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With Artificial Intelligence, AI researchers have given communities a means to solve problems that human beings once thought unsolvable.
A web search, for instance, this implementation of Artificial Intelligence systems has provided a means to solve complex problems that arise from gathering and filtering through information to find the best answer for users.
For Artificial Intelligence technologies to accomplish a task, several processes and techniques divided into the branches of Artificial Intelligence are required.
There are many different branches of AI, but there are seven types of AI, such as artificial narrow intelligence and self-aware AI.
After reading this article, you will understand nine essential branches of Artificial Intelligence systems, how they function, and how they solve complex problems.
What is Artificial Intelligence (AI)?
Artificial Intelligence can be defined simply as computer systems or software systems that utilize human intelligence beyond the capacity of a human brain.
Artificial Intelligence is computer science and data science that implements human behavior and intelligence. It enables machines to efficiently perform and accomplish tasks that require human beings to dedicate more time than necessary.
By learning from each experience and improving to provide the fastest reliable possible outcome, Artificial Intelligence evolves as time passes, and the branches of Artificial Intelligence make this possible.
AI branches such as Natural Language Processing, Deep Learning, or Fuzzy Logic are some of what provides the processing or decision-making ability that computer systems require to provide very near accurate solutions to real-world problems.
What Are the Branches of Artificial Intelligence?
Through computer science, the branches of Artificial Intelligence each offer a way for computer recognition to implement human behavior.
Thinking, learning, understanding language, planning, or bias are ways for computer systems to mimic human behavior to solve real-world problems.
For instance, Machine Learning as a branch of Artificial Intelligence mimics the human ability to learn, process, and predict a possible outcome. It also relies on data that needs to be correct before Machine Learning can function satisfactorily.
Robotics is another branch of Artificial Intelligence that helps perform tasks that can cause a massive strain on human beings’ muscles. Robotics functions through computer systems and machines to control the movement of massive objects at a more efficient rate.
Below are the nine branches of artificial intelligence we will be discussing:
- Machine Learning (also known as ML)
- Supervised Machine Learning
- Unsupervised Machine Learning
- Reinforcement Machine Learning
- Deep Learning
- Neural Networks
- Natural Language Processing (also known as NLP)
- Expert Systems
- Fuzzy Logic
- Robotics
- Computer Vision
- Cognitive Computing
1. What is Machine Learning?
Machine learning is one of the most advanced branches of Artificial Intelligence. As the name implies, it can provide computer systems with the ability to learn through data science and solve complex problems without human assistance.
In this branch of Artificial Intelligence, programmers teach computers how to solve complicated problems by performing complex mathematical computations on large datasets.
Machine learning algorithms are imputed in computer systems to learn, process, analyze, translate, interpret data, and perform tasks.
Self-driving cars, web searches, speech recognition, stock exchange prediction, and fraud detection are but a few implementations of machine learning techniques.
Machine learning is further sub-categorized into:
- Supervised learning,
- Unsupervised learning,
- and Reinforcement learning.
Supervised Machine Learning
Supervised Machine learning (also known as Supervised Learning) is a Machine Learning sub-category that trains machine learning algorithms using labeled datasets. The labeled datasets give machines the ability to classify data or predict outcomes.
AI researchers combine inputs and correct outputs to create labeled datasets. The computer system that implements this artificial intelligence branch learns through these datasets.
Supervised Learning may be implemented in predictions such as risk analysis or stock exchange prediction, weather prediction, estimated time of destination arrival, or the fastest route to take home.
They make predictions with available data such as walking speed, the amount of traffic, time of day, weather conditions, etc.
Supervised Learning uses all present data to provide a possible future outcome.
Unsupervised Machine Learning
Unsupervised Machine Learning (also known as Unsupervised Learning) is quite the opposite of Supervised Learning. Its dataset is unlabelled.
It uses these unlabelled datasets to discover patterns that were previously undetected. Once it gathers discovered patterns, it can then use them to provide output without any pre-existing notion of what they should be.
With unsupervised learning, a computer system can perform complex processing tasks.
This sub-category helps with fraud detection. It finds all kinds of unusual and unknown patterns in data and helps broaden the possible output of data. It also does not require manual intervention like labeling of data.
Although it is diverse, do not expect precise information or closely accurate answers from it.
Reinforcement Machine Learning
Reinforcement Machine Learning (also known as Reinforcement Learning) is a sub-category that rewards positive experiences and punishes negative experiences gathered by a computer system.
It is designed to be rewarded for positive behaviors and equally punished for negative ones. This algorithm encourages a trial-and-error tactic training method that guides the system through the desired path.
With each experience, the Reinforcement Learning algorithm develops and performs tasks as desired.
Reinforcement learning has been implemented in gaming, healthcare, traffic light control, and self-driving cars, to mention a few.
This sub-category is quite helpful but also hard to control. Since its system relies on exploration, it may need help to consistently find the best results in an environment that changes frequently.
It will therefore take more time to learn in such an environment, which can become a limiting factor, especially in an urgent situation.
2. What is Deep Learning?
Deep Learning is a branch of Artificial Intelligence that functions through a neural network. Its neural network simulates the behavior of a human brain by combining several data inputs, weights, and biases that allow it to identify, categorize, define data and perform a task with a significant amount of accuracy.
The deep learning neural network is designed as an interconnected multiple-layered structure of more than three layers, with each layer refining and optimizing the level of accurate results provided by the last.
The progression of data through the deep neural network is called forward propagation. The input layer acquires the data for processing within the middle layers, and the output layer accumulates the final result of all processing within the middle layers.
Another process that the deep neural network can execute is backpropagation. This procedure is a reverse version of the forward propagation.
In backpropagation, deep learning uses algorithms to calculate errors in results and adjust them by moving backward through the multiple layers to train the model.
With forward propagation and backpropagation, the deep learning neural network provides near-accurate results, corrects for error, and gradually becomes more accurate as it moves back and forth through the neural network layers.
Since it learns from large amounts of data, it requires no pre-processing of data like Supervised Learning. It also requires minimum intervention from any human expert at the initiating stage, making it much more self-taught and time favorable.
Deep Learning has been implemented in many aspects of our daily lives. It is used to design digital personal assistants, credit card fraud detection, speech recognition, risk analysis, and many more methods of prediction.
3. What Are Neural Networks?
The Neural Network may be identified as a more shallow version of the deep learning branch of Artificial Intelligence. It possesses the same entities as deep learning in the aspect of having neurons and layers.
But what makes a neural network so different is that they attempt to learn by mimicking human behavior.
A neural network refers to a computer system of neurons implementing mathematical functions and statistical techniques to solve real-world problems. It is a computer science that performs several neural network functions by implementing the potential of a human’s nerve and nervous system.
A human brain comprises an infinite number of neurons. By incorporating cognitive science into computer systems, this branch of Artificial Intelligence can code brain neurons into layers of neural networks replicating a human’s brain neurons.
The neurons in the computer system become a mathematical function that gathers and classifies information per the neural network structure.
The neural network comprises three layers of neurons that incorporate the potential of the human brain into Artificial Intelligence. These layers are an input layer, a middle layer, and an output layer.
The input layer receives data, the intermediate layer processes data, and the output layer compiles all the processed data to provide an output. Layers are connected by variables such as weight or bias, which determine their final output.
As one of the most significant branches of Artificial Intelligence to society, neural networks have been implemented in market research, fraud detection, and stock exchange prediction.
A neural network can solve several world problems with a high level of notability. Still, one of its drawbacks is the level of accuracy its layers of neurons provide. Deep learning provides multiple layers which offer more neurons.
4. What is Natural Language Processing?
Natural Language Processing (also known as NLP) is a branch of Artificial Intelligence or computer science that uses computer algorithms to enable computer systems to read better and understand human natural language just as much as a human being should.
By mimicking human natural language processing and understanding abilities, computer algorithms have been created for a computer system to act with the same amount of human intelligence.
A computer system can now understand human languages, process them and extract meaningful information with the assistance of Natural Language Processing Artificial Intelligence.
A significant aspect of life involves communicating with computer systems by writing, typing, or speaking. This branch of Artificial Intelligence has become an enormous problem solver in society by providing computer systems with the ability to do so.
If you spoke into a microphone recently and it was able to execute the task required, there’s a great chance that NLP was involved in making that possible.
NLP has been implemented in many applications such as sentiment analysis, speech recognition, text translation, google search engine, automated readable summary text, market research, digital personal assistants, and many other applications.
5. What are Expert Systems?
Expert Systems are a branch of Artificial Intelligence that aims to provide users with expert knowledge on a requested topic. The knowledge provided by expert systems is made possible because of accumulated feeds of expert knowledge from several aspects of life.
An expert system completely relies on the data from its knowledge base, which several human experts provide.
Expert systems are available at all times, requiring less thinking time than a human. The knowledge gathered by expert systems comes from several reliable expert sources, reducing the risk of fatal errors.
With the amount of knowledge gathered by this branch of Artificial Intelligence, answers are usually very accurate and precise. Anyone can use an expert system for any situation where knowledge is needed more urgently than it can be acquired from an expert human.
6. What is Fuzzy Logic?
Fuzzy Logic is a branch of Artificial Intelligence that functions by imitating human thought logically to find answers to deep questions.
Unlike standard logic, which narrows all answers to a wholly TRUE or FALSE concept, Fuzzy Logic derives a value within the standard logic and identifies it as an answer. This value is an intermediate value, which resides between TRUE and FALSE.
Fuzzy Logic is an uncomplicated Artificial Intelligence tool made to implement machine learning techniques that can provide better answers to uncertain questions.
Fuzzy Logic is a branch of Artificial Intelligence that does not give basic answers, making it the perfect Artificial Intelligence for complex questions requiring critical thought. However, that also makes Fuzzy Logic the worst possible Artificial Intelligence for answering a simple question.
7. What is Robotics?
Robotics is a branch of Artificial Intelligence that combines computer science, mechanical engineering, and electrical engineering to create robots with the ability to sense an environment and its entities and act by it to accomplish the desired goal.
Robots are the by-product of robotics, and they are computer systems that carry out the demands of their creators.
Robotics has significantly advanced over the years. The combination of several other branches of Artificial Intelligence with robotics has increased the potential of robots.
Robots are primarily applied to perform many physical activities that might be too tasking or dangerous for humans. But as of now, there’s ongoing research to improve robots AI and give them a better grasp of human nature.
8. What is Computer Vision?
Computer Vision is a branch of Artificial intelligence that deals with visual data. It relies heavily upon its knowledge base and cannot learn without it.
Just as it is human behavior to relate what they see to what they have seen, so does the Computer Vision Artificial Intelligence function. It is trained to teach itself to understand visual data and discern different images from each other based on gathered data.
Computer Vision uses deep learning and a convolutional neural network to identify objects and notice their distinctive nature.
Through deep learning, Computer Vision will help the computer system look—break images down to pixels with tags—at an object. The tagged images from the object then go through convolutions and allow the computer to perceive objects.
Typical applications of this branch of Artificial Intelligence are image categorization, facial recognition, market research, and Google Lens, which can bring up images related to a reference.
9. What is Cognitive Computing?
Cognitive Computing is a branch of Artificial Intelligence that aims to better equip computer systems with the same level of thinking and decision-making ability possessed by humans.
It is a combination of computer science and cognitive science that mimics the human thought process. Incorporating cognitive science, it provides a means for computer systems to carry out complex tasks without needing human assistance mid-way.
With the help of this branch of Artificial Intelligence, computer systems are expected to execute tasks with the same level of discretion as a regular human.
Cognitive Computing applies to critical thinking, speech recognition, image detection, and many other parts of life.
Summary
Artificial Intelligence has brought a great deal of technological advancement into the world. With the help of the different branches of Artificial Intelligence, such as Machine Learning or Cognitive Vision, it has provided communities with the ability to live a more fulfilled life.
With the help of Artificial Intelligence, humans can now do more past research and a little amount of work at the beginning. It all depends on the implemented branch of Artificial Intelligence.
As the different branches of Artificial Intelligence are continually developing, one can expect more technological advancements to surface over time—so watch out.
