Prompt-to-OS (P2OS)：通过集成AI生成模型革新操作系统与人机交互
Prompt-to-OS (P2OS): Revolutionizing Operating Systems and Human-Computer Interaction with Integrated AI Generative Models
AI总结：
本研究提出Prompt-to-OS (P2OS)人机交互新范式，以大型生成模型为核心接口实现自然语言交互，简化操作并支持个性化与高可及性，同时指出隐私安全等挑战，为操作系统变革提供构想起点。
摘要：
本文提出了一种突破性的人机交互范式，彻底革新了传统的操作系统概念。在这一创新框架内，用户向机器发出的请求由一个相互连接的生成式AI模型生态系统处理，该系统可与传统软件应用无缝集成，甚至取而代之。这一范式转变的核心是大型生成模型，例如语言模型和扩散模型，它们充当用户与计算机之间的中央接口。这种开创性方法利用先进语言模型的能力，让用户能够与计算设备进行自然语言对话。用户可以直接向系统表达自己的意图、任务和查询，无需使用明确的命令或进行复杂的导航操作。语言模型会理解并解读用户的提示，生成并展示符合语境的有意义响应，从而实现流畅直观的交互。这一范式转变不仅简化了用户交互，还为个性化体验开辟了新的可能。生成模型可以适应个人偏好，从用户输入中学习，持续提升自身的理解能力和响应生成质量。此外，它还能增强可访问性，因为用户可以使用语音或文本与系统交互，满足多样化的沟通偏好。然而，这一富有远见的概念也带来了重大挑战，包括隐私、安全、可信度以及生成模型的伦理使用问题。必须建立强有力的保障机制，以保护用户数据，防止语言模型被潜在滥用或操纵。虽然这一范式的完全实现仍遥遥无期，但本文为设想这一变革性潜力提供了起点。
英文摘要：
In this paper, we present a groundbreaking paradigm for human-computer interaction that revolutionizes the traditional notion of an operating system. Within this innovative framework, user requests issued to the machine are handled by an interconnected ecosystem of generative AI models that seamlessly integrate with or even replace traditional software applications. At the core of this paradigm shift are large generative models, such as language and diffusion models, which serve as the central interface between users and computers. This pioneering approach leverages the abilities of advanced language models, empowering users to engage in natural language conversations with their computing devices. Users can articulate their intentions, tasks, and inquiries directly to the system, eliminating the need for explicit commands or complex navigation. The language model comprehends and interprets the user's prompts, generating and displaying contextual and meaningful responses that facilitate seamless and intuitive interactions. This paradigm shift not only streamlines user interactions but also opens up new possibilities for personalized experiences. Generative models can adapt to individual preferences, learning from user input and continuously improving their understanding and response generation. Furthermore, it enables enhanced accessibility, as users can interact with the system using speech or text, accommodating diverse communication preferences. However, this visionary concept raises significant challenges, including privacy, security, trustability, and the ethical use of generative models. Robust safeguards must be in place to protect user data and prevent potential misuse or manipulation of the language model. While the full realization of this paradigm is still far from being achieved, this paper serves as a starting point for envisioning this transformative potential.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
