This June, I had the opportunity to join the AI & Big Data Expo, where I got a lot of insight on the developments in Artificial Intelligence. The event got me thinking about how we use A.I. and Big Data within Anchormen as well as how big organizations integrate upcoming technologies in their day-to-day operations.
There was one presentation in particular that sparked my interest; many of my data science colleagues are familiar with Computer Vision and Object Recognition. Both are technologies with various real-life applications which are becoming more and more common.
Side note: If you are interested in the topics, you can check out our “Data Science Digest” series. We’ve discussed opportunities and Anchormen projects with both technologies – Computer Vision and Object Recognition.
Very simplified, Computer vision is an interdisciplinary field that deals with how algorithms can be made for gaining high-level understanding from digital images or videos. At Anchormen, we have some amazing use cases such as analyzing pictures and training a neural network to automate radiology image analysis for classifying tumors and fractures and more recently – working together with TV and digital advertisers to predict consumers’ response to different advertising commercials before they are even launched.
But KLM apparently has been working with Computer Vision as well! During the presentation, they revealed their unique way of using the technology.
As you probably know, KLM Royal Dutch Airlines is the major airline company in the Netherlands. It’s the oldest airline in the world (next year, they are celebrating 100 years since their founding in 1919) and has more than 35,000 employees. They’ve been looking into ways of making the preparation of airplane take-off automated through the use of data. My first thought was, “Ok, sounds awesome…but how?”.
There are a lot of cameras that constantly have the airplanes in sight. While an airplane is idle, there is a luggage carrier and a fuel carrier arriving, and the hatch doors are being opened to prepare for passenger intake. All these steps are monitored by personnel, but could also be recognized by an AI system which can coordinate the whole process and the track necessary resources. KLM are already making the first steps towards such a system.
During the expo, they showed an image indicating when the luggage carrier arrived and how long it took to put the luggage on the plane. This is only one example, but they visualized all of the steps in the process. It became immediately evident that some steps could be done in parallel, thus decreasing overall processing time.
For me this was an eye-opener for the variety of Computer Vision applications in other industries. Potentially their algorithm could be extended by orchestrating resources in such a way that they are on location when needed and overall processing time can be decreased. All in a fully automated manner! I can’t wait to see how this can improve the future of air travel.