Nearly every industry in the world has adopted artificial intelligence in some way, shape or form. As artificial intelligence becomes more efficient, it is becoming increasingly common for companies to find ways to automate tasks. From chat bots with great communication skills to street savvy self-driving cars, we are seeing surprisingly advanced applications of artificial intelligence. One that BZBGEAR is mostly excited about is the application of AI in the AV industry.
Most have probably wondered, how does artificial intelligence work? To answer that question briefly, most AI applications use machine learning and deep learning to become smarter. Compared to humans, AI can process and summarize large sets of data (big data) at impressive speeds. This is what makes AI so efficient at learning and handling simple tasks. AI has also become more effective at mimicking human learning methods, which is what deep learning is.
When AI is applied to a machine, it gives the machine the ability to adapt, reason and provide solutions for itself. Even in new situations, machines powered by AI are still effective. When put in a new environment, the AI will use previous data inputs to problem solve based on its given task and understanding of the new environment.
When using machine learning, AI uses algorithms and patterns in data sets to make inferences and predictions. For example, if you ask an AI application that uses machine learning “How does AI work?”, it will gather all of the existing information available to it that describes what AI is and develop a summary for you.
Deep learning on the other hand is a bit more complicated. Deep learning is a subset of machine learning which attempts to mimic the neuro networks of human beings to figure out how humans learn certain things. Deep learning in AI is seen in applications such as virtual assistants, facial recognition and self-driving vehicles.
When a computer uses deep learning, it will go through much the same process as a toddler that is learning to identify a pet cat and dog. What essentially happens is, the computer using deep learning will receive non-linear data inputs (non-linear data means there is not yet a clear relationship between the variables). From there, it will take what it learns to develop statistical models as output. The computer will then repeat this process until its output reaches an acceptable level of accuracy. The number of processing layers the computer uses in this learning process is what inspires the label “deep learning”. Similarly, a toddler will eventually be able to naturally recognize the difference between the dog and the cat by learning the different attributes of both. Along the way, the parent or teacher will have to teach the child and correct their mistakes. These processing layers will eventually lead the child to be able to identify the pets with accuracy.
A subset of deep learning is computer vision. Deep learning betters computer vision which provides the computer with great accuracy for object detection and image classification. This leads us to an aspect of AI that is vital for the AV industry, which is image recognition. Image recognition is the ability of a software program to recognize people, objects, places, writing and actions in images. A computer can use machine vision tech in combination with a PTZ camera and AI software to reach quality image recognition.
An example of an AV and broadcasting product that can do this is the BG-ADAMO by BZBGEAR. The ADAMO is a PTZ auto-tracking camera that uses the latest human detection AI algorithms to exhibit unparalleled tracking speed and accuracy.
Overall, BZBGEAR is excited to see where advancements in AI will take the AV industry. To learn more about BZBGEAR, visit: BZBGEAR.com. To check out the BG-ADAMO AI powered PTZ camera, visit this page.
