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.