What Is Artificial Intelligence?

When I was in grad school, I used to think artificial intelligence meant robots. Not metaphorical robots, the literal kind. Machines with arms, blinking lights, a monotone voice that said things like, “Affirmative, human.” I blame too many late-night reruns of 2001: A Space Odyssey and Blade Runner.

I thought AI was some unreachable science reserved for genius-level PhDs locked in Silicon Valley research labs. And frankly, for a long time, I avoided it.

It wasn’t until years into my teaching career, helping students debug their Python projects, building curriculum for data science classes, working with researchers in education tech, that I realized something shocking: most real-world AI is remarkably… ordinary. And useful.

It doesn't take over the world. It doesn’t even try. It just helps.

It summarizes emails, recommends playlists, drafts headlines, schedules meetings, predicts spam. It’s not magic, it’s a mirror of the data it sees. And learning how to use it has quietly become a life skill, one that I wish more people felt empowered to explore.

So, in this lesson, I’m not going to throw equations at you. I’m not going to ask you to train a model or write a line of code (not yet, anyway). What I am going to do is walk you through what AI really is, where it came from, what it can (and can’t) do, and most importantly, how it might show up in your life starting this week.

Defining AI, minus the buzzwords

Let me start with a definition I use with my first-year students:

Artificial Intelligence is the science of making machines perform tasks that normally require human intelligence.

That’s it. That’s the whole thing.

If a machine can understand language, make decisions, solve problems, or learn from experience, we call it AI. It doesn’t mean it’s smart. It means it’s trained.

I remember explaining this to a team of HR managers at a workshop. One of them asked, “So… is Grammarly AI?” Yes, I replied. And so is your Google Maps route. And your Netflix recommendations. None of these systems are conscious, but all of them are doing a version of what a human might do with enough time and data.

Let me give you some categories to anchor this:

In reality, everything you and I interact with today falls under Narrow AI. And you don’t need to understand calculus or neural networks to benefit from it, you just need to understand the interface.

A brief history of how we got here

In 1956, a group of scientists gathered at Dartmouth College to talk about the idea of teaching machines to "think." That meeting coined the term artificial intelligence. Back then, the goal was simple: get computers to mimic human reasoning.

Now, I wasn’t around in the '50s (though some days it feels like I’ve been in classrooms that long), but I was around when things started getting interesting in the early 2000s.

Here’s a timeline I use in my AI workshops:

I still remember seeing the ImageNet breakthrough in 2012. A neural network learned to identify objects in images, cats, dogs, cars, with an accuracy that beat all previous systems. That single event launched the modern wave of deep learning, the same technology that powers the tools you’re hearing about today.

But what fascinates me more than the algorithms is the access. We’ve moved from labs to laptops. From PhDs to PMs. Today, you can type a sentence into a chatbot and get a draft response in seconds. You don’t need to build the machine, you just need to learn how to talk to it.

What AI actually looks like

Let me share a story from a learner I mentored, Karen, a nonprofit program coordinator.

Karen didn’t consider herself tech savvy. But her work involved writing grant applications, coordinating community events, and emailing dozens of partners weekly. She came to me asking if she needed to “learn AI.” I said, “You already are, you just haven’t realized it yet.”

She used Grammarly (NLP-based AI) to check her writing. She used Otter.ai to transcribe board meetings. She even asked Google Sheets to autofill quarterly data predictions. That’s AI, practical, accessible, invisible in the best way.

Here are some more everyday AI tools:

None of these tools required Karen to “know AI.” They only required her to recognize where she was stuck, and ask, “Could a machine help me with this?”

That mindset shift is more powerful than any tutorial.

How AI actually works

Let’s strip away the jargon and talk about how AI gets smart.

At its core, AI learns patterns from data. It doesn’t understand, it remembers.

Here’s the loop:

1. You give it data. Lots of it. Pictures, words, numbers, behavior.
2. It finds patterns. This is the “training” process.
3. You give it new input.
4. It predicts an output based on what it’s seen before.

That’s it. It’s basically a glorified autocomplete on steroids.

When I explain this to younger students, I compare it to baking. Data is the ingredient list. The model is the oven. The training is the recipe you follow over and over until it’s perfected. Inference is pulling out a fresh loaf, using what you learned before.

What AI can do… and what it definitely can’t

If I could shout one message from the rooftops of the internet, it would be this:

AI is not intelligent the way humans are.

It can generate content, yes. But it can also hallucinate facts, miss context, or give wildly inaccurate advice.

I once fed a chatbot some code from a student assignment and asked for feedback. It returned a polished explanation, but suggested a fix that would break the logic entirely. Helpful? Yes. Trustworthy? Only if you know how to spot the mistake.

That’s why human oversight is non-negotiable. You are the final editor. The conductor. The storyteller. AI is the assistant.

Conclusion

If there’s one thing I hope you take away from this lesson, it’s that you don’t need to be “technical” to learn AI. You just need to be curious.

In fact, your strength might be your background. Writers, designers, analysts, teachers, you know what good output looks like. AI can’t create purpose or strategy. That’s your job.

As we move into this new age of automation, I want to remind you:
The future isn’t AI-powered, it’s human-powered, with AI as a tool.

In the next lesson, we’ll explore how to actually start using AI tools to boost your productivity, and you’ll see how a well-formed prompt can often be more useful than any line of code.