The Role of Neural Networks in Artificial Intelligence

Artificial Intelligence, also known as AI, is an essential concept in modern science and technology advancements. AI's main idea is to build smarter machines that can go through a logical decision-making process based on previous experiences. Thus, the intention is to make smarter computers that can learn from past experiences to make more intelligent and informed decisions. 

To achieve this, computer technology researchers came up with a concept to build computers that emulate the human brain. This approach helps computers to operate similarly to human beings. To make this possible, researchers came up with the concept of neural networks. 

A neural network is essentially hardware or software that mimics the behavior of neurons in the human brain. Typically neural networks involve machine learning and deep learning as integral parts of Artificial Intelligence. 

Artificial neural networks are the critical components of machine learning and are inspired by the brain's neuron functionalities. An artificial neural network is essentially an elaborate process of synthesizing information that uses remarkably advanced processors using their dynamic state response to external inputs. In essence, artificial neural networks are wires and silicon that are interconnected like the human dendrites and neurons to imitate a human brain's typical functionalities. 

What is the Basic Structure of ANNs

Artificial Neural Networks constitute several nodes that act the same way as the neurons of the human brain. These ANNs are interconnected to one another for communication. The artificial neurons then create a network of nodes that can perform simple to complex data analysis and operations. Typically, after every operation is completed, the results, also known as the output, are transmitted to the next other neurons. These results or outcomes from a node are known as a node value or activation in computing. 

However, this is not the entire idea behind machine learning. The nodes, like in the human brain, have a learning mechanism. All the created links are given a certain weight. However, the weight values can be altered. The process of adjusting the weight values over time results in learning. 

The Learning Strategies of Artificial Neural Networks

Artificial intelligence is brought about by the learning processes of artificial neural networks. Usually, there are different approaches to teaching or learning in schools. Similarly, various techniques have been used to drive the learning process of artificial neural networks. Below are the three main learning methods in practice today.

• Reinforcement Learning

This is a complex learning process that is mainly based on observing the environment of the neural networks. Artificial neural networks are designed to learn by analyzing the environment and determining whether the decision is negative or positive. If the ANNs observe a negative result at any time, the entire network adjusts the weights to make a positive or correct decision in subsequent events. 

• Supervised Learning 

In this learning strategy, there has to be a "teacher." The supervised learning process involves a system that will periodically feed the network with sample data that gives information about which "teacher" is well conversant with the topic at hand. 

The process is structured to take guesses about a particular situation; however, it has to crosscheck with the "teacher's" correct answer before it makes a final decision. This process ensures that the artificial neural network makes enough adjustments to its guesses based on the suggested outcome to minimize errors. 

• Unsupervised Learning

Unsupervised learning is used whenever no sample data is available for cross-checking purposes. For example, imagine you need to search for a hidden pattern randomly. Unsupervised learning approaches this problem using a clustering method. It subdivides several unrecognized patterns and performs a separation operation based on the pre-existing data sets. 

The Roles of Neural Networks on Artificial Intelligence. 

When talking about Artificial Intelligence and its works, it is almost impossible not to mention neural networks. This is because these two go hand in hand. In this section, we analyze some of the roles of neural networks in AI.

• Adaptive Learning

Neural networks are typically responsible for all the learning processes of a machine. These tiny yet complex networks absorb the given data sets and use the information to solve tasks. Usually, the ability to learn is powered by this feature, which in turn results in artificial intelligence.

• Real-Time Operation

Neural networks are powerful, and in some cases, they are designed to handle tasks and calculations simultaneously. However, for this functionality to be appropriately utilized, special hardware explicitly designed to handle such capabilities is used. 

• Redundant Information Coding through fault tolerance

Neural networks are responsible for holding the network together whenever a fault occurs. As a result, they are often capable of retaining data even when a section of the network experiences a failure. 

• Prognosis

Neural Networks can make future projections from already established models. This feature is the technology behind weather and traffic predictions. 

• Self Organisation

A neural network can reorganize itself and develop new inferences based on the data it is currently receiving. This capability makes it self-dependent and therefore facilitates artificial intelligence. 

Neural networks are remarkable computer brains that can extract useful and meaningful information from crude data. This feature is vital in determining patterns and trends that would otherwise escape the human eye. The power of machine learning and artificial intelligence makes it easier for humans to solve tricky puzzles, identify hidden patterns, and use a wide range of data to teach computers to make better decisions. 

The ability to make current decisions, however, isn't the end game of artificial intelligence. With the capabilities of neural networks continually increasing by the day, it is possible to have "expert" neural networks that can be used to make near-accurate projections. 

These technological advancements are universally beneficial to humanity. The primary aim of artificial intelligence to make life easier and more efficient for humans. These advancements have already led to market-ready applications such as facial recognition, speech-to-text transcription, handwriting recognition usually used in processing checks, pattern recognition in general, and even complex data analysis. Some other applications include signal processing and weather predictions. 

What Are Some Popular Applications of Neural Networks in Real Life

As earlier mentioned, neural networks have had a broad range of use cases. It is an integral part of the technological revolution towards artificial intelligence. The concept of neural networks has been used widely to perform tasks that ordinary machines or computers cannot do. However, these tasks can be quickly done by humans because of their intelligence and decision-making process. Below are some popular applications of Neural Networks in real life. 

Industrial Applications 

Neural networks are used in the manufacturing industry to perform several tasks. For example, they are used in chemical product design analysis, project bidding, process control, machine diagnosis, and dynamic modeling of chemical processes. 

Financial and Business Applications

In business and finance, neural networks are used in hedge fund analytics, fraud detection, and marketing segmentation. In addition, some innovative startups, such as untapt, are famous for using artificial intelligence and neural networks to solve corporate challenges such as hiring efficiency and low employee retention. 

Also, neural network applications have been used to develop business and financial applications in several fields. For instance, neural networks have been used for automatic bond rating and trading advisory systems in the financial securities field. Likewise, in banking, credit card attrition, fraud and risk evaluations, and loan delinquencies are all applications of neural networks.  

Anomaly Detection

Since artificial neural networks are the best tools for pattern recognition, they can identify unusual occurrences. Thus, ANNs can be used to detect anomalies and generate useful output from a comprehensive data set. 

Medical Applications

Medical practitioners have repeatedly used neural network-powered technologies to perform several medical tasks. For instance, doctors have used neural network-powered devices to perform cancer cell analysis, prosthetic design, EEG, and ECG. 

Similarly, transplant process optimizations, expense reduction, emergency room test advisement, and hospital systems improvement are all products of the application of neural networks.

Automotive and Aerospace Applications

Both automotive and aerospace industries have benefited from the technological advancements of AI. For example, aircraft are analyzed using neural network-powered technologies used to aircraft control systems, flight path simulations, high-performance auto-piloting, and aircraft component fault detectors. Similarly, Automotives have virtual sensors, improved guidance systems, warranty activity analyzers, and power trains that are all neural network engineering products. 

Technology has advanced at a much higher rate in recent years, and it continues to do s. With the successful implementation of neural networks in machine learning, there are endless possibilities for artificial intelligence capabilities. 

Artificial intelligence has been integrated into several industries. As a result, many researchers are looking for ways to make human life more efficient through technology and AI. In the recent future, AI will be part of our everyday life, impacting the decision-making process in financial, medical, educational, banking, and business institutions. 

Finally, neural network technology is still being refined to enable artificial intelligence to make more accurate and near-perfect decisions. Therefore, several adjustments are being made to ensure perfection in artificial neural networks' learning and decision-making processes.