AI Dungeon Run | Andrew Ng's AI For Everyone – Chapter 2: Building AI Projects
Chapter 2 dives into how AI projects actually get built, with lots of real-world examples. The data matters more than you'd think — and AI automates tasks, not jobs.
Chapter 2 gets into the mechanics of AI projects, with plenty of concrete examples.
Machine Learning, for instance, requires collecting data, training a model, then testing it in the real world. A Data Scientist collects data, analyzes it, and turns hypotheses into actions. Either way: data is everything.
When starting a project, it’s recommended to pair an AI Expert with a Domain Expert, because:
- AI Expert: Knows what AI can and can’t do technically
- Domain Expert: Knows what actually matters to the business
But the key question throughout the process is: how can AI automate tasks, not jobs?
This hit home for me — I’m currently building a Research AI tool at work. AI can’t replace a Researcher, but it absolutely can help a Researcher finish their daily tasks faster.
So if you find AI can’t complete a Researcher’s entire job and conclude it’s “dumb” — that’s a misplaced expectation. We should expect task-based automation, not job-based automation.
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