Recursive learning models: AI that improves from its own results

The third one, and this is what I'm excited about because this is what I've been working on for about the last four or five days. When I told you at the very beginning I was updating my, my content accelerator, and that's recursive learning models. And basically what that means is the AI is constantly learning from its own outputs. So, for example, making ads see this already or making ads sees this already. In other words, the ads are going out there, it's collecting the data from the interaction, the conversions, all kind of stuff, and then it's improving upon itself. Well, this has been really good for a lot of the big models, but it's coming to all of us, and that's what I've been working on. So as an example I mentioned before, Swing Point Media, we make content for clients. We make blogs, videos, social media posts and emails. We do more than that, but that's basically the bulk of what we do. And so what this is going to do now, this new piece that I'm building and adding to my already existing workflow, we've been using for now three years to create the content, it's now going to be smarter. In other words, it will, every time it sends out a piece of content, and it will then grab the data from that content. The analytics, whether it's a blog post, it's a social media post, it's a video, whatever it is, and then it will bring that information back in and learn from it and then improve it on the next, on the next go. So it's constantly changing. It's getting better and better every time. Can you imagine having an employee that every time they do a task, they do it better, they do it faster, their results get better? Well, that's kind of what we're doing. It's called recursive learning. And we're going to see that more and more.