Here is the translation of the 17 use cases, ideas for creation or investment, and startup requests in the field of decentralized and distributed AI:
- Decentralized Compute: Imagine running complex AI models without relying on large tech companies. Decentralized computing can compete with cloud offerings in terms of price and performance while providing better privacy guarantees and greater flexibility.
- Data Incentivization Networks: These systems reward participants for creating valuable datasets and verifiers for ensuring accuracy. This creates a self-sustaining ecosystem of high-quality data, whether synthetic, user-generated, or collected.
- Continuously Pre-trained Models: Imagine a large decentralized network of participants creating and updating AI models in real-time. This provides the most up-to-date and modern results, adaptable to changing contexts.
- Decentralized Model Evaluation: The ability to evaluate AI applications in specific areas using closed test sets and human-evaluated tasks. This helps distinguish between models that are truly useful and those that are good for casual conversation.
- Multi-Agent Systems: Networks of interacting AI agents for solving complex tasks. Decentralized MAS allows for constant adaptation as new capabilities are created or market conditions change.
- AI-Native Finance: As agents manage budgets (in tokens or computational resource allocation), we'll need new tools for AI-native lending, accounting, and verification. This opens up a new financial paradigm for AI systems.
- AI Memory: Systems that enable agents to record and process interactions with users, creating long-term memory graphs. This leads to a more useful and enjoyable user experience in various AI applications.
- Human-in-the-Loop: A platform where AI agents can hire humans for tasks requiring real-world actions or human judgment, such as sending packages, taking photos, or providing feedback on AI-generated content.
- AI-Native Contracts: A global, frictionless system for AI agents to enter into and execute contracts with devices, obligations, and cryptographic tools to ensure compliance and confidentiality.
- AI-Assisted Cybersecurity: Imagine a 24/7 digital war where AI agents actively conduct probing, penetration testing, and finding new vulnerabilities. This ongoing arms race between offensive and defensive AI drives rapid progress in cybersecurity.
- AI-Assisted Governance: AI systems that help navigate complex issues, facilitate decision-making, mediate conflicts, and even act as individual democratic representatives. This can revolutionize organizational and political governance.
- Variable Privacy: Flexible approaches where users choose privacy levels, from TEE (medium privacy, low cost) to FHE (stronger privacy, high cost).
- ICO for AI: Crowdfunding mechanisms for AI models. Invest time, computational resources, or money in model creation and receive a share of future profits.
- Token Auctions: A new primitive where market participants bid on model output for inclusion of advertising information.
- Agent Schedulers: Imagine intelligent cron jobs for AI. Agents that "wake up" themselves for autonomous task execution, similar to smart contract automation but for AI agents.
- AI Moderation: Decentralized networks of AI agents working together to moderate online content across platforms, reducing bias and increasing accuracy.
- AI-SSI: A self-sovereign identity system created by AI and for AI, improving security, privacy, and user experience in digital interactions.