AI, machine learning, and deep learning are terms often used interchangeably, but they are not the same — and confusing them makes it harder to understand the field. This episode clears the fog by breaking down how these layers of terminology connect. We’ll begin with Artificial Intelligence as the broadest category: any system designed to mimic aspects of human thought. Within that sits machine learning, where computers improve performance by finding patterns in data rather than relying solely on fixed rules. And within machine learning lies deep learning, a powerful subset that uses multi-layered neural networks to handle tasks like vision, speech, and natural language at unprecedented scale.You’ll also hear why these distinctions matter in practice. Traditional AI still has value in symbolic reasoning and expert systems, while machine learning dominates in predictive analytics, and deep learning fuels the breakthroughs behind self-driving cars, virtual assistants, and generative text systems. We’ll cover tradeoffs in interpretability, data needs, and computational demands, showing why organizations choose one approach over another depending on their goals. By the end of this episode, you’ll be able to explain clearly what separates AI, machine learning, and deep learning — and why those differences matter not just for exams or interviews, but for making sense of real-world technologies. Produced by BareMetalCyber.com, where you’ll find more cyber prepcasts, books, and information to strengthen your certification path.

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