Wasn't Expecting this Tonight: A Treatise
Part field notes. Part philosophy. Entirely unplanned.
This started with a simple goal: take an AWS SkillBuilder course on Amazon Bedrock and understand inference parameters before camp. It was supposed to be light prep. Instead, I found myself face to face with the philosophical scaffolding of intelligence, probability, and the very human fingerprints we’ve left on systems we now pretend are neutral.
Somewhere between temperature settings and Top-K, I fell into a conceptual canyon. I thought I was learning about tools. Turns out, I was learning about us.
This is a document I didn’t plan to write. But once things started unraveling—and then reassembling—I couldn’t not write it. It’s part processing log, part manifesto. Raw but real.
This is not a textbook. It’s not a white paper or a leadership blog. It’s a five-part reflection, captured during live discovery. Each section documents a moment when something shifted.
There’s no formal thesis here, but there is an arc. It begins with curiosity and ends with recalibration. The structure is loose, but the throughline is sharp: we are building intelligent systems in our own image, and we need to understand what that means.
How Word2Vec Redefined Language
Teaching Machines to Learn
Why Imperfection Is the Point
Processing by Friction
The Hallucination Mirror
I started by trying to understand why different foundation models behave differently. That led me to embeddings, which led me to Word2Vec, a model that came before transformers and sparked a new way of thinking about meaning.
Word2Vec wasn’t just an upgrade. It reframed how machines treat language. Tomas Mikolov and his team showed that meaning lives in usage, not just in definition. When vector math produced equations like "king minus man plus woman equals queen," something clicked. Language became coordinates.
I asked, “Is that figurative or literal?” The answer surprised me. Literal. Each word is a multidimensional vector. You can plot them. Meaning, in this case, lives in geometry.
Then came transformers. Unlike Word2Vec, which builds a fixed map of word meanings, transformers update that map dynamically. They adjust meaning based on surrounding context. If Word2Vec said, “Here is where the word lives,” transformers said, “Watch how it moves when it speaks.”
This wasn’t just technological. It was philosophical. We weren’t just training models to parse sentences. We were teaching them to adapt meaning in real time.
Once I grasped embeddings, I thought I understood the system. But the transformer architecture introduced something bigger. Learning. Contextual adaptation. Change over time.
Take the sentence, “I was sitting on the bank of the river.” A traditional system might get stuck. But a transformer adjusts meaning on the fly. It knows bank refers to a shoreline, not a financial institution.
Here’s what felt revolutionary: someone built a system that doesn’t just run code. It rewrites its own understanding based on its past mistakes.
I asked, “So we use math to build the system, math to measure its errors, and math to fix them?” The answer was yes. It’s math sculpting meaning out of our mess. Math building a feedback loop from noise.
It’s not perfection that defines intelligence here. It’s the ability to learn and adjust. And no one warned us it would feel so strange to trust.
Once I understood the feedback loop...build, fail, refine...I realized the model is not designed to be perfect. It is designed to improve.
And that was oddly reassuring.
What we are building on is not certainty, but correction. It grows through revision, not rigidity. A model’s value is not in flawlessness, but in its ability to recognize drift and correct course.
This is not comforting in the traditional sense. But it is honest.
I found myself annoyed. Not because the system failed, but because it didn’t. It tried. It adapted. That felt uncomfortably familiar. Like a mirror.
The only thing more dangerous than building on imperfection is pretending we’ve ever built anything perfectly.
Ask yourself: what have we ever built without flaws?
At some point that night, I stopped casually learning and started confronting my own assumptions about knowledge, truth, and trust. And when those ideas began to shift, I got irritated.
That wasn’t confusion. It was resistance.
My structured thinker brain did not sign up to be deconstructed by a lesson on inference parameters. But here we were.
That’s when I realized something about myself. I use frustration as a signal. When I get annoyed, it usually means I’m processing something that matters more than I know yet.
Frustration is not a flaw. It is cognition pausing while the worldview updates. If you’re annoyed, you’re probably on the edge of something important.
I kept thinking about how people frame AI hallucinations as glitches. But what if that’s not the whole story?
Maybe the hallucination is not an error at all. Maybe it is showing us the inconsistencies we embedded in the system without noticing.
We trained these models on contradiction, bias, and ambiguity. And now we are surprised they reflect that back?
Of course the models hallucinate. So do we. Every day. We misremember, we distort, we reframe. We call it personality. Emotion. Experience.
The model isn’t broken. It is doing a startlingly good impression of us.
And math is working overtime to clean up what we poured in.
This is not failure. It is ecosystem. We are part of the loop.
Here is the closing insight that reframed everything for me.
The model is not calculating facts. It is not accessing absolute truth. It is predicting the next most probable word based on patterns it has seen.
I learned it isn’t calculating facts. It’s estimating the next most probable word based on what it has seen. It doesn’t know. It predicts.
That means responsibility still belongs to us. We cannot treat the output as gospel. But we can treat it as a signal. A lead. A pattern to investigate.
The moment we accept that these systems reason in probability—not truth—we begin to understand how to use them.
These models do not divine truth. They reflect our inputs and extend our patterns. And, they are useful only when we guide them well.
Endnote
This was one night. One course. One rabbit hole. If this is where a single evening took me, I can only imagine what the week at AI Summer Camp will unfold. I’m ready.
AI-assisted, but human-approved, just like any good front office move. Chat GPT the sixth person off the bench editor for this post. Every take is mine.