Deep machine learning is becoming an intrinsic part of much of what we do. It is no surprise then that law enforcement is focused on leveraging deep machine learning in its work so that departments can gain vital intelligence faster.

It is imperative that law enforcement officers apply these new technologies to solve crimes since criminals themselves are not shy of using them. They are using the anonymity of the dark web, rapid tech advancements, and digital currency to commit the most heinous of crimes, such as trading in illicit drugs, child pornography, and sex trafficking. It is only right that the same technology is used to apprehend them.

Deep learning for crime fighting uses deep neural networks to identify victims and speeds up search and rescue times. Unlike traditional, linear machine learning algorithms, it is hierarchical, which means it is based on increasing complexity and abstraction to process information.

The actions triggered and the results produced by these technologies are comparable to human experts. For example, some of these advanced systems match sexual exploitation images on the dark web with missing children information, Amber and Silver Alerts, and then combine traffic camera footage to locate vehicles involved in such cases.

So, what is deep machine learning?

Traditional computers perform specific tasks. Deep machine learning uses programs that learn independently, evolve with exposure to new data and adapt understanding based on those factors.

As more data is fed into the systems, the complex patterns of recognition allow them to recognize and identify objects and make predictions with a high degree of accuracy.

This means officers do not have to sift through mountains of data. Many intelligent and innovative new products offer unmatched innovation to the ways law enforcement agencies solve crimes. Companies like Motorola are introducing advanced analytical products to assist in crime fighting.

Machine-learning solutions and their advanced algorithms use predictive rules that automatically recognize anomalies in data sets. They reduce the number of false alerts by filtering out incorrect or irrelevant information.

Detractors, however, point out risks associated with the processes. Biased conclusions could be drawn from AI based on factors like gender, ethnicity, and age. There is also a high risk for the data to be breached. But with proper risk management in place, this risk can be controlled, and machine learning can be used to fight increasingly sophisticated crimes.