Machine Learning and Artificial Neural Networks

Artificial intelligence is a rapidly growing field with many levels and subsets that make it essential knowledge for any medium to large-sized company looking to expand its reach in the era of digital commerce. In order to stay competitive, businesses need to embrace artificial intelligence and use it to its full potential. With the help of artificial intelligence, companies can automate repetitive tasks, improve decision-making, and better serve their customers as the world becomes more digitized. In this article, we will go over two specific branches of artificial intelligence: machine learning and artificial neural networks.

Machine Learning (ML)

Machine learning involves training a computer to do what we do naturally when we learn. For example, machine learning allows software applications to become more apt at predicting outcomes without the machine being explicitly told to. Usually, machine learning uses historical data as its source of information to predict new outcomes.

Machine learning can be supervised, unsupervised, semi-supervised, and reinforced. Supervised machine learning involves the use of labeled data sets fed by humans to the application to train algorithms to classify data or predict outcomes. Unsupervised machine learning uses unlabeled data to categorize and analyze data, discovering hidden groupings and patterns without the need for human intervention on its behalf. Semi-supervised machine learning combines the previous two types, as data scientists may feed labeled data to the machine to train it initially to predict outcomes but then feed it more data so that it can find patterns on its own and categorize the data. Reinforcement machine learning is used when there are rules for a multi-step process; the data is fed to the machine, and the machine is given positive or negative cues based on whether it is doing its task correctly. After a few times, algorithms become more predictive and correct on their own, and the algorithm can manage and categorize data on its own successfully. In summary, we can train a computer to read and understand written data on a document, recognize spoken language and convert it into text, predict outcomes based on related variables, and uncover hidden definitions and categories in seemingly unrelated data.

Machine learning has a wide range of uses. Recommendation engines are a common use case for machine learning. Other popular uses include fraud detection, spam filtering, malware threat detection, business process automation (BPA), and predictive maintenance.

Artificial Neural Networks (ANN)

While computers can carry out many tasks that humans can’t, the human brain is a step ahead regarding common sense, imagination, and inspiration. However, computers themselves are starting to become more like the human brain. Artificial neural networks (ANNs) are made to replicate the human brain as closely as possible by copying the way biological neurons signal to one another. In fact, ANNs are also called simulated neural networks (SNNs), which better resemble their function to simulate the human brain.

According to IBM, artificial neural networks are a “subset of machine learning and are at the heart of deep learning algorithms.” ANNs are made up of node layers, containing an input layer and an output layer with multiple layers in between. Each node has an associated weight and threshold that is activated when exceeded. After the node is activated, it then sends a signal to the next layer. Eventually, the information fed to the input node is outputted by the output node. Like Natural Language Processing (NLPs), ANNs take a vast amount of training and data to improve their accuracy over time. However, once developed, ANNs can be used as a powerful tool in data processing and classification.

There are multiple types of neural networks which are used for different purposes. These include feedforward neural networks (or multi-layered perceptrons (MLPs)), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). MLPs have an input, output, and multiple hidden layers. Data is used in training these networks to become more accurate. CNNs are similar to MLPs but mainly used for image and pattern recognition. These networks use convulsions to carry out repeated tasks for identification. Finally, RNNs use feedback loops to make predictions about future outcomes. These networks are primarily used in stock market predictions and forecasting.

Applications of ML and ANN in Business & Digitization

Machine learning is providing game-changing benefits for businesses across industries. By utilizing the ability to self-improve given more data, machine learning fits perfectly in the modern business environment where data is constantly being generated. This data applies itself to a wide variety of tasks, including predictive maintenance, fraud detection, and recommender systems. Machine learning is also involved in the process of digitizing business operations. By automating repetitive and low-value tasks, businesses can free up employees to focus on higher-level tasks. In short, machine learning is revolutionizing the way businesses operate. The increased capacity and efficiency of ML in comparison to human workers is quickly becoming an essential tool for any company looking to stay relevant in a market experiencing exponential growth.

As a more humanized-brain-based form of Machine Learning, Artificial Neural Networks are increasingly being used by businesses to extract insights from digitized data. While general, less complex machine learning is usually focused on automation and repetitive tasks, ANNs take on higher stakes issues in a business. Typically, they help businesses to make better decisions by analyzing large amounts of data that would be impossible for humans to process.  For example, ANNs can be used to analyze customer purchase histories to identify trends and make recommendations for future purchases. They can also be used to anticipate customer demand and optimize marketing campaigns. In many cases, the use of ANNs can provide a competitive advantage by allowing businesses to make better decisions and take action more quickly than their rivals. As businesses continue to digitize their operations, the use of Artificial Neural Networks is likely to become even more widespread.


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