Author: FLock.io; Translation: 0xjs@Golden Finance
We are entering a defining era in the development of AI, one in which the convergence of generative AI and blockchain is opening up new frontiers of digital innovation.
We explore this paradigm shift in depth, charting the journey from established centralized AI frameworks of Web2 to decentralized innovations of Web3.
Web2 AI and Web3 AI stack and track projects
The Foundation: The AI Technology Stack for Web2
The AI landscape in the Web2 era can be best understood through a layered approach, each representing a key aspect of AI development and deployment. This breakdown provides a clearer perspective on how the individual components interact and contribute to the overall AI ecosystem. These layers include:
Computation Engine
At the core of AI capabilities are centralized cloud services like AWS and Azure. These platforms are the cornerstone of AI operations, providing the computing power necessary to analyze complex data and perform machine learning tasks at scale.
Basic Model
Centralized model training platforms, represented by entities such as OpenAI and Anthropic, have played a crucial role in democratizing AI. These platforms have made advanced AI capabilities accessible, sparking a widespread surge in AI applications across a variety of fields.
Data Management
The efficiency of AI in Web2 relies heavily on effective data management systems. Tools such as Scale AI and Mechanical Turk simplify data preprocessing, ensuring it is ready for analysis. Meanwhile, platforms such as Pinecone, Databricks, and Snowflake play a key role in managing and storing massive datasets, facilitating easy access and manipulation.
Agents and Applications
The versatility of AI in Web2 is best demonstrated in its wide range of applications. From marketing tools like Jasper and copy.ai to breakthrough innovations in healthcare brought about by Glass, the applicability of AI spans across multiple sectors, demonstrating its transformative potential.
Tools and frameworks
This layer has greatly shaped AI development in the Web2 era, with fundamental tools and frameworks for AI agent development, such as Langchain and LlamaIndex. Platforms such as BabyAGI, AutoAGI, and Camel have been essential to enhancing agent development, providing specialized environments for creation and refinement. Collectively, these elements have enriched AI agents with complexity and advanced capabilities, marking key advances in AI capabilities during this period.
Market Mechanism
The emergence of platforms such as HuggingFace and GitHub has fostered a collaborative ecosystem that allows for the sharing and discovery of AI models and tools.
Evolution: A decentralized AI framework for Web3
Transitioning to Web3, our exploration of AI enters a new dimension. Building on our layered approach from Web2, we delve deeper, illustrating how decentralization and blockchain can redefine AI projects. The unique properties of Web3 require additional layers, specifically privacy and authentication, showing how these transformative technologies can foster a more secure, transparent, and collaborative AI environment. This nuanced approach reveals the profound impact of decentralization on the development of AI, paving the way for a future where trust, ownership, and innovation thrive.
Computing Collaboration
Blockchain technology is revolutionizing computing collaboration, moving from centralized cloud services like AWS to a decentralized global network of resources. Projects like Render, Akash, and Gensyn are proof of this evolution, enabling participants to contribute computing power and foster a more inclusive ecosystem.
Model development
Unlike centralized platforms like OpenAI that dominate Web2, Web3 is embracing decentralized model building. This new approach (exemplified by the Bittensor Network) fosters collective contributions from a global community.
Data democratization
Web3 rewrites the rules. No more data locked in corporate vaults. Instead, imagine a vibrant community collaborating to annotate data for groundbreaking research, from medical analysis to self-driving car training. Tools like IPFS and Filecoin ensure secure distributed storage, empowering individuals to control their own data and be rewarded for their contributions. This is more than just data — it’s a new social contract for ownership, collaboration, and shared progress.
Data Privacy
Blockchain becomes a shield for your information. Imagine training AI on sensitive medical data without exposing the patient’s identity, with the help of advanced encryption and zero-knowledge proofs. Companies like Privasea and Modulus Labs are leading the way, shifting the paradigm from intrusive, centralized models to privacy-first AI. This is the promise of Web3: AI that learns and grows while keeping your data safe and under your control.
verify
Solutions like EZKL and Worldcoin offer secure, self-sovereign identity management without the need for a centralized platform to store your login credentials. Imagine being able to log into an AI application without relying on Facebook or Google, enhancing security and giving you control over your digital identity.
AI-driven analytics
Platforms like Dune Analytics and NumerAI leverage cutting-edge AI algorithms to transform data analysis in Web3. Imagine seamlessly exploring complex DeFi protocols, identifying investment opportunities, and making informed decisions, all powered by AI in a transparent and community-driven ecosystem.
Agent
Web3 enables the development of intelligent agents like Autonolas and Fetch.ai that are able to navigate the decentralized web and autonomously perform tasks on your behalf. Imagine AI agents managing your investments, negotiating decentralized transactions, or optimizing your resource allocation across Web3 protocols, ushering in a new era of automated and personalized services.
Final Thoughts
The synergy of blockchain and AI in the Web3 space heralds a new era of innovation, combining blockchain’s strengths in data ownership, transparency, and ethical governance with the advanced capabilities of AI. This integration paves the way for enhanced data ownership, greater transparency in AI systems, and facilitating direct data monetization. It also promises to minimize AI’s energy footprint and promote ethical AI development.
