Of the seven patterns of AI that represent the ways in which AI is being implemented, one of the most common is the recognition pattern. The main idea of the recognition pattern of AI is that we’re using machine learning and cognitive technology to help identify and categorize unstructured data into specific classifications. The unstructured data could be images, video, text, or even quantitative data. The power of this pattern is that we’re enabling machines to do the thing that our brains seem to do so easily: identify what we’re perceiving in the real world around us.
The recognition pattern is notable in that it was primarily the attempts to solve image recognition challenges that brought about heightened interest in deep learning approaches to AI, and helped to kick off this latest wave of AI investment and interest. The recognition pattern is broader than just image recognition, however. In fact, we can use machine learning to recognize and understand images, sound, handwriting, items, face, and gestures. The objective is to have machines recognize and understand unstructured data. The recognition pattern of AI is such a huge component of AI solutions because of its wide variety of applications.
The difference between structured and unstructured data is that structured data is already labelled and easy to interpret, but unstructured data is where most entities struggle. Up to 90% of an organization’s data is unstructured data. It becomes necessary for businesses to be able to understand this and interpret this data and that’s where AI steps in. Whereas we can use existing query technology and informatics systems to gather analytic value from structured data, it is much harder to use those approaches with unstructured data. This is what makes machine learning such a potent tool when applied to these classes of problems.
Machine learning has a potent ability to recognize or match patterns that are seen in data. Specifically, we use supervised learning approaches to machine learning for this pattern. With supervised learning, we use well-labeled training data to teach a computer to categorize inputs into a set number of identified classes. The algorithm is shown data repeatedly, and uses that data along with training labels to train a neural network to classify data into those categories with some accuracy. The system is making neural connections between these images and it is repeatedly shown the image over and over or similar images and the goal is to eventually get the computer to recognize what is in the image based off training. Of course, these recognition systems are highly dependent on having good quality, well-labeled data that is representative of the sort of data that the resultant model will be exposed to in the real world. Garbage in is very much garbage out in these sort of systems.
The many applications of the recognition pattern
The main objective of the recognition pattern is for a machine system to be able to essentially look at unstructured data, categorize it, classify it, and otherwise make sense of what otherwise would just be a “blob” of untapped value. Applications of this pattern can be seen across a broad array of applications from medical imaging to autonomous vehicles, from handwriting recognition to facial recognition, voice and speech recognition, to identifying even the most detailed things in videos and data of all types.in things like autonomous vehicles. Machine-learning enabled recognition has added significant power to security and surveillance systems, with the power to observe multiple simultaneous video streams and recognize things such as delivery trucks or even people who are in a place they ought not be at a certain time of day.
The business applications of the recognition pattern are also potent. For example, in online retail and ecommerce industries, there is a need to identify and tag pictures for products that will be sold online. Previously humans would have to laboriously catalog each individual image according to all its attributes, tags, and categories. Nowadays, machine learning-based recognition systems are able to quickly identify products that are not already in the catalog and apply the full range of data and metadata necessary to sell those products online without any human interaction. This is a great place for AI to step in and be able to do the task much faster and much more efficiently than a human worker who is going to get tired out or bored. Not to mention these systems can avoid human error and allow for workers to be doing things of more value.
Not only is this recognition capability being used with images, it’s also used per dominantly to identify sound in speech. There are lots of apps that exist that can tell you what song is playing or even recognize the voice of somebody who is speaking. Another application of this type of pattern recognition is recognizing animal sounds. The use of automatic sound recognition is proving to be valuable in the world of conservation and wildlife study. Using machines that can recognize different animal sounds and calls can be a great way to track populations and habits and get a better all-around understanding of different species. There could even be the potential to use that in areas such as vehicle repair where the machine can listen to different sounds being made by an engine and tell the operator of the vehicle what is wrong and what needs to be fixed and how soon.
One of the most widely adopted applications of recognition pattern of artificial intelligence is the recognition of handwriting and text. While we’ve had optical character recognition (OCR) technology that can map printed characters to text for decades, traditional OCR has been limited in its ability to handle arbitrary fonts and handwriting. Machine learning-enabled handwriting and text recognition is significantly better at this job, in which it can not only recognize text in a wide range of printed or handwritten mode, but it can also recognize the type of data that is being recorded. For example, if there is text that is formatted into columns or a tabular format, the system can identify the columns or tables and appropriately translate to the right data format for machine consumption. Likewise, the systems can identify patterns of the data, such as Social Security numbers or credit card numbers. One of the applications of this type of technology are automatic check deposits at ATMs. Customers insert their hand written checks into the machine and it can then be used to create a deposit without having to go to a real person to deposit your checks.
The recognition pattern of AI is also applied to human gestures. This is something already heavily in use by the video game industry. Players can make certain gestures or moves that are that then become in-game commands to move characters or perform a task. Another major application is in the retail world where customers are being helped through being able to try on things virtually. It’s even being applied in the medical field by surgeons to help them perform tasks or remove them or even to train people on how to do certain tasks before they have to perform them on a real person. Through the use of the recognition patterns, machines can even understand sign language and translate and interpret the language as needed without human intervention..
In the medical industry, AI is being used to recognize patterns in various radiology imaging. For example, these systems are being used to recognize fractures, blockages, aneurisms, potentially cancerous formations, and even being used to help diagnose potential cases of tuberculosis or coronavirus infections. Medical professionals are predicting that with just a few years, machines will perform the first analysis of most radiology images with instant identification of anomalies or patterns before they then go on to the human radiologist counterparts for further evaluation.
The recognition partner is also being applied to identifying counterfeit products. Machine-learning based recognition systems are looking at everything from counterfeit products to potential drug smuggling. The use of this pattern of AI is impacting every industry from using images to get insurance quotes to analyzing satellite images after natural disasters to assess damage.
Given the strength of machine learning in identifying patterns and applying that to recognition, it should come as little surprise that this use case of AI will continue to see widespread adoption. In fact, in just a few years we might come to take the recognition pattern of AI for granted and not even consider it to be AI. That just goes to the potency of this pattern of AI.