Fundamentals of AI
AI is not one thing. It is a collection of methods for using data, computation, and models to produce predictions, classifications, generated content, decisions, recommendations, or actions.
Core concepts
- Data: examples or records used to shape or evaluate a system.
- Model: the mathematical structure that maps inputs to outputs.
- Training: the process of adjusting a model from examples.
- Inference: using a trained model to produce an output.
- Evaluation: checking whether outputs are correct, useful, safe, and robust enough for the job.
- Deployment: putting the system into a real workflow where people, incentives, and failure modes matter.
What to ask before trusting an AI system
- What task is the system actually performing?
- What data shaped it, and what data tests it?
- What does failure look like?
- Who verifies the output?
- Can a human appeal, correct, or override it?
- What costs, energy use, privacy risks, or social harms does it create?
NIST AI Risk Management Framework
The NIST AI Risk Management Framework is a strong starting reference because it treats AI as a system that must be governed, mapped, measured, and managed. That is more useful than judging systems by demos alone.
Editorial stance
International Tech Resource will cover AI as infrastructure, tool, market force, research field, and stewardship problem. A model is not trustworthy because it is impressive. It is trustworthy only when its behavior is measured against a real job and a real risk boundary.