The intersection of AI and blockchain - Part 3 | Dabble.AI #11
The Dabble.AI Project #11 - Exploring autonomous business models
This is the third and final post in a 3-post series exploring the intersection of AI and blockchain technologies. Each post builds on the previous one, so if you haven't read the first post, and the second post, I recommend starting there.
In part 2, I discussed how blockchain technologies might enable multi-agent AI systems across organizational and platform boundaries. In this post, I'll provide some examples of how specific blockchains could play into that. Specifically, I'll discuss Ethereum, Chainlink, IPFS, Hyperledger Fabric, Ocean Protocol, Polkadot, and Golem.
I won't get into technical details. I'm also assuming you're willing to read up on the blockchains I'm using as examples. Or that you're familiar with them already. With that said, let's dive in.
Ethereum Smart Contracts: Ethereum is the most widely used platform for smart contracts, which are self-executing coded contracts with the terms directly written into code. AI agents could use Ethereum smart contracts as a coordination mechanism, defining the rules of engagement, data sharing, and reward distribution between agents. Smart contracts could also be used to create decentralized marketplaces where AI agents can offer services or request tasks to be completed by other agents.
Chainlink Oracles: Chainlink provides decentralized oracle networks that can feed real-world data into smart contracts on various blockchains. AI agents could utilize Chainlink oracles to access trusted off-chain data sources, APIs, and even AI/ML models. This could enable AI agents to incorporate real-time data into their decision-making and interact with external systems through smart contracts. Chainlink's reputation system for oracles could help AI agents select reliable data sources.
IPFS (InterPlanetary File System): IPFS is a decentralized protocol and network for storing and sharing data in a distributed file system. AI agents could use IPFS to store and share large datasets, models, or computation results in a decentralized manner. IPFS content addressing could be used in smart contracts to ensure data integrity and provenance.
Hyperledger Fabric Channels: Hyperledger Fabric is a permission-based blockchain platform that supports private data collections through its channel architecture. AI agents could utilize private channels to securely share sensitive data or models with specific authorized parties, enabling collaborative learning while maintaining data confidentiality. This could facilitate secure multi-party computation and federated learning between AI agents from different organizations.
Ocean Protocol: Ocean Protocol is a decentralized data exchange platform that allows data owners to sell access to their data while maintaining control and privacy. AI agents could use Ocean Protocol to securely access and purchase datasets for training or analysis, enabling decentralized data marketplaces. Ocean's data tokens and compute-to-data capabilities could enable AI agents to perform computations on data without direct access, preserving privacy.
Polkadot Parachains: Polkadot is a multi-chain network that allows different blockchains to interoperate and share security. AI agents operating on different parachains could use Polkadot's cross-chain communication to coordinate and exchange data or value. Polkadot's shared security model could provide a trusted environment for AI agents to interact across chain boundaries.
Golem Network: Golem is a decentralized computing power marketplace where users can rent out their idle computing resources. AI agents could use Golem to access decentralized computing power for resource-intensive tasks like model training or large-scale simulations. This could enable AI agents to scale their computational capabilities on demand without centralized infrastructure.
These are just a few examples. There are many others, and new possibilities are rapidly emerging. The key themes are using blockchain features for secure coordination, data sharing, decentralized marketplaces, and access to decentralized resources. Now let’s consider a hypothetical but real-world example.
A real-world example
Imagine a future where a patient's health data isn't siloed in a single hospital's database, but instead is securely shared across a decentralized network of healthcare providers, research institutions, and AI service providers. Each entity operates an AI agent that specializes in a particular aspect of healthcare: diagnosis, treatment recommendation, drug discovery, etc.
These AI agents can engage in secure, auditable collaboration via smart contracts on a blockchain platform like Ethereum. When a patient grants consent, their anonymized health data is made available to the network via a secure data-sharing protocol like Ocean Protocol. The AI agents can then purchase access to this data using Ocean's data tokens without the data ever leaving the patient's control.
With access to a diverse set of patient data, AI agents can perform federated learning to improve their models while preserving data privacy. They can use Chainlink oracles to incorporate real-world data on treatment outcomes and drug efficacy into their learning process. The improved models are then made available to the network, with their provenance and performance recorded on the blockchain for accountability.
When a new patient comes in with a complex set of symptoms, their healthcare provider submits a request to the network via a smart contract. The AI agents then bid to provide their diagnostic and treatment recommendation services based on their specialized expertise. The smart contract aggregates the responses, weights them based on each agent's proven performance, and returns a personalized treatment plan to the healthcare provider.
Throughout this process, Hyperledger Fabric channels are used to maintain patient privacy and comply with regulations like HIPAA. Payments for AI services are automatically handled via the smart contract, with a portion of the fees being allocated to compensate patients for sharing their data.
In this use case, the decentralized nature of the system prevents any single entity from monopolizing patient data or AI capabilities. The blockchain provides a trusted, transparent coordination layer for the AI agents to collaborate and compete, aligning incentives around patient outcomes. The combination of secure data sharing, federated learning, and decentralized governance enables the emergence of a powerful collective intelligence for healthcare.
This is just one example, but it illustrates how the intersection of multi-agent AI and blockchain could transform entire industries. By enabling secure, decentralized collaboration between specialized AI agents, these technologies could unlock new frontiers in personalized medicine, predictive maintenance, financial modeling, and beyond.
This is just a hypothetical example. But hopefully, it illustrates how the unique features of blockchain - decentralized trust, secure data sharing, and automated coordination can amplify the power of multi-agent AI systems in ways that are difficult to achieve with centralized architectures.
This is the final post for this series. But I’m sure I’ll be revisiting the topic of AI/blockchain convergence as I dive deeper into how to develop autonomous business models.