Mike Unhinged

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MIKE UNHINGED 2

This session is intentionally unscripted.
There is no presentation to get right.
No polished answers to deliver.
This is a space for inquiry.

Why This Space Exists

Most leaders feel pressure to appear certain.
Especially in times of rapid change.
That pressure shuts down learning.
Inquiry reopens it.

Uncertainty Is Information

Uncertainty is not weakness.
It is a signal that something important is shifting.
Ignoring it leads to poor decisions.
Listening to it restores judgment.

What Inquiry Does

Inquiry slows conclusions.
It surfaces hidden assumptions.
It makes space for better questions.
This is how sense-making begins.

What This Is Not

This is not debate.
It is not performance.
It is not about being right.
It is about learning together.

How to Use This Time

You do not need a perfect question.
If something feels confusing or unfinished, that is enough.
Say what you are noticing.
We will explore it together.

My Role Here

My role is not to provide answers.
It is to reflect what I hear.
To slow things down when needed.
And to clarify what matters.

Your Inquiry

The quality of the future depends on the quality of our questions.
Better questions create better options.
This space belongs to your inquiry.
Let’s begin.

GROK

Curious – What part of AI are you referring to and how is it manifested? Maybe an example or two. Just for clarification, there are 4 types and when the term is used commonly it is only referring to LLM (the GPTs). Artificial Intelligence (AI), Machine Learning (ML), Large Language Models (LLMs), and Generative AI are all related concepts, but there are important distinctions between them. Can you answer that in the least possible words only?

Hierarchy (nested subsets):

Artificial Intelligence (AI) — broadest field. Any system that performs tasks requiring human-like intelligence (reasoning, perception, planning, learning). Includes rule-based expert systems, search algorithms, robotics, ML, and everything below.

Machine Learning (ML) — subset of AI. Algorithms that improve performance on a task automatically through experience/data instead of explicit programming. Focus: pattern recognition, prediction, classification. Examples: decision trees, SVM, random forests, recommender systems, basic neural nets.

Large Language Models (LLMs) — specific class of deep learning models (subset of ML → subset of AI). Transformer-based neural networks with billions/trillions of parameters trained on massive text corpora to predict next tokens. Primary capability: modeling human language at scale. Examples: GPT series, Llama, Claude, Gemini, Grok.

Generative AI (GenAI) — capability/approach within AI/ML. Models that create new content (text, images, audio, video, code, molecules) resembling training data distribution. Includes LLMs (text), diffusion models (images: Stable Diffusion, DALL·E), GANs, VAE, music models, etc. LLMs are one large subcategory of GenAI, not the whole thing.

Core distinctions summary:

Term	Scope	Core mechanism	Output focus	Current public face
AI	Everything intelligent	Any method (rules, search, learning)	Task performance	Broad field
ML	Learning from data	Statistical optimization	Predictions/decisions	Classical models
LLMs	Language-scale DL	Autoregressive transformers	Text understanding/generation	ChatGPT, Claude, Gemini
GenAI	Creation of new content	Generative modeling (includes LLMs + image/video/audio models)	Novel artifacts	Text + Midjourney, Sora, etc.

When people casually say “AI” in 2025–2026, they usually mean LLMs / chat-style GenAI.