The idea of analytics in security applications is not a new one. In fact, some in the industry have been (over)promising its capabilities for years.
“AI and deep learning were launched around five years ago with much hype rather than an educational approach, which left both systems integrators and end users a bit confused on the differences between AI, machine learning and deep learning,” says Jason Burrows, Western sales director at IDIS America, Coppell, Texas. “Some early offerings disappointed as they were launched prematurely, before engines were fully trained and able to recognize objects reliably and accurately. The cost and complexity of early-to-market AI applications made users question the value of deployment, configuration and operator use. This was compounded by privacy concerns and a greater storage burden. As a result, many end users were reticent, and systems integrators were unclear on which solutions would be best suited to meet their customer’s needs.”