Artificial Intelligence (AI): Advancements and Applications in Security
Artificial Intelligence (AI) has fascinated people for years. The idea of creating a machine capable of independent thinking, decision-making, and responding appropriately has led to numerous successes in both entertainment and professional fields. The use of this technology in areas such as retail, pharmaceuticals, and security is no longer a distant possibility.
What Stage is AI at Currently?
When people talk about AI, terms like “machine learning” and “deep learning” are often mentioned. To understand AI’s current position, it’s helpful to become familiar with a summary of the various approaches taken by planners and developers to advance and further develop this effective technology.
Pattern Recognition vs. Machine Learning vs. Deep Learning
Since the idea of AI was first introduced, multiple approaches have been used to enable computers to think independently. The first method involved pattern recognition. Specifically, developers designed programs that trained computers to recognize certain features and patterns to identify objects.
Later, this approach evolved to include algorithms that taught computers to think, leading to what we now call machine learning. With the help of algorithms and vast amounts of data (used as examples of correct and incorrect responses), computers use math and statistics to extract patterns and classify results, thus performing a type of basic thinking. However, issues arise when there is insufficient data for learning or when there are no obvious or recognizable patterns, such as when the environment is constantly changing. In these cases, the system doesn’t perform well.
The next step in AI’s evolution that has caught everyone’s attention is deep learning. The biggest difference with this approach is its ability to identify important features for recognition without relying on a programmer/operator’s input.
This system, inspired by the human brain’s function, consists of multiple neural layers that apply various algorithms to segregate high-level data. The lower layers act as “sensors,” focusing on small parts of an image and passing the information to higher layers, which combine and interpret the data into a larger pattern or image. This process continues until the system can recognize and understand the image.
Use Cases in Security
Understanding how deep learning fits into security settings is easy, especially since one of the key elements for “learning” is data, and there is a vast amount of data available in video surveillance. Given this technology’s ability to accurately identify objects, its integration makes systems smarter. Therefore, it can be applied in various security applications, from facial recognition and vehicle detection to license plate recognition and crowd behavior analysis.
This system can also be programmed to automatically analyze data from surveillance systems, speeding up search processes, freeing human operators from focusing on minor details, and preventing the need to spend significant time viewing large amounts of recorded images and videos.
With AI’s continued advancements, it’s interesting to think about how these innovations will be integrated into existing surveillance technologies to enhance security. Who knows, the future may be closer than we think.
Source: a&s Magazine