Supervised learning Generative AI data collection sampling Foundation Models Chatbot Chatbot reinforced learning Trustworthy AI WatsonX Large Language Models Fairness Value Alignment WatsonX Training Data Wizard of Oz Prototyping bias transparent data Value Alignment NLP AI Ethics Fairness Supervised learning Model Hallucination bias transparent data Foundation Models Explainability reinforced learning Unsupervised learning Generative AI Human in the Loop Robustness Wizard of Oz Prototyping Model Drift Model Drift deep learning Machine Learning Explainability Large Language Models data collection sampling Training Data AI Human in the Loop Unsupervised learning Robustness AI Ethics Neural Network Trustworthy AI Neural Network Model Hallucination AI Machine Learning Supervised learning Generative AI data collection sampling Foundation Models Chatbot Chatbot reinforced learning Trustworthy AI WatsonX Large Language Models Fairness Value Alignment WatsonX Training Data Wizard of Oz Prototyping bias transparent data Value Alignment NLP AI Ethics Fairness Supervised learning Model Hallucination bias transparent data Foundation Models Explainability reinforced learning Unsupervised learning Generative AI Human in the Loop Robustness Wizard of Oz Prototyping Model Drift Model Drift deep learning Machine Learning Explainability Large Language Models data collection sampling Training Data AI Human in the Loop Unsupervised learning Robustness AI Ethics Neural Network Trustworthy AI Neural Network Model Hallucination AI Machine Learning
(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.
Supervised learning
Generative AI
data collection sampling
Foundation Models
Chatbot
Chatbot
reinforced learning
Trustworthy AI
WatsonX
Large Language Models
Fairness
Value Alignment
WatsonX
Training Data
Wizard of Oz Prototyping
bias transparent data
Value Alignment
NLP
AI Ethics
Fairness
Supervised learning
Model Hallucination
bias transparent data
Foundation Models
Explainability
reinforced learning
Unsupervised learning
Generative AI
Human in the Loop
Robustness
Wizard of Oz Prototyping
Model Drift
Model Drift
deep learning
Machine Learning
Explainability
Large Language Models
data collection sampling
Training Data
AI
Human in the Loop
Unsupervised learning
Robustness
AI Ethics
Neural Network
Trustworthy AI
Neural Network
Model Hallucination
AI
Machine Learning