Gabriele Tolomei, Cesare Campagnano, Fabrizio Silvestri, Giovanni Trappolini
{"title":"Prompt-to-OS (P2OS): Revolutionizing Operating Systems and Human-Computer Interaction with Integrated AI Generative Models","authors":"Gabriele Tolomei, Cesare Campagnano, Fabrizio Silvestri, Giovanni Trappolini","doi":"arxiv-2310.04875","DOIUrl":null,"url":null,"abstract":"In this paper, we present a groundbreaking paradigm for human-computer\ninteraction that revolutionizes the traditional notion of an operating system. Within this innovative framework, user requests issued to the machine are\nhandled by an interconnected ecosystem of generative AI models that seamlessly\nintegrate with or even replace traditional software applications. At the core\nof this paradigm shift are large generative models, such as language and\ndiffusion models, which serve as the central interface between users and\ncomputers. This pioneering approach leverages the abilities of advanced\nlanguage models, empowering users to engage in natural language conversations\nwith their computing devices. Users can articulate their intentions, tasks, and\ninquiries directly to the system, eliminating the need for explicit commands or\ncomplex navigation. The language model comprehends and interprets the user's\nprompts, generating and displaying contextual and meaningful responses that\nfacilitate seamless and intuitive interactions. This paradigm shift not only streamlines user interactions but also opens up\nnew possibilities for personalized experiences. Generative models can adapt to\nindividual preferences, learning from user input and continuously improving\ntheir understanding and response generation. Furthermore, it enables enhanced\naccessibility, as users can interact with the system using speech or text,\naccommodating diverse communication preferences. However, this visionary concept raises significant challenges, including\nprivacy, security, trustability, and the ethical use of generative models.\nRobust safeguards must be in place to protect user data and prevent potential\nmisuse or manipulation of the language model. While the full realization of this paradigm is still far from being achieved,\nthis paper serves as a starting point for envisioning this transformative\npotential.","PeriodicalId":501333,"journal":{"name":"arXiv - CS - Operating Systems","volume":"39 12","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Operating Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2310.04875","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.