AI technology has developed fast in the last three years, driven by the introduction of GPU chip architectures and advanced Machine Learning / Deep Learning algorithms. Our New Report looks at how the combination of these two developments have allowed data to be processed at much higher speeds and given computers the ability to learn without being explicitly programmed.

Machine learning is a subset of AI encompassing a range of algorithms to enable a trend or pattern recognition over time. It can be supervised, i.e. with expert training, or unsupervised, with no inputs from humans. Deep learning is a subset of machine learning and has applications in today's world, from speech recognition to image recognition and even biomedical informatics.

In general, one can think of it as a cascade, many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. Deep learning when perfected will be much more accurate tool than traditional symbolic reasoning for AI analytics.

While deep learning is rapidly becoming the new star of video analytics actual commercial implementations only started to enter into the video surveillance market in the last two years. In the first three months of this year there has been a significant number of new products launched on beta trials with some delivering exciting results.

Neural networks are loosely designed based on the biology of our brain. They try to simulate how we humans think. Simulating the interconnects between neurons, where the neurons can connect to any other neurons within a certain physical distance. So artificial neural networks have several layers of connections and directions of data propagations. Their applications include pattern analysis, classification etc. which are all based on the learning of multiple layers of features or representations of data. The higher level features are divided from the lower layer features to form a hierarchical representations.

Silicon Valley seems to agree that these two advances, GPU and Deep Learning Algorithms, are about to have their moment. AI Technology can and will make a direct and massive contribution to increasing the performance and value of video surveillance solutions and a significant proportion of these can be applied directly.

However video surveillance is by no means an island and in many cases it will ultimately need to be connected with the wider IoT, to form huge Big Data sets and possibly run off a Blockchain platform, for security. The developments of these technologies are still a long way from being perfected but their interconnection could deliver the intelligent video analytic solutions that have been promised by suppliers for some time.

Blockchain-based platforms are emerging as one of many options to securely connect artificial intelligence with IoT at the network edge. Combining AI and IoT will enable cities and buildings to respond to changes in the environment and to the input of new information whilst Blockchain can help create marketplaces for data and their exchange and also improve data privacy. This in turn will make it much more likely that databases will be shared.

It's important to keep in mind that the technology surrounding blockchain is still in its early stages. There are many experiments underway, from A.I. to IoT, to new types of digital platforms, but it will take time for these solutions to develop and to reach consumers and businesses.

This article was taken from our new report 'The Global Market for Intelligent Video Analytics 2018 to 2023' which looks specifically at the impact of AI on video surveillance and analytics.