Humans are amazing creatures and what we’re capable of is truly magical when you take a step back and think about it. We can hear unfamiliar sounds and they mean absolutely nothing, but a combination of sounds – the strumming of a guitar or beat of a drum – can come together to form a composition that we not only recognize, but also know the musician and lyrics to. These patterns we detect unlock a different reality.
Equally as magical, we are living in a day and age where supercomputers are being trained to become anything we want them to be whether it’s IBM Watson proving itself as the Jeopardy champion or Amazon’s Echo serving as a personal assistant. The power behind this dramatic shift in computing lies in Artificial Intelligence (AI).
Looking at Echo as an example, “Amazon recently announced that the Alexa AI powering its Echo and other hardware has now learned 1,000 ‘skills’ (up from just 135 in January),” according to Gizmodo. The fact that computers can continually learn and become more intelligent by the day is both mind-blowing and exciting at the same time.
At Soteria Intelligence, we are riding this new wave innovation by using social media data combined with AI to combat one of the biggest problems in the world today: lone wolf attacks.
By analyzing historical data on lone wolves – whether attacks occurred or not – there are a variety of patterns we’ve uncovered that not only paint a picture of psyche, but also help detect lone wolves as they are being born versus when they actually act. AI plays a key role in this.
As opposed to software of the past where clearly defined rules produce a specific result based on inputs (if this, then that), AI allows us to train our technologies based on historical data as well as a healthy diet of new data, including input from analysts and other critical data points like emerging trends.
If we wish to have the power to identify potential lone wolves on the horizon, we must form a thorough understanding of what drives them, their actions, outside influences, and so much more. We must then search for such pre-incident indicators through a variety of mediums, especially social media. This is a monumental task when looking at big data, and where humans themselves fail.
On the other hand, a marriage between man and machine grants the power to solve problems on a very large scale. And that’s our mission.