AI For Everyone
Overview
AI For Everyone (DeepLearning.AI / Andrew Ng) is a non-technical course that covers AI strategy, workflow automation, and how to work effectively with AI teams. Targeted at business leaders and non-engineers.
What is AI?
- ANI (Artificial Narrow Intelligence) — AI for one specific task (spam filter, image recognition). This is all current AI.
- AGI (Artificial General Intelligence) — human-level general reasoning. Not yet achieved.
Machine Learning is the most important branch of AI today: systems that learn from data rather than explicit programming.
Key Concepts
| Concept | Simple Definition |
|---|---|
| Supervised Learning | Learn from labelled examples (input → output) |
| Data | The fuel of AI — more quality data = better models |
| Features | Input variables the model learns from |
| Training | Fitting the model to data |
| Inference | Using the trained model on new data |
AI in the Enterprise
AI Project Workflow:
- Identify a business problem AI can solve.
- Collect and label data.
- Train and evaluate model.
- Deploy and monitor.
- Improve iteratively.
What AI can and cannot do:
- Can: automate tasks with structured input-output, find patterns in large datasets.
- Cannot: reason with common sense, handle completely novel situations, explain itself reliably.
Building an AI Team
- ML Engineer — builds and trains models.
- Data Engineer — builds data pipelines.
- Data Scientist — analyses data, selects features, evaluates models.
- AI Product Manager — defines what to build and why.
AI Strategy
- Start small with high-value, feasible projects.
- Build a data strategy early — data is a moat.
- Invest in AI literacy across the organisation.
- Pair domain experts with AI engineers.
Responsible AI
- Bias in → bias out. Audit training data.
- Be transparent about AI-driven decisions.
- Consider fairness across demographic groups.
- Have a human-in-the-loop for high-stakes decisions.