AI Skills Beyond Developing
summary
Argues that building AI requires more than coding: a six-step CRISP-DM cycle from business understanding through deployment, with business understanding as the most common failure mode (one project wasted four months answering the wrong question). Recommends embedding ethics from the start, mapping domain skills (cars, psychology, drawing) to each application, and using tools like computer vision and cognitive services for data collection. Notes language choices Python vs R and stresses math/statistics and analytical thinking as essential.
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chapters (6)
- 0:00Introduction and background
- 2:29Data in the modern era
- 6:11Defining artificial intelligence
- 10:26Core skills for AI careers
- 15:26The AI development process
- 21:18Q&A and closing thoughts
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