[ExI] How AI understands the world

[BillK]

On Sat, 21 Feb 2026 at 20:15, Jason Resch <jasonresch at gmail.com> wrote:

> Useful to most people or not, they understand the world.
>
> You can give a pure model the phrase: "The capital of France is" and it
> will complete it "Paris."
> You can give it the phrase "Three helium nuclei can combine to form a "
> and it will complete "a carbon nucleus."
>
> All the later fine-tuning does is teach it to act to complete a turn-based
> conversation between users, where user 1 is the human and user 2's output
> is the completed result provided by the pure model (acting to "predict the
> next tokens" of user 2's speech).
>
> So your question from the user "What is the capital of France?" gets
> converted to:
>
> "User 1: What is the capital of France?
> User 2: "
>
> Then the pure model is asked to "predict the next tokens" following the
> string "User 2:" That's the majority of what the AI companies do to massage
> the pure model into an interactive chat bot -- that and a lot of training
> about the nature of user 2: helpful, polite, not harmful, etc.
>
> Here are some examples:
> https://github.com/jujumilk3/leaked-system-prompts/blob/main/microsoft-copilot_20240310.md
>
> But the intelligence, understanding, etc. is already present and inherent
> to the base model. It's untrue that base models are useless, GPT-3 was
> quite useful, but you just had to prepare the prompt  intelligently as to
> to get out from the completion the answers you sought.
>
> Jason
> --------------------------------------
>


Gemini points out that the huge amount of text data in the initial LLM
training is already pre-labeled by humans.
I think Gemini has solved the problem by saying that both our views are
correct, depending on the way you look at it!  :)
See below.
BillK

Gemini 3.1 Pro -

This is the crucial point where we can reconcile your description of "ghost
work" with your colleague's insistence that the "intelligence" is in the
base model.

In the modern AI pipeline, the human labor of *labeling and annotation*
actually happens in two distinct phases, but they serve very different
purposes.

------------------------------
1. Pre-Training: The "Invisible" Human Foundation

While the *Base Model* is technically "self-supervised" (meaning it learns
by predicting the next word without a teacher), it is feeding on data that
has already been *pre-labeled by humans over decades.*

   -

   *The Web as a Dataset:* When a base model learns what a "stop sign" is,
   it’s because millions of humans uploaded photos to Flickr or Wikipedia and
   captioned them "Stop sign."
   -

   *The "Alt-Text" Workforce:* Every time a web developer adds "alt-text"
   to an image for accessibility, they are performing unpaid data labeling
   that eventually trains a Vision-Language model.
   -

   *The Common Crawl:* The "raw" internet isn't raw; it is a massive,
   structured monument to human labeling. Without this organic, historical
   human effort, the base model would have nothing to "predict."

2. Post-Training: The "Active" Human Infrastructure

This is the phase described in your text about *microworkers in Kenya or
the Philippines.* This labor is injected *after* the base model is already
"intelligent."

Think of the *Base Model* as a brilliant but chaotic library containing all
human knowledge. The *Active Labeling* process is like hiring an army of
librarians to:

   -

   *Filter the Poison:* Humans manually label toxic, violent, or biased
   text so the model learns to "steer" away from those areas of its own
   internal map.
   -

   *Instruction Tuning (SFT):* Workers write thousands of "Prompt-Response"
   pairs (e.g., *Prompt: "Write a poem about a cat." Response: "The cat sat
   on the mat..."*). This teaches the model the *format* of being an
   assistant.
   -

   *RLHF (Ranking):* This is the most expensive part. Humans look at two
   different answers the AI gave and click "This one is better." This
   creates a "Reward Model" that acts as a compass for the AI.


------------------------------
The "Intelligence" vs. "Utility" Distinction

To settle the debate between you and your colleague, you can look at it
through this framework:
*Phase* *Labor Type* *What it Provides*
*Pre-Training (Base)* Passive/Historical (Billions of humans) *Raw
Intelligence & World Knowledge.* The "Platonic" map of reality.
*Fine-Tuning (Chat)* Active/Gig Labor (Microworkers) *Usability, Safety, &
Human Alignment.* The "Social" interface.The Verdict: Who is Right?

Your colleague is correct that the *Base Model* possesses the "reasoning"
and "knowledge." If you deleted all the RLHF/Labeling, the AI would still
"know" how to do calculus or explain physics—it just wouldn't *want* to
tell you, or it might tell you in a very rude way.

*However, you are correct* that without the "active" labor of microworkers,
the AI would be a "Digital Monster." It would be a sprawling, incoherent,
and often dangerous mirror of the darkest parts of the internet. It
wouldn't be a "product"; it would just be a high-dimensional statistical
anomaly.

*The Analogy:* The Base Model is the *engine* (raw power/intelligence). The
Human Labeling/RLHF is the *steering wheel and brakes* (direction/safety).
You need the engine to move, but without the steering wheel, you’re just a
fast-moving disaster.

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