🔰 Neural Networks 🔰

8 min readMar 1, 2021

Today, neural networks (NN) are revolutionizing business and everyday life, bringing us to the next level in artificial intelligence (AI). By emulating the way interconnected brain cells function, NN-enabled machines (including the smartphones and computers that we use on a daily basis) are now trained to learn, recognize patterns, and make predictions in a humanoid fashion as well as solve problems in every business sector.

What Are Neural Networks?

A branch of machine learning, neural networks (NN), also known as artificial neural networks (ANN), are computational models — essentially algorithms. Neural networks have a unique ability to extract meaning from imprecise or complex data to find patterns and detect trends that are too convoluted for the human brain or for other computer techniques. Neural networks have provided us with greater convenience in numerous ways, including through ridesharing apps, Gmail smart sorting, and suggestions on Amazon.

The most groundbreaking aspect of neural networks is that once trained, they learn on their own. In this way, they emulate human brains, which are made up of neurons, the fundamental building block of both human and neural network information transmission.

How the Biological Model of Neural Networks Functions

What are neural networks emulating in human brain structure, and how does training work?

All mammalian brains consist of interconnected neurons that transmit electrochemical signals. Neurons have several components: the body, which includes a nucleus and dendrites; axons, which connect to other cells; and axon terminals or synapses, which transmit information or stimuli from one neuron to another. Combined, this unit carries out communication and integration functions in the nervous system. The human brain has a massive number of processing units (86 billion neurons) that enable the performance of highly complex functions.

How Artificial Neural Networks Function

ANNs are statistical models designed to adapt and self-program by using learning algorithms in order to understand and sort out concepts, images, and photographs. For processors to do their work, developers arrange them in layers that operate in parallel. The input layer is analogous to the dendrites in the human brain’s neural network. The hidden layer is comparable to the cell body and sits between the input layer and output layer (which is akin to the synaptic outputs in the brain). The hidden layer is where artificial neurons take in a set of inputs based on synaptic weight, which is the amplitude or strength of a connection between nodes. These weighted inputs generate an output through a transfer function to the output layer.

How Do You Train a Neural Network?

Once you’ve structured a network for a particular application, training (i.e., learning), begins. There are two approaches to training. Supervised learning provides the network with desired outputs through manual grading of network performance or by delivering desired outputs and inputs. Unsupervised learning occurs when the network makes sense of inputs without outside assistance or instruction.

A Brief History of Neural Networks

Neural networks date back to the early 1940s when mathematicians Warren McCulloch and Walter Pitts built a simple algorithm-based system designed to emulate human brain function. Work in the field accelerated in 1957 when Cornell University’s Frank Rosenblatt conceived of the perceptron, the groundbreaking algorithm developed to perform complex recognition tasks. During the four decades that followed, the lack of computing power necessary to process large amounts of data put the brakes on advances. In the 2000s, thanks to the advent of greater computing power and more sophisticated hardware, as well as to the existence of vast data sets to draw from, computer scientists finally had what they needed, and neural networks and AI took off, with no end in sight.

Why Do We Use Neural Networks?

Neural networks’ human-like attributes and ability to complete tasks in infinite permutations and combinations make them uniquely suited to today’s big data-based applications. Because neural networks also have the unique capacity (known as fuzzy logic) to make sense of ambiguous, contradictory, or incomplete data, they are able to use controlled processes when no exact models are available.

According to a report published by Statista, in 2017, global data volumes reached close to 100,000 petabytes (i.e., one million gigabytes) per month; they are forecasted to reach 232,655 petabytes by 2021. With businesses, individuals, and devices generating vast amounts of information, all of that big data is valuable, and neural networks can make sense of it.

Tasks Neural Networks Perform

Neural networks are highly valuable because they can carry out tasks to make sense of data while retaining all their other attributes. Here are the critical tasks that neural networks perform:

Classification: NNs organize patterns or datasets into predefined classes.
Prediction: They produce the expected output from given input.
Clustering: They identify a unique feature of the data and classify it without any knowledge of prior data.
Associating: You can train neural networks to “remember” patterns. When you show an unfamiliar version of a pattern, the network associates it with the most comparable version in its memory and reverts to the latter.

Neural networks are fundamental to deep learning, a robust set of NN techniques that lends itself to solving abstract problems, such as bioinformatics, drug design, social network filtering, and natural language translation. Deep learning is where we will solve the most complicated issues in science and engineering, including advanced robotics. As neural networks become smarter and faster, we make advances on a daily basis.

Real-World and Industry Applications of Neural Networks

Here’s a list of neural networks engineering applications currently in use in various industries:

Aerospace: Aircraft component fault detectors and simulations, aircraft control systems, high-performance auto-piloting, and flight path simulations
Automotive: Improved guidance systems, development of power trains, virtual sensors, and warranty activity analyzers
Electronics: Chip failure analysis, circuit chip layouts, machine vision, non-linear modeling, prediction of the code sequence, process control, and voice synthesis
Manufacturing: Chemical product design analysis, dynamic modeling of chemical process systems, process control, process and machine diagnosis, product design and analysis, paper quality prediction, project bidding, planning and management, quality analysis of computer chips, visual quality inspection systems, and welding quality analysis
Mechanics: Condition monitoring, systems modeling, and control
Robotics: Forklift robots, manipulator controllers, trajectory control, and vision systems
Telecommunications: ATM network control, automated information services, customer payment processing systems, data compression, equalizers, fault management, handwriting recognition, network design, management, routing and control, network monitoring, real-time translation of spoken language, and pattern recognition (faces, objects, fingerprints, semantic parsing, spell check, signal processing, and speech recognition)

Business Applications of Neural Networks:

Real-world business applications for neural networks are booming. In some cases, NNs have already become the method of choice for businesses that use hedge fund analytics, marketing segmentation, and fraud detection..

Here are current examples of NN business applications:

Banking: Credit card attrition, credit and loan application evaluation, fraud and risk evaluation, and loan delinquencies
Business Analytics: Customer behavior modeling, customer segmentation, fraud propensity, market research, market mix, market structure, and models for attrition, default, purchase, and renewals
Defense: Counterterrorism, facial recognition, feature extraction, noise suppression, object discrimination, sensors, sonar, radar and image signal processing, signal/image identification, target tracking, and weapon steering
Education: Adaptive learning software, dynamic forecasting, education system analysis and forecasting, student performance modeling, and personality profiling
Financial: Corporate bond ratings, corporate financial analysis, credit line use analysis, currency price prediction, loan advising, mortgage screening, real estate appraisal, and portfolio trading
Medical: Cancer cell analysis, ECG and EEG analysis, emergency room test advisement, expense reduction and quality improvement for hospital systems, transplant process optimization, and prosthesis design
Securities: Automatic bond rating, market analysis, and stock trading advisory systems
Transportation: Routing systems, truck brake diagnosis systems, and vehicle scheduling.

“With the advancement of computer and communication technologies, the whole process of doing business has undergone a massive change. More and more knowledge-based systems have made their way into a large number of companies”.

The Future of Neural Networks

Here are some likely future developments in neural network technologies:

Fuzzy Logic Integration: Fuzzy logic recognizes more than simple true and false values — it takes into account concepts that are relative, like somewhat, sometimes, and usually. Fuzzy logic and neural networks are integrated for uses as diverse as screening job applicants, auto-engineering, building crane control, and monitoring glaucoma. Fuzzy logic will be an essential feature in future neural network applications.
Pulsed Neural Networks: Recently, neurobiological experiment data has clarified that mammalian biological neural networks connect and communicate through pulsing and use the timing of pulses to transmit information and perform computations. This recognition has accelerated significant research, including theoretical analyses, model development, neurobiological modeling, and hardware deployment, all aimed at making computing even more similar to the way our brains function.
Specialized Hardware: There’s currently a development explosion to create the hardware that will speed and ultimately lower the price of neural networks, machine learning, and deep learning. Established companies and startups are racing to develop improved chips and graphic processing units, but the real news is the fast development of neural network processing units (NNPUs) and other AI specific hardware, collectively referred to as neurosynaptic architectures. Neurosynaptic chips are fundamental to the progress of AI because they function more like a biological brain than the core of a traditional computer. With its Brain Power technology, IBM has been a leader in the development of neurosynaptic chips. Unlike standard chips, which run continuously, Brain Power’s chips are event-driven and operate on an as-needed basis. The technology integrates memory, computation, and communication.
Improvement of Existing Technologies: Enabled by new software and hardware as well as by current neural network technologies and the increased computing power of neurosynaptic architectures, neural networks have only begun to show what they can do. The myriad business applications of faster, cheaper, and more human-like problem-solving and improved training methods are highly lucrative.
Robotics: There have been countless predictions about robots that will be able to feel like us, see like us, and make prognostications about the world around them. These prophecies even include some dystopian versions of that future, from the Terminator film series to Blade Runner and Westworld. However, futurist Yonck says that we still have a very long way to go before robots replace us: “While these robots are learning in a limited way, it’s a pretty far leap to say they’re ‘thinking.’ There are so many things that have to happen before these systems can truly think in a fluid, non-brittle way. One of the critical factors I bring up in my book is the ability to establish and act on self-determined values in real-time, which we humans do thousands of times a day. Without this, these systems will fail every time conditions fall outside a predefined domain.”