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The future of Artificial Intelligence in Security Deep Learning, along with huge increases in processing power and data gathering capabilities, is bringing new intelligence to the ways security is performed, from threat anticipation to robot and drone patrols. The idea of machines that could act and think like men has been around for a long time. In Greek myth, Talos was a giant bronze man who guarded the island of Crete by throwing stones at the ships of unwanted visitors, acting as the very first security guard. The earliest computers were designed as “logical machines” that reproduced human capabilities such as basic arithmetic and memory, their engineers essentially seeking to create mechanical brains.
In AI, the goal has long been to create devices that would think like human beings, act like them, or both. As technology progressed, researchers in AI concentrated on mimicking human decision-making processes to carry out tasks in ever more human
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ways. To do that, they needed to incorporate one of humanity’s most fundamental characteristics—the ability to learn. Now, 60 years after Arthur Samuel created perhaps the world’s first successful self-learning program, Deep Learning systems are at the cutting edge of AI and are beginning to have a profound effect on security systems and protocols. Predicting the future with AI At its core, Deep Learning relies on data. This data is fed into neural networks that mimic the way people think and understand the world. These networks also hold a number of advantages, such as speed, accuracy and lack of bias. And their capabilities have the potential to be huge. For example, researchers at MIT have created a system which can
technically predict the future, albeit in a currently limited way. For the security sector, being able to predict how people might behave is incredibly valuable. Human beings have always possessed this capacity, but historically computers have only been able to utilise data which already exists. Predictive deep-learning algorithms, such as the one being utilised at MIT, point the way to AIcreated simulations of ever greater accuracy. Deep learning works on a system of probability. The neural network is able to make statements, decisions or predictions with a degree of certainty, based on the data fed to it. A feedback loop, which either senses or is told whether its decisions are right or wrong, modifies the approach it takes in the future. In this sense, it is able to learn. Where CCTV
August/September 2021
