Artificial Intelligence and Intelligent Automation: What’s the difference?


Both artificial intelligence and intelligent automation are often misunderstood, however, and when it comes to AI, the hype is spreading faster than the actual science.

Both artificial intelligence and intelligent automation are often misunderstood, however, and when it comes to AI, the hype is spreading faster than the actual science.

Courtesy of BigStock.com — Copyright: Shade Lite

Advances in both automation and artificial intelligence have paved the way for real-life solutions that can help organizations save money and resources. The technology can be used for necessary but tedious, time-consuming tasks that would take humans much longer and be more prone to error. Both artificial intelligence and intelligent automation are often misunderstood, however, and when it comes to AI, the hype is spreading faster than the actual science.

Subsets of artificial intelligence such as machine learning and deep learning can help organizations sift through their data and tackle real-world solutions such as facial recognition or people counting. Intelligent automation can further help organizations by using existing data and automating analysis based on that data, ultimately helping to improve operations and workflow, as well as reducing redundant responses. But neither technology is “intelligent” in the sense that they can think or act like humans. We are many years away from that.

Nevertheless, both technologies have real solutions that can be deployed today, giving organizations real benefits. To understand some of those benefits, we must first understand what AI and IA are, their limitations, and how intelligent automation can be effectively deployed.

Artificial Intelligence

Artificial intelligence is often talked about and yet many capabilities are misinterpreted, undefined or misunderstood. Misunderstanding the capabilities of AI will often lead to unrealistic expectations. In data science, AI refers to a fully functional artificial brain that is self-aware, intelligent and that can learn, reason and understand. While advancements in what is referred to as AI technologies have come a long way and will continue to do so, the reality of AI, however, is very different from an intelligent computer that can learn and make decisions like a human. In practice and as it relates to the physical security industry, AI is a technology that runs a series of algorithms, searches through large databases or does calculations swiftly to provide deeper insights. The results can help users make decisions more quickly and efficiently depending on the application. General examples of applications that fall under “AI” would be facial recognition, object detection or people counting.

Because it’s a very broad term, when used without clarification AI may often fail to meet expectations. In truth, what is possible today are actually subsets of AI, such as machine learning techniques that include neural networks and deep learning. For example, deep learning uses task-specific algorithms to help train a computer to properly classify inputs. To do this, programmers essentially teach a computer by inputting a very large amount of data with corresponding labels, improving the technology’s ability to recognize new inputs.

While highly advantageous for well-understood applications, current AI technology has its limitations. Specific use cases and algorithms can certainly help organizations find greater operational efficiency, but it cannot teach itself completely new tasks or automatically make sense of data that it hasn’t been first taught. In addition, it can be difficult for users to interpret how an AI technology, such as deep learning, came to a decision or output.

AI does not give meaning to something on its own, but it can allow users to make more knowledgeable decisions and perform tasks more efficiently. If investigators run a face captured on video through a facial recognition database, for example, it’s important to know that faces that come back as matches are not guaranteed positive matches but rather meet the probability requirements previously programmed into the algorithm. In other words, it can be a very valuable and necessary start for users to eliminate some mundane legwork and facilitate more informed investigations or decisions.

Defining Intelligent Automation

So where does intelligent automation fit into all of this? Intelligent automation also allows users to eliminate tedious tasks or mundane legwork, helping them make quicker, more informed decisions. It can automate some of those decisions too. IA integrates both automation and data together. One way to look at IA is that it uses an organization’s existing data from different technologies and enables large-scale data analysis to automate operations and improve productivity.

In the physical security industry, IA can combine and automate many different datasets together such as thermometer readings, video, incidents, facial recognition, license plate recognition, map-based data and other records. Data sources can be correlated and leveraged together to assess specific situations or problems. As with AI technologies, intelligent automation is most capable when deployed for specific, well-defined situations and problems. If one system finds “A” and another system finds “B”, then Intelligent Automation should do “C.”  This is automation using existing intelligence to analyze textual or situational data with available systems and come up with appropriate measures to take. It can also simplify performance across organizations by automating data analysis and checking for faults or inconsistencies.

Intelligent Automation in the Real World

For organizations to get the most out of intelligent automation, the technology should have a clearly defined environment where the emphasis is on the human input with machines doing the heavy lifting and not on the machines making decisions. With IA, humans review and approve machine decisions to help better drive outcomes.

In order for IA to improve operations and give organizations valuable intelligence, it’s imperative to clearly define expectations to understand where IA can be deployed and add value.

In the physical security industry, IA is used to produce insights from cross-domain data sources.  The system ingests data (video feeds, access control alarms, ALPR alerts), and leverages that data for business intelligence, and improve workflows.

Let’s take, for example, a building that has several systems, including temperature sensors, airflow sensors and a centralized security system. Intelligent automation can be used to automatically pull video, send a map of where an incident is located, and sound an alarm in the event that both the temperature spikes greatly and the airflow sensor states “danger,” suggesting the possibility of a fire or chemical spill. The technology can initiate a specific standard operating procedure (SOP) when necessary such as unlocking specific doors, notifying management, etc.

Benefits of Intelligent Automation

IA is particularly useful for applications with large amounts of data that would otherwise be insufficient or impossible for humans to handle. It can automate repetitive, well-defined tasks that could take a user a significant amount of time.

In addition to saving time and money, intelligent automation can also help organizations drive innovation. Automating processes can take the load off the employee, allowing them to concentrate on more innovative, creative, highly skilled tasks and on making more knowledgeable decisions.

Advances in technology are ushering in a new era of great potential for intelligent automation that can benefit just about any business or organization by leveraging available data and opening up interesting possibilities. When an application calls for it and when expectations align, intelligent automation can be extremely valuable, allowing organizations to improve operations, lessen the chance of error or fraud, enhance customer experience and streamline workflows.

About the Author:

Dr. Sean Lawlor is the Lead Data Science at Genetec, Inc. based in Montreal, Quebec, Canada. He received his Ph.D. from McGill University in 2016 in Electrical Engineering where he focused on unsupervised modeling and inference in traffic networks. He is an expert in both supervised and unsupervised learning problems. He currently leads Genetec’s team which investigates and assesses the applications of machine learning and signal processing techniques to improve efficiency and intelligence in Genetec’s Security Center and cloud platforms.



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