> So, here’s an honest question: which approach is actually easier, more cost-effective, and energy-efficient?
Really it's more like three questions.
1. Easier? I guess that depends how you define ease, but it largely depends on what resources you have available to you. If I'm Meta and I already have a ton of compute and AI training expertise but don't have relationships with all of the airports, stadiums, etc., their approach is probably easier. You'd have to spin up new teams of people all over the world to get beacons everywhere you want them.
2. Cost-effective? I don't know enough about the costs of your solution to give an accurate answer here, but again it just seems like they're probably already spending resources training models on a huge number of images of the world, so maybe not a lot of incremental cost here.
3. Cost efficient? I would assume your approach wins here.
Really it's more like three questions.
1. Easier? I guess that depends how you define ease, but it largely depends on what resources you have available to you. If I'm Meta and I already have a ton of compute and AI training expertise but don't have relationships with all of the airports, stadiums, etc., their approach is probably easier. You'd have to spin up new teams of people all over the world to get beacons everywhere you want them.
2. Cost-effective? I don't know enough about the costs of your solution to give an accurate answer here, but again it just seems like they're probably already spending resources training models on a huge number of images of the world, so maybe not a lot of incremental cost here.
3. Cost efficient? I would assume your approach wins here.