someonenear youdisagreesaudibly"I willcomeback tothat"someonesays: "asyou know",but you don'tknow.Aphonegoes offplots areway toosmallsoundis tooloudSpeakeris justfacing theslidesYourattentionsnaps backhearing "inconclusion"we are 10minutesbehindscheduleWay toomuch texton a slideQuestion iscompletelymisinterpretedby thespeakerYou can'thear thespeakerwellmissingslidenumbersunnecessarythank-youslideQuestionis just acommentwe reachslide 20 ina 12" talksomeoneis skepticalof machinelearningscatterdata looksjust like ablobreallybad fitUnfamiliarjargonsomeonenear youdisagreesaudibly"I willcomeback tothat"someonesays: "asyou know",but you don'tknow.Aphonegoes offplots areway toosmallsoundis tooloudSpeakeris justfacing theslidesYourattentionsnaps backhearing "inconclusion"we are 10minutesbehindscheduleWay toomuch texton a slideQuestion iscompletelymisinterpretedby thespeakerYou can'thear thespeakerwellmissingslidenumbersunnecessarythank-youslideQuestionis just acommentwe reachslide 20 ina 12" talksomeoneis skepticalof machinelearningscatterdata looksjust like ablobreallybad fitUnfamiliarjargon

SWGO Bingo - Call List

(Print) Use this randomly generated list as your call list when playing the game. There is no need to say the BINGO column name. Place some kind of mark (like an X, a checkmark, a dot, tally mark, etc) on each cell as you announce it, to keep track. You can also cut out each item, place them in a bag and pull words from the bag.


1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
  1. someone near you disagrees audibly
  2. "I will come back to that"
  3. someone says: "as you know", but you don't know.
  4. A phone goes off
  5. plots are way too small
  6. sound is too loud
  7. Speaker is just facing the slides
  8. Your attention snaps back hearing "in conclusion"
  9. we are 10 minutes behind schedule
  10. Way too much text on a slide
  11. Question is completely misinterpreted by the speaker
  12. You can't hear the speaker well
  13. missing slide numbers
  14. unnecessary thank-you slide
  15. Question is just a comment
  16. we reach slide 20 in a 12" talk
  17. someone is skeptical of machine learning
  18. scatter data looks just like a blob
  19. really bad fit
  20. Unfamiliar jargon