Artificial Intelligence - Neural Networks

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What are Artificial Neural Networks (ANNs)?

Artificial Neural Networks (ANNs) Artificial Neural Networks are parallel computing devices, which are basically an attempt to make a computer model of the brain. The main objective is to develop a system to perform various computational tasks faster than the traditional systems.

The inventor of the first neuro-computer, Dr. Robert Hecht-Nielsen, defines a neural network as −

"A computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.”

Basic Structure of ANNs

The idea of ANNs is based on the belief that working of human brain by making the right connections, can be imitated using silicon and wires as living neurons and dendrites.

The human brain is composed of 86 billion nerve cells called neurons. They are connected to other thousand cells by Axons. Stimuli from external environment or inputs from sensory organs are accepted by dendrites. These inputs create electric impulses, which quickly travel through the neural network. A neuron can then send the message to other neuron to handle the issue or does not send it forward.

ANNs are composed of multiple nodes, which imitate biological neurons of human brain. The neurons are connected by links and they interact with each other. The nodes can take input data and perform simple operations on the data. The result of these operations is passed to other neurons. The output at each node is called its activation or node value.

Each link is associated with weight. ANNs are capable of learning, which takes place by altering weight values. The following illustration shows a simple ANN −

Artificial Neurons Vs Biological Neurons

Some of the differences between artificial neurons and biological neurons are tabulated below.

Feature	Biological Neurons	Artificial Neurons
Structure	Complex with cell body, dendrites, and axons.	Simplifies mathematical function.
Functionality	Signals interpreted through action potentials and neurotransmitters.	Computes weighted sums and applies an activation function.
Communication	Uses chemical and electrical signals for inter-neuron communication	Communicates through numerical data and mathematical models
Processing Speed	Slower transmission due to chemical processes.	Extremely fast computations based on electronic processing.
Energy Efficiency	Highly energy efficient and is capable of operating on small amounts of energy.	Generally less energy-efficient, varies by architecture and implementation.
Complexity	Highly complex, involves various types of neurons and glacial cells, and intricate chemical signaling.	Simpler in structure but can scale up to model complex behaviors through layers of neurons in neural networks.
Execution	Operates in real-time within biological systems.	Operates in digital environments, requiring hardware and software.
Learning	Learns through experience and can generalize in complex ways.	Learns through datasets and optimization techniques, often requiring large datasets.

Types of ANNs

Neural networks are classified into different categories based on factors like their depth, the number of hidden layers, and the I/O capabilities of each node. Types of neural networks include −

• Convolutional Neural Networks
• Deconvolutional Neural Networks
• Recurrent Neural Networks
• Feed-forward Neural Networks
• Modular Neural Networks
• Generative Adversarial Networks

Topologies of ANNs

There are two Artificial Neural Network topologies − FeedForward and Feedback.

FeedForward ANN

In this ANN, the information flow is unidirectional. A unit sends information to other unit from which it does not receive any information. There are no feedback loops. They are used in pattern generation/recognition/classification. They have fixed inputs and outputs.

FeedBack ANN

Feedback ANN have connections that loop back, which allows to fed back information into the structure. This structure enables to handle sequential data and temporal dependencies, making them suitable for tasks like time series prediction and language modeling.

Working of ANNs

In the topology diagrams shown, each arrow represents a connection between two neurons and indicates the pathway for the flow of information. Each connection has a weight, an integer number that controls the signal between the two neurons.

If the network generates a “good or desired” output, there is no need to adjust the weights. However, if the network generates a “poor or undesired” output or an error, then the system alters the weights in order to improve subsequent results.

Machine Learning in ANNs

ANNs are capable of learning and they need to be trained. There are several learning strategies −

• Supervised Learning: It involves a teacher that is scholar than the ANN itself. For example, the teacher feeds some example data about which the teacher already knows the answers.

  For example, pattern recognizing. The ANN comes up with guesses while recognizing. Then the teacher provides the ANN with the answers. The network then compares it guesses with the teacher’s “correct” answers and makes adjustments according to errors.

• Unsupervised Learning: It is required when there is no example data set with known answers. For example, searching for a hidden pattern. In this case, clustering i.e. dividing a set of elements into groups according to some unknown pattern is carried out based on the existing data sets present.
• Reinforcement Learning: This strategy built on observation. The ANN makes a decision by observing its environment. If the observation is negative, the network adjusts its weights to be able to make a different required decision the next time.

Applications of ANNs

They can perform tasks that are easy for a human but difficult for a machine −

• Aerospace: Autopilot aircrafts, aircraft fault detection.
• Automotive: Automobile guidance systems.
• Military: Weapon orientation and steering, target tracking, object discrimination, facial recognition, signal/image identification.
• Electronics: Code sequence prediction, IC chip layout, chip failure analysis, machine vision, voice synthesis.
• Financial: Real estate appraisal, loan advisor, mortgage screening, corporate bond rating, portfolio trading program, corporate financial analysis, currency value prediction, document readers, credit application evaluators.
• Industrial: Manufacturing process control, product design and analysis, quality inspection systems, welding quality analysis, paper quality prediction, chemical product design analysis, dynamic modeling of chemical process systems, machine maintenance analysis, project bidding, planning, and management.
• Medical: Cancer cell analysis, EEG and ECG analysis, prosthetic design, transplant time optimizer.
• Speech: Speech recognition, speech classification, text to speech conversion.
• Telecommunications: Image and data compression, automated information services, real-time spoken language translation.
• Transportation: Truck Brake system diagnosis, vehicle scheduling, routing systems.
• Software: Pattern Recognition in facial recognition, optical character recognition, etc.
• Time Series Prediction: ANNs are used to make predictions on stocks and natural calamities.
• Signal Processing: Neural networks can be trained to process an audio signal and filter it appropriately in the hearing aids.
• Control: ANNs are often used to make steering decisions of physical vehicles.
• Anomaly Detection: As ANNs are expert at recognizing patterns, they can also be trained to generate an output when something unusual occurs that misfits the pattern.