kaal:claim:4855607-002

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.

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.

Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024), Model Overview: Deep Learning Models, p. 9
https://ssrn.com/abstract=4855607 · source PDF

Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607

• scaling driven improvement of deep learning accuracy

empiricalsupport: evidencedfailure: Compute scaling wallfamily: scalability-and-throughput-limitinstitutional-design

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