6 Major Sub-Fields of Artificial Intelligence

Rancho Labs
7 min readJul 14, 2021


Artificial intelligence (AI) is the field of computer science that focuses on developing intelligent computer systems, or systems that display the features we associate with intelligence in human behaviour, such as language comprehension, learning, reasoning, problem solving, etc.

It’s all about giving machines the ability to mimic human behaviour, particularly cognitive capabilities. Artificial intelligence, machine learning, and data science, are however all interconnected.

We shall learn about Artificial Intelligence and its six primary sub-fields in this blog.

Branches of AI Based on Capabilities

Linguistics, prejudice, vision, planning, robotic process automation, natural language processing, decision science, etc. fall under the artificial intelligence domain. Let us learn more about some of the most important AI sub-fields in depth.

1. Machine Learning

Machine Learning is one of the most dynamic fields in advanced technology, and it creates a lot of hype every time a new product is released by an organization that uses ML techniques and algorithms to deliver products to the customer in an innovative manner.

Machine Learning is a sub-field of artificial intelligence that is based on the concept that systems/machines can learn from data, recognise patterns, and make decisions with little or no human interference. We give machines access to information and allow them to learn for themselves. It’s simply getting a computer to perform a task without specifically programming it to do so.

To create a comprehensive ML system, programmers use advanced mathematical skills to build machine learning algorithms that are coded in a machine language. In this manner, machine learning helps us to categorise, interpret, and estimate data from a data set.

Furthermore, data professionals pick different forms of machine learning algorithms for what they want to predict from data based on the sorts of data available. The algorithm used to train the machine can be Supervised, Unsupervised or Reinforcement Learning.

ML has given us self-driving cars, image and speech recognition, useful online search, and a variety of other uses in the last several years. It essentially corresponds around applications that learn from their mistakes and improve their decision-making ability or prediction accuracy over time.

2. Neural Network

The neural network is a field of artificial intelligence that makes use of neurology ( a part of biology that concerns the nerve and nervous system of the human brain). It incorporates cognitive science into machines to execute tasks. The neural network mimics the human brain, which has an infinite number of neurons, and the neural network’s purpose is to code brain-neurons into a system or computer.

In basic terms, a neural network is a set of algorithms that are used to identify elemental correlations among large amounts of data. In a neural network,a neuron is a mathematical function (such as activation functions) whose job it is to gather and categorise data according to a certain structure. To perform tasks, the network heavily relies on statistical techniques such as regression analysis.

Neural Networks are widely used for fraud dete ction, risk analysis, stock-exchange prediction, sales prediction, and many other purposes.

3. Natural Language Processing

NLP is a branch of computer science and artificial intelligence that allows computers and humans to communicate using natural language. It’s a method of computational analysis of human languages. By mimicking human natural language, it allows a machine to comprehend and interpret data.

NLP is a method for searching, analysing, comprehending, and extracting information from textual input. NLP libraries are used by programmers to instruct computers how to extract meaningful information from text input. Computer algorithms can verify whether an email is trash or not by looking at the subject of a line, or the content of an email, which is a typical example of NLP.

Utilizing NLP has a number of advantages, including:

  • It enhances document accuracy and efficiency.
  • It can generate readable summary text automatically.
  • It is particularly beneficial for personal assistants like Alexa
  • It allows organisations to use chatbots for customer assistance.
  • It helps in making sentiment analysis easier.

Text translation, sentiment analysis, and speech recognition are examples of NLP applications. Twitter, for example, uses NLP to filter forbidden/terroristic language from various tweets, while Amazon utilises NLP to interpret customer feedback and improve their experience.

4. Deep Learning

It is a process of learning in which the machine processes and analyses the input data using a number of ways until it identifies a single acceptable output. It’s also referred to as self-learning of machines. To map the raw sequence of input data to output, the machine uses a variety of random programmes and algorithms.

Deep learning would observe all possible human traits and behavioural databases, and it will undergo supervised learning. This procedure includes:

  • Detection of various human emotions and expressions.
  • Identify humans and animals based on pictures, such as specific signs, markings, or traits.
  • Recognize and memorise the voice of various speakers.
  • Video and audio data conversion into text data.
  • Identification of right and wrong gestures, classification of spam, and fraud activities (like fraud claims).

After gathering and analyzing large datasets, clustering of related datasets is accomplished by comparing similar audio sets, pictures, or documents using the available model sets.

By performing repetitive takes and self-analyzing, machines would achieve solutions to the problems.

5. Cognitive Computing

The objective of Cognitive Computing is to initiate and enhance human-machine interaction to accomplish complex tasks and help in problem-solving.

While working with humans on a variety of jobs, machines learn and comprehend human behaviour and feelings in a variety of situations, and then recreate the human thought process in a computer model.

The machine learns to interpret human language and image reflections as a result of this practise. Hence, cognitive thinking combined with artificial intelligence can create a product with human-like actions and data processing skills.

In a situation of complex problems, cognitive computing is capable of making accurate decisions. As a result, it is used in areas where solutions must be improved at the lowest cost possible, and it is acquired by natural language analysis and evidence-based learning.

Google Assistant, for instance, is a perfect example of cognitive computing.

6. Computer Vision

Computer vision is an important component of artificial intelligence because it enables the computer to identify, analyse, and interpret visual input from real-world pictures and visuals by capturing and intercepting it.

It uses deep learning and pattern recognition to extract visual information from any data, including images or video files within PDF documents, Word documents, PowerPoint presentations, XL files, graphs, and photographs, among other formats.

If we have a complex image of a collection of items, then simply seeing the image and memorising it is difficult for most people. A sequence of transformations to the picture may be incorporated by computer vision to extract bit and byte detail such as sharp edges of objects, uncommon design or colour used,etc.

This is accomplished by utilizing a variety of algorithms that use mathematical expressions and statistics. To see the world around them and react in real-time situations, robots employ computer vision technologies.

This component is widely utilised in the healthcare sector to assess a patient’s health status by using MRI scans, X-rays, and other imaging techniques. Computer-controlled vehicles and drones are also used in the automobile industry.

Humans are regarded as the most intelligent species on the planet because they possess analytical thinking, logical reasoning, statistical understanding, and mathematical or computational intelligence, which enable them to solve any issue and analyse large amounts of data. Similar cognitive capabilities are incorporated into different sub-fields of AI.

Although there are several applications and sub-fields of AI, we have described the six major branches which are most common and widely employed in various applications around us.

Artificial intelligence is built for machines and robots considering all of these skill combinations, and it enforces the capacity to solve complex problems in machines that are similar to those that can be done by humans.



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