How AI, IoT and Big Data Will Affect the Security Industry

Stanley Security’s IoT expert Moiz Neemuchwala explains why Big Data and artificial intelligence are poised to have a major impact on the security industry.

According to James Barrat, author of “Our Final Invention” and expert on the subject, artificial intelligence (AI) is the last invention the human race would ever need to continue to develop and perform tasks usually requiring human intelligence.

In the future — and, to be superior to their ancestors — machines and computer systems will reinvent themselves on their own. It may sound a bit scary, but definitely fascinating too!

Engineers and data scientists are pushing this frontier to the next level and setting the stage for this future to be a reality. Artificial intelligence is powered by processing data; however, data needs to be generated first and the security industry is playing a big role.

Data is generated in a number of ways, with sensors being the most common form and what has allowed for the age of Internet of Things (IoT, or Industry 4.0). In other words, sensors communicate with each other while yielding various forms of data.

For example, a video camera collects/records video streams with each frame consisting of an immense amount of data. This information is collected in data lakes, which makes it accessible when needed.

AI can now be enabled through deep learning — a new area of machine learning research, which, over the years, has evolved from pattern recognition, the simplest form of learning. Large swaths of data, commonly known as Big Data, can be brought together to provide insights that were difficult to comprehend.

This ability makes machines learn and capture decisions on their own, based on parameters which they continually absorb and update as they process more data.

For example, the camera that captured the video frames comprehends everything it sees and defines several more parameters based on the millions of frames collected over time.

One of the most commonly used algorithms for deep learning is neural networks, derived from how the brain operates using neurons, hence the name. Neural networks are used in multiple applications like pattern recognition, optimization, prediction of outcomes, etc. — the first AI-based, self-driving car used neural networks, for instance.

Advances in computational technology along with advanced statistical modeling have made deep learning an actuality.