ReadOS / Technology
AI and Machine Learning in Practice — Part 2
Understanding artificial intelligence, language models and practical automation decisions.
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- 01Technology is a means, not a strategy: start with the outcome, then test the ecosystemA technology proposal can gather momentum before anyone asks what it is for. How to define the outcome first, compare real alternatives and check whether an option can be adopted.
- 02Testing decision systems before you trust themHow to validate automated decision systems: performance metrics, rare-event simulation, robustness to model errors, trade-off analysis, adversarial testing and responsible deployment.
- 03Tests that cannot fail: checking whether your assurance could ever find a problemA check that passes whether or not the problem exists tells you nothing, yet feels like assurance. How to spot tests that cannot fail, from padlocks to AI bias audits, and replace them.
- 04The real cost of running AI: tokens, latency, hardware and the choice between local and cloudAI bills grow quietly as usage and prompts grow. How to calculate cost per request and per outcome, manage speed, size hardware for local models and decide between cloud and local.
- 05Vision, documents and speech: practical multimodal AI in operationsAI that reads documents, understands photos and transcribes speech can remove hours of data entry and note taking. Where it works, how to measure accuracy and how to keep people in control.
All chapters preserve the original KEVOS Insights content. These topic-based reading collections are organised alphabetically; chapters may be read in any order.
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