A Convolutional Neural Network (CNN) is a form of artificial intelligence that plays a key role in the AI ecosytem due to its ability to analyze and understand visual data.
The need to decipher and understand images and video material is a crucial part of making AI world aware.
While the tech might sound complex, the concept behind it is relatively simple when broken down.
How does a CNN work?
A CNN works similarly to how our human eye processes images.
In the same way we identify objects using our vision, a CNN will scan an image and use mathematical algorithms to detect patterns and features.
Instead of using a human brain as the interpreter, the CNN will instantly run through a large series of mathematical processes to identify the relevant patterns.
It does this through the use of something called “kernels,” which are made up of tiny pieces of an image that the network has previously been trained to recognize. In this way the network can recognize specific features of any image, such as edges or shapes.
As the kernel algorithm scans the image, it learns to recognize these patterns and integrate them into an overall understanding of what the image represents, for example a ‘car’, ‘building’ or even a ‘cat’. It’s almost like an AI version of building a jigsaw puzzle.
Once the kernels have identified these features, the CNN combines all this data into individual pixels, creating layers of information that tell it what’s happening in the image.
This process is repeated across multiple layers, each one capturing more complex patterns until the network can make a final decision based on its training.
The early layers identify basic elements (like lines and curves), intermediate layers put these together to identify things like wheels or ears, and the final ‘deep’ layers assemble everything together to recognize complex objects such as a plane or bus.
What makes a CNN so cool is the fact that it’s able to learn for itself what features are important for recognition. It doesn’t have to be given specific rules, instead it discovers patterns by using the thousands of sample images it’s been trained on, and from that data makes a final decision.
In case you think this is all very esoteric and nerdy, CNNs are deployed in a huge range of applications we use every day.
Things like unlocking our phones with our face, identifying road signs in self-driving cars, tagging people in our photo galleries and even translating text in images.
They also play a vital role in medical imaging, helping doctors identify diseases by analyzing X-rays or MRI scans.
Another clever thing about CNN’s is the fact they’ve been designed to distill images down to their basic important components, while ignoring extraneous features which are less important (for example the background).
This makes them faster and more accurate in identifying image elements, although sometimes they can miss important features because of this speed.
CNNs need a lot of training data to operate optimally, and anyone who’s laughed at a mis-classification of a face in a photo will know they make mistakes.
However, these clever little bits of AI tech will likely revolutionize how we interact with our environment going forward.
Chances are, if you one day find yourself talking to your oven, toaster or television, there will be some form of CNN in the background making sense of any images included in the chat.
