Hanke’s actually got awards from CIA for his work at In-Q-Tel investing in Keyhole/Niantic, so yeah, safe to assume that the agency invested specifically to have players collect data. Considering many Pokémon were on or near military bases around the world… not hard to assume what CIA’s real goal was.

https://futurism.com/the-byte/pokemon-go-trespassers-militar...

I was wondering about the privacy implications: given a photo, the LGM could decode it to not just positioning, but also time-of-day and season (and maybe even year, or specific unique dates e.g. concerts, group activities).

Colors, amount of daylight(/nightlight), weather/precipitation/heat haze, flowers and foliage, traffic patterns, how people are dressed, other human features (e.g. signage and/or decorations for Easter/Halloween/Christmas/other events/etc.)

(as the press release says: "In order to solve positioning well, the LGM has to encode rich geometrical, appearance and cultural information into scene-level features"... but then it adds "And, as noted, beyond gaming LGMs will have widespread applications, including spatial planning and design, logistics, audience engagement, and remote collaboration.") So would they predict from a trajectory (multiple photos + inferred timeline) whether you kept playing/ stopped/ went to buy refreshments?

As written it doesn't say the LGM will explicitly encode any player-specific information, but I guess it could be deanonymized (esp. infer who visited sparsely-visited locations).

(Yes obviously Niantic and data brokers already have much more detailed location/time/other data on individual user behavior, that's a given.)

> Colors, amount of daylight(/nightlight), weather/precipitation/heat haze, flowers and foliage, traffic patterns, how people are dressed, other human features (e.g. signage and/or decorations for Easter/Halloween/Christmas/other events/etc.)

I mean, in theory it could. But in practice it'll just output lat, lon and a quaternion. Its going to be hard enough to get the model to behave well enough to localize reliably, let alone do all the other things.

The dataset, yes, that'll contain all those things. but the model won't.

You don't know for sure the model won't contain non-location data, like I noted the additional blurb vaguely said: "And, as noted, beyond gaming LGMs will have widespread applications, including spatial planning and design, logistics, audience engagement, and remote collaboration."

> will have widespread applications

There are a lots of "coulds" "ifs" and "shoulds". But how do you tokenise all those extra bits? For it to function as a decent location system, it has to be "invariant" to weather/light conditions. Otherwise you'll just fall back to GPS.

At it's heart, its a photo -> camera pose (location) converter. The bigger issue is how do you stop it hallucinating the wrong location when it has high uncertainty. That's before you get into scaling issues so that a model can cope with bigger than room scale pointclouds.

the first "public" VPS was released a while ago, yet six years later we still don't see widespread adoption of visual based location, even though its much much more accurate in an urban environment.

Google maps has more data than PGO could ever hope to have.

But you only use Maps when you need directions.

I don’t think this is sarcasm.

Until pretty recently, phone telemetry data was a free-for-all, and if you’re, say, in legal trouble, a map of the location of your phone over the past… however long you’ve had your phone is immediately available.

https://en.wikipedia.org/wiki/In-Q-Tel is a major investor into Niantic.

Upload a picture of a bad guy in an office lobby to pokegpt and ask it where he is.

Or Google Lens. Regardless this isn't the CIA, it's the NGIA.