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There’s a moment in this episode — somewhere around the time Gavin compares AI to predicting the color red in a pattern of blue-blue-blue-red — that I realized just how wrong we’ve been getting this whole AI conversation. Understanding AI for L&D is not only about helping our people use AI. So let’s break down the basics so that we know all about Machine Learning, Generative AI, and Hallucinations

Because most people professionals, let’s be honest, don’t want to get into the weeds of neural networks and probability matrices. But they do want to know:
“Can I trust this thing?”
“Will it replace people?”
“Is it just parroting the internet or actually ‘thinking’?”

And more than anything — they want someone to explain it to them in a way that doesn’t make them feel stupid.

So that’s what we set out to do in Episode 2 of the series. We tackled the language of AI — and the anxiety that often comes with it — by starting where every good learning experience starts: with curiosity and a safe space to ask “dumb” questions (which, spoiler: none of them are dumb).

So, What Is Machine Learning — Really?

Gavin, who leads AI strategy conversations with Fortune 50 clients at AWS, broke it down like this:

“If I asked you to predict what color comes next in a pattern of blue-blue-blue-red, and you guessed red… congratulations, you just used machine learning.”

At its core, machine learning (ML) is about finding patterns in enormous amounts of data to make predictions or categorizations. Think of how Netflix recommends your next binge, how your bank flags suspicious charges, or how Google Maps reroutes you around traffic. Those aren’t just programmed features, they’re the output of models trained on millions of data points.

Researchers at MIT define machine learning as “the process by which computer systems learn from experience, often with minimal human intervention.”

It’s not magic. It’s statistics on steroids.

But, and this is the kicker, traditional machine learning systems are only as good as the patterns they’ve seen before. They don’t do well with new or unusual data. If they’ve never seen it, they’ll either ignore it or get it wrong.

Enter: Generative AI (And Why It Feels So Human)

This is where the AI story takes a sharp turn — and where most of us start to feel that creeping sense of both wonder and panic.

Generative AI — the engine behind ChatGPT, DALL·E, Claude, Gemini and others — goes beyond recognizing patterns. It uses them to create new things. Text, images, ideas, answers. And it feels weirdly… human.

“If machine learning sees ‘the sun is…’ and picks the most common next word — yellow, for instance — generative AI goes a step further. If you say, ‘I was sweating in my car because the sun is…’, it doesn’t say yellow anymore. It says hot, or unbearable, or something else that fits the story.”

That’s because generative AI is trained on language models so massive they make the old-school ML systems look like a flip phone. These models don’t just know facts — they understand context. That’s why a tool like ChatGPT can write you a bedtime story in the voice of a pirate, or summarize a policy doc in plain English, or mimic your company’s tone in a job description.

As of early 2024, OpenAI’s GPT-4 and Anthropic’s Claude 3 are among the most powerful large language models (LLMs) in use, trained on trillions of tokens (fragments of words) pulled from the open internet, academic papers, books, and more. (2)

But here’s the thing: even they don’t know what they “know.” They just predict what words statistically make the most sense next.

Which brings us to… hallucinations.

Wait… Did My AI Just Make That Up?

Yes. Yes, it did.

Welcome to the world of AI hallucinations, where your friendly chatbot confidently tells you something that sounds correct but is completely fabricated.

“It’s not trying to lie,” Gavin explained. “It’s just doing its job: generating words. If it runs out of solid data, it’ll still try to complete the sentence — just with… improv.”

This happens more often than people think. In fact, research by Stanford and UC Berkeley in 2023 found that language models can hallucinate at rates ranging from 3% to 27%, depending on the prompt and domain. (3)

The danger? It sounds so good you believe it.

And for people leaders using AI to summarize policies, write onboarding materials, or build training — this is where ethical use gets very real, very fast. If you don’t fact-check AI outputs, you risk spreading false information with absolute confidence.

So Should L&D and HR Even Use This Stuff?

Absolutely. But with intention.

AI is a phenomenal starting point — a thinking partner, a springboard, a research assistant. But it’s not a replacement for your judgment, your voice, or your human empathy.

It can write a draft. But only you can sense whether the tone is too cold.
It can suggest questions. But only you know what makes sense for your team.
It can generate learning content. But only you can make it resonate with real people.

“You can’t treat it like a super-intelligent colleague,” Gavin said. “It doesn’t know what it’s saying. It just knows what usually comes next.”

The key for People professionals is not just using AI, but learning how to guide it — with smart prompts, clear parameters, and a critical eye. This is where the skill of prompt engineering comes in — and we dig into that more in Episode 3.

Where to Go From Here

If you’re in HR or L&D, and you’ve been quietly avoiding AI because it feels too technical or too risky — you’re not alone. But that’s not a reason to sit this one out.

As Gavin said in this episode:

“Anyone can access this now. It’s not just for the Googles of the world anymore. The difference between people who thrive in this shift and those who don’t will come down to curiosity — and a willingness to learn what you don’t know.”

So start experimenting. Use AI to rewrite a boring email. Ask it to summarize a meeting. Try giving it a prompt and then asking, “Now poke holes in this.”

The more you use it — with care — the better you’ll get. And more importantly: the more human your work will become. Because when AI takes over the repetitive stuff, what’s left is all the nuance, coaching, leadership, and creativity that no machine can replicate.

Not yet, anyway.

Sources:

1. MIT Technology Review, “What is Machine Learning?”
   
2. OpenAI, GPT-4 Technical Report, March 2023
   
3. Maynez et al., “On Faithfulness and Factuality in Abstractive Summarization,” Stanford NLP

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