Understanding the Difference Between AI, ML, and Deep Learning

March 24, 2021


With the advancement in Computer Science, new technologies are coming out every day. We are slowly moving into the era of Big Data. To handle this large amount of data from various sources, efficient manipulation of the data is needed. That is why, Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (AI) are starting to come into the picture. Nowadays, almost every firm is looking forward to new customized solutions, which include the involvement of AI, ML, and DL. In this article, we are going to discuss the influence of AI, ML, and Deep Learning in every aspect and try to differentiate them according to their features and applications.

Artificial Intelligence, Machine Learning and Deep Learning are very much crucial to demonstrate the functionalities of Data Science. They are closely related to computer brains, but they differ in their definition. Artificial Intelligence or AI is the superset of every algorithm or concept related to Data Science. While Machine Learning or ML acts as a subset of Artificial Intelligence, it primarily deals with making computers learn on their own. 

On the contrary, Deep Learning, or DL, is a subset of Machine Learning which deals with large data sets. Deep Learning algorithms try to mock the activity of the human brain. Let us now understand what each of these terms means specifically to draw a comparison among them.

Artificial Intelligence

Obsession with automation has been a long-awaited track for humans since the beginning of technology. Alan Turing asked if machines could think, back in the 1950s. Artificial Intelligence enables the machine to think on its own. It contains a vast major section of learning algorithms. So, to summarise, if you want a computer to solve problems on its own, you will need AI. Artificial Intelligence can be primarily divided into three parts: Artificial Narrow Intelligence, Artificial General Intelligence, and Artificial Super Intelligence. 

Artificial Narrow Intelligence: This is the simplest form of AI which is present in real-world applications. This idea mainly deals with training the machine to solve one particular task on its own. Machines with Artificial Narrow Intelligence or ANI are intelligent but cannot solve multiple cognitive tasks. These models work on a particular well-defined dataset and thus cannot perform emotional or sentiment-driven decision-making steps. Autopilot or self-driven cars are an example of this kind.

  • Artificial General Intelligence: Artificial General Intelligence or AGI is also known as strong AI. This concept deals with machines mocking the exact nature of human cognition. AGI machines can work in a way that is almost indistinguishable from human minds. Although this kind of Artificial Intelligence does not exist in today’s models. These machines will be able to analyse sentiments and emotions to map the exact order of human actions.
  • Artificial Super Intelligence: This is the most abstract form of Artificial Intelligence. Artificial Super Intelligence or ASI is a concept of machine intelligence that surpasses human Intelligence. These hypothetical machines will be able to solve even more significant problems and will perform tasks beyond the reach of human minds.

Machine Learning

Machine Learning or ML acts as a subset of Artificial Intelligence which operates with algorithms to recognise datasets. ML machines can improve their understanding of any concept without human intervention. It merely analyses statistical datasets and decides over probability. Most of our regular day services often use recommendation systems that predict our favourite shows and songs. Search engines optimise the results and produce the most beneficial output. These all are examples of some of the most well-designed machine learning models.

Machine Learning gets primarily differentiated into two categories: Supervised Learning and Unsupervised Learning. Supervised Learning deals with reading datasets to understand patterns in them. This learning helps the machine to recognise new unlabelled data. On the other hand, Unsupervised Learning talks about differentiating a given unlabelled dataset into possible clusters of similar properties.

Deep Learning

Deep Learning or DL is a unique case of machine learning. DL tries to mock the decision-making process of the human brain. One of the most significant examples of a deep learning model is the neural network. This algorithm replicates the neuron network of the human brain and learns different datasets through that. Neural Networks can be of any type like Convolutional Neural Network and Recurrent Neural Network. These networks help in image recognition, speech recognition, and even in understanding multiple objects. 


Using the different applicative aspects of Artificial Intelligence, Machine Learning, and Deep Learning brings many real-world problems to the verge of a solution. As these algorithms are rigorous and expensive, they require high computation powers. These tasks are suitable for cluster-associated GPUs only. With E2E cloud services, implementing Artificial Intelligence models has been much simpler. The E2E cloud server provides cloud infrastructure at a very low cost with efficiency. The AI-enabled models of the E2E cloud help in securing the customer data. Hence, it is one of the best platforms to implement Artificial Intelligence or Machine Learning models.

Throughout this article, we have gone through the concepts of these three crucial topics of data science and differentiated them from one another. We came to know the intermediate relationship between Artificial Intelligence, Machine Learning and Deep Learning. As we see, the umbrella of AI covers all of ML and DL under it. ML is the method to make machines learn, whereas DL is the process of achieving that by mimicking the human brain. In this way, we can differentiate among these closely related terms. 

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Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

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Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

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  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

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State> Next state> Action> Reward

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What is Q-Learning Algorithm?

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  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
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