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    <loc>https://www.openmercury.com/research/the-economics-that-decide-ai-growth-or-crisis</loc>
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      <image:title>Research - The Economics That Decide AI Growth or Crisis - Make it stand out</image:title>
      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
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      <image:title>Research - The Economics That Decide AI Growth or Crisis - Make it stand out</image:title>
      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
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      <image:title>Research - The Economics That Decide AI Growth or Crisis - Make it stand out</image:title>
      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
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      <image:title>Research - The Economics That Decide AI Growth or Crisis - Make it stand out</image:title>
      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
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    <loc>https://www.openmercury.com/research/the-economic-engine-of-ai</loc>
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      <image:title>Research - The Economic Engine of AI - Make it stand out</image:title>
      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
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    <loc>https://www.openmercury.com/research/how-a-transformer-model-loses-attribution-a-step-by-step-example</loc>
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    <loc>https://www.openmercury.com/research/why-ai-isnt-built-for-attribution-understanding-transformers</loc>
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      <image:title>Research - Why AI Isn't Built for Attribution: Understanding Transformers - Make it stand out</image:title>
      <image:caption>Figure: The transformer architecture (encoder-decoder) with multi-head self-attention (green) as its key component. Each layer allows tokens to attend to (look at) other tokens in the sequence, mixing information from across the input. (source: NIPS'17)</image:caption>
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      <image:title>Research - Why AI Isn't Built for Attribution: Understanding Transformers - Make it stand out</image:title>
      <image:caption>Figure: Authorship Tags Lost in the Funnel — Training abstracts away source identity into distributed parameters.</image:caption>
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    <image:image>
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      <image:title>Research - Why AI Isn't Built for Attribution: Understanding Transformers - Make it stand out</image:title>
      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
    </image:image>
    <image:image>
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      <image:title>Research - Why AI Isn't Built for Attribution: Understanding Transformers - Make it stand out</image:title>
      <image:caption>Figure: An example attention visualization (from an English-to-French translation model) focusing on the word “it” in the sentence “The animal didn’t cross the street because it was too tired.” The model’s attention (lines) connects “it” primarily to “animal,” effectively baking the context of “animal” into the representation of “it”. While this improves understanding, it demonstrates how information from one part of the text is blended into another – with no mechanism to label where that information originally came from. (source: Google Research)</image:caption>
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      <image:title>Research - Why AI Isn't Built for Attribution: Understanding Transformers - Make it stand out</image:title>
      <image:caption>Figure: Who Said It? Dialogue between Alice and Bob is summarized incorrectly by an AI bubble — a visual metaphor for quote misattribution.</image:caption>
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    <image:image>
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      <image:title>Research - Why AI Isn't Built for Attribution: Understanding Transformers - Make it stand out</image:title>
      <image:caption>Figure: The Confident Hallucination. AI confidently types realistic citations — some real, some fabricated — illustrating how form replaces verification.</image:caption>
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    <image:image>
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      <image:title>Research - Why AI Isn't Built for Attribution: Understanding Transformers - Make it stand out</image:title>
      <image:caption>Figure: Partial Fixes for Attribution — Current research directions for restoring provenance.</image:caption>
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    <image:image>
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      <image:title>Research - Why AI Isn't Built for Attribution: Understanding Transformers - Make it stand out</image:title>
      <image:caption>Figure: Unmixing the Cake — Distributed learning makes source disentanglement nearly impossible.</image:caption>
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      <image:title>Research - Why AI Isn't Built for Attribution: Understanding Transformers - Make it stand out</image:title>
      <image:caption>Figure: The Broken Chain of Credit — When attribution disappears, credit and accountability follow.</image:caption>
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      <image:title>Research - Why AI Isn't Built for Attribution: Understanding Transformers - Make it stand out</image:title>
      <image:caption>Figure: From Black Box to Glass Box — The future of AI: from opaque synthesis to transparent citation. Left: opaque black AI cube outputting “unknown source”. Right: transparent cube with visible source icons and citations.</image:caption>
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