Understanding the tools we depend on

Ideas are equal.
Opportunities are not.

Young Chloe draws a house with crayons. An untouched revolver lies away from her drawing.

Jason Kridner · Northville educators

Opening, 0:00–2:00. Introduce the line as a saying RJ uses, with the wording Jason recalls. Do not claim a verified original author. Ask the audience to hold two children in mind: one discovering what she can make, another surrounded by extraordinary things she cannot see how to make. We owe children more than exposure to powerful products. The ability to read a program’s logic and reason about what it will do is part of their opportunity to direct their own lives. Today we will inspect two mechanisms on paper, then ask who can understand, change and retain the tools. This hour will not solve the economic or institutional barriers. It should make those barriers harder to overlook.

The opening illustration is an existing AI-generated image. The untouched gun is symbolic background inspired by a shirt image, not a binary choice offered to a child or a causal claim about violence. Do not point to it or build a threat narrative.

Chloe & Bridget · fictional story

“Birds need somewhere to sit.”

A crooked chimney, a snapped purple crayon, an enormous evening.

Story frame 1 · illustration to develop

Chloe & Bridget · fictional story

“Could you make it darker?”

Factory pigment and molds; a classroom batch with more blue.

Story frame 2 · illustration to develop

Chloe & Bridget · fictional story

“Can we stay?”

A wall-sized pencil tree; strokes she recognizes; one small branch at home.

Story frame 3 · illustration to develop

Chloe & Bridget · fictional story

She still has the two purple crayons.

An ugly clay house, lit windows, then the closed factory entrance.

Story frame 4 · illustration to develop

Chloe & Bridget · fictional story

The house answers, softly.

Impossible rooms, rain becoming music, a visitor who asks to stay.

Story frame 5 · illustration to develop

Chloe & Bridget · fictional story

“Mine doesn’t do anything.”

Bridget’s drawing slips under a placemat. Chloe pulls up a chair.

Story frame 6 · illustration to develop

Chloe & Bridget · fictional story

“But I want to take it.”

Two old tools lead to one sign-in. The exported house cannot move.

Story frame 7 · illustration to develop

Chloe & Bridget · fictional story

“I don’t know yet.”

A retirement notice beside the house with the rain. Chloe’s hand leaves the mouse.

Story frame 8 · illustration to develop

Chloe & Bridget · fictional story

What can the child carry away?

Bridget’s drawing lies between them. The rain keeps playing.

Story frame 9 · illustration to develop

Understanding the tools we depend on

Understanding the mechanism

Read the logic.
Predict its behavior.
Change what it does.

7:00–10:00. Return from the story to the educational responsibility. Students can use agents to help write code and still learn to inspect the logic themselves. The important difference is whether they can explain and test the mechanism, rather than depending entirely on another system to make it work. This is practical, conditional agency, not total independence. Reusing code is valuable; the question is whether the path to understanding and changing it is available.

Do not reduce this to “computers execute reliably, but AI makes mistakes.” The point is the accessible reasoning route: for an explicit algorithm and stated inputs, we can sometimes derive an outcome before running it, and teach why. Repeatability alone does not give us that explanation. Neither a successful demo nor access to a source file guarantees comprehension.

Invite one example of a classroom activity that ends at operating a product, then one that exposes an underlying mechanism. Keep this brief. The upcoming activity is deliberately small enough to understand completely.

Understanding the tools we depend on

Nine shuffled cards

One remembered card.
Two rounds.
Position five.

A provisional activity for rehearsal

10:00–12:00. Proposed adaptation of NRICH Spot the Card, not a final activity decision. Work in pairs, with any nine distinct playing cards per pair. Remove jokers; no full-deck sort or prepared arrangement is necessary. One person deals, one remembers a card. Let them shuffle openly. Before anyone selects a card, predict that after the two prescribed rounds it will be fifth from the top. Do not claim you can name it before receiving any information. The named column is an input to this algorithm.

Deal one card at a time face up, left to right, into three columns, building three rows top to bottom. The spectator silently picks one visible card and names only its column. Use the following slide and printed activity sheet for the collection order. An observer can check the operations; nobody needs to act as a hidden assistant.

NRICH, Spot the Card

Understanding the tools we depend on

The same short procedure

Deal left to right, three columns.
Name the remembered card’s column.
Collect that column in the middle.
Repeat the deal and collection once.

12:00–18:00. Exact ordering matters. Treat each column as a list from top row to bottom row. Pick up the other columns and the selected column so that the new packet, read from TOP to BOTTOM, is: all three cards of one unselected column in row order; all three cards of the selected column in row order; all three cards of the other unselected column in row order. Do not casually scoop the grid into a pile. One easy method is to lay those three lists end to end on the table, then assemble the face-down packet starting with the LAST card and finishing with the FIRST on top.

Deal that packet again, starting with its top card, left to right, three rows. Spectator names the column again. Collect exactly as before. Count from the top: the fifth card is the remembered card. Turn it over. If it fails, inspect the trace and packet orientation before repeating; human execution can make mistakes without changing the algorithm’s guarantee.

Allow roughly six minutes for pairs to perform and check. This is an estimate requiring rehearsal. Do not spend the entire hour recovering a confusing collection instruction. Use the numbered printed trace or demonstrate a single pair if needed.

NRICH, Spot the Card

Understanding the tools we depend on

Why position five?

Any starting position
4, 5 or 6
5

18:00–23:00. Lay out the intermediate packet and count positions. After the first collection, the chosen column occupies positions 4, 5 and 6. In the second deal those three positions all land in the middle row, one per column. After putting the chosen column between the other two, its middle card occupies position 5. This works for every initial ordering, provided the procedure and the reported columns are correct.

Ask partners to explain this before running it a second time. The second run can start from a fresh shuffle. They now predict from the mechanism rather than merely believing a repeated performance. Extend only if time permits: what if we put the selected column first? Trace positions instead of guessing.

The test supplied with this deck enumerates all nine starting positions and both orders of unselected columns at both collections. It verifies 36 traces; it does not measure classroom timing or prove a general-purpose computer.

NRICH, Spot the Card

Understanding the tools we depend on

A small instruction set

Rules + memory + repetition

Turing completeness concerns what can be computed.

23:00–27:00. The card procedure is one finite algorithm. A more general machine can read and write memory, choose a next step based on a value, and repeat. A Turing-complete model can simulate any Turing machine, given unbounded storage and enough time for a computation that halts. Finite physical machines have resource limits. Some problems are undecidable and some programs never halt; “any algorithm runs to completion” would be false. Not every simple machine is universal. The card trick does not establish universality.

The Turing test is a different question about behavior in an imitation game, not the range of computable functions. Keep the terms distinct. A compiler is a program that translates a language into another representation a machine can execute. Students need not become compiler specialists to benefit from seeing such a translation and understanding that it is a mechanism, not magic. No compiler activity is required in this hour.

Turing machine background

Understanding the tools we depend on

Where is the work happening?

Colored-pencil illustration of the chess automaton, with a concealed operator visible inside its cabinet.

The Mechanical Turk · artistic reconstruction

Understanding the tools we depend on

People behind the examples

Someone chooses the task.
Someone judges the answer.

Understanding the tools we depend on

A word from the hat

“Tell a short story about
a surprising discovery at school.”

One next word, or STOP

Understanding the tools we depend on

Read the whole response again

Fresh suggestions.
A fresh draw.
One more word.

33:00–39:00. After each selection, read the prompt AND the entire response so far. Empty the hat. Everyone now writes a fresh next-word or STOP suggestion based on the new context. Repeat until STOP is drawn or the eight-word cap is reached. Never cut up a prewritten sentence or keep old slips for a changed context. Save the selected words in order.

Reset to the identical prompt and empty response. Repeat the same rules for a second run. Do not deliberately alter the first word, split the group, or change the rules to manufacture a difference. Random selection supplies the randomness. Different outputs are possible, not guaranteed. If two stories match, that is an honest result. A third run is optional only if ahead of time.

Six minutes for two short runs is a hypothesis, not tested classroom timing. Have slips ready. If groups are slow, finish the second run with fewer words and use the resulting partial texts in discussion. Do not silently turn the activity into a single person writing the story.

Understanding the tools we depend on

Same prompt. Same process.

Which word came next?
What changed after it?

39:00–43:00. Invite two tables to read their outputs. At a particular step count the candidate slips. If four of six slips say “door,” its draw probability was four sixths in our setup. That is an empirical frequency of these people’s suggestions, not a measurement of a language model. A selected word changes the context, so the next set of proposals can change. STOP is a possible draw, not a majority vote.

This models sampling during generation. It does not train a neural network. People supply the candidate distribution from their own learning and judgment. A language model calculates a distribution over tokens using learned parameters and the input context; tokens may be parts of words. Generation can sample from that distribution. Some decoding choices are deterministic, so randomness is not the defining difference between a learned model and explicit program logic. Temperature is optional extra detail and is omitted here.

Understanding the tools we depend on

Where do the probabilities come from?

Training changes parameters.
Generation uses them.

43:00–46:00. Offer a bounded intuition: during training, examples supply a prediction task; a learning procedure changes many numerical parameters to reduce its training error. In a neural language model those parameters help transform the context into scores for possible next tokens. They are not a readable list of instructions written by an author for every situation. Learned associations can be useful and also reflect accidental regularities or gaps in the examples.

Compare the card proof: we derived an exact destination from the stated rules. The hat made the influence of context and selection visible, but it did not expose how a neural network learns representations. A model can repeat an answer consistently without giving us a comparably simple justification that it is correct on a new input. Explicit programs can also be buggy, complex or undecidable to analyze in general. We choose mechanisms and verification methods appropriate to what we depend on.

Understanding the tools we depend on

People teach and judge

Demonstrations
Comparisons
Further training

InstructGPT, 2022

46:00–49:00. InstructGPT began with pretrained GPT-3. Human example responses supported supervised fine-tuning. People ranked outputs; those comparisons trained a reward model. Further reinforcement learning used that model’s scores. This is a specific historical workflow, not a complete description of every current model. Ask: whose preferences became the target, and what happens when people disagree?

Human feedback improved the reported behavior, but the researchers still described factual mistakes and bias. The hat exercise sampled suggestions; this training process changes model behavior before later generation. The hidden human in the historical Turk, distributed paid work, and training feedback are three different arrangements of human work. Keep the distinctions visible.

OpenAI, InstructGPT

Understanding the tools we depend on

Who can build the tool?

Data. Computation.
People. Infrastructure.

Using a model ≠ training its foundation

49:00–54:00. Compare what the classroom just needed with producing a large pretrained model: gathered and prepared data, substantial computation, technical work, evaluation and continuing infrastructure. GPT-3’s paper describes a 175-billion-parameter model. That is a historical scale example, not a current price quote or a minimum size for useful machine learning. Training a small model, adapting an existing model and training a frontier-scale foundation model from scratch are very different undertakings. Individuals can do valuable work with smaller and openly available systems, while competing at the largest scale is highly unequal.

Relate this to Chloe. Access to an impressive service is real opportunity, but it can carry conditions that the user cannot change. A downloadable result may not preserve the running system, the ability to edit it, or the right to take it elsewhere. Avoid claiming all subscriptions are bad or all open-source systems solve access. Ask which dependencies can be replaced, inspected, repaired or exited and which cannot. A crayon-making activity has no classroom-scale equivalent for reproducing an entire modern AI stack. That gap is part of the subject.

GPT-3 paper

Understanding the tools we depend on

After the activity ends…

What can this child
understand, change
and carry away?

54:00–60:00. Return to Bridget’s drawing and Chloe’s uncertainty. Give pairs two minutes: choose an activity they teach, name the mechanism children actually inspect, and name a dependency that remains beyond their control. Then invite two responses. Leave at least one minute for the last question or an unresolved concern.

Re-state the central commitment: give students opportunities to read logic, predict behavior under stated conditions and make a machine carry out their intentions, even if AI helped produce the code. Successful exposure to a product does not by itself provide that understanding. The mechanisms we examined are small and comprehensible. The larger barriers include access, cost, ownership, complexity and intimidation, and an hour cannot remove them. Teachers can nevertheless become much more attentive to what opportunity an activity leaves with a child.

The phone tool and microphone-triggered image reveal remain optional future investigations. This deck contains no listener, recording, live model call or mandatory hardware demonstration.

Ideas are equal.Opportunities are not.

“Birds need somewhere to sit.”

“Could you make it darker?”

“Can we stay?”

She still has the two purple crayons.

The house answers, softly.

“Mine doesn’t do anything.”

“But I want to take it.”

“I don’t know yet.”

What can the child carry away?

Understanding the mechanism

Nine shuffled cards

The same short procedure

Why position five?

A small instruction set

Where is the work happening?

People behind the examples

A word from the hat

Read the whole response again

Same prompt. Same process.

Where do the probabilities come from?

People teach and judge

Who can build the tool?

After the activity ends…

Ideas are equal.
Opportunities are not.