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September 21, 2021
The first blockchain appeared in 2008 as a new type of distributed database or ledger technology.
It stores and manages files of information into groups of data called blocks, linked together to form a chain.
As well as the data itself, each block contains an immutable record of exactly when it was created which cannot be corrupted, lost or changed without the network knowing about it.
Gartner categorizes blockchain technology in four evolutionary stages.
The early phase of blockchain was built on top of existing systems with limited distribution capabilities either within or between enterprises.
The current phase is characterized by blockchain-inspired solutions designed to address specific operational issues and generally include distribution, encryption and immutability.
The next phase of blockchain offerings will deliver on the full value proposition, including decentralization and tokenization.
In the final phase of the Gartner model, post-2025, enhanced blockchain solutions will fully harness complementary technologies such as AI and IoT.
Naturally, many companies are already investing in large quantities of data along with the related technologies needed to extract real value from it.
The terminology may vary from industry to industry, but the clear objectives remain; to increase revenues and efficiency in the current offerings and to develop new data-driven businesses for the future.
The challenge is that we are producing more data than we can process, let alone use for decision making.
AI applications can solve this problem, whilst also learning about and associating those items relevant to the task.
However, it is also critical that we check these machine learning-based decisions to build trust and confidence in their outcomes and, like all technologies producing automated conclusions, it should be verified and audited for accuracy, preferably by humans.
The EU General Data Protection Regulation (GDPR) states that any decisions made by a machine or algorithm must be readily explainable with the right to obtain details and if desired, to opt-out of any machine-based decisions completely.
It is backed up by impressive fines if breached.
By connecting a distributed, decentralized, and immutable ledger that can record the data that goes through a decision made by machine learning, which is essentially a centralized process, can make it more coherent and understandable.
Enter blockchain: it can help to establish the attribution, understanding, and justification of those decisions and outcomes.
By storing the key data elements as transactions on a blockchain, the system can be meaningfully verified, audited, and if necessary, adjusted.
The Swedish mapping, cadastral, and land registration authority, Lantmäteriet, has been working with blockchain to do just that.
All the data from land transactions are fed into an AI process then used by chatbots, instead of humans, to answer FAQs.
Instead of recording and dissecting thousands of conversations, each inquiry can be tracked and the stored answers and data reviewed and audited, then refined for future use.
This also makes it possible to trace and determine why decisions are made – in effect blockchain’s key function of building trust and transparency forces AI to explain itself and its actions, making it the perfect complementary technology.
Individually, AI and blockchain offer many exciting opportunities, but together they offer more than the sum of their parts.
By combining the predictive power of AI with the robustness of blockchain, enterprises can build safer, smarter, more transparent, and more cost-efficient business automation systems.
Most people currently associate blockchains with some form of cryptocurrency, where anyone can download the P2P client software, view the ledger, and interact with the blockchain.
These public blockchains can be described as fully decentralized where in addition to the distributed database there is also no single entity in overall control.
They are designed to preserve an individual user’s anonymity and treat all users equally.
Currently, public blockchains require considerable processing power to perform tasks and the processing power on standard computers varies wildly.
The next generation of blockchains are using AI to determine the capability of each machine and allocate different tasks and node types based on past performance and specification levels.
This enables rapid allocation of resources to optimize the network and in public blockchains, with thousands of nodes, this would be a huge, time-consuming, and continuous overhead for both non-AI and human operators.
AI can boost blockchain efficiency far better than humans or traditional computing.
For enterprise applications in many industries, public blockchains pose several challenges around privacy and control, where it does not suit an enterprise to allow every participant full access to the entire contents of the database.
As a result, a new generation of private blockchain is emerging where a single authority or organization ultimately retains control, and no one can enter this type of network without proper authentication.
Private blockchains can look more like centralized networks but they offer all the distributed benefits, whilst retaining some overall control to improve privacy and eliminate many of the illicit activities often associated with public blockchains and cryptocurrencies.
Private blockchains are, by definition, ‘permissioned’ and transactions verified faster than public blockchains, because participants are often either known or trusted and, as only a few nodes need to manage data, transactions can be supported and processed at a much higher rate.
For reasons of performance, as well as accountability and cost, private blockchains are more suited to enterprise applications, where the technology empowers and supports the business rather than individual users.
One of the key applications of private blockchain is the smart contract - a computer program or business logic that automatically executes, controls and documents legally relevant events and actions, according to the terms of a contract or an agreement.
Smart contracts reduce or eliminate the need for trusted intermediators and cut losses from fraud and other malicious or accidental exposures.
They can also be used to create new types of digital assets or tokens, thus opening new application areas. Integrated with AI systems, smart contract technology can speed up this process by providing automated real-time vulnerability scanning and debugging of the contract file.
This enables the owners to be alerted to any kind of security vulnerability before the final contract is made available to the client.
The insurance industry can use blockchain Smart Contract APIs to make external calls to predictive AI systems to determine acceptable risk and associated premium costs.
The front-end system gathers the data, usually from the prospective client input. The business logic makes calls to external data systems with AI determining the predicted risks based on the client profile.
The answers are returned for the logic to decide to offer or decline the business and the details are stored on the blockchain before finally informing the client.
To make Smart Contracts truly smart the application would then enable acceptance by the client, issue the contract and automatically provide the cover. No human intervention is required, and all key data is immutably recorded for audit at any time in the future.
Similar examples can be found for fleet management operations where predictive maintenance schedules are called upon to ensure pre-emptive servicing and parts replacement are applied to transport fleets.
The Smart Contract will even book the service slot and inform the fleet manager to bring the vehicle in.
Further use cases exist for digital collectibles, event ticket systems, polls, digital licenses, digital identities, and notary services.
In the next generation blockchains emerging now, Smart Contracts will support and execute any type of business logic required by the application.
AI and blockchain are two of the most exciting and influential technologies of this decade.
According to Gartner, the business value generated by blockchain will reach $176 bn by 2025 and $3.1 trillion by 2030, whilst AI is expected to create $391bn in business value by 2025 according to reports by Grand View Research.
Private blockchains combined with AI provide more opportunities to utilize the technology for B2B use cases and applications will deliver greater performance, privacy, reliability, and transparency.
Large enterprise blockchain solutions will be custom developed according to their specific business needs and SMEs will take advantage of cost-effective pre-packaged solutions and Blockchain-as-a-Service options.
The convergence of AI and blockchain is still in its infancy but expect to see the convergence of these technologies gaining pace and becoming mainstream across sectors from financial services, supply chain, and telecoms to health and insurance.
Jonas Lundqvist is the CEO of Haidrun. He founded the firm to help businesses harness private blockchain technology to meet increased demands on efficiency and security, whilst providing new revenue and value-producing opportunities.
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