176
Niantic uses Pokémon Go player data to build AI navigation system
(arstechnica.com)
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
this post was submitted on 19 Nov 2024
176 points (98.4% liked)
Technology
73455 readers
1192 users here now
This is a most excellent place for technology news and articles.
Our Rules
- Follow the lemmy.world rules.
- Only tech related news or articles.
- Be excellent to each other!
- Mod approved content bots can post up to 10 articles per day.
- Threads asking for personal tech support may be deleted.
- Politics threads may be removed.
- No memes allowed as posts, OK to post as comments.
- Only approved bots from the list below, this includes using AI responses and summaries. To ask if your bot can be added please contact a mod.
- Check for duplicates before posting, duplicates may be removed
- Accounts 7 days and younger will have their posts automatically removed.
Approved Bots
founded 2 years ago
MODERATORS
What do we need this for? Not clear what it does
Seems to be a tool for taking an image and Geo-locating it, even in cases where other sources of data like Googles street map cars are insufficient.
The issue is it's going to be trained only on places people go. My understanding is that most geolocating systems are more focused on the uncommon or less traveled locations. Maybe I'm wrong, but I guess they're probably tested it out before announcing it.
Maybe they will start/already have putting pokemon in locations that they need data for to make a more comprehensive map.
Seems questionably reliable as an independent system. Maybe could help refine more traditional systems? Not sure I understand a use case for this.
It definitely has applications in defense/Intel spaces. Knowing where a photo was taken can be really useful info.
AI delivery bots maybe? It's basically an aggregate of "here's where it's possible/common to walk" so it's not useful for driving/flying AI. Also useful for marketing, knowing where foot traffic is.
The article says they're treating it as a Large Geospatial Model (like a Large Language Model), so it seems like you could use that as a predictive way to navigate between two points. With an LLM it spits out phrases based on context. The LGM would return paths based on context.