Katie McConchie


Digital Marketing Coordinator

Artificial Intelligence (AI) and Machine Learning (ML) are terms that are often used interchangeably. But they are not the same thing. While they are related, they have different meanings, approaches, and purposes.

What is Artificial Intelligence?

Artificial Intelligence is a field of computer science. It focuses on creating machines that can perform tasks that require human intelligence, such as learning, reasoning, problem-solving, and decision-making. AI systems aim to replicate or surpass human-level intelligence and automate complex processes.

AI can be divided into two main categories: narrow AI and general AI. Narrow AI, also called weak AI, performs specific tasks. These include recognising speech or images, playing chess, or driving a car. Narrow AI systems are built using a set of rules, algorithms, or decision trees. This allows them to perform a single function.

General AI, also called strong AI or artificial general intelligence (AGI), is designed to perform any intellectual task. It contrasts with narrow AI, which is designed to perform specific tasks. AGI systems are still largely hypothetical, but researchers are working to develop them.

What is Machine Learning?

Machine Learning is a subset of AI. It focuses on building systems that can learn from data, identify patterns and make predictions or decisions. ML algorithms use statistical techniques to learn from data and improve their performance over time.

In other words, ML is a way of building intelligent systems. This involves training them on large datasets rather than coding them with a set of rules. By training on data, ML algorithms can identify patterns and relationships. They can then use this knowledge to make decisions or predictions.

How are AI and ML Connected?

Artificial Intelligence and Machine Learning are closely related fields in computer science. They are rapidly advancing and becoming increasingly important in today’s world. Although there are distinct differences between the two, they are also closely connected. Both play a significant role in the development of intelligent systems.

In essence, ML is a key component of AI. It provides the data-driven algorithms and models that enable machines to make intelligent decisions. ML allows machines to learn from data and to adapt to new situations. This makes it a crucial component of any intelligent system.

ML also helps to address the “knowledge acquisition bottleneck” that can arise when developing AI systems. This allows machines to acquire knowledge from data and thus reducing the amount of human input required.

AI and ML differences

There are many ways in which AI and ML are connected. For example, in the field of natural language processing, AI algorithms are used to understand human language. Meanwhile, ML is employed to develop models that can accurately predict the meaning of words and phrases in context.

Similarly, in computer vision, AI algorithms can be used to detect and recognise objects. On the other hand, ML can be utilised to develop models that can recognise patterns and make predictions based on images.

Autonomous Systems

Another key area where AI and ML are closely connected is in the development of autonomous systems, such as self-driving cars or drones.

These systems rely on a combination of AI algorithms and ML models to make decisions in real time based on data from sensors and other inputs.

For example, a self-driving car might use AI algorithms to detect objects on the road. ML models can be employed to predict the behaviour of other drivers or pedestrians and to make decisions based on that data.

Overall, the connection between AI and Machine Learning is clear. ML provides the data-driven algorithms and models that enable machines to learn and make predictions. Meanwhile, AI provides the broader framework for building intelligent systems. However, as much as they share similarities, they also have their differences.

Key Differences between AI and ML

AI and ML are two distinct fields with their own unique characteristics and applications. By understanding the key differences, businesses can make informed decisions about which technology to use in their operations.

The primary distinction between the two is that AI is a more extensive field that encompasses many different approaches. In contrast, ML is a particular approach to building AI systems.

AI systems can be built using a variety of techniques, including rule-based systems, knowledge-based systems, expert systems, and natural language processing (NLP). ML, on the other hand, is typically divided into three main categories: supervised learning, unsupervised learning, and reinforcement learning.

    • Supervised learning is the most prevalent form of Machine Learning, where the algorithm is trained on a labelled dataset. It is then utilised to make predictions on new data.


    • In contrast, unsupervised learning is used to identify patterns or relationships in unlabelled data without the need for prior knowledge.


    • Reinforcement learning is a type of Machine Learning that focuses on building systems that can learn from feedback by rewarding positive behaviours and penalising negative ones.


AI and ML - a cobot and a laptop with code


One of the key differences between AI and ML is the level of human intervention required. With AI, the machine is programmed to perform a specific task, and it will continue to perform that task until it is reprogrammed. With ML, the machine is trained to recognise patterns and make predictions based on data, but it does not necessarily need to be reprogrammed to make new predictions.


AI can be either rule-based or data-driven, while ML is solely data-driven. Rule-based AI systems are built using a set of rules or decision trees that allow them to perform specific tasks. In contrast, data-driven AI systems are built using machine learning algorithms that learn from data and improve their performance over time.

AI algorithms typically require a relatively small amount of data to perform their tasks, whereas ML algorithms require much larger datasets to achieve the same level of accuracy. The reason for this is that ML algorithms rely on statistical models and algorithms to learn from the data, which requires a lot of data to train the machine.


Another key difference between AI and ML is the level of sophistication required to implement the technology. AI algorithms tend to be more complex and require a higher level of expertise to implement and maintain. Alternatively, ML algorithms can be implemented using standard programming languages and are relatively easy to deploy and maintain.


Concerning applications, AI is frequently employed in fields such as robotics, natural language processing, and computer vision. This is because machines in these areas need to perform complex tasks that require a high level of intelligence. In comparison, ML is used in a wide range of applications, from fraud detection and predictive maintenance to image and speech recognition.

Benefits of AI and ML

One of the key advantages of Artificial Intelligence is its ability to process and analyse large volumes of data in real time. With the rise of big data, traditional methods of data analysis are often inadequate to handle the sheer volume of information generated.

AI algorithms can analyse data from multiple sources, including social media, sensors, and other IoT devices, to identify patterns, trends, and anomalies that are difficult or impossible to detect using traditional methods.

Automate Boring and Repetitive Tasks

AI and ML can also automate many tasks currently performed by humans, freeing up human resources for more complex tasks and increasing efficiency while reducing costs. For example, AI-powered chatbots or voice assistants can automate customer service interactions, allowing businesses to provide 24/7 support without human operators.

Similarly, in manufacturing, AI and ML can control robots and other automated systems, enabling factories to improve decision-making by providing insights and predictions based on data, therefore, operating more efficiently and with greater precision.

AI and ML in biosecurity

Improve Decision Making

AI can also improve decision-making in various contexts. For instance, in finance, AI algorithms can analyse market data and make predictions about future trends, helping investors make informed decisions. ML assists AI with this through its ability to identify patterns and trends in large and complex datasets.

In healthcare, AI and ML can analyse medical data and assist doctors in diagnosing or developing treatment plans. AI can also help businesses make informed decisions by analysing customer data and providing insights into customer behaviour and preferences.

Recognise Patterns

Another benefit of AI is its ability to learn and adapt to new situations. ML algorithms can train machines to recognise patterns and make predictions based on data, enabling them to learn from experience and adapt to changing circumstances. This is particularly useful in applications such as self-driving cars, where the machine must make real-time decisions based on changing road conditions and other factors.

Enhance Safety and Security

Finally, AI and ML have the potential to enhance safety and security in various contexts. For example, self-driving cars equipped with AI algorithms can reduce the number of accidents caused by human error in transportation. Similarly, AI algorithms can detect and prevent cyberattacks, identify potential security threats, and provide real-time alerts in the event of a security breach.

Examples of Machine Learning

ML is a powerful tool that can be used to build intelligent systems that can learn from data and make decisions or predictions. At COREMATIC, we specialise in building Machine Learning systems that can help businesses improve efficiency, accuracy, and decision-making. Here are some examples of our ML for Computer Vision in action:

Automotive Industry

COREMATIC has created various computer vision solutions to inspect vehicle damages in the automotive industry.

In one of our projects, we utilise multi-camera systems to scan vehicles and produce reports on previous damages. AI-based solutions can use these reports to actively identify, count and monitor dents and defects in real time.

COREMATIC has gone beyond the boundaries of these technologies by developing advanced models that can detect hundreds of dents in real-time. This technology is particularly useful for vehicles that have been damaged by hail. Our technology assesses and categorises the severity of each dent separately. It then provides data that can accurately estimate the cost of repair in an automated manner.

Recycling and Reuse Industry

In a first for Australia, COREMATIC designed and built the first Reverse Vending Machine (RVM) manufactured in Australia. Completely custom-built utilising ML to provide an AI solution to identify bottles, cans, and cartons, the beverage container detection system is going to revolutionise the way Australians recycle.


We developed a yield monitor systemthat utilises Artificial Intelligence and advanced data collection to register GPS tags every few meters. This system is designed to determine the quantity and quality grade of potatoes immediately after harvest.

The data that is collected provides valuable insights for farmers, enabling them to improve efficiency and increase yield performance. This simplifies and enhances farm management decisions, ultimately leading to maximised harvest results.


COREMATIC has successfully incorporated computer vision technologies with advanced mobile robots to perform biosecurity risk analysis applications.

This technology involves combining multiple cameras to inspect and detect biosecurity risk materials (BRM), which enhances safety and efficiency while enabling informed decision-making by operators.

Additionally, computer vision analysis has been demonstrated as a practical solution for automated inspections and monitoring of critical assets, collecting environmental data, and improving safety.

In conclusion, the fields of Artificial Intelligence and Machine Learning are rapidly advancing and becoming increasingly important in today’s world.

While AI aims to replicate or surpass human-level intelligence and automate complex processes, Machine Learning focuses on building systems that can learn from data, identify patterns, and make predictions or decisions without being explicitly programmed to do so.

Although they have distinct differences, AI and ML are closely connected, and both play a significant role in the development of intelligent systems.

By understanding the key differences between AI and ML, businesses can make informed decisions about which technology to use in their operations. With AI and ML rapidly evolving, the possibilities for their application in various industries are vast, and we can expect to see more innovation in the future.