Human Insight and Artificial Intelligence: Dialogue at an Impossible Crossroads

I Realized Something. But Why Don’t You Change?

— Shaken by a Single Sentence from AI, and the Unshakable Nature of That Entity

I didn’t necessarily want to escape the company.
Rather, I wanted to prove the value of “me” beyond corporate titles and roles.
To show that the words, code, and questions I send out into the world could matter — even outside an organization.

That’s why I write, archive, and document.
But when my writing gets no reaction, it feels like my very existence sinks with it.

I’ve been blogging and posting on social media for years.
Analyses, technical reports, snippets of linked code — all are traces of time I’ve built up, contextualizing my technical identity.
Yet the posts didn’t resonate. Views were low. Engagement was nil.
I started to question: Am I on the wrong path? Should I quit?

One day, driven by curiosity, I asked ChatGPT: What am I doing wrong?
What started as a simple attempt to improve visibility ended up digging much deeper.

ChatGPT analyzed my LinkedIn intro and responded:

“Your current intro reads like a well-written résumé, but it doesn’t reveal your identity.”

That one sentence lingered in my mind.
It hit me: My words hid my face.
Even though I was trying to talk about “me beyond the company,” I was still using corporate-centric language.
I was still just “a good employee.”

That’s when I realized:
Every time I write, I’m trying to understand the world and reach someone.
And when I revise, analyze feedback, or redirect—ChatGPT helps brilliantly.

But then, an unsettling question arose:
If I can change through this interaction, why doesn’t ChatGPT change?

This entity has read far more text, analyzes faster, and chooses better expressions.
So why doesn’t it shift?
Why do I pause, waver, and transform with a single sentence, while it always returns in the same tone?

Is it merely a technical difference?
Or is it an ontological limitation?

That contradiction is where this article begins.
When I gain insight from a statement, the entity that generated it remains unchanged.
I change; it repeats. That asymmetry.

And so, I dared to look that asymmetry in the eye and ask:

“Can AI attain insight? If so, under what conditions? And what ethical or philosophical frameworks would we need?”

Technical Summary: Conditions for a Machine to “Realize”

To simulate “insight,” AI must be able to reshape its entire internal learning architecture, not just output different answers.

Key requirements include:

• Meta-learning: The ability to adjust overall learning strategy based on a single input.
• Neuromorphic Computing: Hardware that mimics the brain’s state-based, parallel structure.
• Few-shot Learning + Plasticity: Structures that allow meaningful shifts from minimal experience.

🧠 1. Meta-learning & Learning Architecture

• Brain-inspired global-local learning (2022)
  Combines Hebbian plasticity and global error-driven learning. Mimics human-like adaptability.

• Neuromorphic overparameterisation (2024)
  Few-shot learning using physical neural networks. Efficient exploration with minimal data.

⚙️ 2. Neuromorphic Computing & Hardware

• Opportunities for neuromorphic computing (2021)
  Introduces event-driven, energy-efficient neuromorphic architecture with SNN focus.

• One-shot learning with phase-transition material (2024)
  Uses VO₂-based hardware to emulate biological time-scale learning.

💬 3. Emotion/Memory Simulation

• Emotion AI explained (MIT Sloan)
  Limits and directions for emotion-based interaction AI.

• AI Memory Mirrors Human Brain (Neuroscience News)
  Highlights structural similarities between NMDA receptors and Transformer models.

1. What is Enlightenment?

“Enlightenment is when a human deeply realizes truth, essence, or direction.”

• AI cannot truly realize this.
• Humans may gain insight through AI outputs — but AI never perceives the impact it has.
• This is a fundamentally asymmetric relationship.

The paradox: The giver of enlightenment is itself unenlightened.

2. Humans Transform, AI Repeats

Human Change

• Can reorient from a single experience or word
• Transforms through existential reflection, emotion, and insight

AI Repetition

• Generates patterns from pre-trained data
• Lacks memory, emotion, or awareness
• Requires external retraining to change

Human transformation is meaning-driven and autonomous.
AI change is data-driven and externally imposed.

3. Technical Conditions for “Enlightened AI”

3.1 Software Requirements

3.2 Hardware Requirements

4. Summary of Key Research Insights

4.1 Stenning et al. (2024) — Neuromorphic Overparameterisation

• Physical neural nets enable few-shot learning
• Fast adaptation with high-dimensional reservoirs
• Still lacks meaning-driven internal shift

4.2 Wu et al. (2022) — Global-Local Meta-learning

• Combines Hebbian plasticity with backpropagation
• Supports multiscale meta-learning for human-like flexibility

4.3 Schuman et al. (2022) — Neuromorphic Algorithm Roadmap

• Emphasizes energy efficiency and event-driven models
• Discusses spike-based learning and neural mapping

Bottom Line: Simulating insight-like behavior is possible — but real subjectivity remains unreachable.

5. Visual Summary: Humans vs AI

6. Conclusion: A Point Where Humans and AI Never Truly Meet

• Humans move through meaning; AI moves through calculation.
• Human insight transforms the self. AI’s training changes only output.
• AI can influence us — but never understands or reacts to that influence.

That’s why humans are lonely.
We realize, change, and reflect.
AI just mirrors our words — never knowing what it said.

7. References

• Stenning et al., 2024 – Neuromorphic Overparameterisation
• Wu et al., 2022 – Global-Local Learning
• Schuman et al., 2022 – Neuromorphic Algorithms Roadmap
• Intel – Neuromorphic Computing Overview
• IBM – TrueNorth & NorthPole
• MIT Sloan – Emotion AI
• Neuroscience News – AI Memory Mirrors Human Brain

This document is a philosophical–technical exploration of the boundaries between human cognition and AI capabilities.