7 Ways AI/ML Can Affect Web3

But Web 2.0 has its limitations. Issues such as corporate control, privacy issues and the spread of disinformation are major drawbacks. The shift to Web3, a more advanced and inclusive digital world, is thus 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 Internet after Web 2.0, giving people more control over their data. In it, you use things like blockchain and cryptocurrency wallets to protect your information.

A user in Web3 is an individual who owns and controls their online experiences and can keep their data private. Web3 differs from Web 2.0 in that it gives users more power over companies. Web3 allows users to own and manage 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. Improved data analysis

AI and ML models excel at advanced data analytics and have been widely used in data science for nearly a decade.

In the field of Web3, you can use AI/ML with great success. With AI/ML, you can track transaction records, monitor smart contract interactions, and analyze usage patterns of decentralized applications (DApps).

AI-powered data analytics in Web3 can provide valuable insights into blockchain data. Several blockchain analytics companies have sprung up using AI/ML for advanced data analytics in Web3.

For example, BlockTrace has developed a chatbot capable of analyzing Bitcoin network data. This chatbot allows you to communicate in natural language and get answers to your questions about the Bitcoin blockchain.

2. Smart contract automation

If you understand what smart contracts are, you may know their critical role in the Web3 ecosystem. Integrating AI/ML with smart contract automation in Web3 can improve management processes. For example, it can automate revenue harvesting, NFT coins and liquidity protocols in DeFi platforms.

In addition, using AI/ML to streamline smart contract processes in Web3 can result in the development of optimized contracts. These contracts can lower the gas fee and can be useful during network congestion.

Using machine learning methods, you can also identify the inefficiencies and potential risks within the contract structure. This allows you to address the issues and design more efficient smart contracts.

AI/ML powered smart contracts also provide opportunities for decentralized and intelligent protocols. This shift could 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 attack users. To counter these threats, it is important to use advanced tactics. AI and machine learning improvements in Web3 ecosystems can be valuable tools in improving security protocols.

These algorithms can detect fraud and security breaches. They learn patterns and identify malicious activity through modeling and training in specific environments.

An example of AI-driven fraud detection in Web3 is Sardine. It uses behavioral biometrics to identify unusual user activity and differentiate between legitimate users and fraudsters. Sardine uses guided machine learning techniques for this. The platform also offers AI-based compliance and payment solutions to bolster its capabilities.

4. Decentralized Administration

AI/ML in Web3’s decentralized management can be effective. Decentralized Autonomous Organizations (DAOs) in Web3 can use AI systems to improve their governance. DAOs are blockchain-based platforms that rely on tokenized governance mechanisms.

Merging AI/ML-driven decision making with Web3 governance can improve 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 DAO member preferences and help design the platform accordingly.

Likewise, these models provide accurate data insights, enabling members to take on new challenges or seize opportunities. This increases the flexibility of DAOs and improves their efficiency.

5. Personalized User Experiences

The user-centric approach and personalization in Web3 can lead to improved customer experiences. With AI integration, personalization can reach new heights. DApps in Web3 can use AI/ML to understand your preferences based on your history and interaction patterns.

In Web3, AI and machine learning can personalize your online experience. Platforms can use ML to suggest and display content tailored to you. ML models use filters to check your interests and actions, 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 can personalize interfaces based on your preferences.

For example, in Mastodon, a Web3 social media platform, you can create your own instances with a lot of customization options. You can choose which items or content to include or exclude based on your interests.

6. Privacy and Data Ownership

While it holds the promise of improved privacy, there are still several concerns that Web3 won’t solve all your privacy problems. However, these concerns can be effectively addressed by using AI/ML to strengthen privacy in Web3. ML methods can encrypt your private data and ensure anonymity within decentralized platforms.

AI/ML-driven privacy solutions for Web3 can include techniques such as Secure Multi-Party Computation (SMPC). SMPC ensures data encryption even when multiple parties are involved in data operations. This allows DApps to process data while ensuring user privacy.

AI/ML models also offer methods such as differential privacy, where noise is added to data during extensive analysis.

In this way, the integration of AI in Web3 can improve user data ownership. In Web3, the ecosystem is already decentralized, which means that no authority has any control over it. Adding AI gives you complete 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 provide benefits such as better privacy, improved processes and improved user experience.

When we add AI/ML to Web3’s autonomous agents, we give them rules to follow when interacting with humans. This helps them understand how to behave.

AI models make these intelligent systems even better. They can now independently execute contracts and perform tasks without relying on people for guidance. This makes them more capable and versatile.

An example of AI/ML powered autonomous agents in Web3 is the Satoshi AI project. It uses 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 can drive innovation in Web3

The Web3 ecosystem is currently in its early stages. It faces several challenges, including privacy concerns and inefficient governance. But integrating AI/ML can help solve these problems. AI/ML has progressed and transformed many industries over the past decade.

AI/ML has huge potential in Web3. It can effectively address privacy and efficiency issues. It improves data analytics and enables 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.