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              "plaintext": "## Understanding AI: From Zero to Informed (Part 3 of 6)"
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              "plaintext": "Understanding AI: From Zero to Informed (Part 3 of 6)"
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              "plaintext": "AI learns by finding patterns in training data and adjusting weights—numerical values that determine how much each piece of information influences its decisions. But this learning process is fundamentally different from how you learn a language: AI doesn't truly *understand*, it predicts what comes next based on probability, which is why it hallucinates so confidently and gets things spectacularly wrong."
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              "plaintext": "How does AI actually learn from training data?"
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              "plaintext": "**AI learns by processing enormous quantities of text, images, or other data while continuously modifying millions (or billions) of numerical weights—the connection strengths between artificial neurons—to correctly forecast the next word, image, or action. This adjustment happens billions of times across trillions of data points, building intricate patterns that appear remarkably intelligent yet operate through sophisticated statistical forecasting.**"
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              "level": 2,
              "plaintext": "Why fine-tuning and RLHF change how AI actually behaves"
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              "plaintext": "**Fine-tuning and Reinforcement Learning from Human Feedback (RLHF) don't instill new factual knowledge; they restructure how AI applies what it has already learned. RLHF works by gathering human evaluations of model outputs and leveraging them to build a preference model, which subsequently steers the AI toward outputs that humans find useful—prioritizing helpfulness over strict accuracy, in essence.**"
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              "level": 2,
              "plaintext": "Why AI hallucinates: it's predicting, not reasoning"
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            "block": {
              "$type": "pub.leaflet.blocks.text",
              "plaintext": "**AI hallucinates because it's fundamentally a prediction machine. It generates text by selecting the statistically most probable next word, repeatedly. Sometimes convincing-sounding inaccuracies rank higher in likelihood than verified facts, particularly when those facts appeared infrequently in training data or when data patterns contradict reality.**"
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              "plaintext": "Read the full post: https://www.klinchapp.com/blog/how-ai-learns-simple"
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  "description": "Discover how AI actually learns by adjusting numerical weights and finding patterns—and why it confidently hallucinates instead of truly understanding.",
  "path": "/blog/how-ai-learns-simple",
  "publishedAt": "2026-06-23T12:15:59.466Z",
  "site": "at://did:plc:a4f2ydt43slmk3iyvypgsr3d/site.standard.publication/3mox4gp5kmk2g",
  "tags": [
    "ai-fundamentals",
    "explainer",
    "beginners"
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  "textContent": "## Understanding AI: From Zero to Informed (Part 3 of 6)\n\nUnderstanding AI: From Zero to Informed (Part 3 of 6)\nAI learns by finding patterns in training data and adjusting weights—numerical values that determine how much each piece of information influences its decisions. But this learning process is fundamentally different from how you learn a language: AI doesn't truly *understand*, it predicts what comes next based on probability, which is why it hallucinates so confidently and gets things spectacularly wrong.\n\nHow does AI actually learn from training data?\n**AI learns by processing enormous quantities of text, images, or other data while continuously modifying millions (or billions) of numerical weights—the connection strengths between artificial neurons—to correctly forecast the next word, image, or action. This adjustment happens billions of times across trillions of data points, building intricate patterns that appear remarkably intelligent yet operate through sophisticated statistical forecasting.**\n\nWhy fine-tuning and RLHF change how AI actually behaves\n**Fine-tuning and Reinforcement Learning from Human Feedback (RLHF) don't instill new factual knowledge; they restructure how AI applies what it has already learned. RLHF works by gathering human evaluations of model outputs and leveraging them to build a preference model, which subsequently steers the AI toward outputs that humans find useful—prioritizing helpfulness over strict accuracy, in essence.**\n\nWhy AI hallucinates: it's predicting, not reasoning\n**AI hallucinates because it's fundamentally a prediction machine. It generates text by selecting the statistically most probable next word, repeatedly. Sometimes convincing-sounding inaccuracies rank higher in likelihood than verified facts, particularly when those facts appeared infrequently in training data or when data patterns contradict reality.**\n\nRead the full post: https://www.klinchapp.com/blog/how-ai-learns-simple",
  "title": "How AI Actually Learns: The Simple Version"
}