The current version of the internet, Web 2.0, uses AI and machine learning models in different ways. These models power targeted ads, recommendation engines, chatbots, image generators, and voice assistants.But Web 2.0 has its limitations. Issues like corporate control, privacy concerns, and the spread of misinformation are major drawbacks. So, the shift to Web3, a more advanced and inclusive digital realm, is gaining popularity.As the internet evolves, it becomes crucial to understand how AI and ML will function in Web3.
What Exactly Is Web3?
Before delving into AI integration, it’s crucial to understand Web3. Web3 is the next generation of the web after Web 2.0 which allows people more control over their data. In it, you use things like blockchain and cryptocurrency wallets to protect your information.
Auser in Web3is an individual who has ownership and control over their online experiences and can keep their data private. Web3 is different from Web 2.0 because it gives users more power over corporations. With Web3, users can own and control decentralized platforms. This makes the online world fairer and more inclusive for everyone.
Now, let’s see how AI/ML can make Web3 even better.
1. Enhanced Data Analysis
AI and ML models excel in advanced data analysis, and they have been widely used in data science for almost a decade.
In the realm of Web3, you may use AI/ML to great effect. With AI/ML, you can track transaction records, monitor smart contract interactions, and analyze usage patterns of decentralized applications (DApps).
AI-powered data analysis in Web3 can provide valuable insights into blockchain data. Several blockchain analytics firms that leverage AI/ML for advanced data analysis in Web3 have emerged.
BlockTrace, for example, has developed a chatbot capable of analyzing Bitcoin network data. This chatbot allows you to interact using natural language and get answers to your queries about the Bitcoin blockchain.
2. Smart Contract Automation
If you understandwhat smart contracts are, you might know their crucial role in the Web3 ecosystem. Integrating AI/ML with smart contract automation in Web3 can enhance management processes. For instance, it can automate yield harvesting, NFT minting, and liquidity protocols in DeFi platforms.
Furthermore, using AI/ML to streamline smart contract processes in Web3 can result in the development of optimized contracts. These contracts can reduce the gas fee and can be helpful during network congestions.
Using machine learning methods, you can also identify the inefficiencies and potential risks within the contract structure. It will allow you to address the issues and design more efficient smart contracts.
AI/ML-powered smart contracts also open up possibilities for decentralized and intelligent protocols. This shift can lead to the emergence of automated market makers (AMMs) in decentralized finance (DeFi),dynamic non-fungible tokens (NFTs), and advanced lending protocols. These innovations bring efficiency and intelligence to the Web3 ecosystem.
3. Fraud Detection and Security
In this era, cyber attackers use sophisticated strategies to target users. To counter these threats, it’s important to use advanced tactics. AI and machine learning advancements in Web3 ecosystems can be valuable tools in enhancing security protocols.
These algorithms can detect fraud and security breaches. They learn patterns and identify malicious activities through modeling and training in specific environments.
An example of AI-powered fraud detection in Web3 isSardine. It uses behavior biometrics to identify unusual user activities and differentiate between legitimate users and fraudsters. Sardine employs supervised machine learning techniques for this purpose. The platform also provides AI-based compliance and payment solutions to strengthen its capabilities.
4. Decentralized Governance
AI/ML in the decentralized governance of Web3 can be effective. Decentralized autonomous organizations (DAOs) in Web3 can use AI systems to improve their governance. DAOs are blockchain-based platforms that depend on tokenized governance mechanisms.
Merging AI/ML-driven decision-making into Web3 governance can enhance decentralization. It can detect fraud, protect your privacy, and assess risks within the platform to bring transparency.
AI/ML models are also important for the voting system. They can analyze data to understand the preferences of DAO members and help design the platform accordingly.
Likewise, these models provide accurate data insights, enabling members to address new challenges or seize opportunities. This enhances the flexibility of DAOs and improves their efficiency.
5. Personalized User Experiences
The user-centric approach and personalization inWeb3 may lead to improved customer experiences. With AI integration, personalization can reach new heights. DApps in Web3 can utilize AI/ML to understand your preferences based on your history and interaction patterns.
In Web3, AI and machine learning can make your online experience more personalized. Platforms can use ML to suggest and show content that is tailored for you. ML models use filters to check your interests and actions, and then provide recommendations and content that match your preferences.
Web3 offers more customization options compared to Web 2.0. In addition to content and recommendations, you may personalize interfaces based on your preferences.
For example, inMastodon, a Web3 social media platform, you can create your own instances with a lot of customization possibilities. You can choose what items or content to include or exclude based on your interests.
6. Privacy and Data Ownership
While it holds the promise of enhanced privacy, there are still several concerns thatWeb3 won’t solve all of your privacy problems. However, these concerns can be effectively tackled by leveraging AI/ML to strengthen privacy in Web3. ML methods can encrypt your private information and ensure anonymity within decentralized platforms.
AI/ML-driven privacy solutions for Web3 can encompass techniques such as secure multi-party computation (SMPC). SMPC ensures data encryption even when multiple parties are involved in data operations. This enables DApps to process data while safeguarding user privacy.
AI/ML models also bring methods like differential privacy, which involves adding noise to data during extensive analyses.
This way, integrating AI into Web3 can enhance user data ownership. In Web3, the ecosystem is already decentralized, meaning no single authority controls it. By adding AI, you can have full control over your data, giving you even more power in the Web3 world.
7. Autonomous Agents and Intelligent Contracts
AI/ML can bring autonomous agents and intelligent contracts to Web3. These agents work on your behalf without direct instructions and offer benefits like better privacy, improved processes, and enhanced user experience.
When we add AI/ML to Web3’s autonomous agents, we give them rules to follow when interacting with people. This helps them understand how to behave.
AI models make these intelligent systems even better. They can now execute contracts and perform tasks independently without relying on humans for guidance. This makes them more capable and versatile.
An example of AI/ML-powered autonomous agents in Web3 is theSatoshi AIproject. It utilizes AI to create agents that can interact with decentralized networks. These agents serve as personal assistants, advisors, and decision-making entities, providing valuable assistance in the Web3 ecosystem.
AI/ML May Drive Innovation in Web3
The Web3 ecosystem is currently in its early stages. It faces several challenges, with privacy concerns and inefficient governance being prominent among them. But integrating AI/ML can help solve these issues. AI/ML has made progress and transformed many industries in the past decade.
AI/ML has huge potential in Web3. It can address privacy and efficiency concerns effectively. It improves data analysis and allows for autonomous smart contracts.
AI/ML also focuses on personalization to provide better user experiences in Web3’s decentralized environment. It brings innovation, efficiency, and user-centric experiences to Web3.
