Beamr announced advancing on a new front and reveals its capability to boost Machine Learning for video. Machine Learning and Artificial Intelligence for video have demonstrated immense achievements and have even more tremendous potential. This hot field is expanding fast as part of the computer vision market that is already estimated at more than $20 billion and expected to grow exponentially in the coming years.

But one of the biggest pain points that slows down progress is managing extremely large files and libraries. That is because video files are relatively large, and for training computer networks to recognize moving objects, need lots and lots of them. Think of how recognize a car or a human.

For us, it is an easy task, but not for a computer, either in a single image and certainly in a video. Each movement changes how the object looks, its shape, size and angle. That's why computer networks must scan and analyze countless videos to learn how to recognize if there is a human, a car, a cat or anything else in them.

Players in Machine Learning deal with, not to say are stuck with, large clusters of video files that are extremely difficult to manage, store and transfer. All these technical details sum up to a very clear bottom line for the many companies and start-ups in this field: heavy expenses that hinder their growth. The tests were conducted on NVIDIA DeepStream SDK - a tool for AI-based multi-sensor processing, video, audio and image understanding, which was a natural choice for Beamr as an NVIDIA Metropolis partner.