I'll get all. I'm just going to do a quick demo of one, just one of the machine learning models that we came up with in our Ready for a Search Week workshop. So the task we set was to use these little, oh my, ten in the morning, these little raspberry pies that have quite restricted capabilities with their camera to automatically detect something. And the students could decide what that was going to be. And we were going to use retraining techniques rather than training from scratch. And we were going to use run detection code on the Raspberry Pi and Pi Thin. And just from a research point of view, what we were researching is how accurate are these retrained models, how specific to the training circumstances are they? Because they're built from very general models, but as we see, they end up being very specific models. And ultimately, we want to know whether these can be used in practice and relied upon. So the students pick their own tasks and I'll make some videos for the other ones. This video is demonstrating the car versus person detector, which we set up outside the office. So I'll get this running. And ultimately, this is all we want to show off the student's work. So you can see I'm panning down, they use the scroll hat there to display whether there was a person or a car in the frame. And there's camera hanging down there. And because it's a busy time of day on campus, there's always a person. I've been affected. There's no one in the frame. It'll show you an X to show you there's no way in the frame. And this one ended up being really, really accurate. Now this is the location that it was trained at. But it was only trained with three or four people. It was only trained with a dozen or so positive images and a dozen or so negative images. And it manages to pick every time a person walks past and every time a car goes past. So you can see me struggling with the focus on my camera. But eventually a car will go past and we'll see it detected. That's quite a fun demo. But the most important thing from my point of view is that it's shown that these devices, the power in these devices is enough to run custom detectors. But it's detecting them straight from the camera. There we go. The car has been detected. Taking things straight from the camera reliably. So there's all kinds of use cases for that. We got a lot more research to do in terms of how reliable our dot-circumstances are they reliable? There's a million other variables here. But this is a really interesting starting point. And to show that this is not something magical that we're looking at here, here's the same model running in the office where the workspace we were using. So this is our heater, love lace room. We were using the cam down after the workshop was finished and took a recording. And it thinks that the room is a car. And that's because this model was trained for cars, people, or nothing. It was trained outside. And it has to make a guess about what it's looking at. But I really enjoyed doing the workshop. And I really enjoyed watching people come to understand much more about machine learning. The capabilities in machine learning are exciting. But the restrictions on what it's able to do are substantial. And the students discovered all of that and great demo.