“There is hope, even when your brain tells you there isn’t.” “Life is funny that way. Sometimes the dumbest thing you do turns out to be the smartest.” “No brain at all, some of them [people], only grey fluff that’s blown into their heads by mistake, and they don’t Think.”
What is Neural Network?
A neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network, composed of artificial neurons or nodes
“In neural networks, when data is collected about a particular process, the model that is used to learn about and understand that process and predict how that process will perform in the future is a simplified representation of how a brain neuron works,” said Mark Stadtmueller
History of neural networks
The idea of neural networks began unsurprisingly as a model of how neurons in the brain function, termed ‘connectionism’ and used connected circuits to simulate intelligent behaviour .In 1943, portrayed with a simple electrical circuit by neurophysiologist Warren McCulloch and mathematician Walter Pitts
Since neural networks are best at identifying patterns or trends in data, they are well suited for prediction or forecasting needs including:
- Sales forecasting
- Industrial process control
- Customer research
- Data validation
- Risk management
- Target marketing
But to give you some more specific examples; ANN are also used in the following specific paradigms: recognition of speakers in communications; diagnosis of hepatitis; recovery of telecommunications from faulty software; interpretation of multi-meaning Chinese words; undersea mine detection; texture analysis; three-dimensional object recognition; hand-written word recognition; and facial recognition.
Neural networks in medicine
Artificial Neural Networks (ANN) are currently a ‘hot’ research area in medicine and it is believed that they will receive extensive application to biomedical systems in the next few years. At the moment, the research is mostly on modelling parts of the human body and recognizing diseases from various scans (e.g. cardiograms, CAT scans, ultrasonic scans, etc.).
Neural networks are ideal in recognizing diseases using scans since there is no need to provide a specific algorithm on how to identify the disease. Neural networks learn by example so the details of how to recognize the disease are not needed. What is needed is a set of examples that are representative of all the variations of the disease. The quantity of examples is not as important as the ‘quantity’. The examples need to be selected very carefully if the system is to perform reliably and efficiently.
Neural Networks in business
Business is a diverted field with several general areas of specialisation such as accounting or financial analysis. Almost any neural network application would fit into one business area or financial analysis.
There is some potential for using neural networks for business purposes, including resource allocation and scheduling. There is also a strong potential for using neural networks for database mining, that is, searching for patterns implicit within the explicitly stored information in databases. Most of the funded work in this area is classified as proprietary. Thus, it is not possible to report on the full extent of the work going on. Most work is applying neural networks, such as the Hopfield-Tank network for optimization and scheduling.
Neural Network in Marketing
There is a marketing application which has been integrated with a neural network system. The Airline Marketing Tactician (a trademark abbreviated as AMT) is a computer system made of various intelligent technologies including expert systems. A feedforward neural network is integrated with the AMT and was trained using back-propagation to assist the marketing control of airline seat allocations. The adaptive neural approach was amenable to rule expression. Additionally, the application’s environment changed rapidly and constantly, which required a continuously adaptive solution. The system is used to monitor and recommend booking advice for each departure. Such information has a direct impact on the profitability of an airline and can provide a technological advantage for users of the system. [Hutchison & Stephens, 1987]
While it is significant that neural networks have been applied to this problem, it is also important to see that this intelligent technology can be integrated with expert systems and other approaches to make a functional system. Neural networks were used to discover the influence of undefined interactions by the various variables. While these interactions were not defined, they were used by the neural system to develop useful conclusions. It is also noteworthy to see that neural networks can influence the bottom line.
Security
Neural networks are widely used for protection from computer viruses, fraud, etc.
One of the examples is ICSP Neural from Symantec. It protects from cyber attacks by determining the bad USB devices containing viruses and exploiting zero-day vulnerabilities.
Vehicle building
AI and ML are used in this industry to automate processes. For example, Tesla uses a neural network for the autopilot system in the vehicles. With the help of trained artificial intelligence, it recognizes the road markings, detects obstacles, and makes the road safer for the driver.
Reducing Email Fatigue and Improving Conversion Rates
By only advertising relevant products to interested customers, you also reduce the chances of customers developing email fatigue.
In short, if your advertisements are relevant and interesting customers are more likely to interact.
This drives visits to your website, potentially increasing sales, and helps you to build a strong client-business relationship.
According to dragon360.com 61% of customers say that they are most likely to use companies that send them targeted content.
Applying Artificial Neural Networks in your marketing strategy can save your company both time and money.
By streamlining your marketing approach in this way you will only be targeting the customers most likely to purchase your product.
This streamlined approach of targeting the people most likely to engage can help to increase sales and profits.
Many companies who have adopted targeted or personalized marketing strategies have noticed clear, positive results.
For example, stationery retailers Paperstyle segmented their subscribers into two different groups.
Each group then received targeted emails.
Consequently, the business reported an open rate increase of 244%.
The traffic driven from emails to the website also increased by 161%.
These statistics show that personalised marketing campaigns can deliver real results, benefiting businesses.
Improving Search Engine Functionality
During the 2015 Google I/O keynote address in San Francisco, Google revealed they were working on improving their search engine.
These improvements are powered by a 30 layer deep Artificial Neural Network.
This depth of layers, Google believes, allows the search engine to process complicated searches such as shapes and colours.
Using an Artificial Neural Network allows the system to constantly learn and improve.
This allows Google to constantly improve its search engine.
Within a few months, Google was already noticing improvements in search results.
The company reported that its error rate had dropped from 23% down to just 8%.
Google’s application shows that neural networks can help to improve search engine functionality.
Similar Artificial Neural Networks can be applied to the search feature on many e-commerce websites.
This means that many companies can improve their website search engine functionality.
This allows customers with only a vague idea of what they want to easily find the perfect item.
Amazon has reported sales increases of 29% following improvements to its recommendation systems.
The Future of Neural Networks
“We need to remember that artificial neural networks and deep learning are but one set of techniques for developing solutions to specific problems. Right now, they’re the ‘big thing,’”
— — Richard Yonck, Founder and Lead Futurist of Intelligent Future Consulting and author of Heart of the Machine: Our Future in a World of Artificial Emotional Intelligence.
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.
Conclusion
Neural computers perform very favorably in business and military applications. They do not require explicit programming by an expert and are robust to noisy, imprecise, or incomplete data.
— Divya
