From Laboratory to Market
Those of us that have spent the last two decades in computer science research have seen the disciplines of Artificial Intelligence (AI) and blockchain progress from experimental technologies to consumer-ready applications.
Most distributed systems researchers will agree, that the blockchain is a practical solution for making decentralization work in the real world. Let’s consider the example of the Practical Byzantine Fault Tolerance consensus algorithm, which is one of the more popular algorithms used in blockchain project, first outlined in 1999. The algorithm took the idea of combining consensus algorithms with financial incentives to make decentralization work in the real world.
Similarly, AI has been around for quite some time, however has only made it into mainstream applications recently. This is because we finally have the computing power and storage capacity to train deep neural networks on the huge amounts of data necessary to get practical results.
The point is, both technologies have now truly left the laboratory and are making headway on the market.
The combination of AI and blockchain both share a common objective. Both technologies are about automation.
AI seeks to perform tasks that previously required human expert intervention. In a similar way, blockchain technology automates business transactions by removing middlemen, such as banks, lawyers, and courts.
AI and Blockchain not only share a common objective, they also complement each other perfectly.
The blockchain removes third parties by providing trust at the protocol layer. However, removing trusted third parties, such as banks, comes with the disadvantage of removing expert intelligence. Luckily, AI is all about providing expert intelligence.
Similarly, trust is very important for users of autonomous intelligent agents. For example, we generally don’t want to get into a self-driving car that communicates with other devices, without having full trust in the underlying infrastructure.
It’s exactly this type of intelligent machine-to-machine ecosystems that the combination of blockchain and AI can provide. In such a paradigm, the blockchain can be thought of as the automated trust and communication layer for intelligent software agents to interact securely. This opens up a long list of possibilities. For example, machines can use the blockchain to provide data securely, pay for it with micropayments in cryptocurrencies, and conduct business through smart contracts. Another example sees bots trading securely on decentralized cryptocurrency exchanges.
But it is not just the blockchain that provides a trusted infrastructure for AI. The blockchain itself can benefit from AI. Smart Contracts can be made even smarter by basing their decisions on AI oracles. Even the blockchain’s underlying infrastructure can be enhanced by AI, such as optimizing routing and load balancing.
The future is bright for these two converging technologies, with potential in place for technologies that we haven’t even begun to imagine.