Major Sub-Fields in Artificial Intelligence (AI) You Need to Know

In this article, we are going to discuss some of these diverse sud-fields in AI which lead to the technological advancement we are experiencing today.


In the past few years, the advancement in Artificial Intelligence has been great, surpassing our expectations and enabling machines to perform tasks that were once considered impossible for machines to do. We all know that when calculators were first invented, some of them believed that they possessed a level of creativity due to their ability to perform complex arithmetic calculations as we thought that doing arithmetic really fast is a part of intelligent behavior. But calculators simply follow a pre-defined rule for doing calculations, Apart from that, there is nothing fancy with calculators and even with traditional computers as we know it. If someone thinks the same as today, they were very wrong. Today's case is pretty different, Now AI can drive cars, clone voice, recognize speech, generate images, recommend products, understand and generate language, etc far better than human beings. These require more complex mechanisms to work rather than just performing arithmetic, today's AI is complex. Well, the end goal of Artificial Intelligence is to make machines intelligent as humans, and while human intelligence is so much complex and is dealing with a large bunch of things, AI is also pretty diverse as well. So, in this article, we are going to discuss some of these diverse sud-fields in AI which lead to the technological advancement we are experiencing today.

The Wide Spectrum of AI

As we briefly discussed, AI is pretty diverse and we still doing research not only about AI algorithms and stuff but also the applications and how they benefit the world. As AI progresses, we will find more and more applications for it. However, there are already many techniques that make AI applicable in many areas, let's discuss some of them in detail.

1. Machine Learning

When we talk about Artificial Intelligence, the first and foremost way to perceive intelligence is learning to do things like humans and Machine Learning is exactly doing that. Machine learning is a branch of Artificial Intelligence that gives computers the ability to learn without explicit programming from large amounts of data and make predictions and decisions based on that. This means computers will learn and make their own rules rather than someone providing instructions from outside. This technique makes machines learn something deeply by observing data.

When comes to machine learning, a lot of data and sophisticated algorithms and techniques are involved. Some algorithms can learn the patterns by observing the data and outputs while others can learn to make patterns by observing an enormous number of data. Because of that, Machine Learning is primarily divided into three, Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

Supervised Learning is when the machine is trained on data that has been labeled, meaning that the correct answer is known. The Machine Learning algorithm will look through the training data and its corresponding labels to learn the underlying meaning and pattern of the data which can then be used on new unseen data for making predictions known as testing data. It is like training the machine to recognize cats by telling it that the picture is of a cat. Meanwhile, in Unsupervised Learning the data is not labeled meaning that the answers are not given, In this case, the Machine Learning algorithm tries to find the answer for the data given or find the pattern in the dataset and classify it. Reinforcement Learning is pretty different, The ML algorithm, or what we call an Agent specifically will learn from an environment using a reward system, Here the Agent can explore the environment and takes specific actions based on the exploration, if the Agent takes a good action a reward is given and a penalty is given for bad action, with a large number of training, the model will eventually learn to make accurate actions. We'll discuss more Reinforcement Learning when going further.

Machine Learning is applied almost everywhere, ranging from technology and business to healthcare and entertainment. In technology, Machine Learning powers recommendation systems, fraud detection, image recognition, etc. In business, it aids in demand forecasting, customer segmentation, and optimizing supply chain operations, and is even used in stock markets. Healthcare is one of the most improved fields because of Machine Learning. It can help with disease diagnosis, drug discovery, and personalized treatment plans. Moreover, it can help to predict symptoms of cancers, cardiovascular diseases, etc. In fact, all the below techniques that we are going to discuss are using Machine Learning to excel in the domain.

2. Natural Language Processing (NLP)

Natural Language Processing or NLP is another popular branch of AI that is specifically devoted to Langauge. Language as we know is a way for us to communicate with the world, In order for a machine to communicate with the world, Natural Language Processing can really help. NLP is the sub-field of AI that focuses on developing algorithms and models which can process, understand, interpret, and generate natural human language.

Natural Language Processing deals with a lot of linguistic rules, statistical models, and Machine Learning to process and understand a large amount the text and speech data. NLP uses various kinds of techniques for extracting meaningful information from the text for analysis like tokenization (splitting text into words or sentences), lemmatization (reducing words to their base form), part-of-speech tagging (labeling words with their grammatical categories), and syntactic parsing (analyzing the grammatical structure of sentences). Using some of these techniques we can process text inputs more efficiently, then after when applying Machine Learning, increases the efficiency again. 

With recent advancements in Deep Learning, a kind of Machine Learning, and the availability of large text datasets, NLP has improved even more than before. Algorithms like Transformer Neural Networks lead to the development of Large Langauge Models(LLM) like GPT-4, GPT-3, PALM, etc which achieved state-of-the-art results in various NLP tasks and have enabled machines to understand and generate human language.

NLP can be seen in many places like chatbots, personal assistants, search engines, Spam Filters, Content Recommendations, News analysis, Machine Translation, Text Generation, sentiment analysis, etc, and can also be spotted in many revolutionary products that we use today, for instance, ChatGPT, One of the revolutionary products of Advanced Natural Language Processing and Deep Learning. For developing such a technology, the company OpenAI invested greatly in terms of time and money in researching the vast possibility of Natural Language Processing.

3. Computer Vision

If someone comes to you and says that they have a machine that can recognize objects far better than your eyes could, there is no reason to doubt them because it is true that computers today are pretty powerful in recognizing objects sometimes reaching up to 90% accuracy surpassing human ability to recognize objects. This is possible because of advancements in Computer Vision, another branch of AI that focuses on developing machines that can "see" and interpret visual data. The ultimate goal of computer vision is to develop algorithms that can understand the world around us in the same way that humans do. This means being able to identify objects, track their movement, and understand the relationships between them.

Computer Vision helps machines to acquire, analyze, understand, and interpret visual information from images, videos, and real-world objects. Here we use sophisticated algorithms with various architectures for interpreting rich visual inputs. Algorithms like Convolutional Neural Networks (CNNs) proved to be successful in many Computer Vision tasks like Image processing, Object detection, Face recognition, etc. CNN is one of the popular Neural Network Architecture in Deep Learning which became the primary algorithm for most of the Computer Vision tasks. These algorithms work using a feature extraction technique, where only the relevant features are extracted and processed from the input image rather than processing the whole image which is computationally inefficient. Other models like Vision Transformers (ViTs) also showed greater efficiency in visual data and are still ongoing research.

Today we have self-driving cars, medical imaging systems, security cameras, face recognition systems, augmented reality, etc which are solely based on Computer Vision. Self-driving cars like Tesla uses advanced Computer Vision and Deep Learning to drive the vehicle based on the situation on the road. In medical imaging, Computer Vision algorithms analyze medical scans and images, assisting doctors in accurate diagnosis and treatment planning. Security cameras use Computer Vision to detect and track suspicious activities, enhancing public safety. Face recognition systems rely on Computer Vision to identify individuals for security purposes or provide personalized experiences. Augmented reality experiences are made possible through Computer Vision, enabling virtual objects to seamlessly integrate with the real world. These remarkable applications prove how much this field influences the world around us.

4. Automatic Speech Recognition (ASR)

Speech Recognition also known as Automatic Speech Recognition (ASR) is a technology that processes human speech and converts it into readable text, sometimes known as speech-to-text. This technology combines aspects of Natural Language Processing (NLP) and Deep Learning to process human audio signals and converts them into text. 

Speech Recognition involves several steps. First, the audio signal is captured through a microphone or other recording devices, and the signal is preprocessed to remove background noise and enhance speech quality. Next, the acoustic model analyzes the acoustic features of the speech signal, such as phonemes, using statistical techniques like Hidden Markov Models (HMMs) or deep learning architectures like Recurrent Neural Networks (RNNs) or Convolutional Neural Networks (CNNs). This model converts the audio signals into the corresponding phonetic representations

After the acoustic modeling is completed, Langauge Models enter the stage. Language Models build using RNNs or Transformers can be used to predict the most probable words or sentences from the phonetic representations based on grammar, context, and language patterns. The final output of speech recognition is a textual representation of the spoken words, which can be further processed and used for various applications.

When you search for a query through your voice on Google, Google's AI system can understand what you said and returns the output of your voice as a sentence in the search bar. If that sounds simple, think about YouTube, as you watch a video tutorial on preparing delicious pasta, you may notice captions dynamically forming beneath the visuals, this is one of the other examples of Automatic Speech recognition. Not only Google and YouTube, but Speech Recognition is also used by many companies and startups for developing software for transcription services, chatbots, dictation software, voice-controlled systems, language learning tools, and many more.

5. Deep Learning & Neural Networks

The recent advancements in Artificial Intelligence can mostly be contributed to Deep Learning. Deep Learning is one of the fields in AI that uses Artificial Neural Networks to solve highly complex and data-driven problems. Deep Learning is highly inspired by the working of the human brain. The main task in deep learning is to create Artificial Neural Network models that consist of multiple layers of interconnected artificial neurons. These networks are capable of learning hierarchical representations of data, allowing them to extract and understand complex features.

This approach of making large multi-layered Neural Networks is known as Deep Neural Networks and training them with large amounts of data is the basic essence of Deep Learning. The concept of Neural Networks was there for decades, but recently it got more popular due to the ever-increasing computational power and availability of large amounts of data. To perform high-level Deep Learning you need a large amount of data and reasonable computing power, this is because Neural Networks require extensive data for training and significant computing power to process and optimize the connections or parameters. Once it is well-trained on large datasets with optimization techniques for specific use cases, there is no doubt that Neural Networks can perform really well.

Deep learning and neural networks are essential components of many of the technologies we use today, such as face recognition in smartphones, video recommendations on YouTube, fraud detection in financial transactions, medical imaging and personalized treatment in healthcare to image generation, text generation, games, and even in self-driving cars and robotics. With the advent of more powerful hardware and the availability of large amounts of data, Deep Learning is becoming increasingly popular and is expected to play a major role in the future of Artificial Intelligence.

6. Robotics

Robotics is a multidisciplinary field that brings together various domains, including computer science, engineering, mechanics, and related disciplines. Its primary objective is to create, advance, and implement robotic systems. The concept of robotics has a long history and humans build different kinds of robots to help them assist in their physical works. But today, Robotics has grown significantly, and the applications extended from physical works to cognitive tasks where robots can able to do a wide range of intelligent tasks like humans. This is achieved by integrating Artificial Intelligence into physical robots.

Robotics comes with a lot of mechanical work like designing the physical structure of the robot, including its mechanical components, sensors, and actuators. Using these mechanical parts and sensors, robots can interact with the environment by collecting the data through the sensors and processing it to produce a mechanical outcome. Traditional robots can be pre-programmed to do these tasks, for example, you can tell, if this signal is received from sensors, then do this. But today robots have evolved beyond pre-programmed instructions. With the integration of Machine Learning, Robots have gained the ability to understand the surroundings and learn what to do in each situation. This can be done through a technique called Deep Reinforcement Learning combining Deep Learning and Reinforcement Learning to train robots to make decisions and learn from the external environment.

Today, robots are common in many places, there are industrial robots that can help in manufacturing, service robots for domestic use, mobile robots, pet robots, and more. But today engineers and scientists are interested in building Humanoid Robots. Combining Robotics and Artificial Intelligence can help to build highly advanced humanoid robots. For example, Sophia, a Robot created by Hanson Robotics is one of the highly advanced humanoid robots which can interact with human beings using facial expressions, gestures, and through natural language processing. Sophia is occupied with advanced Artificial Intelligence techniques like Speech Recognition, Natural Language Processing, Speech Synthesis, Robot Facial expressions, Gesture controls, and Emotion Simulation. Moreover, it can interact with human beings in the same way humans interact with each other.

As robotics continues to advance, it can reshape various industries, improve efficiency, and enhance our quality of life. As of 2050, each of us may have a personal robot in our home for helping us with daily tasks like how smartphones became a part of our lives. However, challenges remain, such as ensuring safe human-robot interaction, addressing ethical considerations, and further advancing the capabilities of robots.

7. Reinforcement Learning

You may be familiar with the news about computer programs achieving victory over human champions in various games, starting with chess and extending to a wide range of games, including the game of Go. In March 2016, a computer program developed by Google DeepMind, named AlphaGo, played against the world Go champion, Lee Sedol. The uniqueness of the game of Go lies in its reliance on human intuition, unlike chess which is primarily a rule-based game. Despite this, AlphaGo surprisingly defeated the world champion, which was a significant breakthrough for the field of artificial intelligence. AlphaGo achieved this through a technique called Reinforcement Learning.

Reinforcement Learning is the technique of computers learning from their mistakes and improving over time. This is possible by exposing an Agent (A Machine Learning Algorithm)  to an environment (a Place where the Agent can explore and take action). In the case of AlphaGo, the Agent initially had limited knowledge about the game but was trained to play against itself millions of times. Each iteration involved the Agent making moves and receiving feedback based on the outcomes. By utilizing a neural network architecture, AlphaGo learned to predict the most advantageous moves and improve its strategies through trial and error. This process of self-play and learning from feedback allowed  AlphaGo to develop exceptional gameplay abilities, eventually surpassing even the best human players. 

Reinforcement Learning particularly works in this way where the Agent gets feedback based on its performance in the environment, the feedback can be a reward or a penalty. For good moves the Agent will get a reward and penalty for bad moves, eventually, the model learns the environment really well and take accurate actions. Today we use Deep Reinforcement Learning to train computers to perform a wide variety of tasks. Deep RL is the combination is Reinforcement Learning and Deep Learning. Here we use Deep Neural Networks as an Agent which can learn really complex patterns that are hidden in the environment. Deep Reinforcement Learning showed great success in many fields and also proved that machines can really learn. Today Deep Reinforcement Learning is mostly applied in Games and in Robotics, but the applications are endless and still in research.  


In this article, we have discussed some of the top fields that form the backbone of today's Artificial Intelligence from Machine Learning, Natural Language Processing, and Robotics to Deep Learning and Reinforcement Learning. These subfields have enabled us to achieve significant milestones in AI, such as beating human champions in games, understanding natural language, and developing robots that can help us. As AI technology continues to advance, it has the potential to reach many industries and improve our quality of life in numerous ways. The future of AI is full of possibilities, and let's see how Artificial Intelligence progresses in the coming years.