Page 5 of 8~112 min topic

The history of AI

Diagnose common failures: Deep Blue match

Common failures: great-man stories, inevitability arcs, and demos mistaken for deployment.

~14 min this pageDiagnose common failures — plausible mistakes and warning signs

1Learn the idea

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Chess as spectacle versus research

See it

AI = judgment-like software

Fits

  • Suggest reply
  • Flag odd purchase
  • Draft from bullets

Not by itself

  • Spreadsheet formula
  • Doorbell circuit
  • “Smart” ad copy

Fits = smart tasks · Not AI by itself = fixed rules

Name failures specifically. Sam Rivera refuses the single bucket “the AI messed up” when discussing Deep Blue match at Municipal Museum of Technology. Separate data problems, task-framing problems, interface problems, and governance problems. Each needs a different repair, and only some involve retraining—keep the standing case (redesign an AI timeline so progress is not a straight myth) in the room.

During a real interruption at Municipal Museum of Technology, Sam Rivera stress-tests “Chess as spectacle versus research” on Deep Blue match: one queued question, one hurried call, one hallway challenge. If the idea only works in a quiet workshop, it will not survive the standing case (redesign an AI timeline so progress is not a straight myth).

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Dataset eras that reshaped claims

Build a tiny failure gallery with Deep Blue match and ImageNet era. For each, describe a plausible confident mistake, the first human who should notice at Municipal Museum of Technology, and a fix that is not “ask it again.” Plausibility matters: cartoon failures do not train judgment for Sam Rivera.

Count something crude about Deep Blue match—misses last week, minutes lost, or people affected—and write the number beside ImageNet era. Sam Rivera needs that comparison before anyone at Municipal Museum of Technology declares victory on the standing case (redesign an AI timeline so progress is not a straight myth).

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Forgetting the annotation workforce

List warning signs around Deep Blue match that justify slowing public claims even if a pilot continues privately: missing owners, no logged overrides, identical outputs for dissimilar people, vendors who will not state training scope. When several signs coincide, freeze marketing language tied to the standing case (redesign an AI timeline so progress is not a straight myth).

On “Forgetting the annotation workforce”, Sam Rivera edits language about Deep Blue match the way an editor would: strike “sentient,” “infallible,” and “just a tool” wherever they hide responsibility inside Municipal Museum of Technology. ImageNet era stays nearby as a plain-language control.

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Caption errors Sam refuses to reprint

Write one stop condition for Deep Blue match with authority attached—a named role at Municipal Museum of Technology who can pause use. Stop conditions without authority are theatre. Sam Rivera gets initials on the page before the next launch review, and uses ImageNet era to show what “pause” looks like in a simpler system.

For “Caption errors Sam refuses to reprint”, a second person at Municipal Museum of Technology challenges Sam Rivera’s note on Deep Blue match and asks whether ImageNet era already solves most of the need with less mystery. That challenge is part of finishing the standing case (redesign an AI timeline so progress is not a straight myth), not a delay tactic.

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Before you start

Why this matters

Invent one confident wrong output for Deep Blue match that would look fine in a screenshot. Sam Rivera classifies the miss as data, framing, interface, or governance—and says which fix comes first. Repeat once for ImageNet era with a different class.

Check your understanding

Page assessment

Answer from memory. Completion is saved from this evidence, not from opening the next page.

1. In Sam Rivera’s scene, what bounded task does Deep Blue match perform at Municipal Museum of Technology?
2. Which observation would most change your judgment about Deep Blue match, and why?
3. How should ImageNet era alter the quality bar or the language you use?
4. Who can correct a miss before harm spreads, and what authority do they need?
5. How does this page advance the case: redesign an AI timeline so progress is not a straight myth?

All responses are required.