Welcome to this repository! Here, you'll find a speech assignment that dives into how artificial intelligence (AI) learns in ways that are surprisingly similar to how we humans learn. The goal of this presentation is to break down complex AI concepts into simple, relatable terms that everyone can understand.
The presentation covers three main methods of learning used by AI:
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Supervised Learning: Think of this like a student learning with a teacher. The AI gets labeled examples, just like how a teacher shows a student what is correct or incorrect. For instance, if an AI is learning to recognize cats, it’s given lots of pictures of cats that are clearly labeled, helping it understand what features make up a 'cat'.
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Unsupervised Learning: This is more like a curious child exploring without instructions. The AI doesn’t have any labeled examples, so it figures things out by finding patterns on its own. Imagine giving a child a box of random toys and watching them group the toys based on their similarities—that's what AI does in unsupervised learning.
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Reinforcement Learning: This is just like learning from experience. The AI learns by doing something and getting rewards or penalties, similar to how we learn not to touch a hot stove after getting burned. Through trial and error, it figures out the best actions to take to get the best results.
The idea here is to make these AI learning methods easy to understand by connecting them to human experiences. We also reference credible sources like academic journals, research reports, and popular tech platforms to back up the explanations.
To get started, you can clone this repository to your local machine. Once cloned, check out the speech outline and resources provided, which will help you get a clear and engaging understanding of how AI learns—just like we do!