Is AI right for me?
summary
Shows concrete criteria to decide whether to add AI: start by define the business owner and a clear success metric, then map problems to three tiers—most cases need business intelligence, fewer need predictive data science, and only a tiny set needs full artificial intelligence. Cites real projects (vibration-signature models to predict helicopter part failure, call-record NLP for marketing), warns about skipped problem definition, missing labels, error tolerance, sensitive data and when to use synthetic data or vendor tools like Watson or cloud assistants.
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chapters (25)
- 0:00Intro
- 0:18Ambers AI background
- 3:03Traditional AI
- 5:32AI in 45 years
- 7:17Boston Dynamics
- 7:48Google
- 8:17AI for Business
- 10:19AI Landscape
- 11:03Business Problem
- 11:57Data Science
- 14:27Expectations
- 15:11The Reality
- 16:12The Process
- 16:44Define Your Problem
- 18:57subjectivity
- 21:35inputs and outputs
- 27:17data
- 31:42machine learning
- 33:09business intelligence
- 33:46predictive
- 35:47generative
- 38:19level of effort
- 39:14frameworks
- 39:52AI solution
- 43:02Recap
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