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ANDREW SELDON, GUEST BLOGGER // MARCH 05, 2018
You can’t step out of the door these days without hearing about Artificial Intelligence (AI) and how companies are using it to change the world of security. this is a short article, we will simply use AI as a holdall to refer to Artificial Intelligence, Deep Learning, and Machine Learning, although these are really distinct, yet connected disciplines.
The problem with all this talk is that it’s easy to be caught up in the hype without understanding what AI actually is or the benefits it is supposed to bring. I would suggest we have yet to see real AI at work in the security industry.
From traditional line crossing to the far more complex facial recognition systems out there, as well as the ability to differentiate between humans and animals or inanimate objects, there are some impressive products on the market (and a few not quite as impressive).
But is this AI? The short answer is ‘sort of yes’, and no. The advances in software that led to these features are impressive, but there is much more to come.
Real AI is built on masses of data that is analyzed and categorized (or ‘sliced and diced’ in old Business Intelligence parlance). More importantly, real AI learns from the data it analyses and can make predictions and inferences based on that analysis.
Video analytics today offers the ability to detect when a human is approaching a restricted area and raise an alert. Some vendors offer solutions that can tell the difference between animals and a human, and a dog, for example, won’t generate an alarm. But what if there is heavy rainfall or mist so that even a thermal camera can’t generate images the system can clearly identify as human?
This is where AI comes into the picture, so to speak. As a simple example: by using the historic data as a learning experience, the system would be able to infer that although it can’t clearly identify the object moving into a restricted area as human, it seems to move on two legs and therefore can be classified as human and this warrants raising the alarm.
Similarly, take facial recognition in a retail environment. It’s easy (assuming you have the right equipment) to build up a database of faces and to denote certain faces as VIP guests and others as unwanted guests. What an AI system will do is take that facial database, combine it with other information from the retail environment, and provide the security team with actionable information.
Another simple example. The retail system will alert security that face ‘X’ has entered the premises and that on the past six occasions X was around, someone had their handbag stolen and X left the premises within 10 minutes of the theft. This is not to say X is a thief or that there is video or any other evidence of wrongdoing, but it says there is a correlation between face X and a security event. With this information, perhaps a security operative should be sent to hang around near X, or perhaps the control room should keep an eye on him/her through virtual surveillance.
AI goes far beyond security, of course. Businesses can use the information for operational, human resources, marketing, and other purposes as well, with direct results observable on the bottom line.
The key here is data. Not simply video surveillance data, but any and all data, whether structured or unstructured and the ability to gain access to it easily in a format the system can read and analyze, with the ultimate goal of making connections between seemingly unrelated events.
A real AI system will use multiple sources of data to predict probable events or to infer outcomes based on the patterns it detects in data. It’s far more than simply crossing a line or recognizing a face, it’s what you do with information that defines intelligence – artificial or not.
DILIP VERMA, REGIONAL VP, INDIA // MARCH 26, 2018
Almost all cities are currently looking into or have already taken steps to become safe cities. A few have even begun making the transition to becoming smart cities. The next stage in this urban evolution is the so-called “cognitive” city. Even though the concept of cognitive cities is still in its infancy stage, at the end of our recent 3-part series, we identified two key ingredients: advanced data analytics of large volumes and multiple types of data, and an adaptability which drives resiliency and continuous improvement. Although the advent of truly cognitive cities is well into the future, when they arrive, we’ll be able to distinguish them from both safe and smart cities:
The key to a cognitive city is city-wide cognition of the underlying need, rather than the leveraging of a specific tech or platform. Meaning, to be able and rapidly adopt technologies and practices, thus giving the city a natural protection against vendor lock-in. Smart cities, by contrast, tend to approach ICT as just another utility, like water and sewage, electricity, or garbage collection, which are often operated as regulated monopolies, with long contract periods and expensive financial and legal hurdles in place that prevent adopting another service provider or utility.
Cognitive cities, therefore, are much better positioned to thrive in the face of significant challenges like clean water scarcity, climate change, and providing functional and efficient mass transportation at mega-city scales.
Cognitive cities hold the promise of being more resilient than other urban areas which haven’t made that transition.
In our next post, we’ll take a closer look at how a cognitive city of the future serves its citizens, examine new challenges, and offer an interim view or where we are currently in terms of cognitive cities becoming a reality.