Future Business Journal, 2(1), 15-30.

Brooks, Rodney Allen (1991), “Intelligence without Reason,” in Proceedings of the 12th
International Joint Conference on Artificial Intelligence, ed. M. Ray and J. Reiter
(Sydney, Australia: Morgan Kaufmann, 1991), pp. 569-595.

Chapmann, Joshua (2017), Neural Networks: Introduction to Artificial Neurons,
Backpropagation Algorithms and Multilayer Feedforward Networks, Advanced Data
Analytics, Volume 2.

Quantum Technologies (2020) “ChatGPT and Business, Changing the Game, Revolutionizing Customer Success”.

De Bruyn A., Viswanathan V., Beh Y.S., Brock J.K., Wangenheim F. (2020) Artificial intelligence and marketing pitfalls and opportunities. Journal of Interactive Marketing ,51 pp. 1-37.

Di Persio, L., & Honchar, O. (2016). Artificial neural networks architectures for stock price
prediction: Comparisons and applications. International journal of circuits, systems and
signal processing, 10, 403-413.

Fine, Terrence (1999), Feedforward Neural Network Methodology, Springer, Information
Science and Statistics series.

Ismail, Mohammad, Mohd Awang, Mohd Rahman, and Mokhairi Makhtar (2015), “A Multi- Layer Perceptron Approach for Customer Churn Prediction,” International Journal of Multimedia and Ubiquitous Engineering, 10(7), pp.213-222.

Khan, Salmna, Hossein Rahmani, and Syed Afaq Ali Shah (2018), “A Guide to Convolutional Neural Networks for Computer Vision,” Morgan & Claypool Publishers.

Mandic, Danilo, and Jonathon Chambers (2020), Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability, Wiley.

Murray, G., & Wardley, M. (2014). The math of modern marketing: How predictive analytics
makes marketing more effective. IDC White Paper. Retrieved from us/downloadasset.2014-06-jun-12-15.the-math-of-modern-marketing-how-predictive-analyticsmakes-marketing-more-effective-

Power, D. J. (2016). “‘Big Brother’ Can Watch Us,” Journal of Decision Systems, 25(1), 578-

Sutton, Richard S., and Andrew G. Barto (2018), Reinforcement Learning: An Introduction, Second Edition, MIT Press, Cambridge, MA.

Zhao, Z., Xu, S., Kang, B. H., Kabir, M. M. J., Liu, Y., & Wasinger, R. (2015). Investigation and improvement of multi-layer perceptron neural networks for credit scoring. Expert Systems with Applications, 42(7), 3508-3516.



    Getting Discovered Locally (Local SEO)Compelling Website DesignVirtual Admin Assistants

    Please note that by submitting this form you are to subscribing to our database list.