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.

翻译：尽管生成式AI工具作为通用模型在特定机器学习应用中备受关注，但当前实际部署更广泛的仍是判别式模型。这些已部署的判别式AI工具的关键缺陷在于其缺乏可自适应性与用户友好性——相比之下，生成式AI工具（如GPT4、Stable Diffusion、Bard等）允许非专业用户通过迭代优化模型输入、实时反馈即时生效，从而使用户从初始阶段就能建立信任。受这种新兴协作流程启发，我们开发了一种新型系统架构，使用户能够以类似生成式AI工具的方式操作判别式模型（如目标检测、情感分类等），轻松提供即时反馈并灵活调整已部署模型。该方法在提升这些通用但传统的预测模型的信任度、用户友好性与可自适应性方面具有重要意义。