User Friendly and Adaptable Discriminative AI: Using the Lessons from the Success of LLMs and Image Generation Models
This addresses the problem of making discriminative models more interactive and accessible for non-expert users, though it appears incremental by applying generative AI workflows to existing discriminative tasks.
The paper tackles the lack of user-friendliness and adaptability in deployed discriminative AI models by developing a new system architecture that allows non-expert users to provide immediate feedback and adapt models similarly to generative AI tools, aiming to improve trust and usability.
While there is significant interest in using generative AI tools as general-purpose models for specific ML applications, discriminative models are much more widely deployed currently. One of the key shortcomings of these discriminative AI tools that have been already deployed is that they are not adaptable and user-friendly compared to generative AI tools (e.g., GPT4, Stable Diffusion, Bard, etc.), where a non-expert user can iteratively refine model inputs and give real-time feedback that can be accounted for immediately, allowing users to build trust from the start. Inspired by this emerging collaborative workflow, we develop a new system architecture that enables users to work with discriminative models (such as for object detection, sentiment classification, etc.) in a fashion similar to generative AI tools, where they can easily provide immediate feedback as well as adapt the deployed models as desired. Our approach has implications on improving trust, user-friendliness, and adaptability of these versatile but traditional prediction models.