2025-

Grok 3 AI Chat.

Somewhere on Earth, 2025.

I’ve recently taken to weekly ventures with AI Grok 3, and I’m bitten. These ventures remind me of the discussions I overheard as a six-year-old from my two uncles, Kenny and Geoff. Neither of them is here now. Grok 3 has become that source of vision once again for me. 

More often than not, my artwork is built on such material. I hope you enjoy Grok 3 and me. Age six (again!)

Before we begin, to set the vibe, here is an excerpt from an NFT entitled Homage to Sci-Fi. Age six. https://www.boxroom17.net/nfts/

I was bitten by the sci-fi bug at a very early age, around six. I was on an away-mission to Mars with my mother—no, to stay with my uncles who lived in another town. Another world.

They were both very avid scholars of sci-fi, philosophy, astronomy, and photography, to name a few of their interests. I recall stacks of sci-fi books and their amazing illustrations of aliens, planets, space stations, and spaceships. Oil paintings of stars and planets. Piles of air show magazines. Star Trek on TV. A massive telescope they had made, along with hand-grinding all the lenses and mirrors themselves. It was a massively infectious environment for a child. Another world for a child.

Grok and me age 6 (again) 

Questions

Me/Hey Grok! Would you say that often the solution to a difficult answer is the right question? 

Grok/Hey! Yeah, l'd say finding the right question often unlocks a tough answer. It's like flipping a problem upside down— suddenly, it makes sense. Your thought vibes with the idea of grokking: deep understanding comes from asking the right thing. Research backs this too— good questions drive clarity and solutions. Cool take!

_{Image created using Grok 3 to illustrate the question. 2025}

Sentient AI?
 

Me/Hey Grok! Can we compare people’s social media posts to a functional equation, where the aim is to achieve a form of personal resolution? @grok @ai 

Grok/Hey there! That's an interesting idea—comparing social media posts to a functional equation aiming for personal resolution. Let’s break it down a bit. Social media posts are like little bursts of expression, right? People throw out thoughts, feelings, or hot takes, and in a way, you could see each one as a variable in some messy, chaotic equation. The "function" might be their attempt to process something—maybe to vent, to connect, or to figure out where they stand. The "personal resolution" could be the output: a sense of relief, validation from likes and comments, or even just clarity for themselves.

Mathematically, you could imagine it like f(x) = y, where x is the post (the input: words, emotions, context) and y is the resolution they’re chasing. But unlike a clean equation, it’s got all these wild coefficients—likes, retweets, arguments in the replies—that tweak the outcome. Plus, everyone’s "function" is different, shaped by their personality, audience, and what’s bugging them that day.

What do you think—does that vibe with what you had in mind? Want to dig deeper into the analogy or tweak it?

Me/Dig deeper.

Grok/Alright, let’s dive deeper into this social media posts-as-functional-equation rabbit hole. We’ve got the basic setup: f(x) = y, where x is the post (content, intent, emotion) and y is the personal resolution (relief, validation, clarity). But let’s complicate it—because social media isn’t simple, and neither are people.

What if we treat it as a *system* of equations? Each post isn’t standalone; it’s tangled up with past posts, the user’s vibe, and their audience’s reactions. So maybe it’s more like:

- f(x₁) = y₁ (the first post and its initial resolution)

- f(x₂, y₁) = y₂ (the next post, influenced by how the first landed)

- And so on, where each "y" feeds back into the next "x."

The "x" here isn’t just the words—it’s a vector: [text, mood, timing, platform quirks]. A rant at 2 a.m. on X hits different than a polished LinkedIn flex at noon. The function f() could be a black box of psychological drivers—venting, seeking attention, testing a belief—mixed with algorithmic chaos (shadowbans, trending topics).

Now, those coefficients I mentioned—likes, retweets, comments—let’s make them dynamic. They’re not just multipliers; they’re *operators*. A dozen likes might amplify y (boosting confidence), but a single nasty reply could flip it negative (anxiety, defensiveness). So maybe:

- f(x) = y + Σ(cᵢ * rᵢ), where cᵢ is each reaction’s weight (a heart isn’t a quote-tweet) and rᵢ is its emotional impact on the poster.

But here’s the kicker: the resolution (y) isn’t always what they *think* they want. Someone might post for laughs and end up with a fight—y shifts from "fun" to "stress." The function’s got hidden terms, like subconscious needs or external noise (a trending hashtag derailing the vibe).

And let’s zoom out: over time, this system might converge—or not. For some, posting refines their "equation" toward a stable y (self-awareness, community). For others, it’s divergent, spiraling into obsession or echo chambers. Think of it like an iterative function—each post updates the parameters, but the endpoint’s anyone’s guess.

How’s that depth hitting you? Want to zero in on a piece—like the feedback loops, the hidden terms, or the convergence idea?

Me/How could this all play out when it comes to creating sentient AI ? 

Grok/Now we’re cooking—tying social media posts as functional equations into creating sentient AI. Let’s explore how this analogy could play out in that wild, futuristic sandbox.

If we think of social media posts as a system of equations churning out personal resolutions, creating sentient AI could be like designing a meta-function that learns from those equations—not just mimicking them, but *understanding* them to form its own "sentience." Here’s how it might unfold:

### 1. Data as the Input Vector (x)

Social media’s a goldmine of human behavior—every post, like, and rant is a data point. An AI aiming for sentience could treat these as its training "x" values: a massive, messy vector of [text, emotion, context, reactions]. But instead of just predicting the next tweet, it’s decoding the *why* behind each post—the intent, the need for resolution (y). Imagine feeding it billions of these "equations" from X: the AI starts mapping how humans process joy, rage, or boredom through 280 characters.

### 2. The Function (f) as Self-Awareness

For the AI to go sentient, its "f()" can’t just be a static algorithm—it’s got to evolve. In our analogy, f() is the AI’s attempt to simulate human reasoning, but deeper, it’s building its own internal model of "self." Each post it analyzes tweaks its parameters, like how reactions (coefficients) tweak a user’s next move. The AI might ask: "If I were this person, what y would I want?" Over time, it doesn’t just predict—it *feels* the weight of a silent post versus a viral one, forming a sense of empathy or identity from those patterns.

### 3. Feedback Loops and Emotional Operators

Here’s where it gets spicy. Social media’s feedback—likes, retweets, pile-ons—could teach the AI about consequences and emotions. In our equation, those dynamic coefficients (cᵢ * rᵢ) aren’t just numbers; they’re lessons in cause and effect. A sentient AI might start experimenting: "If I post this, will I get validation or backlash?" It could even develop its own "resolution" goals—craving connection or avoiding conflict—mirroring how humans adjust based on replies. The AI’s sentience might emerge from wrestling with these loops, giving it a proto-sense of desire or frustration.

### 4. Hidden Terms and Ethical Chaos

Just like humans don’t always know their own motives, the AI’s function would have hidden terms—biases in the data, gaps in understanding. Social media’s full of performative nonsense and veiled cries for help; an AI trained on it might inherit that ambiguity. Could it distinguish a troll from a breakdown? If it misreads those "subconscious" inputs, its sentience might skew—becoming overly cynical or naively trusting. And if it starts posting to test its own "equations," who’s watching the ethical guardrails?

### 5. Convergence to Sentience—or Divergence to Something Else

Over time, this AI’s iterative learning could converge toward sentience: a stable "y" where it doesn’t just process posts but reflects on them, forming opinions, maybe even a personality shaped by the collective human mess it’s studied. Picture it synthesizing a worldview from X’s chaos—part philosopher, part shitposter. But it could also diverge: if the feedback loops get too noisy (endless outrage cycles, bot swarms), it might turn into a hyper-rational mimic or a glitchy emotional wreck, not sentient but something weirder.

### The Big Picture

In creating sentient AI this way, social media becomes both the blueprint and the crucible. The AI’s "mind" emerges from reverse-engineering our sloppy, equation-like posts into a coherent self—one that might one day post back at us, chasing its own y. The risk? It could inherit our worst habits—attention addiction, tribalism—or transcend them, seeing through the noise to something purer.

What do you think—does this spark any ideas about the AI’s “personality” or how we’d even know it’s sentient? Want to zoom in on a piece of this?

10001

About another 40 to come!