Blockchain Integration with AI
With extensive experience in blockchain technology, especially in the enterprise sector, I had the opportunity to collaborate with O’Reilly on a book titled Blockchain Tethered AI. Blockchain’s immutable, distributed, secure, and transparent nature makes it ideal for tracking and tracing the development of various components, including crops, valuable items like diamonds, medicines, and software. AI, as an advanced form of software, is growing rapidly, which increases associated risks. These risks arise because AI systems heavily influence decision-making processes, and their accuracy depends on the quality and type of data used for training. If an AI is trained with improper data, it can malfunction, produce inaccurate results, or exhibit bias. Fortunately, these issues can be addressed using blockchain technology, which ensures the integrity and traceability of the data used to train AI systems.
Key Topics Covered in the Book:
1. Creating and Powering AI Marketplaces with Blockchain
Imagine an online store where you can buy and sell AI models safely. Blockchain ensures that every transaction is secure and transparent, so you know exactly what you’re getting and from whom. By using blockchain, these AI marketplaces provide a secure and tamper-proof environment for trading AI models and data. This means that all transactions are recorded and cannot be altered, ensuring trust between buyers and sellers.
In the book, I’ve envisioned a marketplace for AI models produced through a blockchain-based framework. While the implementation details are left for future exploration, I have clear ideas on how to take AI models to the marketplace and sell them securely.
2. On-Chain AI Governance
Think of this as a set of rules that AI has to follow, all recorded on the blockchain. This way, you can always check if the AI is behaving correctly. Blockchain helps create a governance framework where all the rules and regulations that an AI model must follow are written on the blockchain. This makes it easy to monitor and ensure compliance, as all actions are transparent and traceable.
The book demonstrates how artifacts, development processes, and logs created during AI model development can be stored on the blockchain, aiding in the regulation of AI models.
3. Tethering AI to Blockchain Networks
This means making sure that AI can’t work without the blockchain. It’s like having a safety net that ensures all AI actions are recorded and verified. By integrating AI systems with blockchain networks, every action taken by the AI is recorded on the blockchain. This makes the AI’s operations transparent and secure, preventing unauthorized changes or actions.
4. Using Blockchain Crypto Anchors
Think of crypto anchors as special tools that detect if someone tries to mess with the AI. They help keep the AI secure. Blockchain crypto anchors are used to verify the authenticity of data and models used by AI. They can detect any tampering or hacking attempts, ensuring that the AI system remains secure and reliable.
The application stores hashes of log files, datasets, and AI models on the blockchain. During verification, it compares these blockchain hashes with the current hashes of those files. In this way, crypto anchors are implemented to a certain level.
5. Tracing and Correcting AI Actions
If the AI makes a mistake, blockchain can help you trace back and undo that action. The book explains how blockchain can provide a clear and unchangeable record of AI actions, making it possible to audit and reverse any decisions made by the AI. This is crucial for correcting errors and ensuring accountability.
If the AI makes a mistake, blockchain can help you trace back and undo that action. The book explains how blockchain can provide a clear and unchangeable record of AI actions, making it possible to audit and reverse any decisions made by the AI. This is crucial for correcting errors and ensuring accountability.
6. Integration with MLOps
MLOps is the process of managing and deploying AI models. By using blockchain, you can keep track of every step and ensure everything is done correctly. The integration of blockchain with MLOps processes ensures that all stages of AI development and deployment are transparent and secure. This means that every step, from training the model to deploying it, is recorded on the blockchain, making it easy to track and verify.
Conclusion
These features highlight how blockchain can enhance the security, transparency, and accountability of AI systems. By leveraging blockchain, the book suggests that we can create a more trustworthy and robust framework for developing and deploying AI technologies. This integration can address the risks associated with AI and ensure that AI systems operate fairly and accurately.
You can grab your copy from O’Reilly Media