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While there is significant interest in using generative AI tools asgeneral-purpose models for specific ML applications, discriminative models aremuch more widely deployed currently. One of the key shortcomings of thesediscriminative AI tools that have been already deployed is that they are notadaptable and user-friendly compared to generative AI tools (e.g., GPT4, StableDiffusion, Bard, etc.), where a non-expert user can iteratively refine modelinputs and give real-time feedback that can be accounted for immediately,allowing users to build trust from the start. Inspired by this emergingcollaborative workflow, we develop a new system architecture that enables usersto work with discriminative models (such as for object detection, sentimentclassification, etc.) in a fashion similar to generative AI tools, where theycan easily provide immediate feedback as well as adapt the deployed models asdesired. Our approach has implications on improving trust, user-friendliness,and adaptability of these versatile but traditional prediction models.

User Friendly and Adaptable Discriminative AI: Using the Lessons from the Success of LLMs and Image Generation Models