Blockchain and AI, a Perfect Match
The first blockchain appeared in 2008 as a new type of distributed database or ledger technology, which 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, producing a detailed system of record that cannot be corrupted, lost, or changed.
Evaluating Blockchain and AI Relationship
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, post-2025, enhanced blockchain solutions will harness complementary technologies such as AI and IoT.
Individually, AI and blockchain offer many exciting opportunities, but together they offer more than the sum of their parts. By combining the power of AI with the robustness of blockchain, enterprises can build safer, smarter, more transparent, and more cost-efficient data storage and business automation systems.
Today AI is essentially a centralized process and by offering a distributed, decentralized, and immutable ledger that can record all data and variables that go through a decision made by machine learning, blockchain can make AI more coherent and understandable. This makes it possible to trace and determine why decisions are made – in effect it makes AI explain itself and its actions.
Conversely, AI can boost blockchain efficiency far better than humans or traditional computing. Currently, many blockchains running on standard computers often require considerable processing power to perform tasks, which can be reduced by using self-learning algorithms.
Public versus Private
Most people currently associate blockchains with some form of cryptocurrency, where anyone can download the 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.
But for enterprise applications in industries ranging from telecoms, financial services and supply chains to healthcare and insurance, this type of public blockchain poses 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 blockchains 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 are, by definition, ‘permissioned’ and are more suited to enterprises for reasons of performance, accountability and cost. Private blockchain platforms focus on the needs of companies and institutions where the blockchain empowers and supports the business rather than the individual users.
Private blockchains can look more like centralized, controlled 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 blockchain solutions that integrate with AI systems complement and enhance existing IT infrastructure, enabling more intelligent business automation for enterprise customers.
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. Current use cases include digital collectables, event ticket systems, polls, digital licenses, digital identities and notary services.
Integrated with AI systems, smart contract technology can speed up the process and provide real-time vulnerability scanning and debugging of the contract file so that before being made available to the community, the owners can see any kind of security breach.
AI decisions explained
The EU GDP 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.
We are faced with a huge and increasing volume of data being produced every day, which we simply cannot process fast enough to use for decision making. Enter AI. It can assess large data volumes and learn how to connect the variables relevant to its tasks. However, it is critical that we verify these machine decisions to build trust and confidence in their outcomes. 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 then the system can be meaningfully verified, audited, and adjusted. Blockchain’s key function of building trust and transparency makes it the perfect complement to AI.
Blockchain and AI: Better together
Blockchain and AI are two of the most exciting and influential technologies of this decade. AI is expected to create $391bn in business value by 2025 according to a recent report, while private blockchains look set to become the main contributor to blockchain market growth. According to Gartner, the business value generated by blockchain will grow rapidly, reaching $176 bn by 2025 and $3.1 trillion by 2030.
Private blockchains combined with AI provide more opportunities to utilize the technology for B2B use cases and they deliver higher efficiency, 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.
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