Capitalist capital allocation as meta-reward function for AI training model
Machine learning protocols utilize rewards function during training as a means of tuning parameters toward obtaining desirable outputs from a model. One challenge for the current AI industry is the difficulty of translating real-world utility into reward functions for individual models that are trained primarily on training sets of online content, which seldom has an accurate valuation of its real-world utility attached to it. However, computing resources are allocated to different models based on their value to the companies producing and, at a higher layer, capital in the form of investment or revenue is preferentially allocated to those companies whose models are perceived as having higher potential for real-world utility. In essence, this process functions as a meta-reward function for the AI ecosystem overall.
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