First, lets start in the year 2000. (recalling Conan O’Brien’s “In the year 2000” falsetto sung preludes). Not “a look into the future” since its now 2025, but we’re at my senior year in university where I’ve built my first neural network (don’t worry if you don’t know what that is yet). As early as my freshman year I had planned to minor in AI. Psychology classes taught me the biology and methods of learning, and computer science let me build the digital structures of tech that was… not quite ready. Below is my final project to prove it wasn’t AI-generated… because, well, that wasn’t really a thing yet.
Fast forward to November 2022. Like most of the planet, I’m having my first interaction with a Large Language Model (LLM) via a chat app called ChatGPT, jaw on the floor thinking:
“Oh my goodness. This. Is. <PG-rating omission>. Amazing.”
I am in wonderment of its flexibility and fluency with human language. But here’s the thing – I’d been down this road before…
Why Another AI Series? (Or: Please Don’t Tune Out Just Yet)
Look, I get it. It’s 2025 and “AI” has become overplayed, underexplained, and apparently the solution to everything from world hunger to your inability to find matching socks.
But here’s my pitch: Let’s try to make this stuff actually approachable. What if we could document the evolution of AI through a lens that doesn’t require a PhD in machine learning or a tolerance for mind-numbing jargon?
That’s where gaming (and later specifically boardgaming) comes in. Why games? Two reasons that make perfect sense if you think about it:
First, they’re constrained. The problems of a boardgame are well-defined and well-bounded. We’re not trying to optimize hydroponic yields based on gradient descent across 20 million IoT datapoints (yawn). We’re talking about Candyland. Hey, that’s approachable. (link to my github repo on some Candyland analysis)
Second, they’re collaborative. Just like the best technology, games bring people together to solve problems, compete, and occasionally flip the table when someone plays that one card that ruins everything.
So buckle up. We’re going on a journey through AI evolution, one game at a time. And by the end of this series, I hope you’ll understand why that little blinking cursor on your screen is actually performing feats of digital magic will inspire, provoke, and amaze.
The First Aha! Moment: The Text Adventure Game
Enter Zork
For those who missed this particular piece of gaming history, Zork was a text adventure game. I personally played in 1987 on my Tandy 1000SX. No graphics, no sound effects, just you, a computer, and pure imagination. You’d type commands like “go north” or “take sword” and the computer would respond with descriptions of what happened.
West of House
You are standing in an open field west of a white house, with a boarded front door.
There is a small mailbox here.
> open mailbox
Opening the small mailbox reveals a leaflet.
> read leaflet
“WELCOME TO ZORK!
ZORK is a game of adventure, danger, and low cunning…”
Now, what made Zork particularly brilliant wasn’t just the storytelling – it was how it handled your input. If you wrote a strict program like most of that era, it would be painfully literal:
IF input=”Walk North” THEN move north
But if someone typed “move north” it would choke. “Go north” would choke. “Head north” would choke. Those strict, strict if-then-else shackles that made computers feel like that pedantic friend who corrects your grammar at parties (right now my friends may be imagining me, which is unfair. Ok, partially fair).
Zork was different. It was generous with your input. Sorry, it was generous with your LANGUAGE. It seemed… well, it seemed smarter. It could understand that “n”, “north”, “go north”, and “walk north” all meant the same thing. It parsed your intent, not just your exact words.
Want to experience this magic for yourself? You can play a version Zork right in your browser. Go ahead. “Click link. Open Mailbox”. Or, Type “inventory” and feel that nostalgic thrill of text-based adventure.
This was my first taste of what felt like true artificial intelligence. Not because it was actually intelligent, but because it could interpret the messy, imperfect way humans communicate and respond appropriately. In a word: language.
The New Aha! Moment: When Everyone Met Their First LLM
Now, fast forward to November 30, 2022. OpenAI releases (unleashes?) ChatGPT like a digital bomb, and suddenly everyone’s having their own “Zork moment” – but amplified by about a thousand million gazillion.
Remember your first interaction with ChatGPT? That moment when you asked it something expecting a typical search-engine response, and instead got something that felt like… conversation? Like there was actually someone home behind that blinking cursor? You’re again standing before a white house, you opened the mailbox and read the leaflet. It says “Welcome to the Large Language Model (LLM)”. And the Matrix has you Neo.
For most people, it went something like this:
You: “Write me a story about my dog.”
ChatGPT: [Proceeds to write a genuinely good paragraph about your dog, complete with clever wordplay and emotional depth]
You: “Wait… what? How did you… can you make it rhyme?”
ChatGPT: [Rewrites the poem real time in stanzas and cohesion that makes freestyle rappers swoon]
You: “WHAT IS HAPPENING RIGHT NOW?!”
That’s the moment. That’s when the future smacked you in the face. Suddenly, you weren’t just getting search results – you were having a conversation with something that seemed to understand context, nuance, and even humor. And of course you zip through the stages of technology amazement-turned-pause. “What else can it do? How is it doing this? Terminator and Skynet Time? Wait, that answer was bonkers and wrong.”
But here’s the million-dollar question that probably crossed your mind: Is this just a really, really good pile of if-then-else statements, or is this something completely new?
The Great Reveal: It’s Not What You Think
What you’re experiencing with ChatGPT and other Large Language Models (LLMs) isn’t a massive decision tree of if-then-else statements. It’s something fundamentally different: a statistical machine that encodes the relationships between parts of language into pure mathematics.
Stay with me here, because this is where it gets interesting.
The Old Way: If-Then-Else Hell
Traditional programming is like being a very literal genie. You have to specify exactly what to do in every possible situation:
if user_input == “hello”:
print(“Hello! How can I help you?”)
elif user_input == “hi”:
print(“Hi there! What can I do for you?”)
elif user_input == “hey”:
print(“Hey! What’s up?”)
elif user_input == “greetings”:
print(“Greetings! How may I assist you?”)
else:
print(“I don’t understand that greeting.”)
You can see the problem. What about “Hello!”? Or “HELLO”? Or “Well hello there”? You’d need thousands of variations for just basic greetings. It’s like trying to catalog every possible way humans might say something. Spoiler alert: humans are creative, and we’ll find new ways to break your carefully constructed if-then-else castle.
The New Way: Statistical Relationships
LLMs work completely differently. Instead of storing rules, they store relationships. Think of it like this: imagine you had a massive web of connections between every concept, word, and idea that humans have written digitally about. Each connection has a weight – like those red strings on a conspiracy theorist’s wall, but with math.
(Quick technical note: I’m simplifying here for clarity. What’s really happening involves neural networks, transformers (no, not those, Megatron), and attention mechanisms that are way more complex than “weighted connections,” but the core idea – statistical relationships rather than explicit rules – holds true.)
When you type “hello,” the LLM doesn’t look up a rule about greetings. Instead, it thinks: “Based on all the human conversations I’ve seen, when someone says ‘hello,’ what typically comes next?” And then it generates a response based on those statistical relationships.
It’s like having read lots and lots of digital examples of conversations (known as the training data) and being able to predict what a reasonable human response would be. Not because it’s following a script, but because it’s learned the patterns of human communication.
Below shows my project from 2000 where the neural network is modifying its weights during the training process on the data to learn what the right output should be. It reduces the error of its predictions through a series of iterations called epochs. (Thanks to the grader for pointing my Error in red. Such and observation has helped!)
The Magic of Tokens
Here’s where it gets really wild. LLMs don’t even work with whole words – they work with tokens. Think of tokens as the meaningful chunks of language. “Hello” might be one token, “world” might be another, but “ing” might be its own token that often gets attached to action words.
(Another simplification: tokenization is actually more complex and language-dependent than this suggests. But the key insight – that LLMs work with sub-word pieces rather than whole words)
This is like learning language the way kids do – not by memorizing every possible sentence, but by understanding the building blocks and patterns that make language work.
The Storage Miracle
So, does this mathematical wizardry just run in “the cloud” in warehouse sized data centers? No, in fact. You can run some of these models locally on your laptop (with the right caveats). In just 3GB, you get access to an obscene amount of knowledge and cleverness.
“I have no idea what 3GB is, big nerd.”
Well, my friendly neighborhood camera owner, your high-res photos that you snap off with reckless abandon are about 12MB each. That means 3GB is roughly 250 photos. Not a lot – you might snap those off in a month (or a week, you influencer you). Raw images from your fancy camera? That’s about 83 photos worth of space.
In that tiny storage space, you have compressed the relationships between millions of concepts, ideas, and patterns of human communication. It’s like having a library of Alexandria that fits in your pocket and can have a conversation with you. Even WITHOUT the internet. Warning: Sometimes when it reaches into its web, it withdraws incorrect information that it was never trained on. This, is called hallucinating.
(Technical caveat: Those 3GB models like Llama 3.2 are heavily compressed and optimized versions of much larger models. The full-size models that these are based on can be hundreds of gigabytes. Think of it like having a really good highlights reel versus the full game footage – you get the essence, but some nuance is lost in compression.)
From Zork to ChatGPT: The Evolution
Now you can see the progression:
Zork (1980s): Smart parsing of user input through clever programming ChatGPT (2020s): Statistical understanding of human communication patterns
Both feel like magic, but they’re different kinds of magic. Zork was a really clever magician with a great set of tricks. ChatGPT is like having a conversation with someone who has read a massive sampling of language and can improvise responses based on that vast understanding.
Try It Yourself: The Game Master Experience
Want to experience this evolution firsthand? Here’s your homework (its way more fun than writing a fledgling neural network in C++, trust me):
First, play with the old magic: Go try Zork for a few minutes. Notice how it responds to your commands. Feel that sense of “wow, it understood me” even though you know it’s just clever programming.
Then, experience the new magic: Open ChatGPT and try this prompt:
“I want you to play the role of a game master, putting me in a world on my journey through the mysteries of AI. We are at the first encounter: the Large Language Model. My adventure starts here.”
Watch what happens. Notice how it doesn’t just parse your command – it creates an entirely new experience on the fly, complete with narrative, choices, and responses that feel genuinely creative.
That’s the difference between clever programming and statistical intelligence. Both are impressive, but one is following a script while the other is improvising based on patterns learned from millions of human conversations.
What’s Next in Our Journey?
This is just Level 1 in the series (I hope). We’ve established our foundation: Large Language Models and how they represent a fundamental shift from rule-based programming to statistical pattern recognition.
But here’s where things get really interesting. In our next posts, we’ll explore:
- Level 2: When LLMs gained eyes and could see the world (multimodal magic)
- Level 3: When they learned to reason and think through problems step by step
- Level 4: When they became agents that could actually do things in the real world
Each level builds on the last, like unlocking new abilities in a well-designed game. By the end of this series, you’ll understand not just what AI can do, but why it works the way it does – and maybe more importantly, why it sometimes doesn’t work the way we expect.
The future is here, and it’s interactive. Let’s play.
Want to Go Deeper?
If you’re ready to dive beyond the accessible analogies and get into the technical weeds, here are some excellent resources that explain how this magic actually works:
Understanding Transformers & Attention:
- The Illustrated Transformer – Jay Alammar’s brilliant visual explanation
- Attention Is All You Need – The original transformer paper (warning: heavy math ahead)
LLM Training Deep Dives:
- The Illustrated GPT-2 – How GPT models actually work under the hood
- Andrej Karpathy’s “Let’s Build GPT” – Code-along tutorial building a GPT from scratch
Token Magic Explained:
- Hugging Face Tokenizers – Interactive demos of how tokenization works
- OpenAI’s GPT Tokenizer – Play with actual GPT tokenization
These resources will take you from “neat magic trick” to “I understand the mathematical foundations.” Fair warning: once you see how the sausage is made, you can’t unsee it!
Not Recommended:
Review my old class notes on some of the math of neural networks below. If only you, or I, could read my awful handwriting. I guess there are some challenges even AI can’t surmount.
Want to dive deeper into any of these concepts? Have questions about the technical details? Drop them in the comments below. And if you try that ChatGPT game master prompt, let me know what kind of AI adventure it creates for you!Tags: #technoPHILE #AI #LLM #Gaming #Innovation
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