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AI vs ML vs DL vs Data Science

AI vs ML vs DL vs Data Science. This image let us think deep about technology. what is the core difference of all these term.

There is so much confusion around this basic concept. Often people ask me what is the real difference between these terms Al vs ML vs DL vs Data Science.

Each term has a very significant meaning. But understanding the core difference will take a lot of effort to absorb the difference. I can simply write definitions for all these terms let’s check this out. But is this enough to understand the magic behind this black box?

People struggle to find the right article. Which can easily explain the real value and the core value proposition of this wonderful technology. Sometimes I and my friends talk about what would the real use of this technology. And how people directly get the benefit. Because at the end of the day. If technology is not solving the real issue or helping them not to grow the way they want to grow. I think it would be an injustice to all of them.

Artifical Inteligence (AI)


A bunch of scientists at IBM founded the field of artificial intelligence as an academic discipline in 1956. Yes, This is not a new technology. This is a very old concept. But if you see the history of how people arrived at this concept. and who those people are? So to answer that question. A variety of handful scientists from different fields of study thought about creating an artificial brain. It may surprise you to hear that the group of scientists who founded the field of artificial intelligence as an academic discipline in 1956 came from diverse fields such as mathematics, psychology, engineering, economics, and political science.

Sound interesting right? I always believe The origin of all discoveries in technology come from only one source, Philosophy.

About (AI)

The core analogy of this field is to mimic the human brain. We write a program in the AI field that simulates human intelligence into a machine and programs it to think and behave like humans. This field involved developing algorithms, which analyze data and perform human-like actions.

For example, understanding natural language like a human, and recognizing images to understand the things inside the image.

Find the object from this image. This image is illustrating object where AI can classify.
Find the object from this image.

If I asked you from this image, how many objects are in this and what are those objects? So you can easily identify with your bare eye. because you know these objects before. How Cats and Dogs look alike.

But by asking the same question to the machine you need to feed some intelligence into the machine so that it can identify these objects. To make a machine talk like a human and the process that involves making a machine talk comes under the artificial intelligence domain.

There are other examples like an application that can give question answers like IBM Watson. decision-making system which can take marking budget decisions. where to spend and where not to spend. and the list goes on and on. I will cover this in some other blog where I will only discuss AI.

Machine Learning (ML)


The term Machine learning became popular in 1959, and all credit goes to Arthur Samuel. According to him,

Arthur Samuel, also called AI pioneer.
Arthur Samuel

Machine learning is one way to use AI. It was defined in the 1950s by AI pioneer Arthur Samuel as

“the field of study that gives computers the ability to learn without explicitly being programmed.”

— Arthur Samuel

The 80s and 90s were the phases when machine learning came into the mainstream. And people started recognizing separate as a separate field. In the early days Machine learning focused on solving AI problems but after 1990. The focus shifted toward Statistical models, fuzzy logic, and probability theory.

The difference between AI and ML is frequently misunderstood. People had a mindset that Machine learning learns and predicts based on passive learning or you can say learning from the history of data. AI (Artificial intelligence) uses an agent to interact with the environment to learn and take action to maximize its chance of success. We know this technique as Reinforcement learning. I just introduce jargon keywords which we will see in another blog.

Now from the 2020 era, many people started asserting that Machine learning is a subfield of AI. And others still have a view that only an “intelligent subset” of ML should be considered AI.

About (ML)

Now let’s define the term ML (Machine learning). The method or the model we use to train the AI system with a training algorithm to learn from data. Without writing an explicit program for that particular work.

In other words, we can say Machine Learning is a subfield of AI. Where ML algorithms can learn from data to improve the accuracy and performance of AI systems.

How ML is defining the boundary in AI
How ML is defining the boundary in AI

ML is a subset of AI is a very loose term. If we say Machine Learning is a subfield of AI through which we can train algorithms to do AI work. In layman’s terms, ML has a method also called an algorithm to make AI systems more powerful.

Because based on this method we can put human intelligence into machines. The machine learning technique is the way to create a human intelligence system or AI system.

Deep Learning (DL)


So very quick history, This all started in 1962 when Frank Rosenblatt published a paper. “Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms” in Cornell University. Then the natural progression happens people start exploring new ideas around this. And they started working on other deep learning architectures to solve Computer Vision problems.

According to Wikipedia “The term Deep Learning was introduced to the machine learning community by Rina Dechter in 1986″. and then In 1989, Yann LeCun et al. applied the standard backpropagation algorithm.

Things started progressing new architecture started emerging. ANN (artificial neural network), which was introduced in 1943 not used till 2000. SVM (Support vector machine) was the popular choice in the 1990s and 2000s.

The advancement of hardware in 2009 has driven renewed interest in deep learning. Now Deep learning models can train in Nvidia GPUs. And now in 2023, we can see a lot of advancement in new architecture around deep learning popping up.

The neuron of the human brain
The neurons of the human brain

About (DL)

To understand Deep Learning you have to understand the neural networks. Because DL is just a multi-layer neural network. The term neural network comes from brain neurons. I will cover in detail about this in another blog. but the overall concept of a neural network was to create a network of neurons, and the neuron is nothing but a cell that receives an input signal in the form of data (for humans, through the eye they can see things. And once a human sees things can easily identify the object). and process to another neuron with information.

This is Single Layer Neural network. if we add multilayer it will be called deep neural network. DL is subfield of ML
Single Layer Neural network

But if I define this in AI terms then, DL is a subfield of Machine learning through which you can train an AI system.

Deep learning is designed to learn large data and extract information from raw data automatically. The best use of deep learning is when you are working on image data, speech data, and natural language processing.

Data Science (DS)

This topic is one of the buzzwords on the internet. all companies wanted to exploit the area. So let’s easily define this term so everyone can understand.

It is an interdisciplinary field that involves various methods, tools, and techniques to analyze and extract knowledge and insight from data.

Must Read: Exploratory Data Analysis

Data science combines various fields. for example, Statistics, Computer Science, Domain Specific Knowledge, Visualization, data engineering techniques, data quality techniques, and data profiling techniques to analyze and interpret complex data sets.

Data Science involves tasks such as data cleaning, data preprocessing, data analysis, data visualization, and data interpretation.

Basically, as a data scientist, you should have all the core knowledge of analyzing and interpreting data. And create an AI system with the help of Machine learning techniques.


Now coming back to over original discussion, why often do people get confused by these terms? So the simple answer is they do not make a connection or relation between all these terms. okay, let me help with this.

You wanted to create an AI system, so AI is our application layer. now first figure out what type of method will solve the purpose. Can we use a machine learning algorithm or do we have to use a Deep learning technique? once we finalize then we need to analyze and interpret data with Data science techniques.

In summary, AI is the broader field of developing intelligent machines, ML is a subset of AI that involves training algorithms to learn from data, DL is a subset of ML that uses ANNs to model complex patterns in data, and DS is an interdisciplinary field that involves extracting insights from data.

This the way we can define how AI, ML, DL, and Data Science are related. This image tell us the basic difference of all the term. AI vs ML vs DL vs Data science.
How AI, ML, DL, and Data Science are related

Let’s check all related areas in this image.

As we can see Deep learning is a subfield of machine learning and machine learning is a subfield of AI. And in the other hand data science is a field that needs all attention.

Data Science is an interdisciplinary field that uses all the techniques to analyze and extract information from data.


Additional Reading

OK, that’s it, we are done now. If you have any questions or suggestions, please feel free to comment. I’ll come up with more Machine Learning and Data Engineering topics soon. Please also comment and subs if you like my work any suggestions are welcome and appreciated.

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