How can AR image recognition leverage AI and machine learning to adapt to different contexts and scenarios?

An example I’ve seen is an AIRBNB video from back in the day. It was a concept video for a product to be released in the future. The Airbnb guest enters the home, opens their camera, and the camera recognizes where they are within the home. The camera overlays arrows which lead the guest to where the kitchen is. Leads the guest to where the bedroom is. Even leads the guest to the thermostat and then highlights certain buttons on the thermostats so the guest knows how to set the temperature easily. AR and the camera are significantly important as compliantly technologies. Thanks to image recognition and scene understanding, the possibilities are endless.

The language here is a little odd. AI and ML in this scenario are identical, yet the article frames them as if they are different tools providing different functionality. I understand the reason why -- AI appeals to certain people while ML is more resonant for others -- but this is one of those cases where you have to pick a lane and stick to it. It might be better to introduce the topic as being about "ML (machine learning, often generalized as Artificial Intelligence)" or a similar construction.

The key to unlocking AI & ML for AR image recognition is having large datasets with verified & cataloged information. Large dataset unlock amazing Augmented Reality experiences. One of the best examples I have seen comes from companies moving away from GPS to Visual Positioning System (VPS) for users well documented locations. Testing a VPS for walking directions in major cities requires well documented 3D, 2D, Video, & Text datasets down to a street corner. When done right, users can now point their camera at a street corner, building, or street sign and be notified exactly where they are within a given city. This AI& ML + AR unlocks the ability to get camera based directions rather than looking at a flat map on your phone screen.

The future of AR image recognition comes from Synthetic Data being injected into the AI & ML models. Synthetic Data is information that is not real, but based on real world data. A simple example is room interiors built with 3D models. Once these datasets are created, AI & ML can train on these data sets without moral or ethical issues. A great example of this would be training security footage for children climbing over fences in a zoo. A real world data set would require children to climb over fences and possibly injure themselves. While with Synthetic Data, we can quickly make 10,000 or 10 million examples of virtual children climbing over various size fences.