K
Kathleen Martin
Guest
This year marks a major milestone for the security industry: 25 years ago, my company, Axis Communications, introduced the first internet protocol (IP) camera. Might not sound like a big deal, but this marked the beginning of the shift from analog surveillance to today's network solutions. Without the IP camera, modern video/audio solutions and analytics would not be possible. And even as cloud computing has experienced a major rise over the past decade, today's organizations are re-embracing the network edge.
Twenty years ago, another first occurred: the first IP camera with built-in edge analytics (video motion detection) was released. Today, edge devices use analytics for a broad range of purposes, ranging from security to business intelligence, but in the early days of analytics, limiting factors like bandwidth, processing capacity and storage issues hampered the technology's ability to find mainstream success. As these elements have improved, so has the power — and usefulness — of modern analytics.
The prevalence of hybrid systems incorporating both cloud and edge solutions has helped analytics live up to its early promise. Edge devices are reemerging as an essential tool for today's organizations, and the broad range of available analytics tools have helped those organizations make improvements that go far beyond security.
The Rise Of Artificial Intelligence
Artificial intelligence (AI) has become an overused term — today, it is often applied to anything that has to do with any form of digitalization, even if it doesn't technically qualify. It is the relatively recent rise of more complex forms of artificial intelligence, including machine learning and deep learning, that has boosted camera/sensor capabilities. Today, high-powered Deep Learning Processing Units (DLPUs) are enhancing and opening opportunities for new analytics applications. Without this technology, modern analytics would not be possible.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2021/10/05/the-edge-has-re-emerged-to-challenge---and-complement---the-cloud/?sh=248e56bd7ebd
Twenty years ago, another first occurred: the first IP camera with built-in edge analytics (video motion detection) was released. Today, edge devices use analytics for a broad range of purposes, ranging from security to business intelligence, but in the early days of analytics, limiting factors like bandwidth, processing capacity and storage issues hampered the technology's ability to find mainstream success. As these elements have improved, so has the power — and usefulness — of modern analytics.
The prevalence of hybrid systems incorporating both cloud and edge solutions has helped analytics live up to its early promise. Edge devices are reemerging as an essential tool for today's organizations, and the broad range of available analytics tools have helped those organizations make improvements that go far beyond security.
The Rise Of Artificial Intelligence
Artificial intelligence (AI) has become an overused term — today, it is often applied to anything that has to do with any form of digitalization, even if it doesn't technically qualify. It is the relatively recent rise of more complex forms of artificial intelligence, including machine learning and deep learning, that has boosted camera/sensor capabilities. Today, high-powered Deep Learning Processing Units (DLPUs) are enhancing and opening opportunities for new analytics applications. Without this technology, modern analytics would not be possible.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2021/10/05/the-edge-has-re-emerged-to-challenge---and-complement---the-cloud/?sh=248e56bd7ebd
