How I Teach my Second Grade Kid what AI is and How this Relates to BI that Impact your...
summary
Explains AI with concrete kid-friendly experiments — supervised, unsupervised, reinforcement learning illustrated by pen vs apple labels, playground clustering and a robot reward loop — and maps those to BI needs: use CRISP-DM style process, put AI into the modeling layer, and adopt a tech-agnostic stack with APIs. Recommends operational principles like single version of truth and self-service analytics, plus workforce development and enablement, producing a repeatable framework to apply AI in Index Analytics–style BI programs.
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chapters (24)
- 0:00Introduction
- 1:24About the speaker
- 4:31Teaching AI basics
- 8:12Explaining AI with ETL
- 10:01Supervised learning with pen
- 12:16Supervised learning in medicine
- 13:00Entropy with cleanup
- 14:25Unsupervised learning and segmentation
- 15:54Reinforcement learning and robots
- 17:44AI versus traditional programming
- 19:53Programming versus AI algorithms
- 21:51Memorization versus generalization
- 23:17Avoiding overfitting
- 24:09Decision trees explained
- 25:58AI algorithm categories
- 26:20Tesla versus Uber models
- 27:48Using AI versus inventing AI
- 28:56AI investment strategies
- 29:17AI in BI strategies
- 29:59CRISP-DM for AI in BI
- 31:06BI architecture principles
- 31:50AI framework and workforce
- 32:32Speaker's YouTube channel
- 33:45Closing remarks
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