The Blockchain-Enabled Intelligent IoT Economy

Setting the stage

The IoT and consumer hardware industry have seen multiple failures and a few exits over the last 12–18 months (while the B2B side has been doing a bit better overall) and some criticism has been recently made to the industry to slow down.

In spite though of the current push back, the sector is still increasing and attracting capital and talents. Clearly, there are multiple reasons as to why this is the case, but I firmly believe that one of those reasons is the convergence of IoT and Artificial Intelligence with Blockchain as the infrastructural backbone, which is unlocking the next step not only on the tech side but also on the business side.

The industry has indeed evolved from merely creating products, to create networks of products (namely, Internet of Things), to eventually creating Intelligent networks of products (I-IoT). The transition between the first and the second class was straightforward: it was enough to create more and different products and link them together. This generated many new possibilities, but it was clear from day one that it came with a series of issues hard to tackle, such as security/privacy, validation/authentication, and connectivity bottlenecks.

This is where AI and Blockchain come in. The second transition indeed is made possible through a combination of improvements in computing powers, device miniaturization, ubiquitous wireless connectivity and efficient algorithms (Porter and Heppelmann, 2014). The new class of smart products will be (and already are, to some extent) able to monitor, control, optimize, and automatize processes and products with an accuracy previously not imaginable.

Of course, as often happens, the bonus of integrating those fundamental technologies is that they ended up modifying IoT as much as IoT was impacting them in turn.

This convergence is however not accidental, but rather an inevitable necessity almost designed by default: AI needs data, IoT needs intelligence and insights, and both need security and transparent marketplaces.

The magnitude of this convergence is so high that will affect several sectors swinging from energy and manufacturing to home environment, robotics and drones, supply chain, logistics, and healthcare. Every field which is historically data-rich but information-poor will be touched (or should I say brutally hit?) by those technologies.

How Blockchain is changing IoT

Blockchain as a technology is basically providing the IoT stack with a secure data infrastructure to capture and validate data. As simple as that. At least it is a simple statement that contains three different nuances:

• Securing data better: The first one is indeed the concept of storing data securely. We know that blockchain protocols are not designed to heavily store data (they are indeed ledgers, not databases), but they can provide “control points” to monitor data access (Outlier Ventures, 2018).
• Creating the right incentive structure: A blockchain can create the right incentive structure to share IoT data, which is something we are currently missing. Cross-sectional data have been proved to have the most disruptive impact when applied across different industries, but the problem of how and why sharing data in the first place remains. Blockchain (and tokenization) can be used to solve this economic dilemma, and once data are shared can be more easily validated, authenticated and secured.
• Creating a network of computers: Distributing the workload and implementing parallel computing tasks is something it is usually attributed to new AI or High-Performance Computing (HPC) applications, but a blockchain would be essential in this development for authenticating and validating the single nodes of those networks. Some companies that are working on this problem are Golem, iExec, Onai, Hadron, Hypernet, DeepBrain Chain, etc.

How Blockchain can change AI

As I have already previously mentioned, blockchain can affect AI in multiple ways:

• Help AI explaining itself (and making us believe it): The AI black-box suffers from an explainability problem. Having a clear audit trail can improve the trustworthiness of the data as well as of the models and also provide a clear route to trace back the machine decision process, i.e., where data are coming from, who wrote the original algorithm, what data was used for training, etc. It can establish the foundations for “algorithms standards,” as for example which main algorithms, packages, and framework have been developed using a specific training set. This is also essential in machine-to-machine interactions and transactions (Outlier Ventures, 2017), and provides a secure way to share data and coordinate decisions, as well as a robust mechanism to reach a quorum. This is extremely relevant for swarm robotics and multiple agents scenarios, as mentioned by Rob May, who is a tech investor and Talla’s CEO.
• Increase AI effectiveness: A secure data sharing means more data (and more training data), and then better models, better actions, better results…and better new data. A network effect is all that matters at the end of the day. An example of a multi-application intelligence that uses different sets of data is provided by AIBlockchain.
• Lower the market barriers to entry: Let’s go step by step. Blockchain technologies can secure your data. So why shouldn’t you store all your data privately and maybe sell it? Well, you probably will. So first of all, blockchain will foster the creation of cleaner and more organized personal data. Second, it will allow the emergence of new marketplaces such as a data marketplace, which is the low-hanging fruit and it has currently been pursued by companies such as Ocean Protocol, OpenMined, Neuromation, BurstIQ, AtMatrix, Effect.ai, Datum, Streamr, Deuro, Datawallet, etc., a models marketplace (e.g., Dbrain, etc.), and finally even an AI marketplace, that companies like SingularityNET, Fetch.ai, doc.ai, Computable Labs, Agorai, and similars are trying to build). Hence, easy data-sharing and new marketplaces, jointly with blockchain data verification, will provide a more fluid integration that lowers the barrier to entry for smaller players and shrinks the competitive advantage of tech giants. In the effort of lowering the barriers to entry, we are then actually solving two problems, such as providing a wider data access and a more efficient data monetization mechanism. It is also possible that a blockchain-enabled AI will eventually create new organizational structures for intelligent agents to cooperate or compete.
• Reduce catastrophic risks scenario: An AI coded in a DAO with specific smart contracts will be able to only perform those actions, and nothing more because it will have a limited action space.

AI is feeding itself with the new stream of data coming from the physical world and the billions (if not trillions) of sensors and “things” that are capturing and monitoring everything we do.

At the same time though, as soon as an AI starts making sense of IoT data flows, it will:

• Increase data efficiency: An AI will inform those sensors on what data should be captured and stored, and above all where those sensors should be placed to be both more efficient and more effective.
• Save costs: It is fair to think that an algorithm performance should be tested continuously, and once reached the optimal level with data marginal return approaching zero – in other words, a point in which adding more data does not improve the prediction outcome –  an AI will not store or capture more data, resulting in energy, servers, computation, cloud, and infrastructural savings. In addition to that, unplanned downtime prediction is a second cost saving possibility an AI will open for an IoT ecosystem.
• Increase security: An AI could clearly be able to not only fight potential external threats for an IoT network but even predict them. AnChain is doing some interesting work in this field.
• Compute on-the-fly: Edge/fog computing is quickly becoming a hot topic since it allows on-device computation, which in turn reduces the response time for an action, limits the exposures to privacy and compliance issues and solves the huge connectivity bottleneck problem. A few startups are already working in this direction, as for example Foghorn, Mythic, Neureal, SONM, Nebula AI, as well as big incumbents as Google. The company recently released, in addition to federated learning, an entire stack made by an Edge TPU and a Cloud IoT Edge platform. However, things will likely change here due to the rapid development of specialized training and inference chips and the forthcoming introduction of the 5G. Cloud is still necessary for computationally intensive operations and to store data centrally to guarantee an extra layer of security (especially in case of “network disasters”), but custom chips and edge computing algorithms can do most of the operations the final customer needs directly on the device.

How AI can change Blockchain

Although extremely powerful, a blockchain has its own limitations as well. Some of these are technology-related while others come from the old-minded culture inherited from the financial services sector, but all of these can be affected by AI in a way or another:

• Consensus mechanisms: The proof of work or proof of stake are the first consensus mechanisms created but definitely neither the only ones nor the most efficient. AION has recently created a new consensus mechanism called “Proof of Intelligence” where validators are asked to train a neural network and using the parameters of that NN as proof of computation.
• Energy consumption: Mining is an incredibly hard task that requires a ton of energy and money to be completed (O’Dwyer and David Malone, 2014). AI has already proven to be very efficient in optimizing energy consumption, so I believe similar results can be achieved for the blockchain as well. This would probably also result in lower investments in mining hardware.
• Scalability: The blockchain is growing at a steady pace of 1MB every 10 minutes and it already adds up to 85GB. Nakamoto (2008) first mentioned “blockchain pruning” (i.e., deleting unnecessary data about fully spent transactions in order to not hold the entire blockchain on a single laptop) as a possible solution, but AI can introduce new decentralized learning systems such as federated learning, for example, or new data sharding techniques to make the system more efficient. Matrix AI is a company that is leveraging AI to fix some of the intrinsic limits of the blockchain.
• Security: Even if the blockchain is almost impossible to hack, its further layers and applications are not so secure – see what happened with the DAO, Mt Gox, Bitfinex, etc. The incredible progress made by machine learning in the last two years makes AI a fantastic ally for the blockchain to guarantee a secure applications deployment, especially given the fixed structure of the system. Have a look at what, for example, NuCypher is doing in this space.
• Privacy: The privacy issue of owning personal data raises regulatory and strategic concerns for competitive advantages (Unicredit, 2016). Homomorphic encryption, which is performing operations directly on encrypted data, the Enigma project (Zyskind et al., 2015) or the Zerocash project (Sasson et al., 2014) are definitely potential solutions, but I see this problem as closely connected to the previous two, i.e., scalability and security, and I think they will go side by side.
• Efficiency: Deloitte (2016) estimated the total running costs associated with validating and sharing transactions on the blockchain to be as much as $600 million a year. An intelligent system might be eventually able to compute on the fly the likelihood for specific nodes to be the first performing a certain task, giving the possibility to other miners to shut down their efforts for that specific transaction and cut down the total costs. Furthermore, even if some structural constraints are present, a better efficiency and a lower energy consumption may reduce the network latency allowing then faster transactions.
• Hardware: Miners, not necessarily companies but also individuals, poured an incredible amount of money into specialized hardware components. Since energy consumption has always been a key issue, many solutions have been proposed and much more will be introduced in the future. As soon as the system becomes more efficient, some piece of hardware might be converted for neural nets use. The mining colossus Bitmain is already doing exactly this.
• Lack of talent: This is a leap of faith, but in the same way we are trying to automate data science itself (unsuccessfully, to my current knowledge), I don’t see why we couldn’t create virtual agents that can create new ledgers themselves, and even interact on it and maintain it.
• Data gates: In a future where all our data will be available on a blockchain and companies will be able to directly buy them from us, we will need help to grant access, track data usage, and generally make sense of what happens to our personal information at a computer speed. This is a job for (intelligent) machines.