Cross-organizational collaborative development of general technologies for artificial intelligence deep learning systems

Abstract

As the core engine driving the intelligent transformation of global industries, artificial intelligence (AI) deep learning systems have shown a cross-organizational development trend that breaks through the boundaries of a single organization and profoundly reshaping the global industrial ecosystem. The research findings are as follows: (1) The joint cost-sharing model and the aggregated integration decision-making model demonstrate outstanding effectiveness at the technical incentive mechanism level of AI deep learning. (2) When the initial innovation level of the product is relatively high, the AI deep learning system is positively correlated with the effort level and innovation ability of the participants, in the joint cost-sharing and aggregated integration decision-making model, various entities and the entire AI deep learning system have been optimized. (3) Under the aggregated and integrated decision-making mode, the effort level of participants and subjects towards the AI deep learning system is the highest, and thus the level of the AI deep learning system also reaches the optimal level. Previous studies have overlooked cooperative selection models of AI. This article can effectively enhance the depth of collaboration among entities, improve the level of output, and provide a theoretical path for the timely upgrading and development of AI systems.