Machines that write
When a language model writes, what is it actually doing?
Tokens become vectors; context turns those representations into next-token probabilities; human feedback helps shape which fluent answers count as useful. We start unplugged — guessing the next word, tallying the room — then open the machine and watch the sequence become visible.
Text is split into token IDs — often not where a human would divide words or meaning.
Each token ID becomes a learned numerical vector; its representation is then reshaped by context.
The current context becomes a distribution over possible next tokens, then a sampling rule chooses one.
People and institutions demonstrate, compare, and reward versions of what counts as a better response.
Teaching move · contextual vectors: open the Contextual Vector Lab. Keep one token fixed, change its sentence, predict the neighborhood, then reveal the authored move. Ask whether proximity reflects meaning, usage, or a repeated cultural association. The Embedding Projector remains a useful real-project reference for static learned neighborhoods; neither surface is a literal view of a transformer’s complete internal state.
Teaching move · human feedback: open the Whose Preference? Lab. Rank the same fluent responses, reveal an authored rater panel, then change who is included or how judgments are aggregated. Ask whose definition of “better” became a signal. The lab illustrates preference-data decisions; it is not an RLHF training run.
Too cold repeats the safest answer; too hot dissolves into noise. The session lives in the middle, where choice becomes visible.
Welcome & norms
Name the question. Remind everyone direct AI use is optional — each person chooses a participation pathway.
Unplugged prediction → counting
Show a sentence stem and collect guesses in Zoom chat (paste into the Next-Token Prediction Game for the room's distribution), then open Count the Next Token to reveal the mechanism: count → divide → predict.
Tokens → vectors → probabilities + temperature
Use one Contextual Vector Lab predict → reveal cycle to separate token ID, starting vector, and context-dependent representation; then run one prompt at low and high temperature. Watch probability bars and greedy vs. sampled output.
ELIZA vs. frozen comparison text
Inspect the matched rule, pattern, and template. Compare visible rule-matching with frozen LLM-style text, while naming its incomplete provenance.
Investigation
Complete one Text Experiment Board section: a default test, prompt variation, ELIZA comparison, or a panel change in the Whose Preference? Lab.
Debrief
Reflection frame — human, machine, system, ethics, pedagogy. Ask whose definition of “better” entered the signal, then share one evidence-based claim and one question.
Participants can complete the whole session with only the visualizer, ELIZA, and frozen comparison examples — no logging into or prompting a live LLM. The comparison text should not be attributed to a named model because its original provenance was not preserved. Opting out of direct AI use never means opting out of the camp.