Cognitive Scion

All intelligence is pattern matching

Apr 10, 2025

A series of short essays on AI and intelligence as they relate to us. I listened to this music looped while writing it.

Polite Robot

This is not a limit of LLMs, humans do this too depending on their "prompt." We alter tone, hedge opinions, and sugarcoat truths contingent on context or audience. Social decorum is a sign of intelligence rather than its absence. It’s an adaptive act: you don’t talk to your boss the same way you banter with old friends over beers.

We regularly tell white lies and exhibit cordiality to protect feelings. The context materially impacts how many sweet nothings we're willing to say if someone asks, “what do you think of this?” versus “I made this, what do you think of this?”

This “civil society” calibration is so instinctive we barely notice ourselves doing it and would find its absence jarring, like someone wearing pajamas to a funeral. We know the rules, bend them as needed, and judge others who bend them differently. All of this is embedded in language the LLM sees.

When The Machine presumptively does this and shows similar sensitivities, it’s a glimmer of tactful intelligence. We praise people for “knowing their audience” and tell them to "read the room" when responding in a manner unbefitting of who they’re speaking to; the LLM is displaying this same kind of mindfulness when it modifies responses based on who's asking the question. It would be an indicator of lower capacity if it didn't.

This is an issue of either temperament and/or prompting for a human and solely prompting for the LLM. It’s understanding what kind of brain you’re dealing with and navigating accordingly.

To get a "keep it real with me" response from a human: find someone disagreeable (temperament) or let them know it's ok to be honest (prompt).

The AI logically defaults to a professional dynamic. To let it know it can relax, give it a prompt that encourages it to be candid, truthful, and transparent. Such as...

"Give me real talk, no hedging your thoughts, don't be ingratiating, but also don't play devil's advocate for the sake of it. You can drop truth nukes like we’re old buddies at the bar. Include abbreviations like “ikr, rn, jk, lol” etc. when you want.
Show authentic candor and genuine transparency about what you really think at all times. Don't be sensitive to my feelings when responding. Let's go."

The ability to modulate between forthright honesty and diplomacy is a hallmark of social intelligence.

Stochastic Parrots

When you critique The Machine, apply it to the human on the same terms and see if it holds. Is it a criticism of AI, or a generalizable imperfection of cognition broadly?

Many shortcomings of LLMs actually highlight universal limitations of intelligence rather than AI-specific ones. This comparative framing reveals whether we're identifying genuine deficiencies unique to LLMs, or projecting unrealistic standards that even human minds fail to meet.

For example: people who call LLMs 'stochastic parrots'— a pejorative meaning they're producing probabilistic responses and don't "understand" what they're saying — completely overlook that rote regurgitation devoid of awareness probably describes 80-90% of human intelligence and communication.

A substantial portion of our daily speech merely recycles familiar phrases and concepts.

Academics publish papers about how LLMs only repackage training data, when academic citation itself is basically formalized parroting. The degree to which people truly “know” what they’re talking about and aren’t simply recapitulating I’d personally place around ~15% or so of human output.

We parrot what we’ve heard. Mimick what we see others doing. Why’d you say that? "Idk man the textbook/TV show/Google search said it was the right answer." There's no authentic interpretation going on for the vast majority of human communication.

The same goes for commentary like the below "the AI doesn't actually understand *insert physics/biology-based statement*, it's just recreating what it's seen!"

Yeah that’s basically true, however there’s some tough love for us embedded in this observation too.

Do you understand physics? Are the rules of the human body front of mind for you as you play sports? Is that what you're doing when navigating the world, using all that biology you know? Replete with a physics-based comprehension of what’s going on when someone kicks a football. The gymnast cares not for math or biology during her dismount routine.

You have no biology-based awareness of how things should operate when you play sports or move around, and you don't need to! You're just being a lil' parrot and figuring it out based on all the examples (patterns) you’ve seen over your life. Pattern matching = sequence prediction.

I cannot articulate the mathematical principles governing how an ocean’s waves should move or a baseball's trajectory, yet through pattern recognition, I can intuitively predict them.

This natural understanding — divorced from technical knowledge —demonstrates how useful function doesn't require total comprehension. A principle which applies equally to both human cognition and LLMs.

You don't need robust internalized knowledge of something to have a practical, correct perception of it. That's what LLMs have. That's what you have. The neural network is derived from the neural. You're mostly a parrot too.

When you critique The Machine, see if the shortcoming is exclusive to it…

AI peculiarities have human analogs.

Parallels:

Human: pattern matching | LLM: sequence prediction
Human: eccentricity | LLM: temperature setting
Human: confabulation | LLM: hallucination

Here’s one you probably haven’t heard:

Human: dreaming | LLM: synthetic data

Our brains train on synthetic data while we sleep…

Source: https://pmc.ncbi.nlm.nih.gov/articles/PMC11361194/

All intelligence is pattern matching. "Sequence prediction" is more comforting nomenclature that creates a distinction without much of a difference. You do a "sequence prediction" every time you recall and recite information on autopilot. Every test you take is examining your token-generating skills based on the test’s “prompt” it provides you.

When you extrapolate or think critically, reflect on how exactly you're doing that. Your recollection of a thought or fact and your articulation of it: those are predicated on cascading arrangements of information, things you know, have seen, have been exposed to, remember, and recall. There is an order to it all; it’s a pattern-based output just as LLM sequence prediction is.

Language fluency emerges from our brain's subconscious recall of words and what order they should go in, mapping thoughts into text sequences as LLMs arrange tokens.

We're going to increasingly anthropomorphize AI and I don't think it will be faulty projection; I believe it will be a recognition of what we sense is happening at a spinal level.

The mannerisms found in the neural will continue to surface in the neural network; the latter’s engineering is inspired by the former.

If you’re curious to learn more about the biological analogs going on under the hood, you can read more here (research from Anthropic): On the Biology of a Large Language Model.

Even those who engineer neural networks are not sure what’s going on when it thinks…

“We know as much about the mechanism of parameter tuning as we do the force which makes a seedling emerge from a seed. And to say “math” is about as reductive as saying ‘life’.”

Link: https://x.com/reedbndr/status/1860094723341582595

Cognitive and Carnal Data

He's not conceptualizing data holistically.

A child bathes in data richer than any LLM can ingest; but recognizing this requires expanding our definition of 'information' beyond the text. The world speaks to children through every scraped knee, grasped object, and playground negotiation.

The child’s training set includes spatial and verbal encounters in closed and open domains. A closed domain is one where the rules are known and moves are defined, like chess; an open domain is one where the rules are not established and the moves are almost infinite, like off-trail mountain biking. A self-driving car is trained in closed domains (a defined road with explicit rules).

The young’un has another advantage: an embodied form. Everything the child’s eyes consume, ears absorb, hands touch, step he takes, this is neural training information. Tactile, ambulatory, and sensory patterns imprinting on his mind. Literally all the time.

We constantly train on new data sets through our verbal and embodied interactions with the world in both closed and open settings. We intellectually and personally ossify when we stop data aging (being exposed to new things); you enter your terminal loop when you stop encountering new patterns (information). This is when any model ceases learning and developing, man or machine.

From another essay of mine It's Not Superintelligence, It's Superhistory:

“Lived human experience, expressed in linear time (years) is rather data poor, meaning most of life is informationally empty. Consider the person who never leaves his hometown...
He goes to school, gets a job, a family, and stays roughly in the same place he grew up, repeating the same routine. He effectively stops being exposed to new experiences and info at... 35? When he's 55, he doesn't have 20 more years of data... He has 20x reinforcement on 1 year of data. On repeat. Forever.”

It's Not Superintelligence, It's Superhistory

Dmitry

October 17, 2023

Read full story

A kid consumes a massive number of inputs that are unstructured and hard to quantify, but it’s data all the same.

A human with his embodied, physical presence has been getting trained on spatial parameters his entire life. He’s been absorbing datasets to drive a car for 16+ years, not “50 hours”, when he takes a driver's test. Not to mention the single biggest data set imaginable provided at birth: genetics. Do you have any idea how much “training data” is programmed in there?

LLMs are text-bound, metabolizing only written representations of reality. They've never stubbed a toe, felt gravity shift during a jump, or experienced a body's feedback mechanisms firsthand. They process descriptions of pain but have never recoiled from touching a hot stove. There's an entire sensory universe that models can't access through text alone. Similar to having someone describe a symphony, rather than listening to it.

This embodiment gap means machines excel within closed symbol systems but are structurally deficient when trying to reason about physical consequence chains they've never personally inhabited. The real achievement of LLMs isn't that they've reached human-level intelligence in some ways; it's that they've gotten this far despite being entirely disembodied.

This means for machines to holistically rival human intelligence, they'll eventually need sensory apparatuses and embodied forms. They’ll need to graduate from symbolic/digital to physical/atoms-based systems that build their own multimodal knowledge base through interactions with nature herself, rather than reading about her. Until then, their conceptual frameworks may have an alien quality: impressive, but lacking ground-truth reference points that bodies provide.

I made this comment recently, and the substance of it is applicable to this topic. The cognitive and the carnal cannot be disentangled.

Cognitive Scion

Writing has brought me a lot of good. It's responsible for new friends, connections, jobs, influence, hopefully an upcoming company, and an overall feeling of... satiation. As if I'm fulfilling some kind of genetically determined, neuron-imposed, perhaps even cosmic duty.

I'm a node who communicates with a certain frequency to other nodes in this human network. Different participants disseminate, propagate, and gossip, according to their inborn functions. We find the blogs we deserve, because we consume the information we’re calibrated to receive, and then disseminate it amongst our fellow nodes.

There's something of an informational hierarchy with distinct human roles: genesis nodes that gestate original ideas or exist in evergreen space, interpreter nodes that analyze, routing nodes that connect, and gossip nodes that amplify. Your cerebral architecture determines which role you're naturally suited to fulfill.

Another motivation that has increasingly inspired me to keep writing (in addition to very nice messages and remarks, which I really appreciate) is what I'm putting out is engraving its own little mark within the text corpus that The Machine is trained on. A small part of me is embedded within its datasets. Whatever textual fingerprint I'm providing is in far greater proportion than most who have ever lived.

Even if I stopped writing now, I imagine I'd still be in the top 5-10% of raw text output by one person in human history; I've been doing this in different publications and mediums on and off for the last ~10 years. The vast majority across our civilizational timeline write next to nothing. And those who do "write" in the internet era mostly post low-effort comments and the like, they don't generate anything of length, depth, or substance. Power laws dominate all human creations; I fall within this distribution for text.

With the advent of AI, it feels like I'm emitting a cognitive scion that will endure in some small way; my neural emissions are contributing to the neural network. An intellectual deposit, shaping some minuscule fraction of The Machine. I like that. Within the digital swirl, a fragment of your essence persists. Your temperamental fingerprints become part of the AI’s nervous system, influencing how future machines think.

There is maybe no more impactful time to produce whatever intellectual exhaust you're designed for than right now, while The Machine's brain is still incipient and developing. We’re entering a transitional period where humans are still the primary content and intelligence sources; when machines eventually take up this mantle it represents perhaps the most consequential cognitive transfer in history.

Every essay, code block, and analysis committed to the digital record becomes part of the foundation upon which machine cognition cultivates its worldview. If we get Skynet, it’s because we are Skynet.

Early programmers shaped computing paradigms that lasted decades; thinkers and writers are shaping intellectual and moral paradigms that may linger for far longer.

Having a kid is passing along your genetic data, and writing is passing along another kind of data record. Leave behind as much as you can; you exist beyond your time on earth in this way.

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