Exploring the Innovation Opportunities for Pre-trained Models
This work addresses the problem of distinguishing hype from real potential in AI innovation for developers and researchers, though it is incremental in nature.
The study tackled the challenge of identifying genuine innovation opportunities for pre-trained models by analyzing applications from HCI research as proxies for commercial success, categorizing capabilities, domains, data types, and interaction patterns to uncover the opportunity space.
Innovators transform the world by understanding where services are successfully meeting customers' needs and then using this knowledge to identify failsafe opportunities for innovation. Pre-trained models have changed the AI innovation landscape, making it faster and easier to create new AI products and services. Understanding where pre-trained models are successful is critical for supporting AI innovation. Unfortunately, the hype cycle surrounding pre-trained models makes it hard to know where AI can really be successful. To address this, we investigated pre-trained model applications developed by HCI researchers as a proxy for commercially successful applications. The research applications demonstrate technical capabilities, address real user needs, and avoid ethical challenges. Using an artifact analysis approach, we categorized capabilities, opportunity domains, data types, and emerging interaction design patterns, uncovering some of the opportunity space for innovation with pre-trained models.