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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
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
Types of Machine 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.
Deep Learning
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
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
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.
Understanding these differences helps companies and developers choose the right technique to address their problems and take full advantage of these technologies.
Related posts:
- Machine learning in industry
- How smart can an AI be?
- Differences between computer vision, virtual reality and augmented reality
- Deep Learning and its many applications
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