Free preview · K-2 · Smart Machines Around Us

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Unit 1

Smart Machines Around Us

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Smart and Not-So-Smart

Big idea: Some machines just do one thing the same way every time. Other machines seem to notice things and decide — we call those smart machines.

A light switch is a machine, but it isn't smart: you flip it up, the light turns on. Every time. It never decides anything.

Now think about a tablet that knows your face, or a speaker that answers when you talk, or a car that beeps when something is behind it. Those machines notice something about the world and choose what to do. That noticing-and-choosing is the start of artificial intelligence — machines that act a little bit clever.

Look around your day. Which machines just do the same thing every time? Which ones seem to notice and decide? Smart machines are everywhere once you start looking.

Essential question: What makes a machine seem smart?

what is a machineeveryday AIobservation

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Unit 2

Patterns Everywhere

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Patterns Everywhere

Big idea: A pattern is something that repeats so you can guess what comes next. Smart machines are very, very good at spotting patterns.

Clap with me: clap–clap–stomp, clap–clap–stomp, clap–clap… what comes next? You knew it was stomp because you found the pattern.

Patterns are everywhere: red–blue–red–blue beads, day–night–day–night, your morning routine. When you guess what comes next, you are doing the same job a smart machine does. The machine looks at lots and lots of examples, finds the pattern, and predicts what comes next.

That's why a tablet can finish your word before you do, or a music app can guess a song you might like. It found a pattern in what came before.

Essential question: How does finding a pattern help us guess what comes next?

patternspredictionsequences

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Unit 3

Teach the Machine

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Teach the Machine

Big idea: We teach a machine by showing it examples. The more good examples it sees, the better it guesses.

A brand-new machine knows nothing. If we want it to tell a cat from a dog, we show it pictures: "this is a cat… this is a cat… this is a dog…" After enough examples, we show it a brand-new picture and ask: cat or dog?

The machine isn't memorizing — it's finding the rule hidden in the examples (pointy ears? whiskers? a wagging tail?). That's the same skill you practiced spotting patterns.

But here's the catch: the machine can only learn from what we show it. If every cat we showed was tiny and in a corner, the machine might think "cats are always tiny" — and guess wrong on a big cat. Good examples make a good machine. Bad examples make a confused one.

Essential question: How does a machine learn something new?

supervised learningexamplesdata quality

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Unit 4

Being Fair and Safe with Smart Machines

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Being Fair and Safe with Smart Machines

Big idea: Smart machines are tools. Like any tool, we use them in ways that are fair and safe — and we always ask a grown-up when we're not sure.

We learned that a machine only knows what we show it (Unit 3). So if we show it unfair examples, it can make unfair guesses. That's why people work hard to give machines good, fair examples — so the machine treats everyone kindly.

We also keep ourselves safe. A smart machine is not a person and not your friend. We don't tell it private things like our address or password. If a machine ever asks for something that feels wrong, or shows something that feels icky, we stop and tell a grown-up.

And we are honest: if a machine helps us make something, we say so. Smart machines are helpers — we are the ones who decide what is right.

Essential question: How do we use smart machines in a way that is fair and safe?

fairnesssafetyprivacyask a grown-up

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