The estimated% of globalelectricity usedby AI andmachinelearning in 2021The growth inmachine learningmodels will likelyexceed energyefficiencyimprovements inthe coming yearsThe primarysource ofgreenhouse gasemission frommachine learningsystemsExamples of howAI can contributeto both positiveand negativeimpacts onclimate changeDetails abouthow muchenergyChatGPTused in 2023Theindirecteffects ofAITwo categoriesof environmentaleffects causedby AI andmachinelearningHow shiftingcomputing loadsgeographically andby time can helpreduce AI'senvironmentalimpactThe estimated% of globalelectricity usedby AI andmachinelearning in 2021The growth inmachine learningmodels will likelyexceed energyefficiencyimprovements inthe coming yearsThe primarysource ofgreenhouse gasemission frommachine learningsystemsExamples of howAI can contributeto both positiveand negativeimpacts onclimate changeDetails abouthow muchenergyChatGPTused in 2023Theindirecteffects ofAITwo categoriesof environmentaleffects causedby AI andmachinelearningHow shiftingcomputing loadsgeographically andby time can helpreduce AI'senvironmentalimpact

Week 2 Mediation Lesson Reading for Gist - 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
  1. The estimated % of global electricity used by AI and machine learning in 2021
  2. The growth in machine learning models will likely exceed energy efficiency improvements in the coming years
  3. The primary source of greenhouse gas emission from machine learning systems
  4. Examples of how AI can contribute to both positive and negative impacts on climate change
  5. Details about how much energy ChatGPT used in 2023
  6. The indirect effects of AI
  7. Two categories of environmental effects caused by AI and machine learning
  8. How shifting computing loads geographically and by time can help reduce AI's environmental impact