It is increasingly common to hear the term “Artificial Intelligence” in news, books, movies or even everyday conversations. The truth is that, although it seems to come out of a science fiction movie, prediction algorithms are already part of our day to day.

Artificial Intelligence, Machine Learning, and Deep Learning are related but different concepts that are often confused or used interchangeably. In this blog, we are going to explain what each one consists of and the differences between them.

Artificial intelligence

Artificial Intelligence (AI) is a field of computer science that deals with creating systems or programs that perform tasks that normally require human intelligence. This includes abilities such as perception, reasoning, learning, and understanding natural language. For example, an artificial intelligence system can be programmed to identify objects in an image or to answer questions in natural language.

Broadly speaking, the operation of artificial intelligence consists of being able to draw conclusions from a quantity of data that has been previously delivered to it. The algorithm or model learns from that data in such a way that, when we introduce new data, it is able to say what type it is.

Artificial intelligence can use a variety of techniques to perform these tasks, including Machine Learning and Deep Learning.

Machine Learning 

Machine Learning (ML) or automatic learning is a branch of Artificial Intelligence that allows machines to learn from real data without being specifically programmed for it. Instead of following a fixed set of rules, a machine learning system can identify patterns in data and use this information to make predictions or decisions.

Types of Machine Learning

Within machine learning we find three branches: supervised learning, unsupervised learning and reinforcement learning.

Supervised learning

The model must receive labeled data. When new data is introduced, the model is able to predict which dataset label corresponds to it.


Unsupervised learning

Without any references or labels on the data, the model is the one that finds patterns and classifies the data based on their characteristics.


Reinforcement learning

The model learns to make decisions in a specific environment based on a “reward and punishment” scheme.

Streaming video platforms such as Netflix or HBO use Machine Learning to detect patterns in a user’s viewing history and thus recommend movies and series that they might like. It is also widely used in industry to detect defects in parts or help in production.

Deep Learning 

Deep Learning or deep learning is a branch within Machine Learning. While Machine Learning models are based on regression equations and decision trees among others, Deep Learning algorithms use neural networks.

Neural networks act as a set of neurons connected to each other that perform mathematical operations to extract the different parameters and characteristics of the data. Thus, they are able to obtain a result in the classification or detection of future data.

Deep learning requires large amounts of data and processing power to train and tune models, making it more expensive than other machine learning techniques.

Examples of Deep Learning projects or deep learning

Some examples of projects in which at ATRIA we have applied Deep Learning are:

Waste detection in recycling plants

In this project, a computer vision system that makes use of Deep Learning is used to detect unwanted waste at the entrance of separation plants.

Variable character reading

This project consists of the design of a code reading system since its commercial system is turned off due to a high error rate. Through Deep Learning, neural networks were trained in such a way that the software is capable of recognizing and detecting codes with variable typography.

Defect detection in pressed parts

Using Deep Learning, defects in pressed parts are detected early in the manufacturing process.

How to differentiate Artificial Intelligence, Machine Learning and Deep Learning

To sum up, Artificial Intelligence is a broad field that encompasses the development of tools that enable machines to perform tasks for which human intelligence would be needed.

Machine Learning is a subset of Artificial Intelligence. Its purpose is to draw conclusions from data provided. These can be pre-labeled so that the algorithm knows the categories to detect or not. If the data is not classified, it is the algorithm itself that classifies the photos based on their characteristics.

Lastly, Deep Learning is a branch within Machine Learning. The main difference between Deep Learning and Machine Learning is that Deep Learning makes use of neural networks to make its predictions.

The neural network is provided with unlabeled data and it is the one that locates the similar patterns in this data. Put another way, Machine Learning is like learning by doing, while Deep Learning is like having many layers of thought working together to solve complex problems. Both techniques are useful for teaching computers to learn from data and improve their performance on specific tasks.

Understanding these differences helps companies and developers choose the right technique to address their problems and take full advantage of these technologies.

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