In our last episode, we described Machine Learning as the engine that powers AI. So, what is the specific output of that engine? What is created when an AI is "trained"?

The answer is the "model." This is the end product of the training process—the distilled knowledge, captured in digital form.

In this episode, we explain what an AI model is and why it's not the whole product. We cover why a model is a 'frozen' snapshot of knowledge, the danger of 'model drift,' and how biased data creates a flawed tool.

Understanding this will equip you to evaluate the whole car, not just the engine, the next time you see a new AI solution.

🎧 Listen to Episode 004: "Models" now and learn what questions to ask before you trust the black box.

Music generated by Mubert https://mubert.com/render

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