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AI vs ML vs DL: differences and use cases


Kirill Karahainko




Apr 28, 2021

Artificial Intelligence, Machine Learning, and Deep Learning are quite popular technologies used by businesses within different industries. 

However, there are still some misconceptions and misunderstandings when it comes to the difference between AI and ML, AI vs ML interconnection, and the place of DL among these concepts. 

In this overview, we are going to explain:

— What is AI, ML and DL?

— What is the difference between these terms?

— How are these technologies interconnected?

— AI, ML and DL use cases.

Let’s get started!

AI vs ML vs DL — definitions, use cases, and differences

So, let’s find out the purpose of these technologies and the ways they relate to each other. 

What is Artificial Intelligence?

Artificial Intelligence is a collective term that refers to the tools and technologies capable of data classification, analysis, and making smart conclusions based on certain parameters. The key idea of AI is to teach machines to think like a human but solve difficult tasks in a smarter way. 

In part, the capabilities of Artificial Intelligence have already surpassed those of humans. For example, the human brain is unable to comprehensively analyze huge data sets and draw correct conclusions. But Machine Learning and Deep Learning technologies have made this possible for Artificial Intelligence.

To put it simply, AI, ML, and DL are related to each other both generally and specifically. Artificial Intelligence is a general term, while Machine Learning and Deep Learning are the next steps in its advancement.

Types of Artificial Intelligence

There are four types of Artificial Intelligence, and their progression also allows for tracking the connection between AI, ML, vs DL. 

— Reactive machines. Reactive machines are the simplest AI models. They can solve simple tasks, guided by the data set that was provided in the process of model training and in accordance with specified features. 

— Limited memory. Limited memory AI is already an example of Machine Learning. This model uses the insights it received to come up with new, more advanced conclusions within the limits of the dataset. 

— Theory of Mind. The Theory of Mind Artificial Intelligence is the smart system that is able to interact with humans. To create such systems, there is a need for deep learning. Thus, ML and DL are two advanced realizations of Artificial Intelligence, and Deep Learning is the most innovative technology to date. 

— Self-Aware AI. Self-aware Artificial Intelligence will be a new level in AI development. This is the AI type that is expected to power self-driving cars that are also predicted to make the safest decisions for the driver and passengers. 

Artificial Intelligence use cases

Despite some confusion between AI and ML definitions, the usage of these technologies is quite promising for many industries. Below are the main ones that benefit most from these innovations. 

  1. Finance and banking. In the financial sector, Artificial Intelligence, Machine Learning, and Deep Learning are used for fraud detection and prevention, dealing with money laundering and terrorism financing, data analysis and prediction, risk mitigation, and improving customer service. 
  2. Retail and e-commerce. AI and ML unlock unlimited opportunities for merchants. With their help, they may develop data-driven marketing strategies, predict trends, optimize supply chains, personalize their offerings, engage customers across different channels, and much more.  
  3. Healthcare. Artificial Intelligence, Deep Learning, and Machine Learning are also quite promising for healthcare. The tools powered by these technologies may be used for self-diagnosis, remote surgery, safe practices and education, and drug control, among others. 
  4. Agriculture and farming. In this industry, smart technologies are used for quality control, harvesting, water waste reduction, prediction, and analysis. 
  5. Manufacturing and production. This is the sphere where AI, ML, and DL unlock their ultimate potential. As a rule, the technologies are used in conjunction with the Internet of Things, and in this case, they are quite helpful in predictive and preventive maintenance, quality control, supply chain optimization, and emergency response. 

What is Machine Learning?

The key idea behind Machine Learning is, as the name suggests, to teach the algorithm to learn on its own, store that knowledge, and use it in the process of making subsequent conclusions. To make a Machine Learning algorithm learn, you need: 

— A dataset — that is, information your ML model needs to understand what it will be dealing with.

— Features — certain parameters the model needs to pay attention to. 

— Algorithm — this is how the model will use the features to make conclusions within the dataset you have given it. 

Machine Learning use cases

The simplest example of Machine Learning is a classification algorithm; for example, the one that classifies your emails, sending some letters into spam and the others to your inbox.  However, the capabilities of ML go beyond spam filtering. For instance:

— Banking anti-fraud solutions. In this case, when a user gets started with a certain action, like signing into a banking app from a new IP address, an ML algorithm may suspect an illegal attempt and suggest additional measures, such as two-factor authentication. 

— Voice assistants. Siri and Alexa are powered by machine learning as well. They have speech recognition functions and are trained to perform the commands users give. The latest Apple devices come with Siri already based on the deep learning algorithm. 

— Recommendation engines that are one of the most popular AI tools in e-commerce are also machine learning examples. 

However, Machine Learning reveals its beneficial potential even better when it takes the path of Deep Learning. So, let’s find out what DL is and how ML and DL are connected. 

What is Deep Learning?

Deep Learning is currently the most innovative step in the evolution of Machine Learning and Artificial Intelligence. The key idea of the technology is to create something similar to the human brain and teach the model to perceive, classify, and analyze information in the same way the brain does, using neural connections, or neural networks, in the case of Deep Learning.

Deep Learning technology is based on the Theory of Mind type of Artificial Intelligence. That is, it can interact with a person even more efficiently, following commands, maintaining a conversation, and constantly learning during these processes.

Deep Learning use cases

So, deep learning models can:

— recognize speech and languages;

— analyze the most subtle patterns of user behavior;

— translate text and learn from its own mistakes;

— generate texts and designs;

— recognize objects and faces, plus identify human emotions and classify them;

— analyze user sentiment and predict trends.

It is expected that the next stage in the development of deep learning algorithms will be the ability to make independent decisions in a real environment. This ability will power self-driving cars, the purpose of which is to reduce the number of accidents and forever eliminate the possibility of creating dangerous situations on the road.


The capabilities of Artificial Intelligence are amazing, and many businesses are already rushing to take advantage of the best features of this technology. Every year, AI is getting smarter, and best of all, it is becoming even more affordable for small businesses. 

Being completely customized technologies, Artificial Intelligence, Machine Learning, and Deep Learning can help you with solving almost any business problem, while we at Mentalstack can help develop AI tools of any complexity!

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