Exploring the Innovation Opportunities for Pre-trained Models

TL;DR

This paper analyzes how pre-trained models are used in HCI research to identify real-world opportunities for AI innovation, focusing on capabilities, domains, and interaction patterns.

Contribution

It introduces an artifact analysis approach to categorize and uncover innovation opportunities for pre-trained models based on HCI research applications.

Findings

Identified key capability categories of pre-trained models

Mapped opportunity domains for AI innovation

Highlighted emerging interaction design patterns

Abstract

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.