February 3, 2023

The difference between Machine Learning and Artificial Intelligence

For people who do not deal with software technology on a daily basis, the differences between artificial intelligence and machine learning may not be immediately apparent, as both terms seem to describe the same thing - apart from some technical details.

Both terms are closely related. However, they are not the same thing. There are still important points to understand, especially for those who want to implement these technologies in their company.

Today we will look at the main differences between AI and ML.

Definitions and difference:

Artificial intelligence (AI) is a broad field that deals with the simulation of human intelligence in programmable machines that think and learn like humans. Machine learning (ML) is a sub-field of AI and refers to the use of specific algorithms and statistical models that enable machines to improve their performance by learning to deal with data. For comparison, see the figure below.

In other words, AI is the overall concept that machines can perform tasks that normally require human intelligence, while machine learning is a specific way to achieve AI.

The importance of data or knowledge

Classical AI systems, such as rule-based systems, do not need large amounts of data to be created because they rely on predefined rules to perform tasks. These rules are typically created by domain experts who have a deep understanding of the problem domain and can explicitly define the conditions and actions required to perform a task.

An example of this is a rule-based system for recognising objects in an image, programmed to recognise an object as a car if it has four wheels and a certain shape or you have a temperature sensor that alerts the system when it reaches a certain threshold. These rules can be defined by an expert on knowledge or formulas and do not require large amounts of data.

On the other hand, ML systems require large amounts of data to be created, as they learn from data to improve their performance to find the best solution. These systems use algorithms that can analyse data and find patterns that can be used to predict or decide.

An example of this is an ML system for recognising objects in an image that has been trained on a large data set of images and learns to recognise objects based on their so-called features such as colour, shape and texture. This system needs a lot of data to learn from different examples and generalise to new situations. This means that ML systems can find solutions in complex problem spaces that humans have difficulty understanding.

The more data an ML system has, the more accurate it can be. With enough data, the system can learn to recognise the subtleties of the task and make predictions with high accuracy, even in uncertain situations. With too little data, the system may not learn well or generalise, resulting in poor performance.

In summary, classical AI systems do not need a large amount of data as they are based on predefined rules, whereas ML systems need a lot of data as they learn from data to improve their performance. ML systems are suitable in domains where the rules are hard to define, complex or there are many different features.


Abstrakte Darstellung eines Entscheidungsbaums

Rule-based AI:

Expert systems: Expert systems are rule-based AI systems that use a knowledge base to make decisions and perform tasks. The knowledge base is created by experts in the field and contains a set of if-then rules that define the conditions and actions required to perform a task. Expert systems can be used in a variety of applications, such as medical diagnostics, financial forecasting and scientific research.

Decision tree: Decision trees are a type of rule-based system that uses a tree-like structure to represent a series of decisions. Each node in the tree represents a decision and the branches represent the possible outcomes. Decision trees can be used for tasks such as classification and prediction.

These are some common techniques in rule-based AI systems. All use predefined rules to make decisions and perform tasks, and they can be used in a variety of applications depending on the domain in which they are applied.

Machine Learning Methods:

  • Clustering
  • Gradient Boosting
  • Artificial neural network
  • Reinforcement learning
  • ...

Within the methods, there are different approaches to learning from data:

  • Supervised learning: Supervised learning is a type of ML where the system is trained on a labelled data set where the correct result for each input is already known. The system then uses this training data to make predictions on new, unknown data. Common algorithms used in supervised learning are linear regression, logistic regression and support vector machines (SVMs).
  • Unsupervised learning: Unsupervised learning is a type of ML where the system does not receive labelled data. The system must find patterns and relationships in the input data itself. Common algorithms used in unsupervised learning are k-means clustering and principal component analysis (PCA).
  • Semi-supervised Learning Semi-supervised learning is a type of ML where the system is given a small amount of labelled data and a large amount of unknown data. The system must use the labelled data to find patterns in the unknown data. Common algorithms used in semi-supervised learning are self-training and co-training.
  • Reinforcement Learning: Reinforcement learning is a type of ML in which the system learns through interaction with an environment and feedback in the form of rewards or punishments. Common algorithms used in reinforcement learning are Q-learning and SARSA.
  • Deep Learning: Deep Learning is a type of ML based on multi-layer neural networks. These networks are capable of learning complex patterns in data and have been used to produce outstanding results in tasks such as image recognition and Natural Language Processing (NLP). Common algorithms used in Deep Learning are Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).

There are several reasons why there have been major advances in machine learning (ML) systems in recent years:

  1. Availability of data: With the explosion of digital data, there is now an enormous amount of data available that can be used to train ML models. This data can be used to train models that can learn a variety of tasks with high accuracy. Rule-based systems, on the other hand, are based on predefined rules that are usually created by experts, which is a time-consuming and labour-intensive process.
  2. Scalability: ML systems can be easily scaled to handle large data sets and complex tasks. They can be trained on large data sets and then used to perform tasks on new data. These systems are designed to apply knowledge from one domain to a similar domain by re-training them with smaller data sets. Rule-based systems, on the other hand, can be limited by the number of rules they define and the complexity of the tasks they can perform.
  3. Adaptability: ML systems can adapt to new situations and perform a variety of tasks. They can learn from data and improve their performance over time. However, rule-based systems are usually developed for specific tasks and cannot adapt to new situations. Or they can be re-trained on new data without changing the model. Rule-based systems need to be redefined from the ground up.


We hope that we have been able to answer a few initial basic questions about artificial intelligence and machine learning with this article.

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