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 "identifier": "kaal:entity:labeled-data",
 "name": "Labeled data",
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 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0000-0003-0757-275X"
 },
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   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/4734750-031",
   "identifier": "kaal:claim:4734750-031",
   "text": "Supervised learning currently depends on labeled data produced by micro task work, so the evolution and growth of AI is correlated with the evolution and growth of micro task work.",
   "abstract": "currently, supervised learning depends on labeled data that is produced via micro task work",
   "citation": "Wulf A. Kaal, Code Review DAO (2024). SSRN: https://ssrn.com/abstract=4734750",
   "datePublished": "2024",
   "claim_type": "condition",
   "confidence": "argued",
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    "while supervised learning remains more common than unsupervised and reinforcement learning"
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  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/4755632-008",
   "identifier": "kaal:claim:4755632-008",
   "text": "The quality and quantity of labeled datasets directly governs the performance of the neural network's learning algorithm during supervised training, so the evolution of AI is correlated with the evolution of micro task work.",
   "abstract": "The higher the quality and quantity of such labeled datasets the better the AI neural network's learning algorithm during the supervised training process.",
   "citation": "Wulf A. Kaal, AI Learning - Decentralized Governance to Optimize Human Output Datasets for AI Learning (2024). SSRN: https://ssrn.com/abstract=4755632",
   "datePublished": "2024",
   "claim_type": "mechanism",
   "confidence": "argued",
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   "scope_conditions": [
    "applies to the supervised learning training phase"
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   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/4855607-027",
   "identifier": "kaal:claim:4855607-027",
   "text": "Because annotating large datasets is labor intensive and expensive, smart contracts that reward community members with tokens for annotation are needed to sustain a steady flow of high quality labeled data for deep learning.",
   "abstract": "Annotating large datasets is labor-intensive and expensive. Using smart contracts, web3 can incentivize community members to annotate data by rewarding them with tokens. This system ensures a steady flow of high-quality labeled data, crucial for training deep learning models.",
   "citation": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607",
   "datePublished": "2024",
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    "supervised deep learning that depends on large labeled datasets"
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 "description": "3 claims in the published works of Wulf A. Kaal carry the concept tag 'labeled-data'. Derived node: a roster, not an adjudicated definition."
}