Looking Beyond the Hype

Looking Beyond the Hype

The past few years have seen significant advancements in computing power. With this, machines seem to have a greater ability to learn about us and participate in our lives. Whether through product purchase suggestions on Amazon.com and other retail outlets or in our business and professional pursuits, machines are busy learning everywhere around us. Recently, the market has become flooded with buzz words relating to this type of work.

Learning the Proper Terms

Artificial Intelligence, Machine Learning, and Deep Learning are often used inaccurately and interchangeably. Given the significant advancements that have been made in this field, especially in the physical security industry, it is important that we be clear about these terms and their application. Using the term AI loosely only serves to misrepresent what machine learning can do and has the potential to generate misguided and unrealistic expectations.

Artificial Intelligence. AI is a broad term that first appeared in published research in 1956. For years, we understood AI as it appeared in pop culture, which lead to questions of a robot’s emotional capacity or their ability to take over the world. AI denotes a fully functional artificial brain that can reason, evolve, self-learn, and make human-like decisions. Currently, we are many years (or decades) away from this. Using the term artificial intelligence (or AI) related to technology or applications today can be inaccurate and potentially raise unrealistic expectations for those considering the technology.

Today’s examples of what many consider to be AI in our lives— Deep Blue beating a top chess player, Siri recognizing a song, Amazon suggesting a new book—are really examples of increasingly small computers running a series of algorithms, searching through huge databases, or doing a lot of calculations very quickly. With their faster computing power and processing speeds, our current machines are able to comb through a huge amount of data to provide deeper insights. These results can be more accurately categorized as guesses that can help us make decisions more quickly and efficiently.

Machine Learning. ML is an area of artificial intelligence that uses data to help a computer improve performance without being explicitly programmed. Static programming provides a computer with a set of instructions that do not change over time. Machine learning allows programmers to enable a computer to assess and alter its computational processes through training. Specifically, a computer is programmed with algorithms that enable it to determine which features of an input it should use in the identification process to efficiently produce the most accurate output. In a simple example, a computer might be trained to determine whether color or shape is a better indicator for correctly classifying a new input.

Working primarily with data in the form of language, text, video, or images, machine learning uses statistical techniques to enable computer systems to solve problems, make decisions and predictions, or improve the efficiency of specific, narrowly defined tasks.

Supervised and Unsupervised Machine Learning

The two most prevalent types of machine learning are Unsupervised and Supervised: Unsupervised machine learning, also called Data Mining, tackles very narrow problems by analyzing unstructured data—data that has not been organized or labeled in advance—in order to find patterns. With unsupervised machine learning, the computer is looking for discernable patterns in the data and searching for an unknown output or “ground truth.” One of the main focuses in unsupervised machine learning is anomaly detection. In this case, the computer identifies points in a dataset or stream that are outside the normal range without this range being pre-defined.

In supervised machine learning, computers are “trained” to properly classify inputs. This training occurs by providing the computer with structured datasets—data that has been organized or labeled in a predefined manner—that correlate thousands of possible inputs with corresponding labels that the computer understands.

The computer learns these correlations, through training, in order to be able to apply its understanding to new inputs. Once the computer has ingested and classified a new input, programmers must evaluate the “truthfulness” or accuracy of the output that the computer generates.

The programmer must tell the computer how accurate its classification is in order to train the computer to improve its ability to recognize new inputs. For example, if you label and input millions of images of roses and petunias into the computer with their associated labels, through supervised machine learning, the computer will ideally be able to differentiate between future images of roses and petunias at a tolerable rate.

Deep Learning and Working with Structured and Unstructured Data

One of the sub-disciplines under AI includes research in neural networks. Working with structured data, this research analyzes the relationship between inputs and outputs to gain new insights. Deep learning, also known as deep neural networks, is a specific formulation of neural networks that also works with structured data. What is exciting about deep learning is that the accuracies gained lately have often even exceeded what humans can do with specific tasks.

What’s Achievable Today with Deep Learning

In the physical security industry, we are achieving increased accuracy using deep learning to solve structured problems—problems that involve knowing what the output of the data should generally be. For example, automatic license plate recognition (ALPR) is a structured problem because, when we train our algorithms, we work with a data set of raw ALPR images, including letters, numbers, and symbols, to arrive at a classified output. In this case, the output is an image of license plate XYZ123.

At Genetec, we are actively using deep learning for purpose-built solutions that rely on identifying trends and dependencies between features present in the data itself. We are currently using deep learning in AutoVu, our ALPR system, to increase the accuracy and veracity rates of license plate tag reads. By applying computer vision algorithms, we have greatly reduced false positive reads for law enforcement officers when they identify and stop a vehicle of interest. Similarly, KiwiVision Privacy Protector has also been working with deep learning to improve the accuracy of its anonymization tool.

Genetec Citigraf is another example of one of the products that leverages advanced machine learning algorithms to estimate how different types of crime influence the risk of other crimes occurring in the future. For example, it can determine how close in time and space a robbery has to occur to your home to increase the risk of your home being robbed. In this case, there is no “ground truth” in the original problem and the answers are learned from the data. To allow us to pre-emptively handle failures before they occur, we are also using unsupervised machine learning to help our systems predict when they will become unstable. Currently, Security Center provides warnings when you have used 90 percent of the available disk space. Our goal is to have the system inform you that you will exceed available disk space in x number of days when you are only at 10 percent usage.


While advances in deep learning can help us realize greater operational efficiency, it is important to acknowledge that deep learning is not the ‘be-all and end-all’ associated with AI. In fact, it cannot yet teach itself new tasks nor automatically make sense of data through unsupervised learning.

It also has certain limitations. For instance, it can be difficult for users to interpret deep learning methods when trying to identify the steps that a machine takes to take a decision. In addition, it is still limited since it requires a lot of data for training in order to capture complex trends. However, we are seeing significant benefits with our current applications.

Data Stewardship

When it comes to machine learning, at Genetec our aim is to provide end-users with highly analyzed data that will guide them towards making accurate, critical decisions for verified results. The exploratory phases of creating a data science-based solution begins with data. In order to be granted access to data, we need to demonstrate to our customers and partners that we are proper stewards of data and can be trusted with it. An important step towards building that trust is being clear about what data science can and cannot do, which starts with debunking popular assumptions.

We are still a long way away from true AI—machines cannot give meaning to, or make sense of something, on their own. Applications can be developed to use pre-programmed algorithms to discover patterns in data or trained to correctly recognize and classify different inputs. Working with these algorithms we can also allow them to make their own improvements to perform their tasks more efficiently. This is a starting point towards unlocking the potential of machine learning that will allow us to find even more innovative approaches to protecting our everyday and developing safer, more secure environments.

This article originally appeared in the March 2019 issue of Security Today.


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