Machine learning is still the foundation underneath most AI products and IBM's guide explains why that matters

A lot of AI coverage right now focuses on generative AI and LLMs. IBM's machine learning explainer https://www.ibm.com/think/topics/machine-learning is a useful corrective because it covers the broader field that generative AI sits within rather than treating the current wave as the whole story.

The core distinction that matters for understanding the AI landscape: machine learning is the field of systems that learn from data rather than being explicitly programmed for every decision. Generative AI is one class of ML systems. Recommendation engines, fraud detection, spam filters, medical diagnosis tools, and predictive maintenance systems are all ML systems that are not generative.

The reason this matters practically: when people say AI is replacing a job or changing an industry, it is often traditional ML rather than LLMs doing the work. A hiring algorithm or a credit scoring model is ML. Understanding the distinction changes which applications you evaluate critically and which you give a pass because they seem less dramatic.

The article covers the major ML categories in enough depth to navigate most technical discussions without requiring a data science background.

Where in your own professional context is traditional machine learning already present and doing real work, separate from any LLM or generative AI tool?