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In this episode, we investigate the world of self-learning and continuously adapting AI systems—technologies that allow machines to learn, retain, and apply knowledge over time without constant human input. The discussion breaks down key concepts like lifelong learning, incremental learning, and how they form the building blocks of future Artificial General Intelligence (AGI). Listeners will learn about core algorithmic methods such as reinforcement learning, meta-learning, and transfer learning, as well as the challenges these systems face, including catastrophic forgetting and the delicate balance between learning new information and retaining the old. We also explore practical applications of self-learning AI in education, finance, and autonomous systems, and unpack the ethical implications—ranging from data privacy to algorithmic bias—that must be addressed as this powerful technology evolves.

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