What are the more interesting applications of computer vision technology?
There are many applications for computer vision AI and pretty much anything you can imagine a human doing with their vision – we can train AI to do something similar. For example, take out all the noise from what you’re seeing and focus in on objects. If we talk about computer vision at large, we’re talking about things from reading license plates to counting people to detecting cars to spotting sharks at bondi beach with drones to warn surfers. So it’s an infinite solution space, but Xailient solves one critical problem in that space.
And so with this huge, broad brush potential application, how do you identify target markets?
In terms of identifying a beachhead, we are looking particularly at access control. So access control where you’re using, for example, face authentication to let people into a premises through security surveillance. And then gradually we’ll start creeping into other verticals.
The technology behind computer vision AI, what does that look like?
It’s essentially teaching computers how to see like humans, given an image from a camera. You want the AI To detect certain things and then process those things. For example, retail stores want to use their existing cctv cameras to check if people are shoplifting. So you first want to detect the people and then you want to see what they’re doing with their hands. Get an algorithm to then process those images to then generate analytics and you’re then doing the retraining.
Our mission is to enable this future of pervasive, ubiquitous computer vision. We want allow developers to decide what they want to detect and do with images. We provide a platform and they can come with training data.
Let’s say someone wants to detect Varroa mite on bees. Like one of our partners. They would need to come with videos of bees with the Varroa mite. That gets fed into our machine online, which trains the AI to then recognize bees that are carrying mites.
How do you improve and train the AI model over time?