Artificial Intelligence, or ‘AI’ as it is often known, has become a buzzword used by businesses to highlight the technological prowess of many products and solutions, for some time now. But what does it really mean? How intelligent, is ‘intelligent’? And how does it differ from ‘Intelligent Automation’? Dr Sean Lawlor, Data Scientist Team Leader, Genetec, explains.
The world is becoming more automated – from collaborative robots through to computer programs which can sift through thousands of documents almost instantaneously, organisations can now save time and money in new ways. The technology can now be used for necessary but tedious, time-consuming tasks that would take humans much longer and be more prone to error. However, there are aspects of Automation which are misunderstood and misrepresented – I talk here about Artificial Intelligence (AI), where the hype is spreading faster than the aptitude of the technology.
Subsets of AI – like machine learning or deep learning are often referred to as AI, when they are not. In fact, they are closer to intelligent automation than artificial intelligence. Intelligent automation (IA) can help organisations by using existing data and automating analysis based on that data, ultimately helping to improve operations and workflow, as well as reducing redundant responses. But neither technology is truly “intelligent” in the sense that they cannot think or act like humans. We are many years away from that.
Artificial intelligence is certainly a buzzword in security, yet many capabilities are misinterpreted, undefined or misunderstood. Misunderstanding the capabilities of AI will often lead to unrealistic expectations.
In data science, AI refers to a fully functional artificial brain that is self-aware, intelligent and that can learn, reason and understand. While advancements in what is referred to as AI technologies have come a long way and will continue to do so, the reality of AI, however, is very different from an intelligent computer that can learn and make decisions like a human.
In practice and as it relates to the physical security industry, AI is a technology that runs a series of algorithms, searches through large databases or does calculations swiftly to provide deeper insights. The results can help users make decisions more quickly and efficiently depending on the application. General examples of applications that fall under “AI” would be facial recognition, object detection or people counting.
However, the broad nature of the term means that often, expectations and hype exceed its capabilities, and causes disappointment. Currently, only its subsets are tangible, such as machine learning techniques that include neural networks and deep learning. For example, deep learning uses task-specific algorithms to help train a computer to properly classify inputs. To do this, programmers essentially teach a computer by inputting a very large amount of data with corresponding labels, improving the technology’s ability to recognise new inputs. In a real-life scenario, deep learning is being used very effectively in automatic number plate recognition (ANPR).
It is possible to train the system by feeding it raw images of number plates, alongside parameters for it to work within, so it knows it can only class said images with possible outputs. This then lets the system take an image of the back of a car it has not seen before and identify the characters on the plate, along with other useful information such as location, colour and model. For a human, this would be a tedious and time-consuming task – but it’s ideal for computers with minimal human supervision.
Another vertical where machine learning can offer significant value is retail, thanks to its ability to monitor and identify trends. For example, such technology can help stores determine retail conversion rates or the number of people visiting a location versus purchasing.
