What is the difference between artificial intelligence, machine learning and deep learning ?

Deep learning is often confused with machine learning. It should be noted that these are systems that have been developed with artificial intelligence. However, during their design phase, the abstraction layers are not the same. AI as well as machine learning and deep learning also need a lot of data to evolve the learning process. In this article, we will look in detail at the differences between these three technologies, but also at their commonalities.

Artificial intelligence (AI), deep learning (DL) and machine learning (ML) by definition

What is artificial intelligence ?

AI has been developed to take the example of the human brain. Its objective is to perform various tasks that may or may not be accomplished by human intelligence. AI concerns the theoretical development, but also the concrete development of solutions, systems and applications that have the capacity to copy the intellectual faculties of humans.  Among the skills that artificial intelligence can learn are speech, vision, but also language translations and many others.

Machine learning, like deep learning, is a system that is part of artificial intelligence.

What is machine learning?

Machine learning is a system included in artificial intelligence. Developers use ANN technology or artificial neural networks to copy human intelligence when it makes a decision.

ML helps computers to create autonomous learning systems based on the recording, analysis and processing of a large volume of data (datasets). Thus, they no longer need to go through programming in order to learn.

The use of machine learning is focused on identifying the skills contained in the datasets. It also allows statistical modelling to be carried out.

Definition of deep learning

Deep learning (DL) is an approach included in ML. This system uses deep neural networks. They allow the analysis of structures by considering a very large amount of data. These neural networks include algorithms that have been developed based on the specificities of the human brain. Thus, each neural network focuses on a particular layer in order to learn in a targeted manner. The analysis, processing and learning on all these layers allow a specific operation to be carried out.

AI, ML and DL: what are the differences ?

Although machine learning and deep learning are part of artificial intelligence, they do not have the same technological approaches. In order to understand them, it is also necessary to know how to differentiate them.

To make it easier to understand these differences, let’s take the example of an image with a dog and the process of the computer learning to identify the animal.

For artificial intelligence, the intervention of a computer developer is necessary. He or she will be responsible for writing the computer codes that will enable the artificial intelligence to identify the animal in the image. In other words, it is up to the developer to design the learning model that will develop the AI’s knowledge.

Machine learning requires computer developers to train the system to know the characteristics of the dog in the image. To do this, the developers will show images of cats to the system. The system records and analyses them. Depending on the results of the system’s analysis, the developers will make appropriate corrections. This allows for more accurate results as the images are analyzed. With ML, we need to bring in human skills.

As for deep learning, it will classify the identification operations of the animal and its characteristics on the image, by considering different layers. Thus, each layer will analyze a particular characteristic, such as hair, eyes, morphology, etc. The data collected for each layer will then be assembled to identify the dog in the image. The system will also be able to recognize a dog even if it is shown another image.

Some differences to be aware of between ML and DL

When a developer trains machine learning, the time spent on learning is relatively short. However, the quality and accuracy of the result will not be high. With deep learning, the learning of the solution will take longer. The computer needs to store and process a large volume of data. The learning process will also have to take into account various parameters and calculations. The results obtained will thus be more accurate as the analyses are more in-depth and consider various layers.

It should also be noted that ML uses mainly the central processing unit (CPU) for its learning. Applications using machine learning integrate predictive programs or to treat problems in a personalized way.

Deep learning, on the other hand, needs a graphics processing unit or GPU. This is what allows it to store and analyse a large volume of data. It is mainly used to solve more complex problems such as facial recognition, obstacle recognition, etc.

Artificial intelligence, deep learning and machine learning used in the cloud

Over time, cloud technology has developed greatly. This development has also made it easier to access AI solutions, including its sub-domains such as machine learning and deep learning.

Currently, there are many cloud-based AI solution providers on the market. The best known are Microsoft Azure and Amazon Machine Learning. These solutions allow users to access shared spaces and different resources. In contrast to accessing AI solutions in the traditional way, in the cloud, the operation is easier, as is the use of these solutions. Shared solutions in the cloud are also less expensive and have the ability to scale over time.

In addition, the cloud allows for integration and access to solutions such as SaaS, PaaS and others. These allow professionals and businesses to access the benefits of big data storage. They can also benefit from the power of analysis in the cloud.

AI-related services that can be accessed through the cloud include

– The application programming interface or API, associated with AI,

– ML algorithms and codes,

– Natural language processing or NLP,

– Deep learning,

– etc.

To facilitate access to AI solutions in the cloud, the calculation operations are carried out by data canters. The user no longer needs to learn or master data science.