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If you are beginning to learn machine learning, the number of model names can make the field seem much more complicated than it actually is, but most models can be organized into a few major families based on how they learn and the kinds of problems they are designed to solve. Linear and logistic regression learn relatively simple relationships between variables, while decision trees, random forests, and gradient-boosting models make predictions through combinations of decision rules. K-nearest
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Current LLM-based AI models, built on stochastic neural networks, can't truly 'know' what they're doing. They lack the deterministic, logic-based systems needed for true reasoning and reliable output. This fundamental flaw hinders trust and progress. Even if they *wanted* to provide this behavior, they wouldn't be able to because the core architecture isn't designed nor does it support the right logical, semantics, knowledge base that is needed to provide such trustworthy and reliable AI. but DI
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