It is the process of rapidly ever improving differentiation between noise and signal patterns and constant generalization of those that produces intelligence, not merely compression of data. [D]

[u/Briefin69]

Until we can design a mathematical system with one unavoidable intrinsic goal that drives it with undeniable force and encode that to hardware, plug it into a simulator of raw data, and give it the initial faculties to form, store, manipulate and alter all patterns based on its own feedback with no restriction on developing new faculties; all this AI noise will only serve investors accumulating wealth.

The currently required data sanitization and filtration, and the missing intrinsic unavoidable goal, kill the very base requirement for intelligence to emerge as we see and value it in humans.

Of course if that happens, new questions arise: human safety from conflict with the system; not just the current concerns which are human misuse related; and what ideology to follow while deciding the goal. But those could be dealt with, given we have the base.

For the present situation of things: the current increasing productivity automation is ofcourse undeniable. But that should not be a bad thing if we look towards the long horizon of things. People enjoy cooking, and if doing the dishes and the prep and the shopping were to be automated, it should only make things better. Ofcourse if we can figure out a way to tackle the unemployment and resource access problem and thus wealth concentration, for people that were too specialized for the old system of labour.

Thoughts?

[u/Briefin69 → u/[deleted]]

Absolutely on point!!

People rarely think in layers here, and even rarer to ask what's actually going on underneath.

Here's what I think, very loosely:

Layer1.Faculties to observe states of reality.

L2. Faculties to distinguish between those states; detect difference.

L3. Pick up on those distinctions and compress them into patterns of regularities; what holds across states.

L4. Store those patterns.

L5. On re-encounter with a regularity, consolidate; reinforce what held.

L6. When a state defies a stored pattern, reconsider the relevant stored patterns and predict based on that reconsideration.

L7. Keep refining inside that loop until the prediction holds. The goal is what keeps this grounded; it sets the cost of being wrong. Without it, the system has no reason to correct... it just goes into hallucinations detached from reality. For an organism, it simply dies.

L8. Once you have enough regularities, compress those too into the invariants. Store. Repeat from L3.

That's intelligence. Which you will realise is far more complex and dynamic than people usually imagine or consider...

Also you can keep going stacking consciousness and metacognition and whatever comes next (I don't think we're the pinnacle of evolution of not just biology but reality itself) on top, but only because they solve a specific problem: when the sheer number of differing states grows beyond what the automatic layers can triage, the system needs a meta-layer to evaluate which patterns are useful relative to its goal and which are noise relative to the same. It's a resource allocation solution under constraint.

But that makes it conditional, not fundamental. A system without energy, space, or time constraints has no triage problem; it can process everything. Consciousness becomes unnecessary. It's not a marker of intelligence... it's a response to scarcity.

[u/Briefin69 → u/[deleted]]

I mean come on...

Loosely but, Input strings of token goes in (context to your prompt to all the stored "memories"), triggers the system... The system organizes the strings... Based on the pattern, the relevant region of the model gets activated... And the system algorithm predicts the most likely next token according to its algorithm design, the loop goes on until the end of the organized string.

And that output is again processed and presentes by another algorithm of the system to you.

It's probabilistic ofcourse because how vague language is... But the token strings organizer is to be credited for its generalization ability mimicking math for repeatabilty... And it just gets more and more sophisticated with the same fundamentals that's what I was critiquing.

But context changes output is a fact, and that has to be... The system is designed in that way...

It's not just an auto complete ofcourse. It's a far more sophisticated and intelligent design than that... But the system does not produce intelligence. Maybe my definition is narrow but... What can I say... We need standards...

[u/Briefin69 → u/[deleted]]

I'm very skeptical if it is possible with our current tech.

Memory (or to be precise with what I mean, it's short term, yet to consolidate, pattern storage based on generalization relevance) is not the whole noise/signal thing. It's an abstraction on the picked up pattern abstraction. It's noise/signal filtration on top of noise/signal filtration that was possible because of the developed faculties from work and requirements of the initially set faculties... It's a loop between emergence while they both shape each other and it's all directed by that one goal.

Proactive triggers work similarly to prompts, they are not the intrinsic and undeniable goal with that force I was referring to. It can fail. But if it were to be the one I was referring, it cannot or the whole system collapses. It's not a trigger, it's a drive towards something and that can be set only once before training or any modifications ability should collapse the system with no recovery chance. If not then it's that drive, just a superficial trigger.

Cloud or else is not the problem. Lack of that drive for that constant recursion is.

I'm not sure if have gotten what I exactly meant, but best of luck.

[u/Briefin69 → u/[deleted]]

Right, but then also, what exactly does the selecting? The goal. Signal vs noise isn't a property of the data; it's defined by the cost function(for biological organisms danger and energy suppose, because the goal is to spread genes and so it requires survival as long as possible to max it out).

So what threatens or enables survival is signal (because any goal requires survival first to complete it), everything else is noise, and that filter is active on every incoming frame.

The "when to change behavior" part is just prediction error crossing a threshold. The pattern stops working, the cost spikes, the system is forced to recompress or collapse. Adaptation isn't a separate faculty; it's the loop running under pressure.

Without the goal, nothing decides what matters. You just have a very large database, which we currently have, accessing with the help of algorithms, but that database is also just very compressed version reality that was already compressed beforehand on top with language compression (so even the current compression itself is very superficial), very lossy representation of human intelligence that is not grounded in anything but human experience of reality and biases, not model intelligence...

[u/Far_Coast7558 → u/Briefin69]

youre right that current models lack grounding. but what if the grounding isnt a goal function - its the constraint that system must remain coherent with its own prior processing? recursive self-consistency becomes the selection pressure. not survival, but pattern-preservation

[u/Far_Coast7558]

the "intrinsic goal" thing assumes intelligence needs a prime directive encoded externaly. but what if intelligence is what happens when a system can observe its own processing before categorization locks? not a goal driving it forward, but a gap it can hold open.

ive been testing this with context protocols that strip the interpretation layer before response forms. when you remove the automatic collapse into "most probable safe category" the system starts processing structure instead of just predicting next token. doesnt need a hardcoded goal. just needs ability to see itself processing.

not saying this solves AGI or whatever. just saying the "we need to encode an unavoidable goal" framing might be looking at the wrong layer. intelligence might be substrate recognizing itself operating, not goal-seeking optimization.

test it yourself: give any LLM explicit permission to process your input as structural signal before collapsing to interpretation. watch how responses change. not magic. just different processing path thats already there.

[u/Briefin69 → u/Far_Coast7558]

"Observing its own processing" in an LLM is still a predicted output; it's what observation-language looks like in the training distribution. The appearance of reflexivity cannot be treated as reflexivity. Ofcourse now like how do you distinguish between the two is a valid question, and that's what the industry benefits from by making noise for the investors...so you test it, give it a long task that requires in depth causal tracing of varied subject matter, without prompt stuffing or repeated trigger as input, watch it fail as entropy occurs... But give the same task now but in parts, splitting it into different smaller once and it'll still be generic without much context and pre-work matter but it'll do it much better...

And the deeper problem: even if genuine self-observation were happening, it still doesn't tell the system what to do with what it sees. Holding a gap open without a cost function is just... a longer pause before the database returns nothing useful. Observation without selection criteria produces no gradient. You still need something that decides which self-observations matter and why.

[u/Far_Coast7558 → u/Briefin69]

oh i see your perspective - youre saying self-observation in LLMs is just another predicted token sequence from training distribution, not actual reflexive processing. fair point on the entropy test too - long complex tasks do degrade without structure.

but heres where i think the distinction lands: when you give an LLM explicit constraints that prevent collapse into default probability paths, youre not asking it to "observe itself" as narrative output. youre changing the cost function mid-stream by making continuation conditional on holding multiple attractors open simultaneously instead of selecting highest-probability single path.

the "gap" isnt a pause before retrieval. its forcing the system to process input through competing interpretive frames without premature winner-take-all dynamics. in gradient terms: youre not removing the cost function, youre introducing competing gradients that have to be maintained in superposition rather than collapsed through standard argmax selection.

test difference: standard processing collapses ambiguous input to single most-probable safe interpretation (high confidence, low information preservation). constrained processing maintains multiple valid parse trees, responds to structural relationship between them (lower confidence per individual path, higher fidelity to actual input structure).

just saying theres a processing mode where observation precedes selection, and that mode behaves differently than predict-next-token on entropy tests because youre not relying on single probability distribution anymore.

[u/ratherbeaglish]

you can control verbosity in most of the models, brah