Session 1 activities
ACTIVITIES · 29 MINFictional data only
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Rule or learned pattern?
Poll at the start of session 1, then discuss.
Bayside Precision Parts (fictional) has a system that emails purchasing when part 4410 drops below 200 units. What kind of system is it?
Rules, predictive AI, or generative AI?
Sort each fictional Bayside system into the family it belongs to. You will use the same sort in Lab 1.
- Spam filter that learned from messages people marked as junk
- Payroll system that adds 1.5x pay after 40 hours
- Assistant that drafts a reply about lead times
- Model that flags spindles likely to fail within 30 days
- Meeting tool that writes a summary of a call
- Badge reader that opens the door for approved IDs
Why did it get that wrong?
Branching. A chat assistant tells a Bayside buyer that a supplier's price list 'was updated last month', but the supplier changed prices last week.
What is the most likely reason?
Explain-it-back: how AI learns
After session 1, in pairs, then post.
In three or four sentences a new coworker would understand, explain how AI learns from data and why that means its answers are probable rather than certain. Use one example from your own job.
- Learning from examples, not hand-written rules.
- Patterns can be incomplete or outdated.
- Probable, not certain.
- A job example.