What is Pattern Recognition?

With artificial intelligence (AI) solutions taking the world by storm, you must have noticed the term “pattern recognition” being mentioned across news articles, blogs, and tweets about AI.

But exactly what is pattern recognition? How does it work with AI? And most importantly, what purpose does it serve with systems that use synthetic intelligence in the first place?

To help you through these questions, here is a lowdown on pattern recognition meaning as well as its role in the larger progression of AI.

What is Pattern Recognition?

Pattern recognition refers to the practice of identifying patterns in a set of data. In the field of AI, pattern recognition with machine learning (ML) helps systems tell the difference between presented information. This leads AI-enabled systems to perform functions such as image recognition, speech recognition, and natural language processing (NLP).

Pattern recognition started to gain momentum with the development of AI systems that can segment images, understand voice commands, and autocomplete sentences. But it has become even more popular due to AI programs that can now generate images, speak like humans, and write original text from scratch.

This makes pattern recognition a crucial part of modern AI systems and their overall ML-based processes. If you are using an AI system that is able to identify or generate data, you can be certain that it is powered by pattern recognition.

What Are Some Examples of Pattern Recognition?

Pattern recognition can currently be seen across various applications of AI systems. Some of the most popular examples of pattern recognition include the following.

Image Recognition

This refers to systems that can identify different objects and settings in an image. For instance, your phone’s gallery app that can recognize different objects, people, or scenes and categorize them for you uses pattern recognition. Other common use cases of pattern recognition in images include image search or search by image options on search engines.

As a result, whether you ask your phone to recognize the image that you have provided to it or search other images on the internet with keywords, pattern recognition meaning stands out in various applications.

Speech Recognition

In order to understand your spoken words, machines use pattern recognition and identify the words that you deliver to them through voice. This allows speech recognition systems to not only understand what you are saying, but also turn it to text almost instantly.

Speech recognition systems are most commonly found in smartphones. From Apple’s Siri to Google Assistant, personal assistant apps exemplify what is pattern recognition able to do in real life applications. Other speech recognition use cases include voice search and voice-to-text programs that can be found across a range of devices.

Facial Recognition

This is a highly advanced and rapidly evolving type of pattern recognition that identities different people from a set of images. This system can be found across security solutions, but it is also available in utility apps that help you segment your pictures and media through this type of pattern recognition.

For instance, if you use FaceID on your Apple mobile device, you are using facial recognition in a security setting. In a similar fashion, if you are using small thumbnails to find pictures of people or pets in Google Photos, you are also leveraging the same technology but in media instead of live camera feed.

Natural Language Processing

NLP is a highly sought after subset of machine learning. Through ML, AI systems are able to determine the way different words flow with each other. This allows these systems to hold human-like conversation and complete functionalities such as autocomplete and translations with more accuracy than past solutions.

NLP is also demonstrating pattern recognition meaning in generative AI. With systems that can produce human-like text for professional communication or fictional content, you can now write blog posts, articles, emails, as well as stories and novels through AI. These tools are now widely available, which further adds to the popularity of pattern recognition.

Handwriting Recognition

This is one of the oldest examples of what is pattern recognition able to do. Through this practice, systems are able to understand what you write to them through your hand. No matter if your handwriting is slanted, cursive, or both, modern handwriting recognition systems can easily identify it.

These systems are typically found in mobile devices with larger touchscreens, but they are most commonplace in devices with styluses. If you have a drawing tablet or a flagship smartphone, you may use a stylus to benefit from this AI-enabled functionality across a variety of settings.

Fraud Detection

Fraud detection through pattern recognition becomes possible when ML systems identify irregularities in financial statements, where certain transactions stick out of the pattern of regular financial activity. Since these anomalies can be an indication of fraud, AI can help catch and prevent it.

Due to this reason, fraud detection software that is powered by AI has become an integral part of many financial institutions. This makes it a niche application as compared to widely used programs such as personal assistants, but its importance in identifying patterns that can go amiss from the human eye still stands tall.

Medical Image Analysis

Pattern recognition meaning and applications can also extend to medicine and healthcare. By taking the information of visual patterns that indicate the imaging test results of healthy people, AI systems can identify signs of concern in images such as CT scans and MRI scans.

This not only allows doctors and healthcare providers to perform their jobs easily, but also goes a long way in preventing human error during the identification of diseases and conditions. This makes it crucial for many people to understand what is pattern recognition able to achieve in distinct settings.

How Does AI Learn Pattern Recognition?

AI can learn pattern recognition by “training” on data that is fed to the system through multiple approaches. Depending upon the type of AI as well as its intended use case, this can include distinct training processes.

Some of the techniques that are commonly utilized in pattern recognition training include the following.

Supervised Learning

This technique requires labeled data that is used to train the AI system. Through this data, the AI is able to recognize patterns and use that knowledge in its future tasks. These tasks can range from identifying people in images to deciphering words in spoken text.

You can think of supervised learning as a way to teach a specific course to a student, but with the stipulation that the student would not be able to learn new information or patterns on its own. Besides knowing exactly what you are offering to teach, the output from that information is already in your mind.

Unsupervised Learning

This approach uses unlabeled data to help AI understand and identify patterns in the training data. This pattern recognition meaning defines that training the program typically takes less time, but that also depends on the amount of data being fed to the AI program and the end result from it. However, given that the learning remains unsupervised, the output that is generated after this training remains a surprise.

This can be quite helpful if you are trying to train a system with a larger set of data or don’t want to control the output from the start. After learning what is pattern recognition and what is able to achieve in this approach, you can easily identify it in image recognition, speech recognition, and fraud detection among other applications.

Semi-supervised Learning

As outlined by its name, this approach trains AI systems with a combination of supervised and unsupervised learning techniques. In turn, the AI picks up information from both labeled and unlabeled data sets. However, the ratio of data used is not equal. The amount of labeled data is outweighed by the quantity of unlabeled data.

Semi-supervised learning can be helpful in settings where the outcome is the classification of new information that doesn’t fall into the categories of labeled data. For instance, this approach can be ideal for text and image classifications as well as speech recognition.

Deep Learning

This is a more complex approach that uses a vastly large amount of data, which can either be labeled or unlabeled. Besides the enormous size of the data being used, what sets deep learning apart from usual supervised, unsupervised, or semi-supervised techniques is its use of neural networks to identify and extract complex patterns in layers of information.

With that being said, deep learning can use either of these techniques to identify its data, with supervised learning being the most common approach for this process. Through deep learning, AI can establish more effective pattern recognition in images, speech, and text across various applications.

These approaches not only let data scientists and programmers leverage pattern recognition meaning in different settings, but also enable them to develop highly reliable systems for various use cases. This makes sure that AI systems that are trained to identify patterns are able to perform as intended.

What is the Importance of Pattern Recognition?

Pattern recognition is an integral part of AI training, improvement, and functionality. Since computers or machines cannot see, hear, or feel their surroundings like humans can do, they rely on visual, sonic, and contextual cues to make sense of data as well as its associated settings. Pattern recognition makes it possible for AI systems to use these cues in order to understand the data at hand.

By understanding what is pattern recognition and how to leverage its capabilities, data scientists and AI experts can develop systems to simplify and perform a variety of tasks that would otherwise not be possible without human intervention.

This allows AI to perform tasks including but not limited to:

  • Holding verbal conversations with humans in a natural tone, voice, and inflection.
  • Reading, summarizing, and analyzing text content with heightened precision.
  • Recognizing and grouping images from provided data to significant accuracy.
  • Identifying and processing human speech with a higher level of reliability.
  • Generating new text content that emulates the training data.
  • Developing new images that reference the training data.
  • Recognizing facial features and identifying individuals through it.
  • Pointing out anomalies in financial data and transactions.
  • Processing medical imagery and delivering related diagnoses.

Do You Need to Learn Pattern Recognition Before Using AI Systems?

After understanding pattern recognition meaning, you can appreciate how crucial the practice is for the training, development, and overall function of AI solutions. But even though it is important to learn what is pattern recognition and what goals it achieves, it is not necessary to know about this approach to use modern AI systems that are developed to be utilized by anyone.

For instance, if you use image generation tools such as Dall-E 2 or text generation platforms such as Jasper.ai, you can simply use their graphical user interface (UI) to carry out different tasks. With that being said, knowing what pattern recognition is and how it supports AI solutions can help you appreciate the complexity of AI systems and how they are simplified for front-end users.

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Fritz

Our team has been at the forefront of Artificial Intelligence and Machine Learning research for more than 15 years and we're using our collective intelligence to help others learn, understand and grow using these new technologies in ethical and sustainable ways.

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