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 "name": "Compute cost",
 "termCode": "compute-cost",
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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/4855607-002",
   "identifier": "kaal:claim:4855607-002",
   "text": "The computational cost of improving deep learning performance scales so badly that halving the error rate is estimated to require over five hundred times more computational resources, which raises a sustainability problem for the deep learning paradigm itself.",
   "abstract": "improving deep learning performance increases drastically, with estimates suggesting that halving the error rate would require over 500 times more computational resources. This raises concerns about the sustainability and efficiency of deep learning approaches.",
   "citation": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607",
   "datePublished": "2024",
   "claim_type": "empirical",
   "confidence": "evidenced",
   "is_failure_mode": true,
   "scope_conditions": [
    "scaling driven improvement of deep learning accuracy"
   ],
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   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/4855607-008",
   "identifier": "kaal:claim:4855607-008",
   "text": "GPT class models are costly to run, and their closed nature and undisclosed algorithmic details raise transparency and accountability concerns that their performance does not offset.",
   "abstract": "GPT models, in particular, can be expensive to use due to their high computational requirements, and their closed nature and undisclosed algorithmic details raise concerns about transparency and accountability.",
   "citation": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607",
   "datePublished": "2024",
   "claim_type": "failure",
   "confidence": "evidenced",
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