You know that feeling when you type something
completely absurd into an AI image generator?
Like, I don't know, a futuristic neon city being
attacked by a giant Renaissance -era oil painting
of a cat. Oh, yeah. And then five seconds later,
it just hands you this flawless, cinematic masterpiece.
Right. It feels like frictionless, perfectly
clean magic. Boom, there it is. Yeah, but it
creates this massive illusion, right? It makes
you think the machine just inherently possesses
human imagination. It completely hides the really
messy reality of where that intelligence actually
comes from. And today, our mission for this deep
dive is to basically shatter that illusion. We
are looking directly at the X -ray of that magic.
Exactly. Because we are diving deep into the
Wikipedia files on a German non -profit organization
called LAION. That stands for Large Scale Artificial
Intelligence Open Network. Which is a bit of
a mouthful, but a really crucial group to understand.
Yeah, because behind every sleek, minimalist
prompt box you use, there's essentially a massive,
chaotic filing cabinet containing the entire
visual history of the internet. And LAION is
the organization that built the biggest filing
cabinet of all. This is such a critical concept
for you to understand right now because LEI isn't
actually building the AI models you interact
with directly. They aren't making the shiny consumer
apps. Right, they're not the ones selling you
a subscription. Exactly. What they're best known
for is scraping the web to create massive open
source data sets of images and text captions.
These are the foundational data sets, like the
raw fuel, that actually trained high profile
models like stable diffusion or Google Brain's
Imagen. This stuff that powers the whole engine.
Yeah. In our current era of just profound information
overload, understanding exactly how this data
is sourced isn't just a fun technical curiosity.
You really have to know what the machine ate
before you can trust what it spits out. OK, let's
unpack this, because to really grasp how the
whole AI art boom seemed to happen overnight,
you have to understand the incredibly clever
and honestly slightly rogue way Elion actually
collected all this data. It really is fascinating.
I mean, it wasn't a room full of interns just
dragging and dropping photos into a desktop folder,
right? Oh, far from it. The mechanism they used
relied on something called common crawl. So common
crawl is this existing nonprofit that operates
like a well like a digital snowplow a digital
snowplow like that Yeah, just constantly drives
across the public internet downloading raw HTML
code from billions of web pages And then it makes
that massive archive completely free for researchers
to use so I didn't even have to scrape the web
themselves They just use the snowplows leftovers
pretty much what the developers at LA on did
was write a relatively simple script to just
comb through all that crawled HTML, and they
were specifically looking for one very specific
thing, which is the basic image tag. Right, the
piece of code that tells your web browser, hey
go fetch a picture and display it right here.
Exactly. But pulling a picture is really only
half the battle, because an image alone doesn't
actually teach a machine learning model anything
at all. It just sees a bunch of pixels. Right.
The AI needs a label. It needs to know what the
image actually depicts. So alongside those image
tags, the developers targeted the alt attributes
attached to them. Ah, the alt text. That's the
hidden behind the scenes description of an image
on a web page, right? Yeah. It was originally
designed for accessibility. So screen readers
can describe visual content to visually impaired
users. And Elion simply decided to treat those
existing awk attributes as the definitive text
captions for the images. I love this part, because
it requires such a completely lateral way of
thinking about data. Elion didn't build a massive
library filled with stolen books. They just built
the world's largest card catalog, pointing to
where the books currently live on the internet.
That is a perfect way to describe it. But wait,
I have to push back here. Because if you have
ever built a basic website or run a blog, you
know exactly how notoriously garbage alt text
can be. Oh, it's terrible. Yeah. People write
things like, image for final version underscore
real, or they just wildly stuff it with SEO keywords
to rank higher on Google. If LiOn is just indiscriminately
matching raw HTML tags, how do they know the
text is actually an accurate description of the
photo? What's fascinating here is the mathematical
ingenuity they use to solve that exact problem.
Because you're right, raw alt text is way too
noisy to train a highly precise AI. It's full
of junk. So how do you clean it up? To filter
the garbage out, LiOn utilized a model created
by OpenAI called CLIP. You can think of CLIP
like an incredibly strict, mathematically precise
bouncer for a nightclub. Okay, a bouncer. Walk
me through that. So CLIP is trained to look at
an image, read a piece of text, and then calculate
the mathematical distance between them. It scores
how well they actually match. Oh, I get it. So
if the image is a golden retriever catching a
frisbee... and the alt text says golden retriever
playing in park, CLIP gives it a high score and
lets it into the club. Exactly. But if the alt
text says buy cheap sneakers online, CLIP just
throws it straight in the trash. Exactly that.
So they ran their massive chaotic pile of scraped
web links through CLIP using it to automatically
prune the whole data set. Any pairing that scored
below a certain similarity threshold was just
discarded. Wow. And the irony of this process
is just incredibly rich because OpenAI had previously
opened source the underlying code and the weights
for their CLIP model, but they kept the actual
training data set, the 400 million image caption
pairs they originally used to teach it, a closely
guarded corporate secret. Oh, wow. So OpenAI
gave away the bouncer. but kept the guest list
a secret. Yes. So by using the open version of
CII to filter their own scraped web data, LAION
essentially reverse -engineered OpenAI's closed
data set. Wait, really? They used an AI to build
the data set that trains other AIs? They absolutely
did. And the result was their very first release
in August 2021, which they appropriately named
LAION 400MIR, 400 million highly accurate CLIP
-filtered image caption pairs. That is wild.
And we really need to emphasize that card catalog
analogy you made earlier, because it is the entire
key to LAON's operation. They do not host the
images themselves. Right. They aren't hoarding
400 million JPEGs on some massive server farm
somewhere in Germany. Not at all. They only host
the URLs, just the text links pointing to the
images. They shift the entire burden of downloading
the actual heavy image files onto the AI researchers.
Or the tech companies who want to use the data
set to train. their models, which is just a brilliant
logistical loophole. They're literally just providing
the map. A very detailed map. Yeah. But a map
of 400 million things revealed a really fundamental
problem for AI developers. It simply wasn't enough
data. Not even close. Right. To make an AI that
could reliably generate anything the human mind
could imagine, like every art style, every weird
lighting condition, every rare object, they needed
to scale up by an order of magnitude. And that
unprecedented scale is exactly what triggered
a billion dollar legal nightmare. It was inevitable.
In March 2022, LAI and released a successor data
set called LAI Own 5B. That is five billion image
caption pairs. Five billion. Five billion. At
the time of its release, it was the absolute
largest freely available data set of its kind
in existence. And scaling up to that level requires
serious computational resources. I can imagine.
Who even paid for that? This massive effort was
funded by entities like DoodleBot, Hugging Face,
and Stability AI. And Stability AI, notably,
is the company that subsequently used this exact
massive data set to train the wildly popular
stable diffusion model. Okay, here's where it
gets really interesting though. Because when
you create a map of five billion copyrighted
photographs and professional artworks and personal
images, and you do it without asking a single
person for permission, People are going to notice.
Oh, they definitely notice. And they're going
to push back hard. The legal blowback was just
intense and immediate. In February 2023, Getty
Images, the massive global stock photo agency,
launched a huge lawsuit against Stability AI.
Right, because the AI was literally regenerating
their proprietary watermarks. Exactly. Proving
it had basically memorized Getty's catalog. And
LAON was actually named in that lawsuit as a
non -party. But then, the legal crosshairs swung
directly onto Elion itself. Yeah, the real direct
hit came in April 2023, when a German photographer
took direct legal action. He discovered that
his own copyrighted photographs were indexed
within the Elion dataset. Without his permission.
Right. So he sued them in a German court to have
his works explicitly removed from their training
data. And the wildest detail from the source
is here. When he served them with the lawsuit,
Elion actually sent him an invoice right back.
I know. It's hard to believe. They billed him
for the administrative costs of dealing with
what they considered a legally baseless request.
It's just breathtakingly bold. But how did the
courts actually view this? Because common sense
says, hey, that's my photo. You can't use it.
Well, if we connect this to the bigger picture,
the resolution of this specific case is monumental
for the foundational architecture of artificial
intelligence in Europe. In September 2024, the
regional court of Hamburg formally dismissed
the photographer's lawsuit. And this wasn't just
some minor procedural victory. It was hailed
as a landmark ruling on what the legal world
calls TDM exceptions. TDM meaning text and data
mining. Exactly. Text and data mining exceptions
are the specific legal provisions, particularly
Article 4 of the European Union's digital single
market directive. It's very bureaucratic. Very.
But what these provisions do is acknowledge that
in the modern digital researchers really need
to be able to analyze massive amounts of copyright
-protected material computationally. The law
essentially allows this, provided they're doing
it just to extract patterns, trends, or correlations,
and crucially, provided they aren't reproducing
the work to directly compete with the original
human creator. Ah, I see. So it's basically the
difference between reading a thousand Stephen
King books to mathematically analyze his sentence
structure versus photocopying a Stephen King
book to... it out of the trunk of your car. That
is the exact distinction the court made. The
German judges ruled that what LAON was doing,
which was creating a database of links to understand
the statistical correlations between human language
and visual pixels, fell safely under these text
and data mining exceptions. Wow. Even though
a commercial company like Stability AI ended
up using it later. Yes. The court noted that
the data set itself was created for scientific
research. The commercial use later down the line
didn't invalidate LAON's protection. And this
set a massive precedent for copyright law across
the entire European Union. It legally validated
their whole card catalog method. So legally,
they won the undeniable right to scrape the internet.
But here is the massive catch. If you deploy
a digital snowplow to scoop up five billion web
pages, you inevitably scoop up everything. Everything.
The good and the very, very bad. Right. You get
the beautiful museum art and the historical photos,
but you also hit the internet's absolute darkest,
most horrifying corners. The legal victory immediately
gave way to a really severe ethical reality regarding
what happens when you map the unfiltered human
internet. Yeah, and the findings from independent
researchers who started analyzing the data set
are deeply concerning. Because LAO in 5b is completely
open source, right? Anyone with the technical
know -how can download the index and look at
exactly what is inside it. And what did they
find? Well, multiple academic studies published
throughout 2021 and 2023 investigated the contents.
They found that LAON N5B contained highly problematic
material. Researchers documented widespread instances
of rape imagery, non -consensual pornography,
malignant cultural stereotypes, and extreme racist
and ethnic slurs. It's just awful. It gets worse.
A major investigation by the German broadcaster
Bayerischer Rundfunk found large amounts of private
sensitive data. harvested from public sites.
The sources specifically mention private medical
records being found in the data set. I mean,
imagine your own private medical records. Photos
of you at your absolute most vulnerable being
scooped up by a machine learning model simply
because a hospital or a doctor's office misconfigured
their web server and accidentally left a folder
exposed to the public internet. The digital snowplow
doesn't care. It just grabs the image tag. It
just grabs the tag. And then this steady drumbeat
of concern escalated into an absolute crisis.
It really culminated in December 2023 when the
Stanford Internet Observatory released a devastating
technical report on LAI on 5b. They ran an automated
analysis using specialized hashing algorithms
that are specifically designed to detect known
illicit files. And what did the report say? The
report stated they suspected the data set contained
over 3 ,000 instances 3 ,226 to be exact, of
links pointing to child sexual abuse material,
or CSAM. And of those suspected links, 1 ,008
were externally validated by safety organizations.
So what does this all mean? Functionally speaking,
how does an organization even handle that? Because
we know LAON's stated intention was to build
a tool to democratize scientific research. Right.
Their goal was scientific openness. But if your
open source data set is five billion links large,
is human moderation even mathematically possible?
I mean, even if you hired a stadium full of people
to click links and review images all day, every
single day, you can never actually review five
billion images. You couldn't. The sheer vastness
of the data completely defeats human oversight.
Exactly. And this raises an important question
about the fundamental architecture of open source
AI. Can a system be both completely comprehensive,
capturing the entirety of human knowledge, and
perfectly safe at the same time? It seems like
those two things are entirely at odds. They often
are. The Stanford report forced a brutal reckoning
regarding accountability. In response to the
discovery of the CSAM links, LAON took immediate
action. They temporarily removed both the massive
LAI on 5B and the older LAI on 400M data sets
from public access entirely. Just pulled the
plug on them. Yeah. They cited a strict zero
tolerance policy for illegal content and stated
they were taking the links offline out of an
abundance of caution. Because suddenly providing
the map to illegal harmful content becomes a
radically different legal and moral issue than
providing a map to a copyrighted stock photograph.
Exactly. You can't just hide behind a text and
data mining exception when you are indexing illicit
material. So did the data set just stay offline
forever? No. They took the massive data sets
completely offline, and they went to work computationally
scrubbing them. By August 2024, LAON released
a new, highly filtered version of the data set,
which they dubbed Re -LAON F5B. The illicit material
identified by the safety organizations was scrubbed
from the index. Right, but this entire sequence
of events highlights such an incredible, unresolved
tension in the tech world. On one hand, you have
the genuine desire to democratize AI to ensure
that trillion -dollar tech monopolies aren't
the only ones with access to the data that will
build the future economy. Which is a noble goal.
It is. But on the other hand, you have the absolute
necessity of public safety, which is inherently
at odds with blindly vacuuming up the public
internet. It is the ultimate double -edged sword
of the digital age. It really is. But to really
understand LION as an organization, we have to
look beyond just the image -scraping controversies.
Because their mission wasn't just to democratize
text -to -image models. They also wanted to democratize
the text -based AI chatbots. Right. The kind
of large language models you use to write emails
or brainstorm ideas or debug code. Yeah. But
the way they approached building a chatbot was
a massive, fascinating departure from the automated
bulldozer method of LAI and 5b. Very different.
In April 2023, LA ION, working alongside a massive
decentralized group of volunteers, publicly released
an open source AI assistant called Open Assistant.
And the goal there was huge for the time, right?
Incredibly ambitious. Back in early 2023, the
most powerful and capable language models were
locked entirely behind corporate APIs. You had
to pay a subscription and they required massive
centralized server farms to run. Open Assistant's
mission was to provide free access to large language
models that were efficient enough to run locally
on a user's everyday consumer hardware. Which
is a huge paradigm shift. Just imagine having
a top tier, highly intelligent AI running entirely
on your own laptop. graphics card. It's totally
private, it reads your local files, and it doesn't
need an internet connection to beam your data
back to a corporate server in Silicon Valley.
And beyond just running locally on consumer hardware,
the developers wanted this assistant to be able
to dynamically retrieve information from the
web and interact with third -party systems. It
was designed from the ground up to be a deeply
integrated, highly transparent open tool. But
the way they built the training data for Open
Assistant is what I find so compelling. If LARN5B
was an industrial digital snowplow, Open Assistant
was an artisanal, highly manual, crowdsourced
effort. It was incredibly hands -on. Right. They
didn't just scrape a billion random Reddit threads
or Wikipedia pages to teach the bot how to speak.
They relied on a worldwide network of over 13
,500 human volunteers. Yeah. These volunteers
logged into a specially designed Gamify platform
and they manually engaged in a process called
reinforcement learning from human feedback. Or
LHF. Exactly. Through this platform, those 13
,500 humans manually created over 600 ,000 highly
structured data. points. And they weren't just
chatting for fun. They were doing the grueling
work of AI alignment. What does that actually
look like for the volunteers? Well, they would
write original prompts, act out the role of the
AI to write ideal responses, and meticulously
rank different AI outputs. They were explicitly
teaching the model the difference between a helpful,
polite answer and a toxic or unhinged one. It's
essentially a massive global Wikipedia edit -a
-thon. but specifically meant to teach a neural
network how to be a safe and helpful conversationalist.
That's a great comparison. It just proves that
open -source AI development doesn't always have
to mean indiscriminate automated data harvesting.
It can also look like a global community of thousands
of people voluntarily donating their time, their
language skills, and their intelligence to build
something free for the rest of humanity. And
while the Open Assistant project itself has since
been officially shut down, primarily because,
you know, maintaining and serving a live, state
-of -the -art chatbot to the public is just incredibly
resource intensive and expensive, the datasets
and the foundational models those volunteers
painstakingly created remain freely available
today. They're still out there. Yeah, on open
source platforms like Hugging Face, they serve
as a lasting, highly influential testament to
Elion's overarching mission. Elion is relentlessly
pushing for open source AI alignment, trying
to prove that the fundamental tools required
to build and train artificial intelligence shouldn't
be the exclusive proprietary property of a few
megacorporations. So let's bring this all together.
We started with a wildly clever, almost deceptively
simple hack writing a script to search HTML code
for image tags and hijacking the hidden alt text.
Just a few lines of code, really. Right. And
we watched that clever hack scale up into a five
billion link data set that quite literally changed
the trajectory of the global tech industry overnight.
Absolutely. It survived a landmark European Union
copyright ruling that redefined how data can
be used, whether to severe highly publicized
crisis regarding the absolute darkest corners
of the human Internet and ultimately spawned
a massive crowdsourced movement to build a free
open source chat bot from scratch. It is a remarkable,
deeply complicated journey of a nonprofit organization.
Attempting to mathematically map the entirety
of the digital world. It really is and for you
listening This deep dive changes the context
of everything you do online from this point forward.
The next time you type a prompt and see a flawlessly
generated AI image appear on your screen, or
you find yourself having a surprisingly nuanced
conversation with an open source language model,
you aren't just looking at clean, frictionless
magic anymore. The illusion is gone. Exactly.
You know the exact mechanics of it. You know
exactly how much messy, brilliant, legally contested,
and highly controversial human data was poured
into its foundation. You've seen the x -ray.
And looking at that x -ray leaves us with one
final critical realization to think about. If
these massive open source AI models are essentially
giant statistical mirrors reflecting our entire
digital world, absorbing the legally protected
art, the private medical records, the breathtakingly
good, and the profoundly ugly, What happens tomorrow?
What do you mean? Because the internet is currently
being flooded with billions of synthetic AI -generated
images and text, when the next version of a digital
snowplow goes out to scrape the web, it won't
just be mapping human data anymore. It will be
an AI training on the synthetic hallucinations
of other AIs. As that mirror starts reflecting
itself infinitely, what that means for the future
of human knowledge and truth is a question you'll
have to ponder on your own.