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.

翻译：本文提出了一种颠覆性的人机交互范式，从根本上革新了传统操作系统的概念。在这一创新框架下，用户向机器发出的请求由互联的生成式AI模型生态系统处理，这些模型无缝集成甚至取代传统软件应用。这一范式转变的核心是大型生成模型（如语言模型与扩散模型），它们作为用户与计算机之间的核心接口。这种开创性方法利用先进语言模型的能力，使用户能够以自然语言与计算设备进行对话。用户可直接向系统表达意图、任务和疑问，无需显式命令或复杂导航。语言模型理解并解释用户提示，生成并展示具有上下文关联且富有意义的响应，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流程，还为个性化体验开辟了新可能。生成模型可适应个体偏好，通过用户输入进行学习，持续提升理解与响应生成能力。此外，它增强了可访问性——用户可通过语音或文本与系统交互，适应多样化的沟通偏好。然而，这一前瞻性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。必须建立稳健的安全防护机制以保护用户数据，防止语言模型被潜在滥用或操纵。尽管该范式的完全实现仍任重道远，本文旨在为构想这一变革性潜力奠定基础。