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.
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.
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.
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.
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. Modern handwriting recognition systems can easily identify it.
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. AI can help catch and prevent it.
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.
How Does AI Learn Pattern Recognition?
AI can learn pattern recognition by “training” on data that is fed to the system through multiple approaches. 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.
Unsupervised Learning
This approach uses unlabeled data to help AI understand and identify patterns in the training data. The learning remains unsupervised, and the output generated can be a surprise.
Semi-supervised Learning
As outlined by its name, this approach trains AI systems with a combination of supervised and unsupervised learning techniques.
Deep Learning
This is a more complex approach that uses a vastly large amount of data, which can either be labeled or unlabeled. 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.
What is the Importance of Pattern Recognition?
Pattern recognition is an integral part of AI training, improvement, and functionality. It 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.
- Recognizing facial features and identifying individuals through it.
Do You Need to Learn Pattern Recognition Before Using AI Systems?
It is not necessary to know about this approach to use modern AI systems that are developed to be utilized by anyone.
Knowing what pattern recognition is and how it supports AI solutions can help you appreciate the complexity of AI systems.