Large language models like GPT-4 have been trained with trillions of parameters and made available to anyone in the world – for a fee. The availability of these models – powered by the same underlying data – means that differentiation and competitive advantage will come in the form of proprietary data against which others can train their own models. There is some precedent for data collaboration and “give-to-get” models in data sharing, but the end-to-end purchase of datasets and immediate deployment in ML environments remains unsolved.
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