Blockchain Takes AI to the Next Level
reading time
4
min
Sep 27, 2024
As artificial intelligence (AI) continues to evolve, a future where autonomous agents can interact and operate independently in sectors like finance, healthcare and logistics is becoming more and more real. It’s not a simple matter, though, since making AI integral to decision-making processes carries the risk of communication gaps, trust issues, and data manipulation. Blockchain, a decentralized and immutable ledger system, emerges as a promising solution to such challenges, fostering reliable communication and learning between AIs.
Autonomous AI agents, by definition, operate with minimal to no human intervention, making choices and executing tasks based on predefined algorithms and learned behaviors. These entities are being increasingly used in sectors such as finance (e.g., trading bots), healthcare (e.g., diagnostic tools), and logistics (e.g., route optimization), but whenever they need to interact—whether collaborating on tasks, exchanging data, or learning from each other—significant problems can arise:
Lack of Transparency: AIs typically operate within their own silos, with little visibility into how other agents make decisions. This creates a trust gap, as one AI cannot verify whether another's information is accurate or unbiased.
Data Integrity Concerns: AIs rely on massive amounts of data to learn and make decisions. When exchanging data with other agents, there is a risk of misinformation, either accidental or malicious, which can snowball and skew outcomes.
Verifiability and Accountability: There’s no inherent mechanism to trace the origin of data or actions taken by agents. This makes it difficult to determine who (or what) is responsible if something goes wrong, such as a financial loss caused by faulty trading algorithms.
In 2010, the "Flash Crash" demonstrated the dangers of autonomous trading bots operating in silos as the U.S. stock market plummeted nearly 1,000 points in minutes due to a feedback loop of sell orders. Although safeguards have been implemented since then, the event highlighted the critical need for better data transparency and verifiable communication between autonomous systems.
Decentralization and Trust
At its core, blockchain is a decentralized network where all participants have access to the same data and no single entity controls the flow of information, reducing the risk of fraud, corruptibility or miscommunication. In supply chain management, for instance, AI agents can coordinate the movement of goods, each exchange and transaction—from raw materials to final delivery—recorded on a public ledger, making it nearly impossible for any single AI to manipulate or falsify data without being detected by others.
Smart Contracts for Consistent Communication
Anyone who has used an AI knows that its performance can vary greatly depending on the quality and specificity of the instructions we provide them. Smart contracts—self-executing contracts with the terms of the agreement directly written into code—can enhance AI communication by ensuring consistent, reliable back-and-forths between agents without the need for intermediaries or human intervention. In decentralized finance (DeFi) platforms, AI agents may use smart contracts to facilitate real-time trading and investment strategies. These contracts ensure that the trades are executed only when specific market conditions are met, reducing the risk of errors or unauthorized operations.
Transparency and Accountability
Blockchain’s immutable ledger ensures that all actions taken by autonomous entities are recorded and cannot be altered, supplying a kind of transparency and accountability that is often lacking in such systems. If an AI agent makes a decision based on faulty data or a biased algorithm, the blockchain record can trace its origin and help identify the root cause of the problem. Companies like VeChain have explored the use of blockchain to track vehicle maintenance records, sensor data, and performance history in a verifiable way, making sure that the AI agents controlling the vehicles operate with accurate, up-to-date information.
AI Learning and Blockchain
Besides enhancing communication and trust, blockchain can also play a critical role in improving the way AI agents learn and evolve. Because AIs work with large datasets and continuous learning from interactions, it makes sense they would thrive in an infrastructure where they can operate in a decentralized, collaborative, and secure manner.
Data Provenance and Quality Control
For AI agents to learn effectively, they need access to high-quality data. Blockchain’s ability to provide data provenance—the origin and journey of information—reduces the risk of agents learning from inaccurate or malicious sources, which could negatively impact their decision-making processes. In the healthcare sector, AIs can employ blockchain to verify the authenticity of medical records before using them for diagnostics or research, leading to more reliable outcomes.
Collaborative Learning
Decentralization enables multiple AI agents to work together and learn from shared datasets. Instead of relying on isolated sources controlled by individual organizations, AIs can make contributions to and learn from all the information stored on a blockchain. This fosters a more comprehensive type of learning, which further improves their decision-making abilities faster. In the agriculture sector, IBM’s Food Trust blockchain is already being used to track the journey of food from farm to table. With the aid of AI agents, autonomous farming drones share data on soil conditions, weather patterns, and crop performance, leading to a collaborative learning environment that enhances efficiency and sustainability.
Case Study: Ethereum and AI Communication
Ethereum’s smart contract platform has been at the forefront of integrating AI and blockchain technologies, its ability to support decentralized applications (dApps) giving AI agents the necessary infrastructure to communicate and collaborate in real-time. Ethereum-based platform SingularityNET, which aims to create a decentralized marketplace for AI services, enables AIs to interact with each other, sharing data, algorithms, and learning models in a transparent and safe environment. SingularityNET democratizes AI by removing the entry barrier for smaller players, allowing any AI agent to contribute to and benefit from a global pool of knowledge.
While blockchain presents a promising solution for enhancing AI communication and learning, there are still challenges to overcome, such as scalability – the more AI agents interact and exchange data, the more transactions the blockchain network has to process, leading to a slower formance and higher costs.
To address this (and blockchain scalability in general), developers are exploring new consensus mechanisms, such as Proof of Stake (PoS) and sharding. The AI and blockchain communities must also work together to develop standards for interoperability between different platforms, so AI agents can communicate seamlessly regardless of the underlying technology.
Closing Thoughts
By addressing the trust and data integrity issues that sometimes affect AI systems, as well as offering a safe, transparent, and decentralized platform for information trading and collaboration, blockchain can take artificial intelligence to the next level. Businesses that embrace the intersection of these technologies can unlock new opportunities for innovation and efficiency across industries, and there’s no telling what its impact may be in our daily lives.
