Is optimisticabout thefuture ofhuman-AIcollaborationKnows atleast threeprogramminglanguagesHas used agenerative AImodel tocreate art ormusicHassuccessfullydebugged acomplexLLMCanrecommenda good AI ortech relatedpodcastHasattended anICMLconferencebeforeHasexperiencewith fine-tuning a pre-trained LLMHaspublishedresearch onmultilingualLLMsIs excitedabout thepotential ofLLMs ineducationHas used anLLM tosummarizeresearchpapersHas learneda newlanguage inthe last yearCan namethreedifferent LLMarchitecturesIs familiarwith theconcept ofpromptengineeringIs interestedin the ethicalimplicationsof generativeAIHas experiencewith low-resourcelanguages inNLPHasparticipated ina hackathonfocused on AIor LLMsHas traveledinternationallyto attend thisconferenceHas presenteda paper onnaturallanguagegenerationHas collaboratedon a researchpaper withsomeone from adifferent continentHas used anLLM in alanguageother thanenglishHas used agenerative AImodel for anon-academicpurposeHascontributedto an open-source AIprojectHas apreferred AIresearch toolthey canrecommendCan explain thedifferencebetween causaland maskedlanguagemodelsIs optimisticabout thefuture ofhuman-AIcollaborationKnows atleast threeprogramminglanguagesHas used agenerative AImodel tocreate art ormusicHassuccessfullydebugged acomplexLLMCanrecommenda good AI ortech relatedpodcastHasattended anICMLconferencebeforeHasexperiencewith fine-tuning a pre-trained LLMHaspublishedresearch onmultilingualLLMsIs excitedabout thepotential ofLLMs ineducationHas used anLLM tosummarizeresearchpapersHas learneda newlanguage inthe last yearCan namethreedifferent LLMarchitecturesIs familiarwith theconcept ofpromptengineeringIs interestedin the ethicalimplicationsof generativeAIHas experiencewith low-resourcelanguages inNLPHasparticipated ina hackathonfocused on AIor LLMsHas traveledinternationallyto attend thisconferenceHas presenteda paper onnaturallanguagegenerationHas collaboratedon a researchpaper withsomeone from adifferent continentHas used anLLM in alanguageother thanenglishHas used agenerative AImodel for anon-academicpurposeHascontributedto an open-source AIprojectHas apreferred AIresearch toolthey canrecommendCan explain thedifferencebetween causaland maskedlanguagemodels

Human BINGO: Navigating Generative AI and LLMs Across Languages - 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.


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  1. Is optimistic about the future of human-AI collaboration
  2. Knows at least three programming languages
  3. Has used a generative AI model to create art or music
  4. Has successfully debugged a complex LLM
  5. Can recommend a good AI or tech related podcast
  6. Has attended an ICML conference before
  7. Has experience with fine-tuning a pre-trained LLM
  8. Has published research on multilingual LLMs
  9. Is excited about the potential of LLMs in education
  10. Has used an LLM to summarize research papers
  11. Has learned a new language in the last year
  12. Can name three different LLM architectures
  13. Is familiar with the concept of prompt engineering
  14. Is interested in the ethical implications of generative AI
  15. Has experience with low-resource languages in NLP
  16. Has participated in a hackathon focused on AI or LLMs
  17. Has traveled internationally to attend this conference
  18. Has presented a paper on natural language generation
  19. Has collaborated on a research paper with someone from a different continent
  20. Has used an LLM in a language other than english
  21. Has used a generative AI model for a non-academic purpose
  22. Has contributed to an open-source AI project
  23. Has a preferred AI research tool they can recommend
  24. Can explain the difference between causal and masked language models