What is AI and why is it important to Intelligent Document Processing?

Artificial Intelligence (AI) has been a topic of immense interest in both the academic and industrial sectors since the 1950s when computers first began to change how we handle data. Today, AI is being used in almost every industry and forms the backbone of many digital technologies. This brings us to the question we’ll be discussing today: What exactly is AI and why is it important to Intelligent Document Processing (IDP)?

AI and ML

When discussing AI, the term machine learning (ML) is often used in the exact same context. It can be confusing to those unfamiliar with the domain to know which is the right term to use. In very simple terms, ML is a particular type of AI that uses probabilistic approaches and statistical methods. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. That is to say, ML is a branch or subset of AI. Because so many algorithms and models powering predictive applications are based on ML methodologies, you may find that whenever the term AI is used, it is really talking about ML.

Classical vs. Deep Learning 

With these small distinctions in mind, let’s talk about machine learning as it’s found applied in today’s world. There are two major technologies used in the industry today to solve predictive problems: classical machine learning and deep learning. Classical methods in machine learning have been around for decades, even before computers existed. You may be familiar with some of the terms such as logistic regression, support vector machines, K-means clustering or random forests to name a few. These algorithms formed the foundations of many successful products for years and many of them are still found in production environments because they remain powerful predictors for many use cases.

The second approach mentioned earlier was deep learning – an approach responsible for revolutionizing predictive systems related to images, sound and language. Deep learning centers around a learning system called neural networks. Neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems. There are many kinds of neural network types, called architectures, depending on the task. The most common architecture types are convolutional neural networks (CNNs) which are well-suited for any prediction involving images and transformers. Transformers are attention-based neural network architectures that are broadly applied in natural language processing but more recently have also shown success on images as well. Although research into neural networks has been around for many decades, they have only been actively applied since 2012 when the first major breakthrough in image processing occurred on the ImageNet challenge–a benchmark meant to measure how well the tested models were able to recognize objects in images. During that year, CNNs achieved state-of-the-art performance in visual recognition tasks by a large margin, and this began a rush to research and develop new neural network technologies.

AI and ML in IDP

With the above context to guide us, let’s talk about how AI and ML play a role in IDP. When we think about the traditional work associated with document processing, we find repetitive, reproducible work with only minor variations from task to task. It is in these kinds of problem spaces that machine learning really excels. When there are obvious patterns, such as language or visual cues that a human can identify and use to process a document, machine learning can accelerate or even completely automate that task. In a world where the flow of information increases on a daily basis, an IDP platform can save hundreds or even thousands of hours of work and improve data accuracy while freeing up valuable resources.

Ephesoft uses AI technologies in our IDP platform to help customers improve efficiency, productivity and accuracy. It allows people and companies to move and respond faster, saving users 95% of their time to extract and process document data. With AI and ML technology, there is a method to the madness – and this method is the key to working smarter and faster.