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Breaking Down Pattern Recognition: The What, How, and Why

One of the most touted benefits of Artificial Intelligence and Machine Learning is pattern recognition. Pattern recognition makes it possible for us to figure out common traits of behavior in data. It helps in better understanding user behavior or even instances that deviate from the known and regular form of working. 

From fighting crime to accurate medical diagnosis, the applications of pattern recognition are many and diverse. Let’s take a closer look at this booming subset of AI that has become a hotbed of development activity.

What is pattern recognition?

Pattern recognition is defined as the automated recognition of patterns and regularities in data with the help of Artificial Intelligence and Machine Learning. It uses statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics, and similar mathematical computations to deduce patterns. 

A pattern can be described as something that repeats with consistency, as if with a rule or condition. For example, an octave in music.

 

Image 1: An Octave In Music

There are countless similar patterns all around us. In the last five years, we have witnessed an explosion in data creation. It is said that 90% of the data that we have today has been created during the past five years (Science Daily). This humongous amount of data is a treasure trove for enterprises if only they put it to good use. 

Applications of pattern recognition

Although pattern recognition can be applied to any industry and business function, there are specific use cases that stand out from the crowd. These are the most common use cases, the ones that we see and feel in our daily life and can easily resonate with. 

Natural Language Processing

Ever noticed how Gmail is able to predict accurately the next word or syllable you are going to type? It is pattern recognition at play. Google with most of the world’s data within its quick reach is able to figure out the most commonly used phrases, words, and even syllables across all languages. With pattern recognition, it is able to predict the natural language that the user might use in Gmail or any other application.

Image 2: Natural Language Processing 

Image recognition

From artwork to computer-aided designs, almost every image is distinct in its own character. However, there are common traits in all the images that we see as well. Also, the same image could be used several times across the internet. Pattern recognition enables handpicking common traits in images and showcasing them as similar images in terms of color, texture, angles, lines, and so on. Image recognition can also be used for the automatic detection of symbols or signages by autonomous software, like those of self-driving vehicles.

Image 3: Image Recognition

Speech recognition

In case you didn’t know before, the recent trends like voice search, virtual assistants, and even smart home devices all are powered by speech recognition. Speech recognition is a form of pattern recognition that is auditory in nature. The AI system tries to pick up common spindles from the audio signals. It then matches with the spindles that are already stored in the database to understand the audio command. In layman terms, this turns out to be smart speech recognition. Pattern recognition combined with text-to-speech integrations can also understand speech and convert them into text transcripts to be consumed later.

Image 4: Speech Recognition

The role of Artificial Intelligence & Machine Learning in pattern recognition

Like mentioned earlier, pattern recognition is an application that is powered by Artificial Intelligence and Machine Learning. The AI system uses mathematics, statistics, and domain-specific knowledge and data to handpick patterns. Based on the recognized patterns it clubs together data into several common themes for faster data retrieval or anomaly reporting.

Image 5: Role of AI and ML in Pattern Recognition.

These patterns enable the business to take proactive business decisions or even formulate a long-term strategy. From devising pricing plans to fine-turning web pages for maximum conversions, there is a lot that pattern recognition can help with. 

Under the hood, there are two types of AI systems that make pattern recognition possible. They are unsupervised learning and deep learning. In a way, both unsupervised learning and deep learning are sub-divisions of Machine Learning which is a subset of the umbrella technology of Artificial Intelligence. 

Unsupervised learning

Unsupervised learning is a form of machine learning which detects patterns in a data set even if the data is not structured or labeled. The minimal need for human intervention gives it the name unsupervised learning. Also, it is with unsupervised learning that an AI system is able to form its own connections within data. It forms clusters of data which enables it to arrive at conclusions and predictions.

Deep Learning

As the name suggests, deep learning goes one level deeper. It mimics the working of the human bring where connections are made between information to extract an output. In deep learning, the AI system uses artificial neural networks that perform complex patterns like image recognition, audio transcription, natural language processing, etc. 

Pattern Recognition: One of the pillars of cognitive computing

A decade ago, the term cognitive computing and its subsets were not popular as it is today. Thanks to the data explosion, cognitive computing has become a feasible technology. The vast aunts of data are what make AI systems make accurate predictions. 

Pattern recognition is one of the core pillars of cognitive computing. Pattern recognition is a technology that can help understand the data better, give a direction about the common traits in the data, and also help formulate data-driven business decisions. 

Due to the complexity involved, it is not possible for anyone to venture into developing a pattern recognition application. A machine learning development company can help unravel the complexities and build an AI-based pattern recognition application.