Nostr notes by find you on :butterfedy1: fediverse

# Upgrade to #GPLv4?Nothing wrong with #GPLv3, but I feel it does ...

2024-12-15T14:06:07Z

#

Upgrade to #GPLv4?Nothing wrong with #GPLv3, but I feel it does need to be updated to specifically make it unlawful to put the code on any repository owned or operated by an entity that is engaging in software theft (eg. Micros(ha)ft servers). That means no code on git-hub(dot) com.... includes forks and mirrors obviously.

Microsoft supports #softwareTheft via their so-called "ai".

*(NOTE: The new license wouldn't need to add anything about "AI" because the existing license is clear, but it **probably should** explicitly name micros(ha)ft as barred for the sake of clarity.)*

Richard Stallman (npub1q2d…wqyh) Free Software Foundation (npub167r…p2re) Mr. Cliff, B.Sc. 🇨🇦🇱🇧 (npub19rm…nqgc) silverpill (npub19yg…wysk) r (npub1yf4…d8fa)

Want bitcoin adoption? *"Its too dangerous to use ...

2024-12-11T20:35:30Z

In reply to nevent1q…ush5
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**Want bitcoin adoption?** *"Its too dangerous to use computers, its too dangerous to allow people to have uncensorable money, its too complicated, we can't educate people, not even via the television sets in their homes, no TVs should be used only to "entertain". better to only let the "experts" use it and we as god's people, bankers, will destroy anyone who tries to run a business that accepts bitcoin, because pre-crime"*

>I wonder how #Mastodon will be effected as there is no ...

2024-11-30T07:21:27Z

In reply to nevent1q…5uva
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>I wonder how #Mastodon will be effected as there is no central instance to sign up on

all instances will be required to perform some sort of age verification or face tens of millions in fines. From RT: "Platforms that do not take “reasonable steps” to bar people under 16 would face fines of up to $50 million ($32.5 million USD). The new law will fully take effect in 12 months.

Danie van der Merwe (npub142a…95sc)
>wonder if Odysee and Peertube are also excluded for the same reasons as YouTube...

anyone under 16 in australia will not be able to legally publish content on peertube (fwiw, #odysee #bitchute, #gab, #minds, #substack are all cloudflare, which is itself a mass surveillance system setup by ex-nsa folk circa 2009). (EDIT: Under-16s) ***MAY*** be able to only view content like posts and videos but without an account many (edit: big tech) services dont allow viewing content, and by law teens will not be able to have an account.

Hi @npub1jwp…cghx and @npub142a…95sc i noticed your ...

2024-11-30T04:54:41Z

In reply to nevent1q…msty
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Hi HCJ (npub1jwp…cghx) and Danie van der Merwe (npub142a…95sc) i noticed your thoughtful comments and questions on the Australia "social media" ban. The @TechDesk'@'flipboard.social post that u responded to said that the ban applies to big tech however this is false.

Please see above ^^^

On Thursday evening AUSTRALIA JUST MADE A LAW to destroy privacy ...

2024-11-30T04:52:17Z

On Thursday evening AUSTRALIA JUST MADE A LAW to destroy privacy on ALL PLATFORMS that enable "online social interaction" in the name of protecting people under 16 years of age!

The law:

> defines an “age-restricted social media platform” as a service with a “sole purpose, or a significant purpose” to enable “online social interaction” between people, who can “post material” on the service. The legislation does not name any specific service

https://www.rt.com/news/608374-australia-bans-children-social-media/

This is serious, :butterfedyC: fediverse don't want kids on the platform but we cant do age verification either, without destroying #privacy. If fedizens sit back and allow australian teens to have no voice online then we should not be surprised when they come for OUR children outside of #australia.

The only way that :butterfedyA: **fediverse** and :peertube:***PeerTube*** servers in #australia will be able to participate in the network is if we OFFICIALLY embrace [i2p](https://geti2p.net ) (invisible internet protocol).

Help free australia, and by doing so, FREE OURSELVES!

Please boost.

its really good. i mostly use it over bloatFE of the time so i ...

2024-11-21T13:43:12Z

In reply to nevent1q…rnvz
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its really good. i mostly use it over bloatFE of the time so i don't get the full mitra experience, there are a few minor issues. To put some context one of the most bothersome, is that the apostrophe character doesn't end hashtags so #frogzone's hashtagging interests are occasionally stifled.

So we are getting into the details basically. There's a few features i'd like but i'm patient cos it works really nicely. when i use the native ui its super lightweight. maybe as lightweight as bloat itself, not nagging and pushy with status updates. is nice and calm.

i made this #pokemonGoMeme #LGMs #LargeGeospatialModel ...

2024-11-21T13:24:42Z

In reply to nevent1q…42fj
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i made this

#pokemonGoMeme #LGMs #LargeGeospatialModel pistolero (npub1f97…sks4) casey is remote (npub1ax9…8tmg) [@jeffcliff](https://shitposter.world/users/jeffcliff )

!!NOT AN ENDORSEMENT!! ## Towards Complementary Foundation Models ...

2024-11-21T12:43:58Z

In reply to nevent1q…en6t
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!!NOT AN ENDORSEMENT!!

## Towards Complementary Foundation Models

An LGM will be useful for more than mere positioning. In order to solve positioning well, the LGM has to encode rich geometrical, appearance and cultural information into scene-level features. These features will enable new ways of scene representation, manipulation and creation. Versatile large AI models like the LGM, which are useful for a multitude of downstream applications, are commonly referred to as “foundation models”.

Different types of foundation models will complement each other. LLMs will interact with multimodal models, which will, in turn, communicate with LGMs. These systems, working together, will make sense of the world in ways that no single model can achieve on its own. This interconnection is the future of spatial computing – intelligent systems that perceive, understand, and act upon the physical world.

As we move toward more scalable models, Niantic’s goal remains to lead in the development of a large geospatial model that operates wherever we can deliver novel, fun, enriching experiences to our users. And, as noted, beyond gaming Large Geospatial Models will have widespread applications, including spatial planning and design, logistics, audience engagement, and remote collaboration.

The path from LLMs to LGMs is another step in AI’s evolution. As wearable devices like AR glasses become more prevalent, the world’s future operating system will depend on the blending of physical and digital realities to create a system for spatial computing that will put people at the center.

6/6

!!NOT AN ENDORSEMENT!! ## Human-Like Understanding The process ...

2024-11-21T12:42:11Z

In reply to nevent1q…2qhy
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!!NOT AN ENDORSEMENT!!

## Human-Like Understanding

The process described above is similar to how humans perceive and imagine the world. As humans, we naturally recognize something we’ve seen before, even from a different angle. 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. This takes a level of understanding of the physical world, and cultural spaces, that is natural to us, but extremely difficult to achieve with classical machine vision technology. It requires knowledge of some basic laws of nature: the world is composed of objects which consist of solid matter and therefore have a front and a back. Appearance changes based on time of day and season. It also requires a considerable amount of cultural knowledge: the shape of many man-made objects follow specific rules of symmetry or other generic types of layouts – often dependent on the geographic region.

While early computer vision research tried to decipher some of these rules in order to hard-code them into hand-crafted systems, it is now consensus that such a high degree of understanding as we aspire to can realistically only be achieved via large-scale machine learning. This is what we aim for with our LGM. We have seen a first glimpse of impressive camera positioning capabilities emerging from our data in our recent research paper [MicKey (2024)](https://web.archive.org/web/20241120222447/https://nianticlabs.github.io/mickey/ ). MicKey is a neural network able to position two camera views relative to each other, even under drastic viewpoint changes.

{!IMAGE}

MicKey can handle even opposing shots that would take a human some effort to figure out. MicKey was trained on a tiny fraction of our data – [data that we released](https://web.archive.org/web/20241120222447/https://research.nianticlabs.com/mapfree-reloc-benchmark ) to the academic community to encourage this type of research. MicKey is limited to two-view inputs and was trained on comparatively little data, but it still represents a proof of concept regarding the potential of an LGM. Evidently, to accomplish geospatial intelligence as outlined in this text, an immense influx of geospatial data is needed – a kind of data not many organizations have access to. Therefore, Niantic is in a unique position to lead the way in making a Large Geospatial Model a reality, supported by more than a million user-contributed scans of real-world places we receive per week.

!!NOT AN ENDORSEMENT!! ## From Local Systems to Shared ...

2024-11-21T12:35:42Z

In reply to nevent1q…wcq3
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!!NOT AN ENDORSEMENT!!

## From Local Systems to Shared Understanding

Our current neural map is a viable geospatial model, active and usable right now as part of Niantic’s VPS. It is also most certainly “large”. However, our vision of a “Large Geospatial Model” goes beyond the current system of independent local maps.

An entirely local model might lack complete coverage of their respective locations. No matter how much data we have available on a global scale, locally, it will often be sparse. The main failure mode of a local model is its inability to extrapolate beyond what it has already seen and from where the model has seen it. Therefore, local models can only position camera views similar to the views they have been trained with already.

Imagine yourself standing behind a church. Let us assume the closest local model has seen only the front entrance of that church, and thus, it will not be able to tell you where you are. The model has never seen the back of that building. But on a global scale, we have seen a lot of churches, thousands of them, all captured by their respective local models at other places worldwide. No church is the same, but many share common characteristics. An LGM is a way to access that distributed knowledge.

An LGM distills common information in a global large-scale model that enables communication and data sharing across local models. An LGM would be able to internalize the concept of a church, and, furthermore, how these buildings are commonly structured. Even if, for a specific location, we have only mapped the entrance of a church, an LGM would be able to make an intelligent guess about what the back of the building looks like, based on thousands of churches it has seen before. Therefore, the LGM allows for unprecedented robustness in positioning, even from viewpoints and angles that the VPS has never seen.

The global model implements a centralized understanding of the world, entirely derived from geospatial and visual data. ***The LGM extrapolates locally by interpolating globally.***

4/6

!!NOT AN ENDORSEMENT!! ## Our work so far Over the past five ...

2024-11-21T12:33:37Z

In reply to nevent1q…v74f
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!!NOT AN ENDORSEMENT!!

## Our work so far

Over the past five years, Niantic has focused on building our Visual Positioning System (VPS), which uses a single image from a phone to determine its position and orientation using a 3D map built from people scanning interesting locations in our games and Scaniverse.

With VPS, users can position themselves in the world with centimeter-level accuracy. That means they can see digital content placed against the physical environment precisely and realistically. This content is persistent in that it stays in a location after you’ve left, and it’s then shareable with others. For example, we recently started rolling out an experimental feature in Pokémon GO, called Pokémon Playgrounds, where the user can place Pokémon at a specific location, and they will remain there for others to see and interact with.

Niantic’s VPS is built from user scans, taken from different perspectives and at various times of day, at many times during the years, and with positioning information attached, creating a highly detailed [understanding of the world](https://web.archive.org/web/20241120222447/https://nianticlabs.com/news/vps-part-3?hl=en ). This data is unique because it is taken from a pedestrian perspective and includes places inaccessible to cars.

{!IMAGE FROM NIANTIC DID NOT LOAD}

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](https://web.archive.org/web/20241120222447/https://nianticlabs.com/news/q-and-a-with-victor-prisacariu?hl=en ). We receive about 1 million fresh scans each week, each containing hundreds of discrete images.

As part of the VPS, we build classical 3D vision maps using structure from motion techniques - but also a new type of neural map for each place. These neural models, based on our research papers [ACE (2023)](https://web.archive.org/web/20241120222447/https://nianticlabs.com/news/research-ace?hl=en ) and [ACE Zero (2024)](https://web.archive.org/web/20241120222447/https://nianticlabs.com/news/eccv2024?hl=en ) do not represent locations using classical 3D data structures anymore, but encode them implicitly in the learnable parameters of a neural network. These networks can swiftly compress thousands of mapping images into a lean, neural representation. Given a new query image, they offer precise positioning for that location with centimeter-level accuracy.

{!The Wayback Machine does not have this [ScrewTube(?)] video archived}

Niantic has trained more than 50 million neural nets to date, where multiple networks can contribute to a single location. All these networks combined comprise over 150 trillion parameters optimized using machine learning.

3/6

## What is a Large Geospatial Model? Large Language Models (LLMs) ...

2024-11-21T12:21:59Z

In reply to nevent1q…f26l
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## What is a Large Geospatial Model?

Large Language Models (LLMs) are having an undeniable impact on our everyday lives and across multiple industries. Trained on internet-scale collections of text, LLMs can understand and generate written language in a way that challenges our understanding of “intelligence”.

Large Geospatial Models will help computers perceive, comprehend, and navigate the physical world in a way that will seem equally advanced. Analogous to LLMs, geospatial models are built using vast amounts of raw data: billions of images of the world, all anchored to precise locations on the globe, are distilled into a large model that enables a location-based understanding of space, structures, and physical interactions.

The shift from text-based models to those based on 3D data mirrors the broader trajectory of AI’s growth in recent years: from understanding and generating language, to interpreting and creating static and moving images (2D vision models), and, with current research efforts increasing, towards modeling the 3D appearance of objects (3D vision models).

{!IMAGE}

Geospatial models are a step beyond even 3D vision models in that they capture 3D entities that are rooted in specific geographic locations and have a metric quality to them. Unlike typical 3D generative models, which produce unscaled assets, a Large Geospatial Model is bound to metric space, ensuring precise estimates in scale-metric units. These entities therefore represent next-generation maps, rather than arbitrary 3D assets. While a 3D vision model may be able to create and understand a 3D scene, a geospatial model understands how that scene relates to millions of other scenes, geographically, around the world. A geospatial model implements a form of geospatial intelligence, where the model learns from its previous observations and is able to transfer knowledge to new locations, even if those are observed only partially.

While AR glasses with 3D graphics are still several years away from the mass market, there are opportunities for geospatial models to be integrated with audio-only or 2D display glasses. These models could guide users through the world, answer questions, provide personalized recommendations, help with navigation, and enhance real-world interactions. Large language models could be integrated so understanding and space come together, giving people the opportunity to be more informed and engaged with their surroundings and neighborhoods. Geospatial intelligence, as emerging from a large geospatial model, could also enable generation, completion or manipulation of 3D representations of the world to help build the next generation of AR experiences. Beyond gaming, Large Geospatial Models will have widespread applications, ranging from spatial planning and design, logistics, audience engagement, and remote collaboration.

here goes, now this is super creepy stuff so prepare your self. ...

2024-11-21T12:17:11Z

In reply to nevent1q…ztsy
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here goes, now this is super creepy stuff so prepare your self. !NOT AN ENDORSEMENT!

https://web.archive.org/web/20241120222447/https://nianticlabs.com/news/largegeospatialmodel

November 12, 2024

### Building a Large Geospatial Model to Achieve Spatial Intelligence

Eric Brachmann and Victor Adrian Prisacariu

At Niantic, we are pioneering the concept of a Large Geospatial Model that will use large-scale machine learning to understand a scene and connect it to millions of other scenes globally.

When you look at a familiar type of structure – whether it’s a church, a statue, or a town square – it’s fairly easy to imagine what it might look like from other angles, even if you haven’t seen it from all sides. As humans, we have “spatial understanding” that means we can fill in these details based on countless similar scenes we’ve encountered before. But for machines, this task is extraordinarily difficult. Even the most advanced AI models today struggle to visualize and infer missing parts of a scene, or to imagine a place from a new angle. This is about to change: Spatial intelligence is the next frontier of AI models.

As part of Niantic’s Visual Positioning System (VPS), we have trained more than 50 million neural networks, with more than 150 trillion parameters, enabling operation in over a million locations. In our vision for a Large Geospatial Model (LGM), each of these local networks would contribute to a global large model, implementing a shared understanding of geographic locations, and comprehending places yet to be fully scanned.

The LGM will enable computers not only to perceive and understand physical spaces, but also to interact with them in new ways, forming a critical component of AR glasses and fields beyond, including robotics, content creation and autonomous systems. As we move from phones to wearable technology linked to the real world, spatial intelligence will become the world’s future operating system.

1/6

thanks for trying. Unfortunately substack is cloudflare, the ...

2024-11-21T12:13:05Z

In reply to nevent1q…f326
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thanks for trying. Unfortunately substack is cloudflare, the 'C' in C.A.G.E.M.A.F.I.A.

I did some extra research, i think the video is based on [this article](https://web.archive.org/web/20241120222447/https://nianticlabs.com/news/largegeospatialmodel ). Interestingly, EVERYTHING in this #niantic / pokemonGo #scaniverse ecosystem is served by the goog.... gosh, wonder why lol

the webpage by Niantic, the PokemonGo developers(?) is pretty terrible, and requires javascript, i'm going to mirror the article with most pictures below. Might be nice to take some pressure off the internet archive too.

hi Gabriel (npub1z9v…any9), dunno if u want to mirror the video and/or podcast(?) ?

i've long held that pokemon go is a spy tool.... i believe ...

2024-11-21T05:38:22Z

In reply to nevent1q…0m8w
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i've long held that pokemon go is a spy tool.... i believe that its served by google which is something of a cia/nsa cut-out

what does the video talk about? i'll need this on :peertube:***PeerTube*** to watch it, as i cant use peter thiel's #rumble which asks for a govt issued phone number in order to use.

so yeah, a summary with a source would be helpful.

that's fair but how to resolve the issue with an identified ...

2024-11-18T16:42:45Z

In reply to nevent1q…mrfl
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that's fair but how to resolve the issue with an identified person reaching out to Wikileaks over a platform owned by a US military contractor? maybe they need to have a warning on the bio of the account?

also what if the person or people managing the account can be identified/targetted by the brainchipped one? that might be a risk in the future too.

So #Wikileaks are still on the 'brainchipped one's ...

2024-11-18T16:09:11Z

So #Wikileaks are still on the 'brainchipped one's platfrom'. I realize that Wikileaks haven't published much in a long time, but I do think that its vital Wikileaks move to :butterfedyC: fediverse as i don't believe it is prudent to be on a server owned by a US military contractor.

If they were to move to :butterfedyA: fediverse how many instances would they need to have an account on to reach everyone and minimise the risk of attacks?

Do you think they'd even need multiple accounts?

This is a serious question. BOOSTS welcome and appreciated.

Did anyone hear about bigtech investing in #nuclear energy to ...

2024-11-03T14:20:58Z

Did anyone hear about bigtech investing in #nuclear energy to supposedly power 'ai'? here's a supposed article that sounds like it's copy-pasted straight from the nuclear lobby's press release (you can tell because they write "clean energy" as opposed to *"toxic waste generating energy that poisons children and placed in bombs to damag the #genetics of people in parts of the world bankers want to harvest"*).

Amazon bought a nuclear-powered #datacenter in march and now they are investing $500 million in "small modular reactors". The nuclear lobby must be thrilled that everyone seems to be too distracted with the election to even talk about this. This news from amazon came a day after google announced it has big plans to go nuclear over the next decade or so.

in case anyone needed another reason to boycott m$, amaz, and goog....

#pollution #cancer #eugenics #warmachine #weaponsindustry #amazon #google #aws #microsoft #refugeecultivation #modernslavery #datacenters

most people are not very smart.... they cant see the link between ...

2024-08-04T23:59:19Z

In reply to nevent1q…x2ef
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most people are not very smart.... they cant see the link between the bombs that the uk specialize in aiming at ppl in faraway places and the influx of refugees.

murdoch press be like "NEW COOKS at resturant! bombing up new refugees GREAT SUCCEXX!!!1"