Here is All You Need to Know About Artificial Neural Networks (ANNs) and Machine Learning (ML)
Artificial Intelligence (AI) is a field of science that involves the simulation of human intelligence in machines. The main goal of AI is to program machines to do tasks that typically require human intelligence. AI is particularly useful for performing repetitive, high-volume tasks, such as converting audio conversations into written text. Over time, though, AI has gotten better at performing more complicated tasks, such as confirming the identity of a person by verifying their digital signature.
Machine Learning (ML) is a branch of AI that focuses on the use of data and algorithms to simulate the way that people learn, with the intent to train machines to perform tasks without the need for explicit programming. Deep Learning (DL) is a type of ML technique that employs complex, multi-layered neural networks to teach machines to, in essence, learn by example and achieve high levels of recognition accuracy.
What is an ANN?
Artificial Neural Networks (ANNs) are a type of machine learning algorithm that teach machines to process data in a manner that simulates the human brain. There are many types of ANNs, which vary based on their number of layers, structure, data flow, and the type of neurons used. One of the oldest and simplest types of ANNs is the Perceptron model, a supervised learning algorithm that classifies data into two different categories, and consists of just two layers with two different node types, including an input and output layer.
A Feed Forward neural network is another type of ANN, which expands upon the Perceptron model by including up to three or more layers of interconnected nodes. In a Feed Forward neural network, the sequence of layers is as follows: 1) An Input layer, 2) Hidden layer(s), and 3) An Output layer. Later on in this article, we will explain how ANNs work and what each node does.
The purpose of an ANN is to display the complex relationship between the input and output layers to discover new patterns. This approach helps machines process large amounts of information, learn from past data, and solve complex problems. ANNs are used in a variety of real-world applications, including speech recognition, image recognition, medical diagnosis, and energy demand forecasting.
What Are ANNs Used For?
Artificial Neural Networks and Machine Learning are used in a variety of real-world applications. They’re deployed to analyze and categorize data, identify patterns and trends, and predict outcomes. This flexibility enables ANNs to be used in many ways, from facial recognition to speech transcription to even performing highly specific medical diagnoses.
Facial recognition is a method of human verification by viewing a person’s face. Through the use of various ML techniques, it collects vast amounts of data – namely, a large database, composed of portrait photos of people – and then uses that data to evaluate different facial characteristics, before finding a perfect match.
Facial recognition software is commonplace these days. You have probably used it to unlock your smartphone, board a flight, or even withdraw money from an ATM. But this isn’t a case of organizations adopting the latest technology just for the sake of it. For airports, the use of biometrics technology, such as facial recognition software, may help speed up boarding times, by removing the need to manually present physical passports and photo IDs to airport staff. With 99 percent accuracy, the software can analyze a face in the system in 2 seconds, and citizens can opt-out of the service if privacy is a concern.
Health care organizations are using ML techniques, such as ANNs, to improve patient care at a reduced cost, as well as assist in the early detection of tumors and heart disease. The use of neural networks could help save patients from experiencing more damage and even save their lives.
In the case of heart disease, a computational predictive system is used to identify and diagnose cardiac disease, by using a problem-related dataset that accurately reflects the pattern of the target class as a reference. For cancer research, ANNs are used to track cell movement, to determine how drugs and gene variants may affect the spread of tumors in the body.
ANNs are even being used to help detect and treat depression symptoms, by constructing neural networks capable of detecting depression symptoms from physical characteristics, with experimental results achieving 83 percent accuracy.
ANN’s are being used in the financial sector in many ways, from predicting stock market pricing to determining credit risk for personal loans. The purpose of using ANN in credit risk is to distinguish between good payers and bad payers. This helps a bank or financial lender determine a payer’s ability to repay their loan, significantly reducing the risk of financial loss for the lender.
For stock market predictions, ANNs are used to collect large amounts of financial data, and then use that data to identify patterns that may help traders predict changes in the fluctuating stock market. Such methods have led to the rise of algorithmic trading, a concept that involves the use of computer programs to follow predefined instructions and place trades based on set criteria, thereby ensuring the most efficient execution of a trade.
How Do ANNs Work?
The structure of an ANN is inspired by the human brain, in the sense that they are a highly interconnected series of nodes, which link together and send signals to one another, enabling the system to process information and make data-driven decisions. A basic neural network has three layers: An Input layer, Hidden layer(s), and an Output layer.
Data is fed into the Input layer which is then processed, analyzed, and categorized. Then, the data passes through one or more Hidden layers where Weights are assigned to the data. The weight of a node helps determine the importance of a variable. Larger weight values contribute more significantly to the output of an algorithm than smaller weight values.
Once weight values are assigned, data passes through the Output node. Whether or not a node activates depends on the given threshold of the algorithm. If the output value exceeds the given threshold, the node will activate. As a result, the output of that node will then become the input of the next node.
Should You Incorporate ANNs and AI Into Your Business?
That depends. What do you hope to achieve from incorporating Artificial Neural Networks and Machine Learning into your business? And more importantly, is the use of AI technology the most efficient and affordable way to resolve the issues you face? These are the kind of questions you will want to ask yourself and get answers to.
For this reason, it is worth approaching a team of AI experts, as they can assess your unique circumstances, choose the right AI technology, and then implement your AI system into your business in the most efficient, affordable, and hassle-free way. More importantly, they can tell you if AI is the best solution for you; and if not, they may recommend another solution that better fits your needs. This way, you receive the right technology for the right job, saving you money and resources.
How do you know which software development team is right for you? Ask to see relevant case studies and examples of their work in the field of AI. This will help give you an idea as to what they have done for past clients and what they can do for you. And you will know if what they have to offer matches your vision and budget. Of course, general enquiries typically come at no cost, no obligation.