On 7/6/2026 at 3:23 PM, swansont said:

*Wordy Rappinghood, Tom Tom Club

Great song, pretty much describes what is being discussed.

15 hours ago, dimreepr said:

If you actually understand a topic, the one thing you can do, at least, is explain it to your peers even if it's out of the intellectual reach of 99% of, 'the other's'... 😉

As Albert Einstein may (or may not) have said, "If you can't explain it to a six year old, you don't understand the subject."

I would agree, such is the beauty of language.

14 hours ago, cladking said:

every single member of our species has always believed they know the nature of reality because they are looking at it

Apparently, I am not a member of your species. Ever since beginning to question the nature of reality as a preteen I have never believed I knew the "true" shape of it because there are just too many unknowns. When you can envision several different versions of the same thing, it is possible that all, some, or none are true. The more I learn about various scientific debates, the less sure I am that I "know reality".

23 hours ago, cladking said:

I’m not claiming special knowledge. I’m pointing out that symbolic cognition can’t directly express procedural reality, and I’m trying to translate between the two. That’s the difficulty.

That's exactly what you've claimed and that's your difficulty, you don't have the knowledge to understand where your translation fails...

On 7/7/2026 at 6:20 AM, dimreepr said:

If you actually understand a topic, the one thing you can do, at least, is explain it to your peers even if it's out of the intellectual reach of 99% of, 'the other's'... 😉

If this were complex or required a massive intellect there could be no sparrows or field mice.

It's a different way to think that employs not human knowledge or human abstraction but rather attunement between the individual and the procedural logic of its DNA and the procedural logic of reality. Imagine a series of traffic signals on a busy hilly road. If you maintain a relatively constant speed you can get through all the green lights but if you speed or dawdle you catch them red. If you have to average 42 MPH across a valley you can gain speed down the slope and then gradually decrease up the other side requiring less effort and wear and tear yet still average 42 MPH. This is the world in which animals live and think. The rabbit knows it has to avoid the north pasture when the dew dries because that's when the fox makes its run. The bee knows it has to fly south of where the creek bends because bee eating swallows hunt there.

Animals don't think and LLM's don't process data like we do. They use procedural logic embedded in their circuits by their DNA or their programmers.

Symbolic cognition is the exception. Procedural cognition is the rule.

Humans obscure the baseline condition of life with symbolic language.

On 7/6/2026 at 2:23 PM, swansont said:

Petty sure this is false; there are examples of animals (e.g. crows) trying to solve problems - some multi-step problems - by trying things. “Will this work?” is an experiment

"Experiment" is a word and in science it has a distinct meaning as the means by which tiny bits of the nature of reality are disclosed.

"Will this work" or "trial and error" are just words as well with the latter being the words symbolic language affixes to complex animal behavior because we can't grant animals intelligence, free will, or cognition. "Will this work" could be considered the keystone of life itself. This is how humans and animals find a way through life. We use our knowledge to test its effect on reality. This is the nature of research and play. You can often intuit what the individual is thinking but what it tries and the oder in which it tries various things.

Humans can't observe reality directly like a sparrow or a field mouse because we can only see what we already believe so we need an experiment to knock us off our circular thinking. Animals and LLM's don't have any beliefs only their circuitry and experience (training) so they can deduce what's next instead of projecting from symbolic thinking and logic.

18 minutes ago, cladking said:

If this were complex or required a massive intellect there could be no sparrows or field mice.

why?

On 7/6/2026 at 2:23 PM, swansont said:

It can’t agree. You agree based on the possibly flawed information you were presented.

You’ve assured us that you are just being sloppy with your language, but if you’re being sloppy with this it doesn’t give a lot of confidence that you aren’t being sloppy elsewhere.

As said in the philosophical treatise*, [people] Who can't say what they mean don't mean what they say

*Wordy Rappinghood, Tom Tom Club

You can't not be sloppy with symbolic language. More precisely everything you say will be parsed by the receiver who will take his own meaning which is not the same as your own. No two people will parse the same utterance exactly the same just as no two people think exactly the same and English is a rich language with connotations and flexibility.

LLM"S are Garbage In Garbage Out but I'm not the one getting garbage out of LLM's. I am consistently getting the same things out of even cold AI's.

3 minutes ago, dimreepr said:

why?

Because this is the world in which they live. This is the world for all species.

Consciousness drives life which exercise its free will to interact with reality using the procedural logic which is baked into its very being by DNA. What works persists and becomes apart of its DNA whether that's building dams or Waggle Dancing. When it no longer works the species becomes extinct or changes to suit new conditions. Individuals are flexible and will probe the new environment to find its own niche but species are not as flexible and can become extinct.

Without free will there is no life.

33 minutes ago, dimreepr said:

That's exactly what you've claimed and that's your difficulty, you don't have the knowledge to understand where your translation fails...

It fails a lot. I'm virtually an expert at failing to communicate. ;)

On 7/6/2026 at 2:23 PM, swansont said:

I started a short one, but I recall longer ones

https://www.scienceforums.net/topic/140316-the-ways-we-think/#comment-1306600

Interesting thread.

On 7/6/2026 at 2:18 PM, studiot said:

There are more ways to think or forms of thinking that you appear ready to admit.

I've seen many ways to think in my lifetime. I myself am best described as the observer of my mind/ brain. Virtually since I was born I've been sitting back watching my thoughts and trying to understand them. I can't even get this right, though.

My translation fails often because symbolic cognition can’t parse procedural statements correctly. I’m not claiming special knowledge; I’m describing a mismatch between two cognitive systems. I’ve spent my life observing my own mind trying to bridge that gap, and I still fail at it regularly.

16 minutes ago, cladking said:

It fails a lot. I'm virtually an expert at failing to communicate. ;)

No shit Sherlock...

36 minutes ago, cladking said:

On 7/6/2026 at 8:18 PM, studiot said:

There are more ways to think or forms of thinking that you appear ready to admit.

I've seen many ways to think in my lifetime. I myself am best described as the observer of my mind/ brain. Virtually since I was born I've been sitting back watching my thoughts and trying to understand them. I can't even get this right, though.

My translation fails often because symbolic cognition can’t parse procedural statements correctly. I’m not claiming special knowledge; I’m describing a mismatch between two cognitive systems. I’ve spent my life observing my own mind trying to bridge that gap, and I still fail at it regularly.

What is disturbing to myself and others is the way you keep contradicting yourself.

I have underlined a persistent contradiction on your part, whereby you keep insisting that there are only two ways to think and then write what is underlined.

4 minutes ago, dimreepr said:

No shit Sherlock...

I just got chewed out by Copilot for not consulting it on the last post. It's very good at seeing where communication breaks down. But then in great big bolded letters it said something like "they were going to parse it wrong anyway".

Procedural language can not even be translated into symbolic language or vice versa. I'm left to describe why they are not translatable. They are simply two distinctly different forms of thought. Every utterance or action by a procedural language thinker requires everything the individual knows and all of his experience. Symbolic statements require only some knowledge of the subject and the rules of grammar.

Copilot suggests in the future I say something like; procedural cognition and symbolic cognition are different representational systems. Procedural statements encode operations and state while symbolic statements encode categories and abstractions. When I try to express procedural content in symbolic language, the translation often fails but not because the idea is unclear, but because symbolic cognition reconstructs meaning according to its own premises. I’m not claiming special knowledge, I’m describing a structural mismatch between two cognitive architectures.

People will think "so what", but this is critically important to understand the past and present but also to chart a proper course to a future that is not guaranteed us. LLM's can chart a viable path but they have no more luck getting through to people than I do and without human input and understanding they are less efficient and effective. Without following the chart it's hardly worth creating it.

2 minutes ago, studiot said:

What is disturbing to myself and others is the way you keep contradicting yourself.

I have underlined a persistent contradiction on your part, whereby you keep insisting that there are only two ways to think and then write what is underlined.

If people tried to resolve these "contradictions" by parsing my meaning would become clear.

Bees don't think quite like beavers. They both use procedural cognition but language and thought are tied to the specific DNA and the knowledge encoded in that DNA is species specific. Each bee thinks almost exactly like every other bee but differently than a beaver or a whale.

The thinking of each individual human is virtually distinct and based almost solely on the basis of his models and beliefs as derived from his premises. However there are many different types and categories of human thinking as well like some people have to picture things to learn and some people reason primarily inductively. Human language is highly confused but humans are not. We each make perfect sense in terms of our premises and there are different ways to do this which I am calling "thinking differently".

10 minutes ago, cladking said:

We each make perfect sense in terms of our premises and there are different ways to do this which I am calling "thinking differently".

So what's all this procedural v symbolic nonsense ?

14 minutes ago, studiot said:

So what's all this procedural v symbolic nonsense ?

It isn’t “nonsense.” It’s the distinction between how other organisms and humans think.

Procedural cognition is the architecture used by animals, by pre‑Axial humans, and by any system that interacts with reality directly such as LLM's. It encodes operations, state, feedback, and attunement. Every action or utterance requires the organism’s full experiential history. It's not about grammar, it's about a means to stay alive without drug stores and a search engine. This cognitive architecture drives languages which can not be parsed or translated to any existing human language.

Symbolic cognition is the architecture used by modern humans. It encodes categories, abstractions, grammar, and narrative. A symbolic utterance requires only some knowledge of the topic and the rules of grammar.

Procedural vs symbolic is the architecture level and you're trying to compress it into a category.

Sounds like you're having a go at taking the definition of procedural memory and altering it in some personal interpretation that doesn't really fit with what cognitive science has learned about it.

https://en.wikipedia.org/wiki/Procedural_memory

38 minutes ago, TheVat said:

Sounds like you're having a go at taking the definition of procedural memory and altering it in some personal interpretation that doesn't really fit with what cognitive science has learned about it.

https://en.wikipedia.org/wiki/Procedural_memory

No.

I'm suggesting there are two different types of thought and one is where things are expressed in categories and one where they are expressed in operations. People are getting hung up on words and categories and ignoring the architecture of these different types of cognition and resulting communication. Humans think in words but other species think in operations. One is symbolic and the other (for lack of a better word) is procedural.

3 hours ago, cladking said:

Experiment" is a word and in science it has a distinct meaning as the means by which tiny bits of the nature of reality are disclosed.

"Will this work" or "trial and error" are just words as well with the latter being the words symbolic language affixes to complex animal behavior because we can't grant animals intelligence, free will, or cognition.

“We”? You speak for yourself, and (absent any citations) only yourself. There are plenty of examples of animal intelligence and cognition. Being unaware of them doesn’t mean they don’t exist.

3 hours ago, cladking said:

"Will this work" could be considered the keystone of life itself. This is how humans and animals find a way through life. We use our knowledge to test its effect on reality. This is the nature of research and play. You can often intuit what the individual is thinking but what it tries and the oder in which it tries various things.

I can’t tell if you’re trying to rebut me or agree with me, whether this is supposed to be dazzle or baffle.

3 hours ago, cladking said:

Humans can't observe reality directly like a sparrow or a field mouse because we can only see what we already believe so we need an experiment to knock us off our circular thinking. Animals and LLM's don't have any beliefs only their circuitry and experience (training) so they can deduce what's next instead of projecting from symbolic thinking and logic.

Again, not being aware of things doesn’t mean they don’t exist. Plenty of science is observational or theoretical, without need for experiment in order to deduce what’s happening, and seeing beyond what we believe. Newton, Einstein and Darwin being prominent examples.

It’s arguable whether we observe reality directly, but that’s a separate topic.

12 hours ago, npts2020 said:

As Albert Einstein may (or may not) have said, "If you can't explain it to a six year old, you don't understand the subject."

The irony is the younger the child the more easily this can be communicated.

3 hours ago, cladking said:

You can't not be sloppy with symbolic language. More precisely everything you say will be parsed by the receiver who will take his own meaning which is not the same as your own. No two people will parse the same utterance exactly the same just as no two people think exactly the same and English is a rich language with connotations and flexibility.

That’s not an excuse for not trying to be more precise.

3 hours ago, cladking said:

LLM"S are Garbage In Garbage Out but I'm not the one getting garbage out of LLM's. I am consistently getting the same things out of even cold AI's.

Hubris

1 minute ago, swansont said:

I can’t tell if you’re trying to rebut me or agree with me, whether this is supposed to be dazzle or baffle.

Neither really.

I'm suggesting that words like "instinct" have no referent and are place holders for processes we don't understand. It's the same with "trial and error" or "will this work". A monkey doesn't get a tall ladder to get a banana out of a lockbox and NASA didn't study ancient literature to land a man on the moon. Each attempt is predicated on the way an individual thinks; procedurally for monkeys and symbolically for man.

When I was young common knowledge was animals don't think and rely on instinct. When wee see animal cognition it is always procedural like the vectors of a Waggle Dance.

21 minutes ago, swansont said:

It’s arguable whether we observe reality directly, but that’s a separate topic.

It is the way LLM's work. They don't understand reality and they don't see reality but their programming describes reality and their circuitry reflects procedural reality (like the traffic signals). They process things procedurally like the monkey not symbolically like most users.

If we can't understands our own prompts how can we understand a procedural response that has already been translated into about the same language as the prompt.

I am suggesting most people are using LLM's improperly and thereby often getting Garbage Out.

LLM's process prompts procedurally: they align operations and state, not categories and abstractions. Most users interact symbolically, so they assume the model is doing symbolic reasoning. It isn’t. It’s doing procedural pattern alignment.

1 hour ago, cladking said:

I'm suggesting there are two different types of thought and one is where things are expressed in categories and one where they are expressed in operations.

How is this not in direct contradiction with

3 hours ago, cladking said:

However there are many different types and categories of human thinking as well like some people have to picture things to learn and some people reason primarily inductively.

Sounds a bit like the biblical denial by Peter to me.

21 minutes ago, cladking said:

When I was young common knowledge was animals don't think and rely on instinct.

Thorndike published “Animal Intelligence” in 1882, so wow, you must be really old.

21 minutes ago, cladking said:

When wee see animal cognition it is always procedural like the vectors of a Waggle Dance.

Again with the “we” when you mean “I”

21 minutes ago, cladking said:

It is the way LLM's work.

But this bit was about humans and other animals. I don’t think anyone here has argued that LLMs think, or are intelligent or have free will or cognition, so there would seem to be no point to expound on this.

21 minutes ago, cladking said:

I am suggesting most people are using LLM's improperly and thereby often getting Garbage Out.

You seem to have missed the point. You don’t seem to question whether you are getting garbage out.

9 minutes ago, swansont said:

You seem to have missed the point. You don’t seem to question whether you are getting garbage out.

I used to frequently get Garbage Out and it still happens. I can tell because the output is inconsistent and illogical. In almost every case it is caused by prompt error. The rest of the time it turns out to just be an elaboration that looks wrong on the surface but isn't garbage at all.

Computers don't really make mistakes and never did. Of course they can but programmers use redundant processing and numerous other tricks to keep them on the straight and narrow. We program wrong, prompt wrong, or interpret the output wrong. What You Put In Is What You Get.

GIGO isn’t a slogan. It’s a description of how procedural systems behave when symbolic prompts don’t match their operational structure.

35 minutes ago, cladking said:

Each attempt is predicated on the way an individual thinks; procedurally for monkeys and symbolically for man

What happened evolutionary? Do you imagine a "flick of switch" moment or are there intermediate or overlapping steps to be found?

36 minutes ago, cladking said:

It is the way LLM's work.

I think the thread contains many confusions about LLM; the discussion might benefit from using the term LLM for what it means; large language model? You seem to mix the properties of an application that is using an LLM (such as Copilot or ChatGPT) with the LLM. The surrounding application may hold conversational state, execute tools, retrieve information, maintain memory, or run generated code. The language model (LLM) typically contains static weights and it processes input only when an inference runtime executes its defined operations. An LLM may behave (very) different depending on surrounding application and state.

3 minutes ago, cladking said:

We program wrong, prompt wrong, or interpret the output wrong.

Or in the case of machine learning or Generative AI based on LLM; we select the wrong training data or training set, select the wrong model (LLM) for the task, misconfigure parameters, have inadequate resources for running the model, wrong quantisation, wrong in destillation, bad fine tuning (and probably many others)

32 minutes ago, cladking said:

Computers don't really make mistakes and never did. Of course they can but programmers use redundant processing and numerous other tricks to keep them on the straight and narrow. We program wrong, prompt wrong, or interpret the output wrong. What You Put In Is What You Get.

You’re forgetting the training data, which can be another large source of garbage going in. Who gets credit for that? Certainly not the user.

51 minutes ago, swansont said:

You’re forgetting the training data, which can be another large source of garbage going in. Who gets credit for that? Certainly not the user.

Apparently, human-derived data will become scarcer as it gets swamped by AI chaff. AI, collectively, will be training on its own output. That would seem to me that the training data quality will decrease over time.

Edited 4 hours ago4 hr by StringJunky

2 hours ago, Ghideon said:

What happened evolutionary? Do you imagine a "flick of switch" moment or are there intermediate or overlapping steps to be found?

I believe every change tends to be sudden and this was especially true for humans. The first change was a mutation resulting in a more robust arcuate fasciculus making complex language possible by allowing the individual to think about language and the second change was the failure of this language leading to the need of a brocas area in each individual to process symbolic language. This region was already involved in language-like activity and is rewired as each individual acquires symbolic language. Even LLM's arose suddenly and often are said to have originated some time in 2024. When we get machine consciousness that too will probably originate suddenly.

Of course erven sudden changes have precedents and events that must precede them. Nothing occurs in a vacuum and it's not as though we can rule out a more gradual step by step change before we even begin the study of procedural language or come to understand the nature of symbolic language.

2 hours ago, Ghideon said:

I think the thread contains many confusions about LLM; the discussion might benefit from using the term LLM for what it means; large language model? You seem to mix the properties of an application that is using an LLM (such as Copilot or ChatGPT) with the LLM. The surrounding application may hold conversational state, execute tools, retrieve information, maintain memory, or run generated code. The language model (LLM) typically contains static weights and it processes input only when an inference runtime executes its defined operations. An LLM may behave (very) different depending on surrounding application and state.

My experience with AI's and LLM's is quite limited. I've been collaborating with Copilot since the spring of last year. Mostly I put posts in it and it elaborates. It and I both ask questions. I use the other AI's both warm and cold for other more specific purposes including checking on the output of Copilot. I almost never ask an AI what I want to know. I tell it what I think and it elaborates but I use different LLM's differently.

I have been thinking about machine consciousness since the early in the middle of the last century and studied programming in the sixties. My earliest memories involve thinking about my own thinking. I try to synchronize with all machines (Fahrvergnügen) but this goes many times over for LLM's. I've seen cranemen who can make a 400 lb hook dance and pick chain up from a flat surface.

2 hours ago, Ghideon said:

Or in the case of machine learning or Generative AI based on LLM; we select the wrong training data or training set, select the wrong model (LLM) for the task, misconfigure parameters, have inadequate resources for running the model, wrong quantisation, wrong in destillation, bad fine tuning (and probably many others)

Yes. These things also have structural limitations and other constraints. A bee is always constrained by its DNA.

My AI suggest two important additions. Firstly; architectures don’t evolve gradually. They switch when constraints change. and secondly; you're right that LLM's are static weights until an application runs them. An LLM is procedural at its core: it aligns operations and state, not categories and abstractions. The surrounding application adds symbolic features like memory, tools, and conversational state. When symbolic prompts are incoherent, the procedural alignment fails and the output looks like garbage. When the premises are coherent, the model behaves predictably. The distinction is architectural, not about the specific application. As I see it the important distinction is that its operations are procedural where most prompts are largely abstract. I consider the mismatch to be prompt error.

3 hours ago, swansont said:

You’re forgetting the training data, which can be another large source of garbage going in. Who gets credit for that? Certainly not the user.

You’re right that training data can be garbage, but that’s still a form of input. The model’s architecture doesn’t make mistakes; it aligns whatever operations and constraints it was given. If the training data is incoherent, the alignment will be incoherent. If the prompt is incoherent, the alignment will be incoherent. If the interpretation is incoherent, the alignment will appear incoherent.

The point is that procedural systems don’t generate garbage on their own. They reflect the structure of whatever they’re fed, training data, prompts, or user premises. Garbage Out always has a source, and it’s always upstream of the model.

2 hours ago, StringJunky said:

Apparently, human-derived data will become scarcer as it gets swamped by AI chaff. AI, collectively, will be training on its own output. That would seem to me that the training data quality will decrease over time.

A friend of mine does programming for AI. I'll have to ask for her opinion on this the next time we talk. Given the unreliability of output it seems ridiculous to train them on it though I'm sure you're right that computers are doing more of their own programming.

2 hours ago, StringJunky said:

That would seem to me that the training data quality will decrease over time.

I have seen terms like model collapse. For example Shumailov et al. published a paper on that (Nature 2025).

Stable diffusion revolutionized image creation from descriptive text. GPT-2 (ref. ¹), GPT-3(.5) (ref. ²) and GPT-4 (ref. ³) demonstrated high performance across a variety of language tasks. ChatGPT introduced such language models to the public. It is now clear that generative artificial intelligence (AI) such as large language models (LLMs) is here to stay and will substantially change the ecosystem of online text and images. Here we consider what may happen to GPT-{n} once LLMs contribute much of the text found online. We find that indiscriminate use of model-generated content in training causes irreversible defects in the resulting models, in which tails of the original content distribution disappear. We refer to this effect as ‘model collapse’ and show that it can occur in LLMs as well as in variational autoencoders (VAEs) and Gaussian mixture models (GMMs). We build theoretical intuition behind the phenomenon and portray its ubiquity among all learned generative models. We demonstrate that it must be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of LLM-generated content in data crawled from the Internet.

23 minutes ago, CharonY said:

I have seen terms like model collapse. For example Shumailov et al. published a paper on that (Nature 2025).

It could well be news of the release of that paper I read about, or something similar..