It can be a minefield to deal with big data! The volume of data generated on a daily basis is rising exponentially and the preservation and security of this information is of utmost importance as our customers are only too aware. As more businesses find themselves accruing large quantities of data, it is something that must be seriously considered to figure out the most business-appropriate way to store their information.
For instance, you can give a neural network two datasets:
Dataset one is called “cat,” which contains pictures of cats
Dataset two is called “no cat,” which consists of images without cats.
Based on these datasets, without ever being told what a cat is, ANN will learn to recognise pictures of cats. It establishes guidelines to explain what each image set has in common between all the images and how the image sets vary from each other.
The simplest words do not do justice to such a complicated structure and method, of course. In fact, ANNs are exceptionally complex systems, and when it comes to understanding and harnessing machine learning, scientists have plenty of work ahead of them. Like human learning and memory, neural networks perform tasks that we cannot programme directly to complete or for which direct programming is necessary. which direct programming would not be realistic, such as:
- Predicting stock market developments
- Identifying authorship of manuscripts based on word choice and style
Machine Learning and Computer Vision
Computer vision is an extremely complex area with a deceptively simplistic definition: automating the kinds of tasks that the human visual system automatically performs to collect information. What this implies in practise is teaching computers to extract data from pictures, whether they are
- Digital images
- Streaming or stored video
- Live footage from a video camera
The applications of this kind of technology include:
- Facial recognition
- Object recognition
- Motion detection and analysis
- Image restoration
How Do Neural Networks Function? Applications of Artificial Neural Networks
The example of cat images may seem dumb, but it shows the primary mechanism by which computers take over tasks once performed by humans using computer vision. The neural network will improve its algorithm as the dataset expands, increase its accuracy and perform properly in increasingly demanding tests.
A more realistic case, maybe, is recognition of handwriting. Although it may be a simple task for a human observer to recognise the letter “A” the programming needed to convey the geometry of “A” is significantly more sophisticated, especially when you consider all the possible variables in a real handwriting sample:
Nevertheless, a neural network trained on a wide range of handwriting samples will learn to understand each letter in all its infinite variations, enabling us to digitise handwritten notes or to decode badly written or partially broken handwriting.
Unlocking computer vision possibilities is far from complete, but in recent years we have made a lot of progress, particularly in terms of complexity. These advances are mainly due to advancements in ANNs and machine learning, both of which will allow the growth and development of computer vision.