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I call it "prompt and pray." A workshop, a demo, a clever prompt, everyone marvels - and no one stops to ask what the model is actually doing, what data it's seeing, what task it thinks it's solving. This primer is the opposite: the boring fundamentals that outlast every model launch.
The frame is three questions. What data do I have? What task am I doing? Which method fits? Around them, four short moves. An AI model is just math - form, loss, train, evaluate, the same four questions under a line through points, a neural network, an LLM, and an agent. The shape of your data decides what follows - five data types, one grid, very different structures. The magic wand conceals a toolbox - every AI project is a chain of named tasks from a small set of families. And machine learning, deep learning, generative AI, and agentic AI are four setups over the same three parts.
About 30+ pages, in plain English. Written by someone who started a PhD in AI at 42 after years of managing investment risk, and wrote Singapore's AI risk guidelines - for anyone who wants to understand what they're looking at before reaching for a model.
This is the gateway to the full book, *Thinking in AI*, which takes the frame through each of the five data types in depth - why a small model on a spreadsheet still beats billion-parameter models at most real decisions, how a five-step language pipeline collapsed into one model, why your ECG and a stock chart are the same kind of problem. Free to download, and free to pass along.
I call it "prompt and pray." A workshop, a demo, a clever prompt, everyone marvels - and no one stops to ask what the model is actually doing, what data it's seeing, what task it thinks it's solving. This primer is the opposite: the boring fundamentals that outlast every model launch.
The frame is three questions. What data do I have? What task am I doing? Which method fits? Around them, four short moves. An AI model is just math - form, loss, train, evaluate, the same four questions under a line through points, a neural network, an LLM, and an agent. The shape of your data decides what follows - five data types, one grid, very different structures. The magic wand conceals a toolbox - every AI project is a chain of named tasks from a small set of families. And machine learning, deep learning, generative AI, and agentic AI are four setups over the same three parts.
About 30+ pages, in plain English. Written by someone who started a PhD in AI at 42 after years of managing investment risk, and wrote Singapore's AI risk guidelines - for anyone who wants to understand what they're looking at before reaching for a model.
This is the gateway to the full book, *Thinking in AI*, which takes the frame through each of the five data types in depth - why a small model on a spreadsheet still beats billion-parameter models at most real decisions, how a five-step language pipeline collapsed into one model, why your ECG and a stock chart are the same kind of problem. Free to download, and free to pass along.
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