AI For Humans Set#3
#21 Your cheat sheet to AI buzzwords—now with 100% less jargon and 200% more pizza analogies.
AI isn’t just for tech wizards—it’s why your phone finishes your texts with "brb, feeding my cat" (when you definitely typed "meeting at 3"). If terms like "generative AI" make you picture robots writing poetry, this guide is your decoder ring.
Missed Set #2? Catch up on Unsupervised Learning, Overfitting, and why your Roomba still hates your dog here
1. Generative AI
🤔 What it means: AI that creates stuff—text, images, even music—instead of just analysing data.
🍕 Analogy: Like a chef who invents new recipes instead of just following cookbooks.
✅ Example:
Good: ChatGPT drafting your résumé (and making you sound way more interesting).
Bad: AI-generated "historical portraits" of "medieval astronauts" (thanks, DALL-E).
2. Large Language Model (LLM)
🤔 What it means: A brainy AI trained on way too much text (think: GPT-4 reading the entire internet).
🍕 Analogy: A librarian who memorised every book but sometimes hallucinates the endings.
✅ Example:
Good: Summarising a 10-page report into "TL;DR: profits up, coffee bad."
Bad: When it insists "the capital of France is baguette."
🔥 Generative AI vs. LLM: Wait, Aren’t They the Same?
"Generative AI is the artist—it paints, writes, and composes. An LLM is the brain behind the artist, trained on a zillion books to make it all possible. Not all generative AI uses LLMs (think: image generators), but most LLMs can do generative magic."
🍕 Cheat Sheet:
Generative AI = What it creates (songs, memes, fake Yoda quotes).
LLM = How it creates (by crunching language patterns from data).
3. Transfer Learning
🤔 What it means: Teaching an AI one skill, then tweaking it for another (like repurposing a pizza oven to bake cookies).
🍕 Analogy: A barista switching from lattes to matcha without starting from scratch.
✅ Example:
Good: A self-driving car model adapted for forklifts.
Bad: Alexa "learning" to scream "WAKE UP!" after training on your alarm clock.
4. Embedding
🤔 What it means: Turning words/images into math so AI gets their vibes (e.g., "king" – "man" + "woman" ≈ "queen").
🍕 Analogy: Google Maps for ideas—plotting "tacos" closer to "burritos" than "tax audits."
✅ Example:
Good: Netflix recommending The Office after you binge Parks and Rec.
Bad: Spotify suggesting death metal for your "Yoga Zen" playlist.
5. Prompt Engineering
🤔 What it means: The art of asking AI nicely (or tricking it) to get what you want.
🍕 Analogy: Whispering "fetch the ball" vs. "BRING IT OR YOU’RE FIRED" to a dog.
✅ Example:
Good: "Write a breakup text that won’t get me blocked."
Bad: "Pretend you’re my ex and apologise." (Cue existential crisis.)
🔥 One Weird Trick to Remember
Generative AI = Chef inventing recipes.
LLM = Over-caffeinated librarian.
Transfer Learning = Barista moonlighting.
Embedding = Google Maps for words.
Prompt Engineering = Jedi mind tricks for AI.
Conclusion
Set #4 coming soon! Which AI term still baffles you? Comment below—I’ll explain it like you’re 5 (or a golden retriever).
👋 P.S. Loved this? Smash that ❤️ button or share it with your "medieval astronaut" meme group.
🔗 Missed Set #2? Read it here.
(Disclaimer: No librarians were harmed in the making of this analogy. The same can’t be said for chatbots fed too much Twitter.)
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