SiMPL #009 From Curiosity to Collective Knowledge: Rediscovering the Power of Questions

Is it just me, or does the word “learning” send you back to your school days? Sure, it’s about formal education, but it’s also so much more. Humans evolved to always be on the lookout for patterns, constantly learning from them, and turning that knowledge into something useful.

Take walking and talking, two things we all take for granted. At birth, we don’t know how to do either. Walking comes from a mix of strength, motor skills, and some bruised knees along the way. Talking? That’s even harder—you start by mimicking sounds, and over time you learn that “ma” means mom. Eventually, you piece sounds together, and a whole world of language opens up.

And then we learn to ask questions. Asking questions is like cracking a code—it’s the fastest way to learn, understand, and grow. Ever notice how some kids can ask a million questions? I think a lot of us stop asking as much as we grow up, maybe because we’re told not to. My uncle just admitted over drinks that he sometimes got tired of all my questions growing up! (So, yeah, thanks, Mom and Dad, for putting up with that.)

When I lead Continuous Improvement teams, I tell them to ask questions like a five-year-old. You don’t need AI or some advanced tool to understand why something is done a certain way; you just need curiosity. Want to improve a process? Ask questions. Want to be a better leader? Ask questions.

This whole process of asking, learning, and adjusting is a key to building resilience.

The Art of Conversation

Once we master the art of asking, we have to learn how to answer. Growing up as a Latino, I learned quickly that you don’t raise your voice to your parents (fellow Latinos, you know what I mean!). This exchange of asking and answering shapes how we connect with others—it’s the essence of conversation.

And with conversation comes deeper connections. Conversations generally fall into three types:

1. Practical – What’s this about?

2. Emotional – How do we feel?

3. Social – Who are we?

If you’re in a discussion where each person has a different conversation type in mind, things can get confusing fast. This is where “matching conversations” comes in—a concept I love from Charles Duhigg’s TED Talk. He explains how shifting from a surface-level “How’s life?” to a more meaningful “How do you feel about life?” can open the door to deeper understanding. He calls this twist a “Deep Question,” and it’s powerful stuff. (Here’s the video link—highly recommend it.)

A Story About Curiosity and Bread

When I met my wife, it wasn’t just a surprise relationship; I gained something just as life-changing—I learned how to make bread. This lesson came from a simple question: “How do you make cachitos?” (a sort of Venezuelan ham-filled bread). Her father invited me over one Saturday to teach me, and what started as curiosity turned into a whole world of sourdough starters and oven burns. Last Friday, I baked a loaf that actually looked halfway decent!

From that one question, I went down a rabbit hole that took me to Google, where I discovered sourdough. Eight years later, I have Djamal, my sourdough starter, and an evolving passion for bread. All because I asked a question.

The Evolution of Learning: From Curiosity to Collective Knowledge

The internet, Siri, Alexa—they’re all about answering questions. Every time you ask Siri to play War Pigs and it plays Baby One More Time, it’s a learning moment (although maybe not the one you wanted). If you provide feedback, and say No, Hey siri play War Pigs by Black Sabbath, it may correct the error, that’s refining a query. It’s Machine Learning—like human learning but on a scale that can crunch unimaginable amounts of data.

Machine Learning also pulls from what I call “collective knowledge.” Just like our shared history with bread-making, which has evolved over millennia, AI gathers data, interprets it, refines it, and creates outputs—whether that’s a bread recipe or a targeted ad. It’s taking all those small pieces and putting them together into something useful.

Human Learning vs. Machine Learning: The Long Journey

In both humans and AI, learning follows two main paths: experience (trial and error) or education (learning from someone else’s experience). Early humans trying to bake bread? They were all about experience. Imagine someone 30,000 years ago trying to figure out which roots were edible and which weren’t—probably not a pleasant process, and definitely a few failed attempts along the way. Over thousands of years, humanity discovered grains like wheat and rye, eventually refining techniques to get that perfect loaf. Today, we know bread-making so well because we’re building on the trial and error of countless generations who figured out the right ingredients, techniques, and even what temperatures work best.

AI, on the other hand, can pull from the internet’s treasure trove of data, giving it a shortcut to what I like to think of as our digital version of humanity’s collective knowledge. AI can “learn” in minutes what took humans a lifetime of experimentation. And this isn’t just limited to bread—AI synthesizes knowledge across countless domains, drawing on billions of data points to improve its responses or predictions.

So, here’s the kicker: what took early humans thousands of years to master and even my own hours of trial and error to learn from my father-in-law, ChatGPT can now deliver in seconds. Want a bread recipe? Just type it in, and bam—you’ve got a tried-and-true recipe without the blood, sweat, and tears (okay, maybe just the flour dust).

It’s the ultimate shortcut—AI putting our collective knowledge to work, from the lessons in ancient bread-making to your next prompt for a perfect sourdough.

Curiosity and Learning in the Modern World

Curiosity isn’t limited to bread or baking—it’s woven into every part of our modern world, from Google searches to election strategies. Today, the curiosity of AI is driven by collective knowledge, a blend of human experience and behavioral data on a massive scale.

Think about it: every time you search, click, or linger a little too long on a post, that’s a data point—one small piece of this larger knowledge pool. And it’s not just about topics; it’s about us—our habits, preferences, and the little patterns we might not even notice in ourselves. That’s why you sometimes get that unnervingly accurate Alexa notification: “One new notification, for Joel, it may be time to reorder vitamins. Want to add them to your cart?” Familiar? It’s not magic; it’s machine learning with a hefty dose of collective data.

And this isn’t limited to products. Remember Cambridge Analytica in the 2016 U.S. election? By gathering what voters cared about, they were able to create targeted ads that influenced real-life decisions. If that sounds like sci-fi, here’s The Guardian article that dives into the full story.

Just like our shared history with bread-making—where countless people learned what worked and what didn’t—AI uses the accumulated knowledge of human actions, preferences, and yes, behaviors, to learn and improve. But that also means it can make some hilarious (or serious) mistakes.

For example, when Gen AI misinterprets sarcasm on a post? It can lead to some funny fails—check out this list of “17 Funny Ways Gen AI Got It Wrong!” for a laugh.

While AI can feel like a super-smart assistant that understands you, it’s still far from perfect. Sometimes, the data isn’t quite right, or AI just doesn’t “get” us the way we think it should. And that’s why we’re still in a world where human supervision is essential, ensuring AI learns in the right direction while we continue to train it.

The Good Work: Book Recommendation!

Let me ask you a deep question: How do you feel when you work? Did your mind jump to your job? Take a step back—let’s think a little bigger.

In this thought-provoking piece, Paul Millerd—yes, the same author of The Pathless Path that I reviewed recently—shares his journey from what looked like the “perfect” career to making a bold choice: quitting his job to explore what truly made him feel alive.

Why this book today? Because it’s about asking the deep questions. Throughout, Paul challenges readers to reflect and reexamine, daring us to rethink what’s truly important.

I’ll be honest—this book reminded me of all the times I’ve “left money on the table” to savor those little things, like morning coffee with my wife. It’s not a self-help book; it’s a call to question the status quo and aim for what you genuinely want. Sometimes, that means making the most ambitious choice of all.

Reading The Good Work brought me back to what I love, what makes me happy, and what I’m still searching for in my own good work. While I’m not quite there yet, I’ve got a clearer idea of where I’m headed. So, if you’re ready for some powerful questions, this book might just be your next read.