Speak to the Machine.
Artificial intelligence does not read English. It does not read Spanish, French, or Japanese. It reads mathematics. Before you can command a machine, you have to see how it perceives the world. This class dismantles the myth of the "thinking machine" and rebuilds it as what it really is: a fast, limited, statistical calculator — and shows you exactly how to work with one.
Explain prediction
Tell the difference between human reading and AI next-word prediction.
Define a token
Say what a "token" is, and why slang shatters into many of them.
Split training vs inference
Know what the AI "knows" versus what it merely calculates.
Quick self-check
Before the mechanics, get a feel for the scale of artificial memory. How many words did the latest AI model have to "read" before it could converse fluently? Drag the fader, then check your guess.
How AI actually reads
It is tempting to imagine a tiny, hyper-intelligent being inside the server, reading your question, thinking, and typing back. That is not what happens.
Imagine a vast library of billions of books, run by a head librarian who speaks no human language at all. This librarian is a master statistician. Handed the phrase "The sky is," they don't look out a window — they calculate probability. Having read trillions of sentences, they know "blue" follows "The sky is" about 92% of the time, while "falling" follows it 4% of the time. So they answer "blue." The machine does not know what a sky is. It only knows the mathematical probability of words appearing near one another.
If the machine only knows the probability of words from what was written in the past, what happens when it's asked to predict the future — or to describe a marginalised culture historically excluded from published books?
AI models are not thinking entities. They are probability engines guessing the most likely next piece of data based on historical patterns.
Raw data → patterns
Picture a mechanical blender. Raw data goes in at the top — newspaper clippings, wikis, handwritten zine pages. The algorithm extracts patterns in the middle, discarding the actual words and keeping only how often they appear together. Probability weights emerge from the spout at the bottom.
Raw data goes in
Books, articles, websites, zines — trillions of words of human text poured into the machine.
Patterns extracted
The algorithm throws away the words themselves and keeps only the statistics of how they co-occur.
Probability weights emerge
What's left is a giant map of likelihoods — the model's entire "knowledge."
Diagram, in words: raw human text is fed into an algorithm; the algorithm breaks it down, discards the words, and saves only the mathematical patterns of how often words appear together. That saved pattern is called a probability weight.
The Token Transformer
Computers can't process letters — they process numbers. To read your prompt, the AI slices your words into chunks called tokens and gives each chunk an ID number. Type a complex word or a piece of slang below and watch the machine chop it apart. Hover a token to see its ID.
A single word fractured into multiple numbers. Those are tokens — exactly what the AI sees. Standard English words are often one token; unfamiliar slang gets shattered into many, because the model has to fall back on tiny letter-chunks it recognises.
Training vs inference
To command AI and protect your data, you must separate two phases of a model's life: training and inference. Blurring them causes most beginner misunderstandings.
Training — building the archive
Like a community archivist compiling a history of local queer nightlife from thousands of flyers and zines, the model aggregates billions of documents over months, on thousands of chips, adjusting its probability map. Then the library is built — and frozen.
Inference — consulting the archive
When you ask a question, the model is not learning from the live internet. It references the probability map it built months ago to infer the next word. It calculates; it does not learn.
A dangerous myth: that typing a private document into a prompt instantly adds it to a global database. During standard inference the model doesn't permanently learn new facts — it holds your conversation in short-term memory until you close the tab. Caveat: the company that owns the platform may log your chats to use in a future training run. That's why opt-out toggles and privacy policies matter for anyone protecting sensitive community data.
Training is studying for the exam. Inference is taking the exam. When you prompt an AI, you're talking to the inference phase: it isn't learning, it's calculating.
Elias sorts the inbox
Elias is an independent zine creator running a busy online store, drowning in hundreds of emails a week. He wants AI to categorise them by urgency.
The emotional prompt
Elias asked: "Can you please be a good assistant and figure out my emails for me?" The AI hallucinated and scrambled the categories — the request was too vague and emotional.
The structured prompt
Remembering AI is a statistical engine, Elias fed it predictable structure:
Rule 1: if the email contains "broken" or "missing" → label URGENT.
Rule 2: if it contains "collab" or "art" → label LOW PRIORITY.
Then he pasted the raw emails below the rules.
Treating AI as a probability engine, not a human assistant, stops you sending vague emotional requests that confuse tokenization. Structure is the only language the machine respects. And Elias first strips any sensitive data (card numbers, addresses) before pasting, since corporate platforms may log the interaction.
Map the frontier
Models are measured in parameters — the mathematical connections formed during training. New ones drop weekly, so any number printed here would be stale fast. Open a search tab and investigate, then log what you find below (saved on your device only).
What to look up
"latest open-source LLM parameter count" · "Llama parameter sizes" · "GPT estimated parameters". Find one small model built to run on a phone, and one massive model built for a data centre.
Breaking the tokenizer
Algorithms struggle with words that were rare in their training data. By watching how a model breaks down words, you can spot its cultural blind spots.
Do this
1. Write a two-sentence paragraph using highly specific community slang, queer vernacular, or niche hobby terms.
2. Open an external tokenizer (e.g. the free OpenAI Tokenizer page) or use the sandbox above.
3. Paste a formal English sentence; note the token count.
4. Paste your slang paragraph; compare the count.
5. Write 2–3 sentences comparing the word-to-token ratio of standard English vs your slang.
Success rubric — tap to expand
Needs work: uses standard English for both tests, getting identical ratios and missing the point.
Getting there: uses moderate slang, notes a slight increase, but doesn't connect it to training data.
Solid: uses complex slang and correctly notes unknown lexicon is chopped into many pieces.
Excellent: notes the fragmentation and argues it proves the training data skewed toward mainstream, formalised text rather than diverse community dialects.
Quiz — 5 questions
Common mistakes
The sentience fallacy
The AI says "I feel" / "I think", tricking your empathy. Fix: remind yourself it's an advanced autocomplete calculator — no intent, no feelings, no memory of you beyond this chat.
The truth fallacy
Treating AI like a search bar and trusting its confident, well-formatted answers. Fix: it predicts plausible-sounding words; it doesn't verify facts. Always check critical facts elsewhere.
The one-word-one-token myth
Counting words instead of tokens, then overflowing the model's memory. Fix: rule of thumb — 100 tokens ≈ 75 English words; far more for slang or technical text.
The emotionally vague prompt
"Make this sound better." "Better" is subjective. Fix: give structure — "Rewrite this: cut 20% of the words, use active voice, professional and urgent tone."
Whose voices are missing?
If an AI can only predict language from the documents it was fed in training, whose voices, histories and cultural contexts might be entirely missing from its internal library?
Optional prompts: How does over-representation of Western, English-language data shape the "personality" of global AI? If you built a dataset for your own community, which zines, archives or voices would you insist on including? Does the phrase "artificial intelligence" grant the technology unearned authority?
You've shattered the illusion of the "thinking machine" — you can see the numbers behind the curtain. Next, we use this to pick the exact right model for your projects.