Artificial Intelligence Neural Network

In the field of science and technology, the term Artificial Intelligence plays a prominent role, and its recent advancements made AI gain more popularity for the concepts of Artificial Intelligence and Machine Learning. The Role of Artificial Intelligence made it possible for machines to learn from experience to perform tasks more efficiently.

The Artificial neural network is one of its advancements which is inspired by the structure of the human brain that helps computers and machines more like a human. This article helps you to understand the structure of Artificial Intelligence Neural Networks and their working procedure.

What is a Neural Network

A neural network is either a system software or hardware that works similar to the tasks performed by neurons of the human brain. Neural networks include various technologies like deep learning, and machine learning as a part of Artificial Intelligence (AI).

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Artificial Neural Networks (ANN)

Artificial neural networks (ANN) are the key tool of machine learning. These are systems developed by the inspiration of neuron functionality in the brain, which will replicate the way we humans learn. Neural networks (NN) constitute both the input & output layers, as well as a hidden layer containing units that change input into the output so that the output layer can utilize the value.

These are the tools for finding patterns that are numerous & complex for programmers to retrieve and train the machine to recognize the patterns.

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Role Neural Network in AI

Most business applications and commercial companies make use of these technologies. Their main aim is to solve complex problems like pattern recognition or facial recognition, and several other applications include -- speech-to-text transcription, data analysis, handwriting recognition for check processing, weather prediction, and signal processing.

Structure of AI Neural Networks

ANN works quite similar to human-brain. By making necessary connections, we can duplicate the working of the brain using silicon and wires which act similar to dendrites and neurons. As stimuli from the external environment are accepted by dendrites in the same way, the input creates electric impulses that travel through the neural network.

ANN consists of several nodes which behave like neurons. The nodes are connected by links (wires) for communication with one another. Nodes take input data to perform small operations on trained data and results of these operations are passed to other nodes (neurons). The output at the node is called its node value. Following is the image representing the basic structure of a neuron. 

Need for Neural Networks

Neural networks have a remarkable ability to retrieve meaningful data from imprecise data, that is used in detecting trends and extract patterns that are difficult to understand either by computer or humans. A trained NN can be made an "expert" in the information that has been given to analyze and can be used to provide projections.

Advantages of Neural Networks

Some of the advantages of neural networks are listed below

• Self-organization: An ANN can generate its own representation of the information that it receives at the time of learning.
• Real-Time Operation: ANN calculations may be done simultaneously, and some special (hardware) devices are manufactured which take advantage of this capability.
• Adaptive learning: Capability to learn how to solve tasks is based on the data given for the training set.
• Redundant Information Coding Through Fault Tolerance: Semi destruction of a network leads to degradation of corresponding performance. Moreover, some networks will have the ability to retain data even when major network damage occurs.

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Working of  AI Neural Networks 

An ANN includes a huge number of processors working parallel, which are arranged in layers. The first layer receives the raw data as input, similar to optic nerves of human eye visual processing. Every successive layer receives the raw input data as output from the previous layer, similar to neurons of the optic nerve receiving signals from those close to it. The final layer generates output. The below image shows several layers.

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Neural networks are adaptable i.e. they can modify themselves according to the training and run parallelly to provide more information about the world. If the network generates a “desired” output, then there is no need to change the trained input data, and vice-versa. If the network generates an “undesired” output resulting in errors, then the system modifies the trained input data to improve the results.

Types of Neural Networks

Neural Networks are of many types and each of these comes with a particular use case. 

Feedforward Neural Network

This is the most common type of neural network. where information travels in uni-direction, that is from input to output. 

Recurrent Neural Network

A more frequently used network type in which data can be transferred in various directions. These possess greater learning capabilities and are often used for complex tasks like learning handwriting or language recognition.

There are also other types that are rarely used, and some of them are Boltzmann machine networks, Hopfield networks, and convolutional neural networks.

Convolutional Neural Networks

Convolutional Neural Networks are similar to ordinary Neural Networks but, with two hidden layers, and they are made up of neurons that have the ability to learn. Every neuron receives some inputs, performs a dot product, and sometimes follows non-linearity. The complete network will show a single differentiable score function i.e., from class scores on one end to the raw image pixels on the other end.

And, have a loss function (e.g. SVM/Softmax) on the fully-connected layer. The tricks developed by developers to learning neural networks still apply.  Choosing the proper network depends on your choice and raw data that is to be given as input must be trained by you according to your preference. At times, we can use multiple approaches, in complex cases like voice recognition.

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How do Neural Networks learn from trained Data?

In the initial stages, neural networks (NN) are fed with huge amounts of data. Training is generally given by providing input and educating the network on what should be the output. For example, facial recognition is the latest technology implemented by many smartphone companies.

Each input is gathered by the identification of matching data, like images of the person’s face, iris, various facial expressions, and all these inputs have to be trained. Providing proper answers will allow it to accommodate its internal data to learn how better it can perform.

Rules must be defined in such a way that, each node decides what to be sent to the next layer considering its own inputs from the previous layer. This is done by considering many principles like genetic algorithms, fuzzy logic, gradient-based training Bayesian method. ANNs are given basic rules related to object relationships. The right decision must be taken in building the rules.

Strategies of Machine Learning in ANN

Artificial neural networks have the ability to learn but they should be trained. There are many learning strategies namely:

• Supervised Learning: It involves a scholar. For example, the scholar gives examples while preaching for a better understanding of the moral. In the same way, ANN implements pattern recognition where it starts guessing while recognizing. Then, the trained data patterns provide the ANN with the answers.
• Unsupervised Learning: It comes to action when there is no sample data set with known answers. Searching for hidden patterns is one such example. The concept of clustering involves dividing the elements into sets of groups, which is based on unknown patterns that are carried out using existing data sets.
• Reinforcement Learning: It is a strategy built based on observation. The ANN takes a decision by considering its environment. If the observations are supposed to be negative, the network adjusts its data to make a different decision for next time.

Applications of AI Neural Networks

Recognition of Image was the first area where neural networks were successfully applied, but the technology expanded to many areas such as

• Natural language processing, translation, and language generation.
• Drug discovery and development.
• Stock market prediction.
• Delivery driver route planning and optimization.
• Chatbots.

Neural Networks currently in Practice

In what real-time applications neural networks are best suited?

A NN has broad applicability to real-time business problems. Now-a-days, they are successfully implemented by many industries including the telecom sectors. Ever since neural networks evolved as a new trend, identifying patterns in data has become much easier as they are well suited for forecasting needs and prediction.

For example, industrial process control, sales forecasting, data target marketing, validation, risk management, and customer research are some of its real-time applications.

To be more specific, ANNs are also used in recognition of speakers in communication (speech recognition), undersea mine detection, the google assistant (SIRI), recovery of telecommunications from faulty software, diagnosis of hepatitis,  words texture analysis three-dimensional object recognition, handwritten word recognition, and facial recognition.

The above ones are specific areas where neural networks are being applied today. Primary uses involve processes that operate according to strict patterns and have a huge amount of data. If the data involved is too heavy for a human brain to understand in a reasonable amount of time, the process of automation is easier through artificial neural networks.

Limitations of Neural Networks

From a technical point of view, one of the biggest challenges is the amount of time it takes to train networks, which often require the acceptable amount of computing power for even complex tasks. The second most issue to be considered is that neural networks are black boxes, in which the user groups the trained data and receives answers.

They are allowed to tune the answers, and the drawback is that they have no access to the exact process of decision-making. This is the reason why researchers are working actively, but artificial neural networks play a very big role in changing day-to-day lives.

Future scope Artificial Intelligence Neural Networks

Being a highly competitive world, we have a lot to gain from neural networks. Their capability to learn through better examples makes them powerful and flexible. Moreover, we need not devise an algorithm to perform a particular task.

We don’t require internal mechanisms for that task. These are well suited for real-time systems as they respond fast with the best computational times because of their parallel architecture.

Neural networks are also contributing to other areas of research like psychology and neurology. In neurology, it is used to investigate the internal mechanisms of the brain and model parts of living organisms. The most exciting aspect of neural networks is that there is a possibility that one-day 'conscious' networks might arise.

Some scientists are arguing that consciousness is a “mechanical property” and conscious neural networks are realistic and are possible. Neural networks have a huge potential and we can get the best from them by collaborating with fuzzy logic, computing, AI, and ML.

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