Artificial intelligence (AI) is an all-encompassing category which includes a wide range of neural networks with different capabilities. Neural networks are segmented by how they approach a particular, unstructured data set or problem, either with a process, algorithm, or machine learning approach. In this article, see how Convergint, partnered with Milestone, can use AI to improve security.
1. The core of learning: shallow or deep
Due to the previous limitations of hardware processing power, machine learning could only deploy shallow learning of very large data sets. This shallow learning looks at data in only three dimensions.
With recent, significant advances in processing power of graphical processing units (GPUs), a deep learning approach can now be utilized, wherein data is analyzed in many more dimensions – which is why it is called “deep”.
Convergint and Milestone leverage a new, GPU-compute platform by re-coding its software to use a new type of coding called parallelization. Software parallelization is a coding technique for breaking a single problem into hundreds of smaller problems. With parallelization, there is a quantum leap forward in how fast a problem can be solved.
2. IoT Frameworks & Aggregation of Data
Milestone’s role as a video management platform company is to develop broad support for all relevant devices. They have a vision to support all top Internet of Things, or IoT, frameworks.
The focus is to continue enabling more and more of those devices across different frameworks into a common data center. Then, Milestone and its partners will continue to advance GPU technology to create a whole new level of processing, helping companies that are using GPU as a parallelization.
Advanced rendering is about creating a whole different type of mixed reality. The BriefCam Synopsis system is an example of mixed reality. It uses real video, extracts objects of interest, and then provides an overlay of augmented reality. Humans cannot look at 24 hours of video in nine minutes. But with Synopsis intelligence can be augmented.
3. Actualized Potential of Augmentation
AI and machine learning are being applied for AI-enabled devices and machines to get very good, low-cognitive functions. For example, humans cannot sit and watch all cameras simultaneously, all the time; their attention does not work that way. But machines are extremely good and detailed at this. People do not see pixels, people see objects. The machine sees the most finite detail available to it, which is the pixel, and within the pixel, it can see more details, which are the shade of colors of that image. By aggregating data, allowing machines to automate responses and solutions, we can augment human interaction and our environment.
4. An Intelligent Revolution
Having AI take over low-cognitive tasks will be the big trend for years to come. With proper aggregation of information, AI can be superior to humans at low-cognitive tasks, and often deliver a better quality of service than humans.
The intelligent industrial revolution is beginning to happen all around the world. It will be very disruptive within the security and surveillance industry — but also insightful and liberating as it frees human efforts for higher cognitive processes that address larger challenges.
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