International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 11 | Nov 2024
p-ISSN: 2395-0072
www.irjet.net
ARTIFICIAL INTELLIGENCE THAT GENERATES IN THE METAVERSE ERA Yashaswini S P1, T Gnanaprasuna2, Supriya S Kyalakond3 1Bachelor of Engineering, Information Science and Engineering, Bapuji Institute of Engineering and Technology,
Davangere, affiliated to VTU Belagavi, Karnataka, India.
2Bachelor of Engineering, Information Science and Engineering, Bapuji Institute of Engineering and Technology,
Davangere, affiliated to VTU Belagavi, Karnataka, India. 3Bachelor of Engineering, Information Science and Engineering, Bapuji Institute of Engineering and Technology,
Davangere, affiliated to VTU Belagavi, Karnataka, India. ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - A type of artificial intelligence known as
"generative AI" is capable of producing original text, images, audio, and video on its own. By addressing gaps in the metaverse's evolution, generative AI offers creative methods for creating material in the metaverse. Products like ChatGPT could improve the way people search, change how content is generated and presented, and open up new channels for web traffic. This should have a big effect on conventional search engine products, spurring innovation and modernization in the sector. This study provides insights for improving the efficacy of generative AI in producing creative material, as well as an outline of the technologies and potential uses of generative AI in the metaverse technology breakthrough.
Fig.1. Comparison of workflow between traditional computer vision techniques and deep learning methods in generative AI. Furthermore, in order to minimize the technical barrier to achieving creativity in the metaverse era, the new technology ChatGPT, which is based on generative AI, is investigated.
Key Words: Virtual AI Agents, AI-Powered Worlds, Deep Learning Algorithms, Computer Vision AI, Edge AI Computing, AI in 3D Modeling, Speech-to-Action AI, Cloud AI Infrastructure.
2. Implementation
Of Different Metaverse Components with Generative AI Support
1. INTRODUCTION
2.1 The theoretical underpinnings of generative AI
By automating intelligent decision-making and producing highly tailored user experiences, artificial intelligence (AI) holds the potential to significantly enhance the metaverse. Customers can perform financial transactions online with more privacy and security thanks to Web3's distributed network design [1–3]. Furthermore, data security and integrity are ensured by the immutable data storage and transfer protocols enabled by blockchain technology. By tackling issues with digital assets and content creation and bridging crucial gaps in Web3's development, generative AI technologies such as Chat Generative Pre-Trained Transformer (ChatGPT) have the potential to become productivity tools in the Web3 era [4]. The industry has paid close attention to the inventiveness and adaptability of generative AI since the introduction of ChatGPT and other similar technologies. With the aid of ChatGPT, which is based on deep learning models and can produce content in a wide range of situations and satisfy a wide range of needs, the effectiveness and Caliber of content creation and distribution may be significantly increased.
© 2024, IRJET
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Impact Factor value: 8.315
As AI technology has developed, generative AI has emerged as a key research topic. Generative AI is a new AI system that can automatically create new material using input data, claim Lim et al. (2023) [5]. computer vision is a crucial theoretical foundation for generative AI since it deals with the processing of picture data to learn new things and produce a variety of content from various image collections. The workflows for deep learning and conventional computer vision approaches are contrasted in Fig. 1. Furthermore, by facilitating smooth interaction between the actual and virtual worlds, several basic computer vision algorithms may improve users' experiences in virtual environments [6–8]. Furthermore, generative AI can help architects swiftly create complex interior spaces, including layouts, finishes, and interior decorations. According to Haleem & Javaid (2022) [9], generative AI can also help architects create complex materials more rapidly. Intricate materials including wood, metal, cement, ceramics, steel, rubber, oak, bamboo, aluminium alloy, copper alloy, titanium alloy, and more can be made by architects using generative AI. Additionally, sophisticated security mechanisms that are impervious to theft can be created using generative AI. In summary, the
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