What we mean by generative AI

[snorts] [music] Before we go anywhere, let's be clear about what we mean by AI because it's actually a broad term that means many

different things. The recommendation

different things. The recommendation engine picking your next video, the spam filter in your inbox, the fraud model flagging a suspicious charge on your card, the system routing your customer

service call.

All of that is AI. None of it, however, is generative. These systems sort, rank,

is generative. These systems sort, rank, classify, and predict. They're

enormously useful, and they're running in the background of your life constantly. They're also not what this

constantly. They're also not what this series is about.

What's changed recently is the rise of generative AI. These are systems that

generative AI. These are systems that produce new content rather than categorizing existing content. Text,

images, code, audio, video. Generative

AI is created through two stages. First,

it's trained on massive amounts of data to learn patterns. That's pre-training.

Then it's refined to be broadly safe, ethical, and helpful. That's

fine-tuning. You'll learn more about these in the next lesson.

Generative AI, at its core, is a prediction system. AI isn't uniformly

prediction system. AI isn't uniformly capable or uniformly unreliable. It's

strong and weak along specific, predictable axes. And most of the time,

predictable axes. And most of the time, the strength and weakness come from the same underlying property of the machine.

An AI can write compellingly because it's a prediction engine. It also

hallucinates because it's a prediction engine. On one end, a capability zone.

engine. On one end, a capability zone.

On the other, a limitation zone. The

mechanism itself is always operating the same way. What varies is where your

same way. What varies is where your specific task lands on that line. The

skill you're building in this series is learning to feel out where those edges are.

Let's do a quick overview of the four properties of generative AI you'll learn in this course.

Next token prediction. Where do the answers actually come from? Unless

you've enabled or directed it to use an external source, the model isn't looking things up. It's writing what comes next

things up. It's writing what comes next based on the content it's been trained on, one fragment at a time. Knowledge.

What does the model actually know? Its

knowledge is broad but uneven, frozen at a training cutoff, and shaped by whatever was in the data it learned from. Working memory. What is the model

from. Working memory. What is the model paying attention to right now? Just like

humans, models don't have unlimited memories. What's in the context window

memories. What's in the context window is what's available to the AI.

Steerability. How much are you in control? These systems are remarkably

control? These systems are remarkably directable, but there can be a gap between what you intended and what actually landed. We'll deep dive into

actually landed. We'll deep dive into each of these properties and how knowing about them can empower you to make good decisions when using AI. The goal here isn't to make you distrust AI. It's also

not to make you fully delegate all your tasks. It's calibrated trust, neither

tasks. It's calibrated trust, neither granting it nor withholding it wholesale.

By the end of the course, you'll be able to ask, "Where does my task sit on the continuum for each property of generative AI? Is this well-trodden

generative AI? Is this well-trodden territory, or am I out near an edge?

What are the stakes if I'm wrong?" With

this model, the behavior of generative AI starts feeling predictable, and that puts you in control.

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