A small tool to view real-world ActivityPub objects as JSON! Enter a URL
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header
to the server to view the underlying object.
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"toot": "http://joinmastodon.org/ns#",
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"Hashtag": "as:Hashtag"
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"content": "<p><span class=\"h-card\" translate=\"no\"><a href=\"https://fediscience.org/@DrYohanJohn\" class=\"u-url mention\">@<span>DrYohanJohn</span></a></span> <br /><span class=\"h-card\" translate=\"no\"><a href=\"https://a.gup.pe/u/cogsci\" class=\"u-url mention\">@<span>cogsci</span></a></span> </p><p>Boosting for my followers - see original post in the thread above.</p><p>The BBS target article on the Language of Thought Hypothesis states:</p><p>"We outline six core properties of<br />LoTs: (i) discrete constituents; (ii) role-filler independence; (iii) predicate-argument<br />structure; (iv) logical operators; (v) inferential promiscuity; and (vi) abstract content."</p><p>It's interesting to think about the extent to which those properties are directly enabled by neural representations using Vector Symbolic Architectures / Hyperdimensional Computing. It hadn't occurred to me to draw a line between LoT and VSA/HDC.</p><p><a href=\"https://aus.social/tags/CogSci\" class=\"mention hashtag\" rel=\"tag\">#<span>CogSci</span></a> <a href=\"https://aus.social/tags/CognitiveScience\" class=\"mention hashtag\" rel=\"tag\">#<span>CognitiveScience</span></a> <a href=\"https://aus.social/tags/CompCogSci\" class=\"mention hashtag\" rel=\"tag\">#<span>CompCogSci</span></a> <a href=\"https://aus.social/tags/ComputationalCognitiveScience\" class=\"mention hashtag\" rel=\"tag\">#<span>ComputationalCognitiveScience</span></a> <a href=\"https://aus.social/tags/VSA\" class=\"mention hashtag\" rel=\"tag\">#<span>VSA</span></a> <a href=\"https://aus.social/tags/VectorSymbolicArchitecture\" class=\"mention hashtag\" rel=\"tag\">#<span>VectorSymbolicArchitecture</span></a> <a href=\"https://aus.social/tags/HDC\" class=\"mention hashtag\" rel=\"tag\">#<span>HDC</span></a> <a href=\"https://aus.social/tags/HyperdimensionalComputing\" class=\"mention hashtag\" rel=\"tag\">#<span>HyperdimensionalComputing</span></a></p>",
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"en": "<p><span class=\"h-card\" translate=\"no\"><a href=\"https://fediscience.org/@DrYohanJohn\" class=\"u-url mention\">@<span>DrYohanJohn</span></a></span> <br /><span class=\"h-card\" translate=\"no\"><a href=\"https://a.gup.pe/u/cogsci\" class=\"u-url mention\">@<span>cogsci</span></a></span> </p><p>Boosting for my followers - see original post in the thread above.</p><p>The BBS target article on the Language of Thought Hypothesis states:</p><p>"We outline six core properties of<br />LoTs: (i) discrete constituents; (ii) role-filler independence; (iii) predicate-argument<br />structure; (iv) logical operators; (v) inferential promiscuity; and (vi) abstract content."</p><p>It's interesting to think about the extent to which those properties are directly enabled by neural representations using Vector Symbolic Architectures / Hyperdimensional Computing. It hadn't occurred to me to draw a line between LoT and VSA/HDC.</p><p><a href=\"https://aus.social/tags/CogSci\" class=\"mention hashtag\" rel=\"tag\">#<span>CogSci</span></a> <a href=\"https://aus.social/tags/CognitiveScience\" class=\"mention hashtag\" rel=\"tag\">#<span>CognitiveScience</span></a> <a href=\"https://aus.social/tags/CompCogSci\" class=\"mention hashtag\" rel=\"tag\">#<span>CompCogSci</span></a> <a href=\"https://aus.social/tags/ComputationalCognitiveScience\" class=\"mention hashtag\" rel=\"tag\">#<span>ComputationalCognitiveScience</span></a> <a href=\"https://aus.social/tags/VSA\" class=\"mention hashtag\" rel=\"tag\">#<span>VSA</span></a> <a href=\"https://aus.social/tags/VectorSymbolicArchitecture\" class=\"mention hashtag\" rel=\"tag\">#<span>VectorSymbolicArchitecture</span></a> <a href=\"https://aus.social/tags/HDC\" class=\"mention hashtag\" rel=\"tag\">#<span>HDC</span></a> <a href=\"https://aus.social/tags/HyperdimensionalComputing\" class=\"mention hashtag\" rel=\"tag\">#<span>HyperdimensionalComputing</span></a></p>"
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