6 Most Important Subsets of AI (Artificial Intelligence)

Once seen as a future technology, artificial intelligence has now become an everyday part of life, with most mobile apps and online solutions using AI directly or indirectly. AI has enabled machines and software to learn and operate effectively without depending on a single set of rules, as earlier machines used to do. It makes the machines adaptive to learning as they perform. This has helped many businesses and organisations simplify their tasks.

Even though the term AI is no longer unknown to anyone, it is still used interchangeably with certain other technologies, which are actually its subsets. It is not anyone’s mistake, as the subsets of AI are often confused with the AI. Thus, being a top-notch AI development company, we thought we would help our audience by sharing our knowledge of AI subsets. In this article, we will learn about the six most important subsets of AI, along with their applications.

It is important to understand these subsets for applying AI effectively across industries. Whether it is healthcare, e-commerce, or BFSI, the subsets of AI have versatile applications in solving complex operations, optimising operations, and innovating in their respective domains.  

Subsets of Artificial Intelligence

There are six major subsets of artificial intelligence:

·   Machine Learning

·   Natural Language Processing

·   Deep Learning

·   Expert Systems

·   Robotics

·   Speech Recognition

 Let’s understand each subset in detail. 

1. Machine Learning 

The foremost subset of artificial intelligence is machine learning. ML empowers AI-based systems to get trained, learn, and improve from experience without explicitly being defined by a rule-based system. The machine learning models analyse data to identify patterns and make predictions for data-driven decisions. Thus, ML helps in bringing automation and high efficiency to various tasks.

Types of Machine Learning

In the machine learning itself, there are three different types of it:

a. Supervised Learning 

In supervised machine learning, the machine learns itself from the known datasets, like a set of training examples, and then predicts the output. A supervised learning agent needs to find out the function that matches a given sample set. Even more, we can classify supervised learning into two categories of algorithms: 

·   Classifications

·   Regression

b. Reinforcement Learning

In reinforcement learning, the AI model is trained by giving some commands. The model performs certain actions, and on each action, it gets a reward as feedback. The performance of the model gets improved on the basis of these feedbacks.

Reward feedback can be either positive or negative, i.e. if the model performs as it was intended to, it gets positive feedback, and vice-versa. The reinforcement learning is also of two types:

·   Positive Reinforcement training

·   Negative Reinforcement training

c. Unsupervised Learning

As the name indicates, in unsupervised learning, there is no training or supervision of the ML model. In this learning method, the algorithms are trained with data with neither labelling nor classification. Unsupervised learning is focused on learning an agent from the data patterns without corresponding output values. There are two categories of algorithms for unsupervised learning:

·   Clustering

·   Association

Application of Machine Learning

·   Fraud Detection: With the capability of analysing patterns in online transactions, ML models can help in preventing fraud in real time.

·   Recommendation Systems: Popular platforms like Amazon and Netflix utilise complex ML-based systems to recommend personalised products or content based on user preferences.

Being a top-notch machine learning development company, we have expertise in developing ML-based apps. 

2. Natural Language Processing

Natural Language Processing is a subset of AI that has played a highly important role in bridging the gap between humans and digital technologies. It has enabled smooth communication between humans and computers. NLP allows systems to process, interpret, and generate human language effectively, making interactions with technology more intuitive.

There are three major processes in NLP:

·   Tokenization: In tokenisation, the text is broken down into smaller units like words or phrases for analysis.

·   Sentiment Analysis: Identifying emotions and opinions expressions in text, often used in social media monitoring or customer feedback analysis.

·   Language Generation: Producing coherent and contextually relevant text, which is mostly used for content generation and chatbots.  

Application of Natural Language Processing

There are three major applications of NLP:

·  Chatbots: AI-driven chatbots are able to understand the queries shared by users in the text format and provide relevant replies to them, improving customer experience and reducing operational costs.

·   Language Translational: Tools like Google Translate improves communication by converting text between different languages seamlessly.

·   Gen AI: Popular Gen AI tools like ChatGPT and DeepSeek are highly dependent on the Natural Language Process to understand the input and provide correct answers.  

3. Deep Learning

Although Deep Learning is a further subset of Machine Learning, its ability and extent make it an independent subset of AI. It provides the ability for machines to perform human-like tasks but without human involvement. With Deep learning, the AI agents can mimic the human brain. It uses both supervised and unsupervised learning to train an AI agent. There is a neural network architecture behind the working of deep learning, and thus, it is also called a deep neural network.

Behind inventions like self-driving cars, speech recognition, image recognition, automatic machine translation, and others, deep learning is the primary technology. The major challenge with deep learning is that it requires a vast amount of data with lots of computational power. 

Neural Network Architecture

In DL, neural networks are made up of interconnected nodes, or "neurons," that process data. Deep learning is very good at processing unstructured data, like text, audio, and photos, because each layer further refines the input. Because of this multi-layered strategy, DL models can perform exceptionally well on tasks that regular ML models might find difficult because of their complexity. 

Layers of Deep Learning

There are three main layers in deep learning:

·   Input Layer: In this first layer, the aim is to receive data input where the neurons broadcast the signal layers above

·   Hidden Layers: There are the middle layers present between the first and the last layer. The hidden layers help to perform computations on inputs; then, the data is transmitted to the last layer.

·   Output layer: This is the last layer of deep learning architecture, and its role is to send back the output to the user. 

Applications of Deep Learning

·   Speech Recognition: Popular virtual assistants like Amazon Alexa, Siri, or Google Assistant use deep learning to understand users’ commands.

·   Autonomous Vehicles: Deep learning algorithms process real-time data from sensors and cameras so that cars can navigate and make decisions independently. 

4. Expert Systems

Expert systems are basically computer programs that depend on human knowledge and programming knowledge for the system to operate. The expert systems use the human expert ability for decision-making. They are used to create solutions to solve complex problems by getting knowledge from humans instead of coding procedures.

The epitome of an expert system is the spelling error suggestion while typing in the Google Search box. These systems rely on “if-then” logic to solve problems and provide relevant & correct suggestions. 

Applications of Expert Systems

·   Medical Diagnosis: Systems like MYCIN assist medical professionals by analysing symptoms and making recommendations for diagnoses or treatments.

·   Troubleshooting Systems: Based on defined principles, applications in the automotive and IT sectors detect problems and provide fixes. 

5. Robotics

Another major subset of AI is robotics, in which mechanical processes are combined with software-based intelligence. Here, the robots are not exactly like the ones that you see in sci-fi movies but are machines that are programmed to do specific actions either semi-automatically or fully-automatically.

With AI, it is possible to create smart robots that can perform certain actions as per their capabilities. To enable robots to perform complex tasks, AI algorithms are necessary. 

Application of Robotics

·   Manufacturing: In the manufacturing industry, robots can help streamline assembly lines, improving productivity and reducing costs.

·   Logistics: Autonomous robots manage various warehouse operations and streamline delivery processes, ensuring efficiency and accuracy. 

6. Speech Recognition

This subset of AI allows machines to interpret and translate spoken language into a format readable by the machine. It is just like talking to a digital device, and the digital device is able to understand what you are saying and perform the required action. It converts the spoken language into written text. By analysing sound waves, it identifies words, phrases, and contextual nuances. There are two major aspects of this:

·   Speech-To-Text Conversion: Transcribing spoken words into text for documentation, communication, and further analysis.

·   Voice Recognition: Identifying individual speakers based on unique voice patterns, ensuring personalised and secure interactions. 

Applications of Speech Recognition

·   Virtual Assistants: AI bots like Siri, Google Assistant, Amazon Alexa, and others use speech recognition to process commands and respond accurately.

·   Accessibility Tools: For individuals with disabilities, it allows for hand-free interaction with digital devices. 

In this article, we have gone through all the major aspects of 6 subsets of AI. At A3Logics, the best AI development company in India, we have expertise in developing custom AI-based mobile applications. Our developers have experience in building apps using each of these AI subsets and building advanced features & functionalities. If you are looking for guidance for AI tools, we can also provide you with AI consulting services. Let us know your requirements.

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