AI is everywhere!
summary
Panelists give concrete guidance: take the Johns Hopkins Data Science Specialization or courses on deeplearning.ai, use Kaggle to practice, and prefer API services like cognitive services and form recognizers for production. They describe a counterfeit-packaging app that combines logo classifiers, OCR and small specialized models plus orchestration code and worked, and a medical wound app that uses face recognition but fails to get enough data for predictive models. Expect AGI decades away, focus on explainability, representative data, and building hybrid systems that combine ML models with rule code.
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chapters (17)
- 0:00Intro
- 0:55Introductions
- 2:58What is AI
- 5:10Preparing for AI
- 6:30Preparing for our careers
- 9:35What should we learn
- 13:40Field Guide to Data Science
- 15:23Using AI to solve real world problems
- 16:55Currently working on
- 22:02AI ethics
- 23:28Data ethics
- 24:33Emotions
- 27:15Explainable models
- 28:16Explainability tools
- 30:04Future of AI
- 33:58Will AI take our jobs
- 35:47Wrap up
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