Is the security industry ready for autonomous AI?
The proliferation of intelligent video analytics powered by underlying artificial intelligence (AI) technologies has been among the biggest trends in the security industry over the past several years. However, skeptics of these solutions are quick to point out that true AI – computers with the ability to analyze a scene and make decisions own their own without a “human-in-the-loop” – is absent from these products and that the machine and Deep Learning algorithms they leverage are only as good as the datasets they are trained on.
But just as AI has improved across segments of the ever-growing Internet of Things (IoT) ecosystem, the same is also happening within security. Enter Corsight AI, which is looking to bring autonomous AI solutions to the industry for facial recognition applications. Borne out of Cortica, a developer of autonomous AI that simulates the neural processes of the human brain, the capabilities of Corsight’s technology extend well beyond what is possible with today’s Deep Learning algorithms.
“By basically trying to mimic the neural network of the brain, we manage to get programming as well as a data structure that give you speed and accuracy but also the ability to learn if an object is actually a different size or shape and get better results,” says Gadi Piran, the company’s CEO. “That is really different from Deep Learning.”
According to Piran, many of today’s popular Deep Learning solutions can be easily fooled if they encounter an object or a person in an abnormal situation.
“Imagine that you give a system a picture of a bottle. You put that bottle standing up, you take a picture and now a system with Deep Learning will know that if they can match the shape, it will be recognized as a bottle,” Piran explains. “But if a person in a video is holding that bottle upside down, Deep Learning usually has a very hard time saying, ‘this is a bottle.’ With autonomous AI, the concept is more of a true understanding of the object and then, no matter what position you give it, it will understand that it is still a bottle.”
One of the things that appealed to Piran about the technology developed by Cortica is that because one of their first applications was autonomous driving, they had the ability to simultaneously detect a multitude of objects at the same time, which stands in stark contrast to many of the video analytics solutions of old that had difficulties accurately identifying relatively low numbers of very basic, everyday items.
“Everything in the analysis, when it looks at the video in the situation, it literally detects everything in a microsecond and they have gotten to the point where they can distinguish between very intricate shapes,” he adds.
