List of Domains of AI | Artificial Intelligence Domains

Domains of AI: Artificial Intelligence is a revolutionary technology which is being considered the future of the world of Computer Science. It is basically capable of performing functions that require the human mind and man force to be done. Despite the hysteria around Artificial Intelligence, it is a domain in which there are immense possibilities, not just in information technology, but also in business, travel, marketing, manufacturing, food and so on.

Domains of AI

Understanding the importance of artificial intelligence, many universities have even started to teach their students about the subject, with some even starting entire four-year courses on AI. As a result, it becomes extremely important to have a basic understanding of the Domains of AI. Thus, in this article, we provide you with the details of the Domains of AI to help you better understand the realm of AI.

However, the following list of Domains of AI is not exhaustive. Since AI is an emerging interdisciplinary field, new additions to the list of Domains of AI are frequent.

List of Domains of AI 

The list of Domains of AI is as follows

1) Machine Learning

Machine Learning (ML) is one of the most popular Domains of AI which allows computers to learn from the data to better its performance. It makes computers learn without being explicitly told to do so. 

Machine Learning works in an exciting way. Algorithms are trained using statistical techniques to classify, predict, cluster and find important insights in data mining projects which subsequently help in decision-making. These algorithms are created using solution-oriented algorithms, such as TensorFlow and PyTorch.

Machine Learning is generally classified into the following two types

  1. Supervised Learning: In supervised Machine Learning, the algorithm is trained in labelled data. For instance, if the algorithm encounters an email that has been labelled “spam”, then it would identify other emails as spam or not spam.
  1. Unsupervised Learning: As the name suggests, in Unsupervised Machine Learning, data is not labelled. Instead, the algorithm is trained to identify patterns and structures in data which is then used to identify, classify or cluster other data.
  1. Reinforced Learning: This type of Machine Learning is Goal-oriented learning in which the algorithm is trained in a specific environment to achieve a certain goal. The computer program learns by feedback which is either a reward or a penalty. This way, it is able to improvise itself. Such a type of ML is used in driverless cars.

2) Natural Language Processing (NLP)

Natural Language Processing is one of the highly used Domains of AI that deals with the processing of natural languages in order to enable computers to understand them. It deals with the programming of computers in a way that it is able to recognize and execute the forms of speech used by humans, including the intricate contextual nuances in them. It is one of the most rapidly growing domains of AI. It involves the following:

  1. Machine translation: It involves the translation of text from one language to another. Popular translation tools such as Google Translate and Microsoft Translator use this technology
  2. Chatbots: Chatbots have recently become extremely popular. These involve answering questions by computers. It is still an evolving technology.
  3. Sentiment Analysis: Sentiment Analysis is used to understand the sentiment of the text. It is widely used in the analysis of content on social media.
  4. Language Understanding and Generation: As the name suggests, Language Understanding and Generation are used to understand the various intentions and emotions behind the text. The same technology is used for the generation of human-like language
  5. Speech Recognition: Speech Recognition is used to recognize and convert human speech to text.

3) Computer Vision

Computer Vision is a domain of Artificial Intelligence that deals with the interpretation and processing of visual data, in the form of images and videos, to help them in understanding the world around them. With the help of Computer Vision, computers can track and understand visual scenes. Among all the domains of Ai, Computer Vision is perhaps the most visual.

Its tasks include acquisition, feature extraction, processing and analysis of images in order to extract high dimensional data from them as explained below:

  1. Image Acquisition: It is the first step that involves the acquisition of an image from an input device such as a camera.
  2. Image Processing: As the image is acquired, the image is processed in order to improve its quality and reduce noise. This involves filtering, resizing, etc. This step is crucial as the extraction of data depends immensely on the quality of the image. 
  3. Feature Extraction: This step involves the identification of important features of an image such as colour, texture, corners etc that help in identifying objects.
  4. Object Recognition: Recognition of objects is perhaps the most important task in computer vision. That detects and classifies semantic objects. This is done by analysing the patterns and structures of objects in the images or videos.
  5. Scene Understanding: The final step is to understand the scene to present a holistic description of the image or video concerned.

4) Robotics

Robotics is perhaps the most popular of all domains of AI, attributed to the culture around Science-Fiction. However, it is a vast field that involves many sub-fields such as Computer Science, Electronics & Communication, Electrical Engineering, Mechanical Engineering, Artificial Intelligence etc. 

Artificial Intelligence in Robotics is still in its early stages of development. Despite that, it is a rapidly evolving field. It involves all the domains of AI including Computer Vision, Machine Learning, Planning and Decision-Making, etc. Each component or domain has its own specific task which is then integrated with other tasks and functions.

The use of Artificial Intelligence in Robotics is set to enhance human productivity. It has a wide-scale application including manufacturing, delivery, and service. medical surgeries, food & travels and so on that have the potential of drastically reducing the amount of labour humans are required to do. However, along with optimism, there exists strong pessimism revolving around the fact that it will consume many jobs

5) Gaming

Gaming is another domain of Artificial Intelligence that has made huge strides. In the gaming industry, AI has achieved a lot including generating non-player characters, creating altogether game worlds, personalizing the gaming experience etc. Perhaps the most prominent examples include chess, ludo, Grand Theft Auto V, The Last of Us etc.

In gaming, Artificial Intelligence is used to enhance user experience by learning the behaviour of the users and learning from it. This has led to artificial bots acquiring characteristics of an opponent and thus altering the difficulty levels of various games. However, like all of AI, this field is also relatively new and there is so much that can be achieved.

6) Virtual Agents

Virtual Agents are computer-generated agents that are driven by algorithms and programs but can act as real agents or various services. This technology is already being used by many corporations to enhance user experience and quality of service. Many companies have created chatbots that act just like human agents in delivering services and addressing customer grievances.

7) Expert Systems

Expert Systems in AI are systems that execute tasks like experts by emulating their capabilities of decision-making. This technology has many applications across various fields such as manufacturing, research, health, business, marketing and so on. Its main components are:

  1. Knowledge Base: It is a collection of facts, rules and knowledge that a computer uses in order to act as an expert in a particular field. The knowledge is inserted into a system beforehand.
  2. Inference Engine: It is basically the part of an Expert System that makes decisions based on general rules, norms and its own experience. The knowledge base is extensively used here so as to act as an expert.
  3. User Interface: This is basically to interact with the user, listen to its problems and provide solutions just as an expert. It is also a source of input.
  4. Knowledge Acquisition System: An expert system cannot function without acquiring expert knowledge in a field. This component is specifically designed to constantly acquire knowledge. It is the most difficult task in an Expert System.

So these were the Domains of AI. We would like to emphasize that since Artificial Intelligence is an emerging field, the list above is not exhaustive. New domains can add or existing domains in AI can merge anytime.

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