No matter how advanced the algorithm, it can’t run without data. This episode focuses on why data is considered the fuel of AI, exploring the different types that drive training and performance. Structured data, such as rows in databases, is contrasted with unstructured data like images, text, and audio. We’ll examine the steps needed to prepare data — collecting, cleaning, labeling, and augmenting — and why quality matters as much as quantity. You’ll also learn about the importance of balanced datasets and how missing or biased data can lead directly to flawed outcomes.We then expand into broader issues of governance and ethics. From open datasets driving research to proprietary datasets conferring competitive advantage, data ownership shapes the AI landscape. Privacy, consent, and regulatory compliance add complexity, especially in healthcare and finance. Synthetic data and federated learning show how innovation continues to expand what counts as usable information. By the end, you’ll see clearly why every AI system reflects the data it’s trained on, and why responsible data practices are inseparable from reliable AI performance. Produced by BareMetalCyber.com, where you’ll find more cyber prepcasts, books, and information to strengthen your certification path.

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“What did the council decide on item 42?” — answered with the speaker and the moment.

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