Author: Chain Research Source: medium Translation: Shan Ouba, Jinse Finance
From the static, one-way communications of Web 1.0 to the dynamic, user-driven landscape of Web 2.0, the Internet has undergone a remarkable transformation in the space of two decades. Now, as we stand on the brink of the Web 3.0 era, we are witnessing the dawn of something even more disruptive: the integration of generative artificial intelligence (AI) into our digital world. This rapidly evolving technology promises to redefine creativity, content creation, and interaction across the vast expanse of the Internet. With the market for generative AI expected to reach $60 billion by 2025, the combination of generative AI and the highly decentralized capabilities of Web3 is expected to transform our online experience.
Unraveling the Mechanism of Generative Artificial Intelligence
Understanding generative AI means studying how it allows machines to create different forms of content, such as text, images, sound, video, and code. This branch of AI uses complex neural networks to analyze large data sets and enhance human creativity, making it a cornerstone of modern technological innovation.
Generative AI is driven by base models, which are large AI frameworks that are able to multi-task and perform non-standard tasks such as summarization, question answering, classification, etc. These models require minimal training to be customized for specific applications, even with limited sample data.
The process of training a generative AI model typically involves supervised learning, where the model is fed a dataset of human-created content along with corresponding labels. It learns to mimic and create new content that reflects human-created examples and categorizes them in a similar manner. This capability goes beyond traditional AI by leveraging complex neural networks such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), enabling the generation of a wide range of content types.
These advances not only enhance human creativity but also introduce new avenues for autonomous content generation, especially in the decentralized realm of Web3, enriching the digital landscape with endless possibilities.
Generative AI within the Web3 framework
Since Generative AI for Web3 involves the fusion of Generative AI methods with the decentralized principles of Web3, here is how this innovative technology works:
Decentralized Data Processing: In conventional AI systems, data processing usually occurs on centralized servers, meaning it is controlled by a single entity. In Web3’s generative AI, data processing is decentralized, meaning it is performed on a network of nodes rather than relying on a single server. This enhances security by eliminating single points of control and failure, as it is in line with the secure and decentralized nature of Web3.
Blockchain Integration: Since Web3 leverages blockchain as its underlying infrastructure, this integration guarantees that every stage of AI content generation is recorded on an open ledger. By serving as a reliable and immutable record, blockchain enhances trust and accountability in the generative AI workflow.
Smart contracts for governance: Smart contracts are self-executing agreements with predefined rules written in code. In the context of generative AI in Web3, smart contracts enable distributed governance. Users collaborate and make decisions through these automated contracts, ensuring a free and transparent approach to managing the generative AI process. This reduces the need for a single authority and fosters a more inclusive decision-making environment.
Tokenization and Incentives: Web3 introduces tokenization, which refers to the process of converting valuable assets into digital tokens recorded on the blockchain. Generative AI processes can use tokens to increase user participation and contribution. Users receive tokens as rewards for validating content, maintaining the network, or actively participating in generative AI activities. This token-based ecosystem creates a direct incentive structure that encourages user participation and improves the overall efficiency of the generative AI system.
Example
Generative AI offers a wide range of applications for the Web3 ecosystem. Some notable use cases include:
Decentralized content creation
Generative AI in Web3 can be used for decentralized content creation, covering text, images, videos, etc. Users on the network can contribute and verify content, thereby fostering a collaborative and diverse digital environment.
NFT Generation and Market
Non-fungible tokens (NFTs) can be generated using generative AI algorithms, creating unique and rare digital assets. Web3 facilitates the creation of NFT marketplaces where these assets can be securely traded, bought, and sold.
AI-driven governance
Generative AI combined with Web3’s governance mechanisms can be used for decentralized decision-making. AI algorithms can help analyze proposals and aid in the consensus-building process within decentralized autonomous organizations (DAOs).
Collaborative AI development
Web3 platforms can leverage generative AI for collaborative AI development, allowing multiple users to contribute to the creation and improvement of AI models in a decentralized and permissionless manner.
Automatic code generation
The use of generative AI in Web3 development has the potential to automate certain aspects of coding, empowering developers to create smarter and more secure smart contracts, decentralized applications (DApps), and other elements related to blockchain technology.
Blockchain game development
Generative AI brings breakthrough opportunities to blockchain game development by enabling the dynamic creation of game assets, environments, and narratives. AI models can generate complex interactive worlds and characters that adapt to players' actions and decisions in real time, creating more immersive gaming experiences. AI-driven procedural content generation can produce infinite variations of quests, levels, and puzzles, ensuring that every player's journey is unique. By integrating these capabilities with the transparent and secure infrastructure of Web3, developers can build decentralized games that provide true ownership of in-game assets, verifiable item scarcity, and new economic models that reward players and creators.
Exploring the Pioneers of Generative Artificial Intelligence in Web3
After considering the use cases of Generative AI in Web3, here are the top Web3 platforms implementing this innovative technology:
Decentraland: Decentraland is a virtual world built on the Ethereum blockchain that is exploring the use of generative AI to enhance the creation and interaction of virtual assets and environments.
SuperRare: SuperRare is an NFT art platform on the Ethereum blockchain that is exploring new possibilities for digital art creation and tokenized ownership using generative AI.
InQubeta: InQubeta is a contemporary AI and web3 cryptocurrency initiative that leverages blockchain technology to connect investors with startups seeking initial funding.
Fetch.ai: Fetch.ai is a decentralized artificial intelligence platform that combines machine learning and blockchain to enable independent agents to perform tasks and interact online.
Augur: Augur is based on a decentralized prediction market system that uses artificial intelligence algorithms to improve the accuracy of predictions and verify the outcomes of cryptocurrencies, sports, and world events.
Nexo: By using artificial intelligence and machine learning, Nexo operates as a cryptocurrency lending platform that assesses the creditworthiness of borrowers and maximizes profits for lenders.
What is the future of Web3 and generative AI?
Artificial Intelligence and Web3 have the potential to change the way we create, share, and control content on the internet. Not only does it change the production and consumption of content, it also changes the landscape of online influence.
The collaborative nature of Web3 combined with the imagination of generative AI offers limitless promise for progress, inclusivity, and user empowerment.
As we navigate the evolving technological landscape, we must address ethical concerns, protect privacy, and ensure fair distribution. The impact of generative AI in Web3 spans fields as diverse as entertainment and finance, making it a critical area to develop and watch in the future.
Pioneers such as ChatGPT and DALL-E, both developed by OpenAI, have made significant leaps in the field of generative AI. ChatGPT embodies the evolution of AI language models designed to generate text based on user input, serving a wide range of purposes from customer support to content creation. Similarly, DALL-E has opened up new areas of digital art and design by generating images from text descriptions, demonstrating the vast potential of generative AI.
