As 2017 winds down, trend watchers are looking ahead to 2018 and thinking about the trends taking shape. Artificial Intelligence is top of mind for many.
What is Artificial Intelligence (AI), and what is the difference between AI, Machine Learning and Deep Learning? According to techopedia, “Artificial Intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.” AI computers might be used for speech recognition, learning, planning and problem solving.
Machine Learning takes AI a step further, allowing computers to be challenged by and learn from new scenarios for testing and adaptation. The goal is for the machines to use pattern recognition and trend detection to “learn” so that it can make independent decisions about similar situations in the future.
Deep Learning collects what Machine Learning computers have learned and uses those algorithms to develop larger networks that mimic the high-powered decision-making capability of the human brain.
AI, Machine Learning and Deep Learning all have significant potential for real-world application, particularly in video security.
The boom in digital video means a voluminous amount of data is available to analyze. Couple that data with more data available via API – weather data, financial data, etc. – and the possibilities for pulling together patterns and making predictions is nearly endless.
“While the technologies aren’t particularly new, this year they have more than ever captured the attention of the market due to various factors: an increase in data that’s available for meaningful analysis, the emergence of hardware devices with high computing power, as well as the maturity of networking infrastructure for both landline and wireless transmissions,” wrote William Pao of a&s International in a recent post on asmag.com.
Some are predicting a boom in AI-driven analysis. “The next step in video analytics is to dive deeper to gain very specific insights into video content, including analyzing human behavior through the use of neural network video analysis. Video will not only be used to track the usual movement of cars and people or detect items left behind, but will also be relied on more frequently to bring behaviors of interest to the attention of security personnel,” said Jammy DeSousa, Senior Product Manager for Security Products for Building Technologies and Solutions at Johnson Controls in the post.
Others are slightly more conservative in their outlook. “Machine or deep-learning is mostly used for video analytics, but I expect the technology will be an important component in many different applications and products in the future. Over time it will become a common tool for software engineers and will be included in many different environments and devices,” said Johan Paulsson, CTO of Axis Communications in the post. “However, the surveillance industry has a history of sometimes over-promising with video analytics, and we are especially conscious of that when it comes to deep learning. We think deep learning has to mature further before it is ready for market in a broader perspective.”
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