Artificial Neural Networks (ANN)

Artificial Neural Networks (ANN) are the pieces of a computing system designed to simulate the way the human brain analyzes and processes information. They are the foundations of Artificial Intelligence (AI) and solve problems that would prove impossible or difficult by human or statistical standards. ANN have self-learning capabilities that enable them to produce better results as more data become available.

Artificial Neural Networks (ANN) are paving the way for life-changing applications to be developed for use in all sectors of the economy. Artificial Intelligence (AI) platforms that are built on ANN are disrupting the traditional way of doing things. From translating web pages into other languages to having a virtual assistant order groceries online to conversing with chatbots to solve problems, AI platforms are simplifying transactions and making services accessible to all at negligible costs.

Artificial neural networks are built like the human brain, with neuron nodes interconnected like a web. The human brain has hundreds of billions of cells called neurons. Each neuron is made up of a cell body that is responsible for processing information by carrying information towards (inputs) and away (outputs) from the brain. ANN has hundreds or thousands of artificial neurons called processing units, which are interconnected by nodes. These processing units are made up of input and output units. The input units receive various forms and structures of information based on an internal weighting system, and the neural network attempts to learn about the information presented in order to produce one output report. Just like humans need rules and guidelines to come up with a result or output, ANNs also use a set of learning rules called backpropagation, an abbreviation for backwards propagation of error, to perfect their output results.

An ANN initially goes through a training phase where it learns to recognize patterns in data, whether visually, aurally or textually. During this supervised phase, the network compares its actual output produced with what it was meant to produce, i.e. the desired output. The difference between both outcomes is adjusted using backpropagation. This means that the network works backwards going from the output unit to the input units in order to adjust the weight of its connections between the units until the difference between the actual and desired outcome produces the lowest possible error.

During the training and supervisory stage, the ANN is taught what to look for and what its output should be, using Yes/No question types with binary numbers. For example, a bank that wants to detect credit card fraud on time may have four input units fed with these questions: (1) Is the transaction in a different country from the user’s resident country? (2) Is the website the card is being used at affiliated with companies or countries on the bank’s watch list? (3) Is the transaction amount larger than $2,000? (4) Is the name on the transaction bill the same as the name of the cardholder? The bank wants the "fraud detected" responses to be Yes Yes Yes No, which in binary format would be 1 1 1 0. If the network’s actual output is 1 0 1 0, it adjusts its results until it delivers an output that coincides with 1 1 1 0. After training, the computer system can alert the bank of pending fraudulent transactions, saving the bank lots of money.

Artificial neural networks have been applied in all areas of operations. Email service providers use ANN to detect and delete spam from a user’s inbox; asset managers use it to forecast the direction of a company’s stock; Credit rating firms use it to improve their credit scoring methods; e-commerce platforms use it to personalize recommendations to their audience; chatbots are developed with ANN for natural language processing; deep learning algorithms use ANN to predict the likelihood of an event; and the list of ANN incorporation goes on across multiple sectors, industries and countries.