Innovating with Artificial Intelligence: Capturing the Constructive Functional Capabilities of Deep Generative Learning

Abstract

As an emerging species of artificial intelligence, deep generative learning models can generate an unprecedented variety of new outputs. Examples include the creation of music, text-to-image translation, or the imputation of missing data. Similar to other AI models that already evoke significant changes in society and economy, there is a need for structuring the constructive functional capabilities of DGL. To derive and discuss them, we conducted an extensive and structured literature review. Our results reveal a substantial scope of six constructive functional capabilities demonstrating that DGL is not exclusively used to generate unseen outputs. Our paper further guides companies in capturing and evaluating DGL's potential for innovation. Besides, our paper fosters an understanding of DGL and provides a conceptual basis for further research.

Generative AI-enabled Knowledge Base Fine-tuning: Enhancing Feature Engineering for Customer Churn

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Towards Artificial Intelligence Augmenting Facilitation: AI Affordances in Macro-Task Crowdsourcing

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