By Nelson Petracek, Global CTO, TIBCO

Lately, I’ve noticed an uptick in creative hypotheses surrounding the ways blockchain systems can be utilized to enhance AI solutions. When I speak of AI, I’m referring to the very broad class of automation and machine learning technologies (deep learning, neural networks, natural-language processing engines, etc.) that manifest in “intelligent” systems and use vast amounts of data to train algorithms and automatically perform specific tasks. Blockchain might aid AI-driven applications by providing decentralized and distributed data architectures to improve the quality of data supplied to AI systems, enhance development of open algorithm marketplaces and exchanges, and even pave the path toward artificial general intelligence (AGI). These approaches are capable of learning to solve myriad problems and accomplish a variety of tasks.

Since turnabout is fair play, I thought it might be interesting to theorize about the ways in which AI might be utilized to enhance blockchain systems.

One interesting proposition could be actually embedding AI within a blockchain platform, thereby improving efficacy. This would extend beyond simply adding a call to an AI algorithm from within a smart contract and move towards making AI a native part of the blockchain nodes that are included in the framework as opposed to simply invoked.

Such AI-driven frameworks could be particularly effective in improving the operation of consensus algorithms. Consensus techniques, utilized to achieve agreement and enhance security in blockchain architectures, are subject to debate and as of yet, there is no general agreement on which means to reach consensus is best. But with the aid of AI, you could doubtless improve performance by making the process more automated, faster, and more efficient. Such inherent system intelligence could be extremely useful in enabling the distribution of data through some of the new “beyond” blockchain architectures that aren’t necessarily based on a linear distributed ledger model. Such next-gen blockchain systems stem from ongoing questions about how to best scale blockchain, and one solution set proposes to base blockchain architectures on non-linear data structures such as directed acyclic graphs (DAGs) or hashgraphs to reduce latency.

But such data structures, if successful, also introduce increased complexity, which obviously calls for new ways to optimize the layout of these ledgers. For example, assume you’ve got a complete, distributed, non-linear network. Where do its ledgers actually sit? And how are people using those ledgers? How are they accessing different nodes? These and other questions could be solved with AI. You could use it to develop a more intelligent and dynamic distribution mechanism for, say, improving hardware utilization — and improve the efficiency of consensus as a result. This would give you the ability to erect a “smarter” architecture with greater visibility into network state and wise network partitioning.

Once you start entering into the realm of partitioning blockchain networks or sharding, the whole point is to do it intelligently — attempting to manage it manually just doesn’t make much sense for something that is global and dynamic and ever-growing. As you put more load on the system, an AI-enhanced network would have more intelligence about its own state and could even partition itself for optimal operation.

This is all particularly applicable in permissioned blockchain networks, where your consensus algorithms aren’t necessarily going to be based on proof of work (PoW) or even proof of stake (PoS). The question on the horizon is how to make it mathematically provable that everything on the chain is secure and valid in a deterministic fashion. And this seems like a question well suited to solving partially with AI.

Going a step further, if one embeds AI into smart contracts (to make them “smarter”), various challenges emerge. These include ensuring that the various nodes running these contracts all come to the same answer (in order to achieve consensus across the network), whether and how to link past transactions to AI execution (to “prove” results), or how to determine if an AI algorithm has “improved” over time and what that means to smart contract execution.

Moving away from consensus and smart contract functionality, there may be opportunity for AI in other blockchain-enhancing applications. AI may be able to assist with the handling of information included within particular transactions.

Normally, when you put information in a blockchain, it isn’t encrypted. Neither is data residing in the nodes themselves “automagically” encrypted. This is part of the impetus for things like the notion of trusted execution environments and enclaves for providing confidentiality protections. In an enterprise blockchain, for example, you don’t necessarily want one participating vendor to see the details of your agreements with all your other participating vendors. There is always a question of how to encrypt transaction information for directed read/write control, and some machine learning developments might hold the answer. Though this is not my area of expertise, it seems possible that homomorphic encryption and deep learning might present opportunities for incorporating greater transaction data confidentiality into blockchain architectures.

Of course, moving too far into the theoretical realm these days leads directly into questions about what the promised dawn of quantum computing might bring to the table, at which point we’ll have to reconsider all our present notions about efficiency and encryption — and even immutability.

My own professional association with blockchain and machine learning/AI technologies is squarely rooted in the enterprise in the here and now. Businesses far and wide are wrangling with the technical, transactional, and organizational integrations required to utilize these powerful and promising technologies and unite them with their existing infrastructures. And with all the new ideas, models, and methodologies emerging on both the blockchain and AI fronts at a breakneck pace, we’ve certainly got our work cut out for us.