Day: January 20, 2022

Lifting the veil: Uncovering the mysteries of Machine Learning

Artificial intelligence (AI) and machine learning (ML) are the new hot and trendy in technology. Yet AI has been around for a while. At Docbyte, we’ve been using rule-based AI to automate sorting documents into categories for our digital mailroom. As for ML, this is where things get interesting as potential benefits are huge for any onboarding or mailroom process.   WHITEPAPER – INTELLIGENT MAILROOM An Intelligent Mailroom (IM) is a technology that quickly processes all inbound documents in any format and then distributes them to the relevant departments across your organization. It supports operations, saves time, and prevents errors and losses. Moving to an Intelligent Mailroom will improve your operational excellence, increasing your responsiveness. Download But for most people, machine learning is as mysterious as distant buildings on a dark, foggy day. Their hazy outlines may give us a general idea of what is involved, but distinguishing a hospital from an office block or counting the windows is only possible by taking a closer look. The same goes for machine learning. As this technology is becoming increasingly important, working its way into our everyday lives, it’s high time to lift the misty veil and dispel some of the mysteries surrounding it.   Machine Learning: The world-renowned Merriam-Webster dictionary defines machine learning as “the process by which a computer is able to improve its own performance by continuously incorporating new data into an existing statistical model.”  Wikipedia gets more technical: “Machine learning algorithms build a mathematical model based on sample data, known as ‘training data’, in order to make predictions or decisions without being explicitly programmed to perform the task.”  Both definitions are spot-on and highlight the most important characteristics of ML: A mathematical and statistics-based method Little to no human intervention in training the model Self-improvement capabilities of the algorithm   ML vs AI: A foray into machine learning territory can soon lead to confusion as distinctions between similar technological concepts – say between ML and AI – may be blurry.  Simply put, AI is the umbrella term for the theory and development of computer systems that perform tasks that would normally require human intelligence, i.e. try to simulate intelligent behavior in computers. On this basis, ML is a tool to create AI.   Two Types of Machine Learning: The two most common and important approaches to creating a model with machine learning are supervised and unsupervised. 1. Supervised: This type of ML can be compared to a student-teacher relationship. The teacher, a human programmer, provides input from which the student, the ML model, learns to infer underlying patterns. The student then applies its learning to new exercises, adjusting its model each time the answers are wrong. The more examples of data the student receive, the better it becomes. Two examples of this type are classification and regression. Classification: You feed your algorithm item characteristics and a set of categories into which these items can be sorted. The algorithm then searches for patterns in how these item characteristics are categorized, so that it can also correctly categorize new items based on its findings.  For instance, if flowers with a certain height and color grow in a specific region, then new flowers with those features should fall into the category that can grow in this region. Regression: Analyzes the relationship between variables and their effect on certain characteristics. For instance, how does the color of a flower impact its price? By discovering these causal effects, the algorithm can accurately set price points for new flowers.   2. Unsupervised: While supervised models entail some form of human interaction and predefined rules, unsupervised models have none. Data is given to an algorithm, which then figures out patterns and characteristics by itself. Again, two examples: Clustering: Instead of asking the algorithm to sort item characteristics into predefined categories, you provide data and let the algorithm define the categories into which it sorts the item characteristics. Topic Modeling: Similar to clustering, topic modeling algorithms extract a predefined number of topics from data they have been fed.   Behind the Buzz: Machine learning is a topic riddled with buzzwords, without people always knowing what they mean. We explain some of them: Data Mining Probably the most commonly heard and confusing buzzword. Contrary to what the name might suggest, data mining is not about digging up new data from various systems. It’s actually digging through your existing mountain of data to find the most useful information, making it closer to data filtering than mining.   Neural Network A machine learning algorithm that mimics the way the human brain works. In essence, it’s a network of neurons where each neuron represents a possible parameter that influences the outcome of an analysis by the network. Based on training, parameters can be switched on or off to turn the input into the correct output. Deep Learning or DL Another umbrella term for techniques and models handling complex problems that require a huge amount of data. With deep learning, the goal is to use neural networks to simulate human thinking. A deep neural network differentiates itself from other neural networks by its sheer size. While a regular network can have 1,000 neurons, for example, those in deep learning scenarios usually number well into the hundreds of thousands. For instance, the latest state-of-the-art natural language processing (see below) model from Google has 340 million parameters. Of course, this makes deep learning quite complex, requiring a significant effort to implement. On the flip side, deep neural networks produce much better results than other ML approaches. For example, in return for added complexity, we get the capacity to interpret language extremely well via algorithms. This means we can now automate document processing to an unprecedented level. DL also shines in its ability to correctly analyze unstructured data, such as images and video.  This enables an even higher level of automation, giving us advanced image search capabilities, face ID, image classification, and more.   Natural Language Processing or Nlp An umbrella term that covers all techniques