The performance of an AI neural network's learning algorithm during supervised training rises with the quality and quantity of the labelled datasets it is trained on, which ties AI progress directly to micro task work.
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The higher the quality and quantity of such labelled datasets the better the AI neural network's learning algorithm during the supervised training process.
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Wulf A. Kaal, Decentralized Mechanical Turk Through Verified Reputation (2018), 1 Problems in Centralized Mechanical Turk, p. 6 https://ssrn.com/abstract=3128900 · source PDF
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Wulf A. Kaal, Decentralized Mechanical Turk Through Verified Reputation (2018). SSRN: https://ssrn.com/abstract=3128900
restated_bykaal:claim:4755632-008 The quality and quantity of labeled datasets directly governs the performance of the neural network's learning...
restated_bykaal:claim:4734750-031 Supervised learning currently depends on labeled data produced by micro task work, so the evolution and growth...
extended_bykaal:claim:4755632-041 Combining decentralized governance with gamification of micro task work is the condition under which gamificat...
extended_bykaal:claim:4734750-032 Because AI progress depends on labeled data from micro task work, the significant limitations of existing micr...
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