Transformative potential of AI and blockchain integration

Written by Tim Carswell

The integration of artificial intelligence (AI) and blockchains harnesses the unique qualities of both technologies to create a dynamic environment where intelligent algorithms and decentralised ledgers blend together to enable the development of innovative solutions for use cases across industry sectors.  There are already examples, expanded on below, of AI and blockchain integration being used in healthcare, supply chain management, financial services, education, IoT device security, energy grid management and agriculture. AI brings its predictive power and learning capabilities while blockchains contributes its immutable ledger and a transparent, secure decentralised framework. Their convergence into a decentralised AI model generates significant synergies and advantages, leveraging the strengths of both technologies whilst mitigating some of their respective weaknesses, for instance, the centralised nature of current AI systems and the inability of blockchain based systems to predict events or take autonomous decisions. The result of the AI and blockchain fusion is systems that are not only more intelligent but also more transparent, secure, scalable, flexible and operationally efficient. 

The majority of AI models currently rely upon a centralised model for training, where an integrated set of servers store data and execute specific AI models using training and validation datasets to make informed decisions. This centralisation makes the data vulnerable to tampering, manipulation and hacking, nor can the provenance and authenticity of the data be guaranteed. As a consequence, AI decisions based upon a centralised model can be flawed leading to defective or undesirable outcomes. However, a decentralised blockchain based AI model enables processing, analytics and decision making based on digitally verified and trusted data in a secure environment without the need for trusted third parties or intermediaries.  Access to the data and control of its use for AI purposes can be determined by blockchain based smart contracts which can also govern transactions and governance decision making among the AI participants.

Smart contract blockchain based autonomous systems can learn and adapt to changes over time and make trusted and accurate decisions that are validated by all the blockchain nodes; such decisions cannot be refuted and can be tracked and traced by all participants. The significant benefits of blockchain based decentralised AI include:

·      Improved trust in robotic decisions

AI, particularly deep learning, is often criticised for its opaque and difficult to interpret ‘black box’ nature but by storing data and AI decisions using blockchain technology it is possible to create a secure, permanent and transparent record of the AI’s decision-making process which can be available for scrutiny and audit thus making AI decisions more understandable and trustworthy.

·      Data security and privacy

The decentralised nature of blockchain prevents there being a single point of failure thereby increasing the resilience of the system and also ensures that data used for AI processing is secure and unmodifiable thus preserving privacy and preventing unauthorised access. AI’s contribution is to enhance blockchain security by using pattern recognition and anomaly detection to identify and mitigate potential threats or malicious activities.

·      Collective decision making

Decentralised and distributed decision-making algorithms eliminate the need for a central authority, allowing many robotic applications to benefit from collective decision making.

·      Decentralised intelligence

Combining individual cyber-security AI agents into a co-ordinated network provides comprehensive security across underlying networks and resolves scheduling issues.

·      Scalability

AI can solve some of the scalability issues confronting blockchain technology by creating more efficient consensus algorithms. AI can also potentially improve a blockchain’s performance by optimising data processing, network partitioning and load balancing. 

·      High efficiency

Integrated AI and blockchain technologies enable the fast, automatic and reliable validation of data and asset distribution.

·      Monetisation of data

An integrated AI and blockchain system can facilitate the creation of decentralised data market places in which individuals and organisations monetise their data by providing controlled and monitored access to it in a secure and privacy preserving environment.

Examples of sector specific AI and blockchain integration use cases:

·      Healthcare.

MedRec is using blockchain for the decentralised management of medical records. AI integration provides insights to facilitate patient care, including predictive analytics for medical outcomes. Blockchain ensures transparency and traceability, reduces fraud and improves efficiency. 

·      Supply chain management

IBM’s Food Trust Project  creates a transparent, immutable record of food items’ journey from food to store. AI is utilised for pattern prediction, predicting food demand and detecting anomalies that could indicate fraud or contamination. Blockchain provides traceability helping to enhance food safety and reduce waste. The result is increased efficiency, reduced costs and improved consumer trust.

·      Financial services

Numerai is a hedge fund which uses a blockchain based marketplace to crowdsource AI models which are then used to make investments in financial markets. Blockchain ensures security and transparent transactions whilst AI is used to make investment decisions. This integration democratises access to financial markets leveraging the ‘wisdom of the crowd’ for improved decision making.

·      Education

Sony has developed a global education system that uses blockchain to centralise the management of educational data including learning history and performance. AI then processes this data to produce individual learning plans, predicted learning outcomes and course optimisation. 

·      Internet of Things (IoT)

Xage Security  uses blockchain to secure IoT devices creating an immutable and tamperproof security fabric. The AI component enables real-time threat detection and manages access control which improves system security and uptime.

·      Energy management

Grid+ is a project which leverages the Ethereum blockchain network to give consumers direct and secure peer-to-peer access to wholesale energy markets for the automated buying/selling of electricity. AI is used to optimise these transactions based on usage data and market conditions which stabilises the grid and potentially saves the consumer money.

·      Agriculture

AgriDigital uses blockchain to provide transparency and traceability in grain supply chains reducing fraud and ensuring farmers get paid accurately and promptly. The AI element enhances the system by predicting market trends and prices, optimising distribution and adding a layer of fraud detection.

Examples of generic projects in the AI and blockchain landscape:

·      Gensyn operates a decentralised marketplace for artificial intelligence development built on blockchain technology.

·      Bagel is a platform focused on streamlining the management and utilisation of large AI datasets, including lightning-fast search and retrieval capabilities for AI applications.

·      MyShell puts the power of AI development directly into the hands of consumers enabling them to create and customise their own AI companions.

·      Onaji is building and operating intelligent algorithms  Its machine learning models can execute decisions on-chain through Ethereum applications like decentralised exchanges, lending protocols, NFT marketplaces, staking protocols and other decentralised finance building blocks.

From democratising AI development to enhancing data management and personalisation in Web3, these blockchain based AI initiatives are at the forefront of the next wave of tech evolution and will increasingly power activity across multiple industry, commercial and financial sectors. However, AI is often criticized for its opaque "black box" nature. Integrating blockchain can create transparent, auditable records of AI’s decision-making processes, but will this transparency be sufficient to overcome scepticism about AI, or will new forms of distrust emerge? Blockchain's decentralized framework ensures data security and prevents unauthorized access. However, as AI requires vast amounts of data, there is a tension between the need for data privacy and the demand for AI-driven insights. Current AI models heavily rely on centralized data systems, which are vulnerable to manipulation. A decentralized blockchain-based AI model can mitigate these risks, but the question remains whether decentralized AI systems can achieve the same level of efficiency and accuracy as their centralized counterparts.