(This is part 1 of a 5-part series of posts attempting to explain the AI landscape in a practical and simple way. See other posts here.)
Since early last year, I’ve been spending a couple of hours a week reading everything I can and talking to anyone I can about AI.
NGL, most of the time it sucks and I’d rather be reading just about anything else. But since I’ve adopted the “hard choices, easy life; easy choices, hard life” mantra of Jerzy Gregorek, I thought I’d get a jump on going through the pain of understanding what implications AI will have on our careers and lives in general. Some of it can be tough to wrap your head around.
One glaring insight I’ve found: there is very little fundamental, accurate human understanding out there. The people building AI are generally awful at explaining it, and the rest of us see it as a sci-fi-ish inevitability we wouldn’t be able to make sense of. Given my skill as a storyteller and my curiosity about the human application of emerging tech, I may be uniquely positioned to be of service here.
So, I’m going to write a series of posts and share some anecdotes on exactly what the hell is happening with generative AI and what it means to us, hopefully in a way that a third-grader can understand.
I’ll get into a lot more depth in the coming weeks, but here’s a framework for consideration to start with:
- Artificial Intelligence is just another technology. Approach learning about and evaluating it in the same way. Sure, it’s changing things faster than any technology before, but that has always been true of emerging tech (thanks to Moore’s law). For me, thinking of it as just another technology makes it less intimidating.
- Think of the overall AI landscape like human evolution, progressing up a hierarchy:
- Artificial Intelligence (AI) as humanity itself.
- Machine Learning (ML) as a lone ranger who knows good spots for hunting through some combination of advice from his Daddy, tips from other wanderers, and trial & error. He usually has something to eat, but he’s lonely, has to do everything himself, and still occasionally goes hungry.
- Deep Learning (DL) as when a group of former lone rangers come together and begin to specialize, sharing information and resources (while also continuing to learn). Some are best with the bow & arrow, and some work to develop even better hunting tools or train hunting dogs. Others develop expertise on animal migrations based on weather patterns or other cues. Together, they can predict where to find dinner more accurately and hunt more efficiently, so the whole community eats daily.
- Generative AI as the innovation made possible once that group’s systems are consistent enough to ensure they always have full bellies. Now, they can focus on other creative endeavors, like inventing barbecue sauce to make that venison more tasty, inventing a new dance, writing a poem, making maps or charts or accounting systems, inventing and mastering a sport, or anything else they can dream up.
- The quality of those creative outputs (Gen AI) is dependent on the quality and consistency of the communal system (DL) and the individuals’ ability to keep learning (ML), and all layers improve with one another.
- This is not a perfect analogy. It does not work like-for-like with every nuance of the AI ecosystem, but it helps with practical understanding.
Hopefully, this can help us move past questions like “What do you think about AI?” which I still hear and is essentially like asking, “What do you think about people (or whales, or monkeys)?” Value judgments aside, AI is part of our reality, and our mental energy is better spent ensuring that those limitless creative outputs are more often than not channeled in ways that assist, rather than harm, our built & lived reality.
What I write about will focus primarily on the outputs & how they impact us–so we’ll be playing in the Gen AI space and letting the developer crowd figure out the other layers. For anyone curious to join me as I explore this, please reach out. I’m not the most tech-savvy person, but since this is all so new, it’s worth keeping in mind that curiosity alone already puts you ahead of the curve.
Next post I’ll get into the first steps you should consider taking for your business.