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AI and Web3 – The integration of artificial intelligence (AI) into web3 is creating exciting new possibilities. As decentralized networks like blockchain become more complex, AI has emerged as a solution to make web3 more efficient, automated, and secure. From powering decentralized applications (dApps) to securing blockchain networks, AI is poised to revolutionize nearly every aspect of web3.
What is Web3?
Web3 refers to the next evolution of the internet based on blockchain and decentralized networks. After the read-write web or Web 2.0, Web3 aims to create an internet that is decentralized, transparent, and user-owned. The core components of Web3 include:
- Blockchain technology like Ethereum which enables decentralized apps and cryptocurrency.
- Decentralized storage systems like IPFS and Filecoin to host website data.
- Decentralized infrastructure providers like Helium and Algorand.
- Decentralized governance through protocols like Compound and MakerDAO.
- Decentralized identity solutions like Ceramic Network.
- Token-based economics and ownership.
By eliminating centralized intermediaries, Web3 puts users back in control of the internet. Applications are built with transparency, privacy and ownership rights at the core.
The Role of AI in Web3
AI or artificial intelligence refers to computers mimicking human intelligence for tasks like visual perception, speech recognition, decision-making and language translation. Popular AI techniques include machine learning, deep learning, neural networks and natural language processing (NLP).
Here are some of the key ways AI is revolutionizing Web3:
Smarter Blockchain Networks
AI is making blockchain networks like Ethereum and Solana smarter and more scalable. Techniques like neuro-symbolic AI create advanced reasoning capabilities so blockchains can dynamically optimize and self-govern their networks.
Startups like Alethea AI and Anthropic are using AI for blockchain optimization, predictive analytics and adaptive policy intelligence. Such AI-powered blockchain networks are faster, highly secure and consume fewer computing resources.
Decentralized networks need powerful cybersecurity against threats like flash loan attacks, NFT scams and Ponzi schemes. AI is automating threat detection, analytics and response time for Web3 cybersecurity.
Machine learning models can rapidly analyze blockchain transactions and activity across nodes to identify any anomalous behavior indicative of an attack. AI cybersecurity also makes smart contract audits and vulnerability assessments more efficient for DeFi applications.
Intelligent Token Curating
With over 16,000 cryptocurrencies on the market, individuals and financial institutions need AI-based intelligence to evaluate and curate tokens for investment or trading strategies.
Natural language processing (NLP) can parse whitepapers and online data sources to assess attributes like tokenomics, roadmap quality, team credentials and community hype for a project. Quantitative AI analysis evaluates on-chain metrics around liquidity, volatility, social sentiment and github activity.
Together, qualitative and quantitative AI analysis can generate investment ratings and predictions for the risk and return potential of crypto tokens.
AI Digital Assistants
Chatbots and voice assistants are making blockchain apps more intuitive and accessible for mainstream web3 adoption. Through conversational interfaces, everyday users can now easily interact with dApps and crypto wallets.
AI assistants created by startups like Alkemi Network offer personalized guidance on asset management, DeFi staking, governance protocols and other aspects of web3. As AI capabilities advance, digital assistants are becoming savvier at explaining complex DeFi concepts.
Enhanced User Experience
Beyond assistants, AI is enhancing web3 user experiences overall through predictive analytics. Historical user data and activity patterns allow AI to customize interfaces, simplify navigation and pre-fetch relevant information on dApps for each user.
Computer vision AI also enables novel interfaces. For example, lens protocol uses AI facial analysis for gesture control over wallets or smart glasses that overlay decentralized apps into any real-world environment.
Efficient Decentralized Computing
Running AI algorithms requires massive computing resources. To make decentralized AI more scalable, blockchain projects like SingularityNET and Ocean Protocol are creating AI-specific blockchains where any node provider can rent out GPU and CPU cycles for AI computations.
Federated Learning is another technique where AI models are trained collectively across nodes in a decentralized network without aggregating user data. Such solutions will democratize access to AI and prevent concentration of power in big tech companies.
Automated Smart Contracts
Smart contracts are what make many of the innovations in Web3 possible, enabling decentralized trading, loans and agreements. But designing bug-free contracts requires high technical skills. AI tools are automating smart contract creation for faster deployment.
Startup Anthropic is using natural language AI to convert plain English descriptions of contract logic into secure Solidity code. Other techniques like genetic algorithms and formal verification can auto-generate optimized contracts for unique use cases. AI-based contract creation lowers the barrier for dApp development.
Deepfakes generated through AI are often seen negatively but have creative applications on web3. Synthetic media can generate interactive AI avatars, VR environments and AR filters. For privacy, users may preferbots over real images.
AI synthetic media on the blockchain also creates new revenue opportunities. Startups are building NFT marketplaces for computer-generated art, voices and music. The applicability of synthetic media will expand as AI capabilities mature.
Decentralized Machine Learning
While AI relies heavily on data, most algorithms are trained by Big Tech firms in a centralized manner on proprietary datasets. To make ML more accessible, decentralized protocols are emerging for collaborative training of shared models.
federated learning, differential privacy, and trusted execution environments (TEEs) to enable decentralized cohorts of data scientists, companies and individuals to jointly produce high-quality AI models without compromising proprietary data.
Together these innovations make the development and monetization of AI models more inclusive. Developer communities get access to rare datasets while individuals earn rewards for contributing high-quality data.
The continued integration of AI into Web3 is inevitable as decentralized networks scale up. AI solutions will catalyze mainstream adoption of blockchain applications. They introduce design efficiencies that were hitherto not possible without centralized intermediaries.
But fully decentralized AI also remains an open challenge. Misalignment between AI incentives and human values is a valid concern. Striking a balance between decentralization and oversight over AI will be critical as adoption progresses. When implemented ethically, AI can hugely empower the collective potential of Web3.