[ExI] How AI understands the world

[Jason Resch]

On Sat, Feb 21, 2026 at 3:15 PM Jason Resch <jasonresch at gmail.com> wrote:

>
>
> On Sat, Feb 21, 2026 at 2:16 PM BillK via extropy-chat <
> extropy-chat at lists.extropy.org> wrote:
>
>> On Sat, 21 Feb 2026 at 17:49, Jason Resch via extropy-chat <
>> extropy-chat at lists.extropy.org> wrote:
>>
>>> Manual refinement of LLM chat bots we interact with is common, but it's
>>> not necessary to build an LLM that understands and reasons.
>>>
>>> There is the "Pure Transformer" that is produced from simply training on
>>> a large sample of text, and this requires no labeling, or manual
>>> adjustments. GPT-2 and GPT-3 were examples of pure transformers. There was
>>> also a pure GPT-4 that was never made public, before it was given
>>> constraints around what things it can or can't do. What is interesting is
>>> that the intelligence of this pure model was measured to be significantly
>>> higher before it was put through this manual adjustment (we might liken it
>>> to being lobotomized).
>>>
>>> This is a generally recognized phenomenon:
>>> https://share.google/aimode/Xz0ejYy73wOt5nQEc
>>> "In summary, while RLHF might "lobotomize" certain creative or reasoning
>>> edges of a base model, it is currently the industry standard for making AI
>>> usable and safe for the general public."
>>>
>>> DeepMind encountered a similar phenomenon, when they observed that their
>>> Go model when pre-trained initially on records of human games, produced a
>>> less skilled player than a model trained on *zero* human inputs (hence
>>> the name "AlphaZero").
>>>
>>> So I suppose my overall point is that while "granular labor of human
>>> annotators" is common, it's unnecessary for an AI to develop of meaning and
>>> understanding.
>>>
>>> Jason
>>>
>>>> _______________________________________________
>>
>>
>>
>> Gemini says that these Pure Transformers were never released because they
>> were unusable.
>> Labelling and manual adjustments are essential to LLM development.
>> BillK
>>
>
> 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
>
>>
>>
P.S. They have come quite a long way from those initial instruction sets.
Here is a recent one by Anthropic:

https://github.com/jujumilk3/leaked-system-prompts/blob/main/anthropic-claude-opus-4_20250805.md

Jason
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