Artificial intelligence (AI) has been creeping into our lives for years. It helps us search for images, delivers personalized playlist recommendations while we listen to music, and blurs the backgrounds of our photos for perfect selfies.
In business, AI is even more impactful. It’s helping extend asset lifespan in utility and logistics companies and making wind farms more efficient so we can all access more sustainable energy.
But as AI expands, it also becomes more complicated. There are multiple types and subsets of AI, ranging from reactive machines and theory of mind to machine learning and deep learning. One of the most impactful AI technologies is unassisted AI. Less talked about than other artificial intelligence tools, unassisted AI is making a big impact on cities, CSPs, and businesses.
In this article, we’ll take a deep dive into what unassisted AI is and how it’s already affecting us.
Unassisted AI: The Basics
Unassisted AI is a form of artificial intelligence that teaches itself to process data rather than requiring human input. This is in contrast to human-assisted AI, which requires human intervention to teach the algorithm rules about how data should be processed.
The result is a tool that processes data more quickly and effectively than humans, with much less setup. Rather than have humans process data—an expensive and time-consuming process—unassisted AI can process large quantities of data in real-time and decide for itself what needs further review and what can be discarded.
Unassisted AI has many applications, but it’s been hugely successful in the enterprise space where big data has made its mark. As a result of unassisted AI, businesses, cities, and communication service providers (CSPs) can work with much heavier datasets far more efficiently.
Unassisted AI processes the information these organizations bring in, separating anomalies from standard data so teams can quickly access insights with less work. Unnecessary data is discarded, saving storage space. This allows for much heavier data loads can be processed much faster and at a much cheaper cost.
What Does Unassisted AI Look Like in Action?
One of the most common forms of unassisted AI is in video analytics.
Video analytics software is incorporated into CCTV monitoring systems, such as the cameras you see posted around cities, like London, or businesses that want more insights on customers.
As the cost of these cameras falls, their use in surveillance systems is increasing. More cameras mean more data to sift through. With the average camera uploading 1MB of data per second and hundreds of cameras running round the clock, data processing becomes a huge problem.
How Unassisted AI Helps
Video analytics uses unassisted AI to solve the data problem, analyzing footage in real-time, and determining what’s important and what isn’t. Unassisted AI technology makes these determinations on its own, without humans having to input rules to explain that a car driving on the wrong side of the road or a person shoplifting is abnormal. It reviews data, determines what’s normal, then flags important changes for review. The more data the system sifts through, the more accurate it becomes.
After using edge computing to analyze footage at its source – the camera – video analytics software sends a fraction of the initial footage for further review and discards the rest. The results are better insights with fewer resources. Unassisted AI also improves privacy, since it quickly discards unnecessary data. That means fewer humans are watching video of citizens walking down a street, and less of that video is stored for long periods of time without due cause.
Unassisted AI and Video Analytics Use Today
Video analytics and unassisted AI technology is being implemented in a number of ways. For instance, in Bristol, UK, a 1700-camera surveillance system is able to run efficiently and effectively without costing a fortune for the council or encroaching on resident privacy.
In business, retail organizations can use unassisted AI-based video analytics to capture store activity and record what captured shopper’s attention, as well as identify the threat of shoplifters. Data on shoppers can be analyzed in real-time so the results of shopper behavior can be stored without the actual footage. And if the system registers an anomaly in behavior analogous with shoplifting, security can be notified immediately.
The Future of Unassisted AI
Video analytics in businesses and cities isn’t the only use case for unassisted AI. The technology could also become a crucial component of the autonomous driving movement, and a key technology that is required to move the IoT and AI industries forward. With unassisted AI in place, connected cars and other IoT devices could use data from their own sensors and surrounding sensors learn more, faster, making them safer and more effective.
Like other types of AI, we’ll likely see major advances in unassisted AI algorithms in the near future, along with its insertion into more applications in our daily lives.