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Niantic plans a “Large Geospatial Model” trained on Pokémon Go player data
- __MatrixMan__ 2y agoI wonder if there's a sweet spot for geospatial model size. A model trained on all data for 1m in every direction would probably be too sparse to be useful, but perhaps involving data from a different continent is costly overkill? I expect most users are only going to care about their immediate surroundings. Seems like an opportunity for optimization.
- janice1999 2y agoI'm sure the CIA already has access. [1] People were raising privacy concerns years ago. [2] [1] https://www.networkworld.com/article/953621/the-cia-nsa-and-pokmon-go.html https://www.networkworld.com/article/953621/the-cia-nsa-and-... [2] https://kotaku.com/the-creators-of-pokemon-go-mapped-the-world-now-theyre-1838974714 https://kotaku.com/the-creators-of-pokemon-go-mapped-the-wor...
- andrewmcwatters 2y ago[flagged]
- Onavo 2y agoMore like Celesteela, after all, you need jet fuel to melt steel beams.
- dgfitz 2y agoGoogle maps has more data than PGO could ever hope to have.
- esafak 2y agoBut you only use Maps when you need directions.
- dgfitz 2y agoI 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.
- deleted 2y ago[deleted]
- astrange 2y agoPeople have a lot of strange beliefs about the CIA. Why would they even care about this?
- BirAdam 2y agoHanke’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-military-bases https://futurism.com/the-byte/pokemon-go-trespassers-militar...
- smcin 2y agoI 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.)
- KaiserPro 2y ago> 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.
- smcin 2y agoYou 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."
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- Jabrov 2y agoI wonder how this can be combined with satellite data, if at all?
- ileonichwiesz 2y agoI don’t see why not. Photos are often combined with satellite data for photogrammetry purposes, even on large scale - see the recent Microsoft Flight Simulator (in a couple days, when it actually works)
- mxfh 2y agoIt's usually aerial data, especially oblique aerial. Bing Maps is still pretty unique in offering them undistorted and not draped over some always degraded mesh.
- CaptainFever 2y agoThis title is editorialized. The real title is: "Building a Large Geospatial Model to Achieve Spatial Intelligence" > Otherwise please use the original title, unless it is misleading or linkbait; don't editorialize. My personal layman's opinion: I'm mostly surprised that they were able to do this. When I played Pokémon GO a few years back, the AR was so slow that I rarely used it. Apparently it's so popular and common, it can be used to train an LGM? I also feel like this is a win-win-win situation here, economically. Players get a free(mium) game, Niantic gets a profit, the rest of the world gets a cool new technology that is able to turn "AR glasses location markers" into reality. That's awesome.
- bongodongobob 2y agoAll they needed was a shit ton of pictures. The AR responsiveness (and Pokemon Go) have nothing to do with it. It was just a vehicle for gathering training data.
- relyks 2y agoI'm pretty sure most of the data is not coming from the AR features. There are tasks in the game to actually "scan" locations. Most people I know who play also play the game without the AR features turned on unless there's an incentive.
- CaptainFever 2y agoThat's good information, thank you!
- anigbrowl 2y agoIt's OK to adjust the title to have more relevant facts or to fix a poorly worded one. Editorializing is more like 'Amazing: Niantic makes world-changing AI breakthrough'.
- n2d4 2y agoThe original title was not poorly worded though. The new one was editorialized to get a certain reaction out of readers — I promise you the responses on this thread would look different with the original title.
- darkwater 2y agoI can really imagine a meeting with the big brasses of Google/Niantic a few years ago that went along - We need to be the first to have a better, new generation 3D model of the world to build the future of maps on it. How can we get that data?" + What about gamifying it and crowd-sourcing it to the masses? - Sure! Let's buy some Pokemon rights! It's scary but some people do really have some long-term vision
- dgfitz 2y agoPokemon Go is built on the same engine as Inverness I think its called. When it launched they even used the same POIs. I think this was ~5-7 years before PGO launched. Edit: I said inverness and meant ingress. Apologies.
- deleted 2y ago[deleted]
- edm0nd 2y agoI think you are thinking of Ingress. No idea what Inverness is. Ingress and PGO share the same portals and stuffs and its what PGO got its data from.
- ClassyJacket 2y agoInverness is a city in Scotland
- travisjungroth 2y agoAlso a tiny town in Marin County, CA and one of my favorite words. It’s just so nice to say. Inverness.
- virodoran 2y agoPokemon Go was launched on the Unity game engine in 2016. Ingress was using a different game engine at the time, and wasn't rewritten into Unity until several years later. Even the backend/server side was significantly different, with them needing to write a shim to ensure compatibility during & after the move to Unity.
- murdockq 2y agoI'm guessing this can be the new bot that could play competitively at GeoGuesser. It would be interesting if Google trained a similar model and released it using all the Street Map data, I sure hope they do. Has anyone done something similar with the geolocated WIFI MAC addresses, to have small model for predicting location from those.
- themk 2y agoI believe I read somewhere that geoguesser AI based on street view data was mostly classifying based on the camera/vehicle set up. As in, a smudge on the lens in this corner means its from Paris. This crowdsourced approach probably eliminates that issue.
- reilly3000 2y agoI’m intrigued by the generative possibilities of such a model even more than how it could be used with irl locations. Imagine a game or simulation that creates a realistic looking American suburbia on the fly. It honestly can’t be that difficult, it practically predicts itself.
- rbrown 2y agoGenuinely impressed Google had the vision and resources to commit to a 10 year data collection project
- relyks 2y agoThis is pretty cool, but I feel as a pokehunter (Pokemon Go player), I have been tricked into working to contribute training data so that they can profit off my labor. How? They consistently incentivize you to scan pokestops (physical locations) through "research tasks" and give you some useful items as rewards. The effort is usually much more significant than what you get in return, so I have stopped doing it. It's not very convenient to take a video around the object or location in question. If they release the model and weights, though, I will feel I contributed to the greater good.
- rbrown 2y agoThey won't. It's the same data collection play as every other Google project Just for clarity on this comment and a separate one, Niantic is a Google spin out company and appears to still be majority shareholder: https://en.wikipedia.org/wiki/Niantic,_Inc.#As_an_independent,_private_company https://en.wikipedia.org/wiki/Niantic,_Inc.#As_an_independen...
- relyks 2y agoGoogle actually has released weights for some of their models, but judging by the fact that this model is potentially valuable, they likely will not allow Niantic for this
- ysofunny 2y ago> Google actually has released weights for some of their models, but judging by the fact that this model is potentially valuable, they likely will not allow Niantic for this which is totally unfair, every niantic player should have access to all the stuff because they collectively made it
- aqfamnzc 2y agoWelcome to the modern internet. While you're at it, please get me access to Google's captcha models facebook face directory Google's GPS location data hoard, (most every android phone on the planet 24/7 (!) and any iPhone navigating with gmaps) And so on and so on All of which I've directly contributed to and never (directly) recieved anything in return
- Jabbles 2y ago> For example, it takes us relatively little effort to back-track our way through the winding streets of a European old town. We identify all the right junctions although we had only seen them once and from the opposing direction. That is true for some people, but I'm fairly sure that the majority of people would not agree that it comes naturally to them.
- tiahura 2y agoThe cia has to be all over this.
- mxfh 2y agoSomehow I always thought something like that would have been the ultimate use case for Microsoft Photosynth (developed from Photo Tourism research project), ideally with a time dimension, like browsing photos in a geo spatio-temporal context. I expect that was also some reason behind their flickr bid back then. https://medium.com/@dddexperiments/why-i-preserved-photosynth-2f670d5c8dec https://medium.com/@dddexperiments/why-i-preserved-photosynt... https://phototour.cs.washington.edu https://phototour.cs.washington.edu https://en.wikipedia.org/wiki/Photosynth https://en.wikipedia.org/wiki/Photosynth at least any patents regarding this will also expire about 2026.
- josh_cutler 2y agoI worked on this and yes it was 100% related to the interest in Flickr. At the time Google Street had just become a thing and there was interest in effectively crowdsourcing the photography via Flickr and some of the technology behind Photosynth.
- oliyoung 2y agoImpressive, but this is one of those "if this is public knowledge, how far ahead is the _not_ public knowledge" things
- UltraSane 2y agoI really want to know what the NSA and NRO and Pentagon are doing training deep neural networks on hyperspectral imaging and synthetic aperture radar data. Imagine having something like Google Earth but with semantic segmentation of features combined with what material they are made from. All stored on petabytes of NVMe flash.
- DrBenCarson 2y agoI’ve published research in this general arena and the sheer amount of data they need to get good is massive. They have a moat the size of an ocean until most people have cameras and depth sensors on their face It’s funny, we actually started by having people play games as well but we expressly told them it was to collect data. Brilliant to use an AR game that people actually play for fun
- UltraSane 2y agoYes it must be almost an exabyte of data.
- AndrewKemendo 2y agoThis is literally what I built my first company around starting in 2012, when Niantic was still working on Ingress I describe it here during 500 Startups demo day: https://youtu.be/3oYHxdL93zE?si=cvLob-NHNEIJqYrI&t=6411 https://youtu.be/3oYHxdL93zE?si=cvLob-NHNEIJqYrI&t=6411 I further described it on the Planet of the Apps episode 1 Here's my patent from 2018: https://patents.google.com/patent/US10977818B2/en https://patents.google.com/patent/US10977818B2/en So. I'm not really sure what to do here given that this was exactly and specifically what we were building and frankly had a lot of success in actually building. Quite frustrating
- singleshot_ 2y agoCall an intellectual property attorney?
- john_minsk 2y agoVery interesting. What is the current state of this tech?
- alpyne 2y agoBrian Maclendon (Niantic) presented some interesting details about this in his recent Bellingfest presentation: https://www.youtube.com/live/0ZKl70Ka5sg?feature=shared&t=12837 https://www.youtube.com/live/0ZKl70Ka5sg?feature=shared&t=12...
- jonplackett 2y agoThis seems like it’d be quite handy to have in an autonomous vehicle of any kind
- piyh 2y agoApplications that I thought of as I read this: Real-Time mapping of the environment for VR experiences with built-in semantic understanding. Winning at geoguesser, automated doxing of anybody posting a picture of themselves. Robotic positioning and navigation Asset generation for video games. Think about generating an alternate New York City that's more influenced by Nepal. I'm getting echoes of neural radiance fields as well. Procedural generation of an alternative planet is the kind of stuff that the No Man's sky devs could only dream of.
- adamredwoods 2y agoAI guided missiles.
- ConanRus 2y ago[dead]
- ggm 2y agoNot wanting to over-do it, but is there possibly an argument the data about geospatial should be in the commons and google have some obligation to put the data back into the commons? I'm not arguing to a legal basis but if it's crowdsourced, then the inputs came from ordinary people. Sure, they signed to T&Cs. Philosophically, I think knowledge, facts of the world as it is, even the constructed world, should be public knowledge not an asset class in itself.
- dev1ycan 2y agoDo you expect every company to release all their data to the public as well or it's just because you're not invested in this one?
- ggm 2y agoI expect any company which collates information about geospatial datasets to release the substance of them, yes. Maybe there's an IPR lockup window, but at some point the cadastral facts of the world are part of the commons to me. I would think there's actually a lot of epidemiology data which also should be winding up in the public domain getting locked up in medical IPR. I could make the same case. Cochrane reports rely on being able to do meta analysis over existing datasets. Thats value.
- scottyah 2y agoThey found a creative way to incentivize the collection of it and paid for the processing. Anybody can collect the same data, I don't see why they would have to release it... It would be nice of them though.
- urbandw311er 2y agoI’ve been saying this about Google Maps for years, especially their vast collection of public transport loading data and real time road speeds. People are duped into thinking they’re doing some “greater good” by completing the in-app surveys and yet the data they give back is for Google’s exclusive use and, in fact, deepens their moat.
- themingus 2y agoInterestingly, Pokemon GO only prompts players to scan a subset of the Points of Interest on the game map. Players can manually choose to scan any POI, but with no incentive for those scans I'm sure it almost never happens. > Today we have 10 million scanned locations around the world, and over 1 million of those are activated and available for use with our VPS service. This 1 in 10 figure is about accurate, both from experience as a player and from perusing the mentioned Visual Positioning System service. Most POI never get enough scan data to 'activate'. The data from POI that are able to activate can be accessed with a free account on Niantic Lightship [1], and has been available for a while. I'll be curious to see how Niantic plans to fill in the gaps, and gather scan data for the 9 out of 10 POI that aren't designated for scan rewards. 1: https://lightship.dev https://lightship.dev
- whatevermang 2y agoPeople complaining here that you are somehow owed something for contributing to the data set, or that because you use google maps or reCAPTCHA you are owed access to their training data. I mean, I'd like that data too. But you did get something in return already. A game that you enjoy (or your wouldn't play it), free and efficient navigation (better than your TomTom ever worked), sites not overwhelmed by bots or spammers. Yeah google gets more out of it than you probably do, but it's incorrect to say that you are getting 'nothing' in return.
- maxerickson 2y agoI'm not sure quite what the ownership is, but Niantic isn't a subsidiary of Alphabet or Google.
- drusepth 2y agoThe company was formed as Niantic Labs in 2010 as an internal startup within Google, founded by the then-head of Google's Geo Division (Google Maps, Google Earth, and Google Street View). It became an independent entity in October 2015 when Google restructured under Alphabet Inc. During the spinout, Niantic announced that Google, Nintendo, and The Pokémon Company would invest up to $30 million in Series-A funding. Not sure what the current ownership is (they've raised a few more times since then), but they're seemingly still very closely tied with Google.
- urbandw311er 2y ago> Today we have 10 million scanned locations around the world, and over 1 million of those are activated and available for use with our VPS service. We receive about 1 million fresh scans each week Wait, they get a million a week but they only have a total of 10 million, ie 10 days worth? Is this a typo or am I missing something?
- gtr32x 2y agoPretty sure there can be multiple "scans" per location is what they are saying
- r00fus 2y agoA location probably requires like a million scans to be visualized properly. Think of a park near your house - there are probably thousands of ways to view each feature within.
- aeturnum 2y agoScans are not always of new locations. They have ~10m established nodes and they get ~1m node scans per week that might be new and might be old.
- themingus 2y agoIt’s possible they meant 1 million frames from scans.
- _j0r5 2y agoFucking cool. Hi old Niantic teammates ;).
- garagemc2 2y agoDon't quite understand the application of this?
- CaptainFever 2y agoGoogle Maps uses this tech for AR navigation: https://www.pocket-lint.com/what-is-google-maps-ar-navigation-live-view/ https://www.pocket-lint.com/what-is-google-maps-ar-navigatio...
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- arnaudsm 2y agoSo that's why Pokemon was notoriously impactful on battery life. They were recording and uploading our videos the whole time?
- CaptainFever 2y agoNo, that is unlikely to be the case.
- andybak 2y agoI don't think so. I wanted to voice this quickly without a detailed rebuttal as yours is the top comment and I don't think it's correct. Hopefully someone will do my homework for me (or alternatively tell me I'm wrong!).
- navaed01 2y agoConversation about ‘players are the product’ of Pokémon go aside… What are some practical applications of an LGM? Seems like navigation is ‘solved’? There’s already a lot of technology supporting permanence of virtual objects based on spatial mapping? Better AI generated animations? I am sure there are a ton of innovations it could unlock…
- CaptainFever 2y agoI hope this tech could help make AR glasses more useful in public, day-to-day life, like a video game HUD.
- wongarsu 2y ago"It could help with search and rescue" jokes aside [1] this seems really useful for robotics. Their demo video is estimating a camera position from a single image, after learning the scene from a couple images. Stick the camera on a robot, and you are now estimating where the robot is based on what the robot has seen before. They are a bit vague on what else the model does, but it sounds like they extrapolate what the rest of the environment could look like, the same way you can make a good guess what the back side of that rock would look like. That gives autonomous robots a baseline they can use to plan actions (like how to drive/fly/crawl to the other side) that can be updated as new view points become available. 1: https://www.xkcd.com/2128/ https://www.xkcd.com/2128/
- krick 2y agoI still don't get what LGM is. From what I understood, it isn't actually about any "geospatial" data at all, is it? It is rather about improving some vision models to predict how the backside of a building looks, right? And training data isn't of people walking, but from images they've produced while catching pokemons or something? P.S.: Also, if that's indeed what they mean, I wonder why having google street view data isn't enough for that.
- drusepth 2y ago> It is rather about improving some vision models to predict how the backside of a building looks, right? This, yes, based on how the backsides of similar buildings have looked in other learned areas. But the other missing piece of what it is seems to be relativity and scale: I do 3D model generation at our game studio right now and the biggest want/need current models can't do is scale (and, specifically, relative scale) -- we can generate 3d models for entities in our game but we still need a person in the loop to scale them to a correct size relative to other models: trees are bigger than humans, and buildings are bigger still. Current generative 3d models just create a scale-less model for output; it looks like a "geospatial" model incorporates some form of relative scale, and would (could?) incorporate that into generated models (or, more likely, maps of models rather than individual models themselves).
- jayd16 2y agoThe ultimate goal is to use the phone camera to get very accurate mapping and position. They're able to merge images from multiple sources which means they're able to localize an image against their database, at least relatively.
- virodoran 2y ago> And training data isn't of people walking, but from images they've produced while catching pokemons or something? Training data is people taking dedicated video of locations. Only ARCore supported devices can submit data as well. So I assume along with the video they're also collecting a good chunk of other data such as depth maps, accelerometer, gyrometer, magnetometer data, GPS, and more.
- firejake308 2y agoIs this related to NeRF (neural radiance fields)?
- farhanhubble 2y agoIt may not be Geospatial data at all and I'm not sure how much the users consented but the data collection strategy was well crafted. I remember recommending building a game to collect handwriting data from testers (about a thousand), to the research lab I worked for long time back.
- m3kw9 2y agoThe data marginally better than what google already have
- fragmede 2y agoWaymo is supposedly geofenced because they need detailed maps of an area. And this is supposedly a blocker for them deploying everywhere. But then Google goes and does something like this, and I'm not sure, if it's even really true that Waymo needs really detailed maps, that it's an insurmountable problem.
- yalogin 2y agoEven before LLMs, I knew they are going to launch a fine grained mapping service with all that camera and POI data. Now this one is actually much better obviously. Very few companies actually have this kind of data. Remains to be seen how they make money out of this
- _qxb9 2y agoWe do this at [name redacted as this was a joke]. When users scan their barcode, the preview window is zoomed in so users think its mostly barcode. We actually get quite a bit more background noise typically of a fridge, supermarket aisle, pantry etc. but it is sent across to us, stored, and trained on. Within the next year we will have a pretty good idea of the average pantry, fridge, supermarket aisle. Who knows what is next
- ryanschaefer 2y agoI’d be interested in how your privacy policy allows this. I can’t find where it mentions photos are stored or used for training purposes…
- ipaddr 2y agoI would be more interested on why you believe something like this isn't baked into most privacy policies. I'm not shocked but I'm shocked you are shocked.
- ryanschaefer 2y agoI’m not exactly shocked that it could exist. But this usage (beyond the scope of processing barcodes) seems like it couldn’t be construed to fit into the normal avenues of data collection under a privacy policy. Also with regard to training specifically, this policy was created in late 2020 so I don’t know how it would cover generative models.
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- moreofthis 2y agoGiving their policy an (admittedly quick) skim there doesn't seem to be any section that mentions AI, LLMs, training any kind of model, using image data from barcode pictures, etc. I'd be very curious to see the explanation of how this is baked into the policy.
- john_minsk 2y agoVery cool. However, I can't fully agree that generating 3d scene "on the fly" is the future of maps and many other use cases for AR. The thing with geospatial, buildings, roads, signs, etc. objects is that they are very static, not many changes are being made to them and many changes are not relevant to the majority of use cases. For example: today your house is white and in 3 years it has stains and yellowish color due to time, but everything else is the same. Given that storage is cheap and getting cheaper, bandwidth of 5G and local networks is getting too fast for most current use cases, while computer graphics compute is still bound by our GPU performance, I say that it would be much more useful to identify the location and the building that you are looking at and pull the accurate model from the cloud (further optimisations might be needed like to pull only the data user has access to or needs access to given the task he is doing). Most importantly users will need to have access to a small subset of 3D space on daily basis, so you can have a local cache on end devices for best performance and rendering. Or stream rendered result from the cloud like nVidia GDN is doing. Most precise models will come from CAD files for newly built buildings, retrospectively going back to CAD files of buildings build in last 20-30 years(I would bet most of them have some soft of computer model made before) and finally going back even further - making AI look at the old 2D construction plans of the building and reconstructing it in 3D. Once the building is reconstructed (or a concrete pole like shown in the article) you can pull its 3D model from the cloud and place it in front of the user - this will cover 95% of use cases for AR. For 5% of the tasks you might want real time recognition of the current state of surfaces for some tasks or changes in geometry (like tracking the changes in the road quality compared with the previous scans or with reference model), but these cases can be tackled separately and having precise 3D model will only help, but won't be needed to be reconstructed from scratch. This is a good 1st step to make a 3D map, however there should be an option to go to the real location and make edits to 3D plan by the expert so that the model can be precise and not "kind of" precise.
- reissbaker 2y agoI'm confused by both this blog post, and the reception on HN. They... didn't actually train the model. This is an announcement of a plan! They don't actually know if it'll even work. They announced that they "trained over 50 million neural networks," but not that they've trained this neural network: the other networks appear to just have been things they were doing anyway (i.e. the "Virtual Positioning Systems"). They tout huge parameter counts ("over 150 trillion"), but that appears to be the sum of the parameters of the 50 million models they've previously trained, which implies each model had an average of... 3MM parameters. Not exactly groundbreaking scale. You could train one a single consumer GPU. This is a vision document, presumably intended to position Niantic as an AI company (and thus worthy of being showered with funding), instead of a mobile gaming company, mainly on the merit of the data they've collected rather than their prowess at training large models.
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- nindalf 2y ago“Concepts of a plan” is often enough to make people think you know what you’re doing. Think most people, here included, got the impression that they had succeeded already.
- blueflow 2y agoMaybe its because the current HN title says "trained" in the past tense?
- jchw 2y agoI'm annoyed that the HN title is still editorialized after multiple hours. It's not like the original title is especially egregious.
- Cthulhu_ 2y agoAnd I get that; one thing that (I think) especially software developers have is a high level knowledge of many different subjects, to the point where IF they ever have to do something in practice, they'll know enough to figure it out. T-shaped people kinda thing.
- ogurechny 2y agoLunduke is happy: “I told you so!” https://www.youtube.com/watch?v=EVmZy95vMUc https://www.youtube.com/watch?v=EVmZy95vMUc
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- nonameiguess 2y agoGoing to try to clear this up from speculation as best I can. Niantic was a spinoff divested from Google Maps roughly a decade ago who created a game called Ingress. This used Open Street Maps data to place players in the real world and they could designate locations as points of interest (POI), which Niantic used human moderators to judge as sufficiently noteworthy. Two years after Ingress was released, Niantic purchased limited rights to use Pokemon IP and bootstrapped Pokemon Go from this POI data. Individual points of interest became Pokestops and Gyms. Players had to physically go to these locations and they could receive in-game items needed to continue playing or battle other Pokemon. From the beginning, Pokemon Go had AR support, but it was gimmicky and not widely used. Players would post photos of the real world with Pokemon overlaid and then turn it off, as it was a significant battery drain and only slowed down your ability to farm in-game items. The game itself has always been a grind type of game. Play as much as possible to catch Pokemon, spin Pokestops, and you get rewards from doing so. Eventually, Niantic started having raids as the only way to catch legendary Pokemon. These were multiplayer in-person events that happened at prescribed times. A timer starts in the game and players have to be at the same place at the same time to play together to battle a legendary Pokemon, and if they defeat it, they'll be rewarded with a chance to catch one. Something like a year after raids were released, Niantic released research tasks as a way to catch mythical Pokemon. These required you to complete various in-game tasks, including visiting specific places. Much later than this, these research tasks started to include visiting designated Pokestops and taking video footage, from a large enough variety of angles to satisfy the game, and then uploading that. They started doing this something like four or five years ago, and getting any usable data out of it must have required an enormous amount of human curation, which was largely volunteer effort from players themselves who moderated the uploads. The game itself would give you credit simply for having the camera on while moving around enough, and it was fairly popular to simply videotape the sidewalk and the running game had no way to tell this was not really footage of the POI. The quality of this data has always been limited. Saying they've managed to build local models of about 1 million individual objects leaves me wondering what the rate of success is. They've had hundreds of millions of players scanning presumably hundreds of millions of POI for half a decade. But a lot of the POI no longer exist. Many of them didn't exist even when Pokemon Go was released. Players are incentivized to have as many POI near them as possible because this provides the only way to actually play, and Niantic is incentivized to leave as much as they can in the game and continually add more POI because, otherwise, nobody will play. The mechanics of the game have always made it tremendously imbalanced in that living near the center of a large city with many qualifying locations results in rich, rewarding gameplay, whereas living out in the suburbs or a rural area means you have little to do and no hope of ever gaining the points that city players can get. This means many scans are of objects that aren't there. Near me, this includes murals that have long been painted over, monuments to confederate heroes that were removed during Black Lives Matter furors of recent years, small pieces of art like metal sculptures and a mailbox decorated to look like Spongebob that simply are not there any more for one reason or another, but the POI persist in the database anyway. Live scans will show something very different from the original photo that still shows up in-game to tell you what the POI is. Another problem is many POI can't be scanned from all sides. They're behind fences, closed off because of construction, or otherwise obstructed. Yet another problem is GPS drift. I live near downtown Dallas right now, but when the game started, I lived smack dab in the city center, across the street from AT&T headquarters. I started playing as something to do when walking during rehab from spine surgeries, but I was often bedridden and couldn't actually leave the apartment. No problem. I could receive sometimes upwards of 50km a day of credit for walking simply by leaving my phone turned on with the game open. As satellite line of sight is continually obstructed and then unobstructed by all the tall buildings surrounding your actual location, your position on the map will jump around. The game has a built-in speed limit meant to prevent people from playing while driving, and if you jump too fast, you won't get credit, but as long as the jumps in location are small enough to keep your average over some sampling interval below that limit, you're good to go. Positions within a city center where most of the POI actually are is very poor. They claim here that they have images from "all times of day," which is possibly true if they literally mean daylight hours. I'm awake here writing this comment at 2:30 AM and have always been a very early riser. I stopped playing this game last summer, but when I still played, it was mostly in darkness, and one of the reason I quit was the frustration of constantly being given research tasks I could not possibly complete because the game would reject scans made in the dark. Finally, POI in Ingress and Pokemon Go are all man-made objects. Whatever they're able to get out of this would be trained on nothing from the natural world. Ultimately, I'm interested in how many POI the entire map actually has globally and what proportion the 1 million they've managed to build working local models of represents. Seemingly, it has to be objects that (1) still exist, (2) are sufficiently unobstructed from all sides, and (3) in a place free from GPS obstructions such that the location of players on the map is itself accurate. That isn't nothing, but I'm enormously skeptical that they can use this to build what they're promising here, a fully generalizable model that a robot could use to navigate arbitrary locations globally, as opposed to something that can navigate fairly flat city peripheries and suburbs during daylight hours. If Meta can really get a large enough number of people to wear sunglasses with always-on cameras on them, this kind of data will eventually exist, but I highly doubt what Niantic has right now is enough.
- KaiserPro 2y agoSo what they are doing is not different from previous "VPS" systems, its how they are doing it. What is a "VPS" At its heart, Visual Positioning Systems are actually pretty simple. You build a 3d point cloud of a place, with each point being a repeatable unique feature that can be extracted from an image (see https://blog.ekbana.com/extracting-invariant-features-from-images-using-sift-for-key-point-matching-675f818ce199 https://blog.ekbana.com/extracting-invariant-features-from-i...) Basically a "finger print"/landmark of a thing in real life that can be extracted from an image reliably. To make that work, you need to generate a large map of these points: https://www.researchgate.net/figure/Sparse-point-cloud-Figure-7-Dense-point-cloud_fig2_351640337 https://www.researchgate.net/figure/Sparse-point-cloud-Figur... Which basically involves taking lots of pictures with GPS tags on where they are. Google has the advantage of street view, Niantic has it's game. Others had to pay a bunch of people to go round a city with cameras. Once you build that pointcloud (which isn't actually that easy, you can't do it all at once, and aligning point clouds is hard.) you can then use trigonometry to work out where a picture is. This is called "re-localization" which is a stupid name. The hard part is the data management. There are billions of points in the world, partitioning the database so that you can quickly locate a picture is the hard part. Hence this approach, which is basically "train a model to do it for us" You still get a "VPS", you still need all that data, but they hope that a model will able to optimize for speed. is it private? No, the original system isn't private. If they've done their job properly, then nothing identifiable will be in the "map" as thats extra data you dont need. What they do with the raw photos, and the metadata that they contain is another matter.
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- zelphirkalt 2y agoThis looks like another use of data not following the original purpose of the collected data. Clearly it should be illegal to use any such data without asking every single user whose data they want to use for consent. And by that I do not mean some extortion scheme.
- mennn 2y agoIt seems I was unable to generate the image for the "SWAT ESPORT" logo at this time. Let me know if you would like me to try again or if you'd like to adjust the description.