Read how AI & Blockchain are revolutionizing industries! Blockchain and AI use cases, machine learning apps, and the future of decentralized intelligence.
Challenges in Blockchain Technology & AI Integration
Future Outlook for Blockchain and AI
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Introduction
Blockchain is the immutable ledger that validates and records data across a network of computers. AI is adept at processing and analysing large amounts of data, and making predictions based on that.
Both AI and blockchain technology are crucial components of the digital infrastructure. Both can complement each other in ways never imagined before. A Spherical Insights study shows that AI and blockchain are a force together and are touted to become a
This article discusses how AI and blockchain help even out each other’s weaknesses, and how blockchain integration with AI can support use cases in diverse sectors.
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What is Artificial Intelligence?
“Artificial Intelligence (AI) uses computers, data and sometimes machines to mimic the problem-solving and decision-making capabilities of the human mind,” defines IBM.
In simple terms, AI enables machines or computers to perform tasks that require human intelligence or intervention. AI consists of subsets such as:
Machine learning (ML)
Natural Language Processing (NLP)
Pattern recognition
Each of these use AI algorithms that are trained on large data training sets and are capable of making predictions, automate repetitive tasks, and support data-driven decision making.
What is Blockchain Technology?
Blockchain refers to a distributed ledger that verifies and stores data in a transparent and immutable way. A blockchain is a decentralized network made up of computers or node, each having a copy of the shared database. Whenever a transaction occurs, it becomes part of a block or group of transactions. The nodes validate the data by majority and the block becomes a part of the chain forever. The process keeps continuing.
Bitcoin Network can be called the first truly decentralised blockchain. Bitcoin allowed peer-to-peer exchange of data and value without any central authority or intermediary. Blockchain tech is already being used in major industries like financial services, healthcare, education, governance, etc. It provides transparency, immutability, security, cost efficiency, automation and a bunch of their benefits. However, the lack of scalability and privacy have proved to be the biggest hurdles in its adoption.
How AI Enhances Blockchain
Artificial Intelligence and blockchain share a synergistic relationship, where each complements the other. In several use cases, AI enables blockchain to improve its security, efficiency, scale, and governance.
Improving Security & Efficiency
AI can assist and train blockchain and its consensus mechanisms for faster transactions, better efficiency, improved scalability, and low latency. AI can scan the source code for bugs by generating test cases. Reciprocally, it can ‘mathematically prove the absence of entire classes of bugs.’
You can train LLMs on smart contracts databases to analyse risk patterns for efficient asset pricing, trade execution, and treasury management. AI can enable dynamic interest rates in loan protocols and continuously monitor and adjust loan terms.
For instance,
Lending protocols Compound and Aave use AI algorithms to manage liquidity.
Gnosis Official and Monarch DAO use AI-based governance models.
Balancer and Bancor use AI-based liquidity chain analysis to keep assets fluid.
Chainalysis and CipherTrace use AI to check and protect transactions against fraud, hacks, or bugs.
AI agents can be called the first-generation AI models that execute orders on behalf of users based on a given command. AI can also help personalise user experience on a blockchain by enabling custom feeds based on transaction history.
Blockchain Scalability Solutions
Owing to AI’s problem-solving capability, blockchain can seek answers for its technical issues. Away from resource-intensive models like Proof of Work, blockchain and machine learning can come together as a consensus layer that uses AI-driven insights related to network demand.
Based on the prediction, blockchain can dynamically adjust energy consumption and build performance, thereby improving scalability. AI can also be crucial in ‘sharding’ - a scalability technique where data is divided across multiple nodes as ‘shards’ and parallelly processed for faster execution. Ethereum, Avalanche, and several other L1s have adopted sharding or are working towards the roadmap.
How Blockchain Enhances AI
Source: PwC | How AI and blockchain can come together
Compared to other technologies, blockchain technology is inherently equipped to scalably provide AI models and applications with tamper-proofing, traceability, and multi-party transparency necessary to govern AI’s inputs/outputs responsibly.
Data Integrity & Quality
AI models are trained on vast data lakes and question snippets to establish patterns and relationships to create rules. These rules are then used to make judgments and create responses for end-user prompts.
However, how AI software constructs billions of parameters through its processing of huge archives of information, along with the resulting prompt responses, takes place in an opaque, black box, and ultimately ungoverned environment. Also, AI models are heavily centralised.
This may result in issues such as data integrity, AI hallucinations, and bias, and may invite copyright infringements. To overcome these challenges, we must control how data is sourced and managed. That’s where blockchain helps.
Charles Adkins, CEO of HBAR Foundation says,”Any AI software makes a decision based on the workflow of its code. If those events are recorded on-chain, you have a very transparent record of the decisions, which can be tracked in the retrospective to identify issues.”
Blockchain is uniquely qualified to serve as a multi-party transparent record-keeping infrastructure for scalably storing, tamper-proofing, and tracing data used in training AI models, providing a solution to many of the risks caused by AI to both rights holders and AI consumers.
Blockchain can ensure data integrity by:
Providing a single source of truth or veracity of data lakes that AI models train on. An organization building its AI model on a blockchain can know the origin of data, its creator, the copyrights attached, etc.
Manage permissions and initiate contracts for data sharing. This protects organisations from unintended copyright infringements and other risks.
Acting as a multi transparency engine, providing real-time visibility and provenance to the parties in the data-sharing contract.
Decentralized AI Models (DeAI)
Among the blockchain and AI use cases, DeAI models are currently the most popular projects. DeAI combines blockchain and AI integration to build distributed, transparent, and intelligent systems. DeAI models bring added security, data ownership, and community-driven development.
DeAI models promise a fresh democratic digital environment away from the centralised AI models. Anyone can participate in a DeAI project. These projects also distribute computational resources and data storage, and function on community-led growth and governance.
Ocean Protocol and Bittensor are decentralised applications that reward individuals for their data contributions and Near Protocol, Akash network, and Filecoin are blockchain applications providing decentralised storage and computing resources.
Applications of AI and Blockchain Synergy
Source: Chainlink | Blockchain and AI applications
Integrating AI algorithms in blockchain systems aids in secure sharing and analysis of patient records, enhancing personalizedHealthcare
Blockchain, with its decentralised and secure database, can store sensitive patient information. AI, in turn, can analyse this health data, which consists of medical scans and records, to identify patterns and predict accurate diagnoses.
Patient Data Management: Integration of AI algorithms in blockchain systems aids in secure sharing and analysis of patient records, enhancing personalised medicine.
Better diagnoses: AI can help doctors make faster and improved diagnoses without revealing any patient data. Doctors can formulate custom treatment plans for their patients.
Data Privacy: AI and blockchain can help in the secure sharing of patient health records or medical research data for clinical trials. AI enhances data management by identifying patterns, trends, and probable outcomes to assist doctors and researchers.
No single point of failure: Since medical data is no longer restricted to a centralised server, there’s no threat of a single point of failure. By moving medical records on-chain, hospitals can make a global registry of patient data accessible to every authorised party.
Clinical Trials: Researchers from different physical locations can collaborate with each other using AI-led blockchain storage systems.
Read this article by TokenFi to understand how AI-enhanced tokenisation can level up clinical Thailand research.
A few healthcare companies employing AI+blockchain functionality include Medicalchain, Avaneer Health, BurstIQ Inc., Chronicled, Inc., CloudMedx, EncrypGen, Guardtime, Path AI, and Patientory.
Financial Services
First DeFi, and now PayFi, is altering the fundamental nature of financial services by bringing them on-chain to add transparency, efficiency, automation, traceability, and a host of new use cases. When combined with AI, decentralised finance can secure optimisation of resources, better price discovery, predictive analytics, and data-based decision making.
Enhanced DeFi: Some of the use cases of AI models in DeFi include Decentralized credit scoring (DCS), decentralized solvers for a globally optimized pathfinder, price prediction, DeFi Credit Risk Analysis, Staking behavior analysis, Trading pattern, cross-chain liquidity analysis, and DAO governance.
Fraud Detection and Prevention: AI and blockchain work together to identify and prevent fraudulent activities in financial transactions.
Risk Management: By identifying patterns and coding smart contracts with LLMs, developers can optimize their smart contracts and detect vulnerabilities in the code. Projects can also use AI to evaluate the robustness of their tokenomics and model.
Dynamic NFTs: AI can bring modularity in NFT usage via functionalities such as NFT recommendation engine, provenance tracking, trend analysis, etc.
Examples of financial services use cases: DEXs like Uniswap, 1inch, 0xProtocol, or CoW Swap facilitate token trading and gather vast amounts of trading data, allowing for the analysis of trading patterns and market dynamics.
Supply Chain Management
When combined with smart contracts over a blockchain, AI models can execute tasks based on predetermined conditions. For instance, when the inventory details are recorded on-chain, the AI model would know when to place an order with an external supplier based on the demand, time of the year, and other analytics captured from previous sales cycles.
Transparency and immutability: When supply chain records are moved on-chain, they benefit from blockchain’s transparency and immutability. This leads to fewer frauds and errors. AI models further ensure any instances of fraud that might happen by detecting patterns.
Cost Optimisation: AI can use the blockchain records of product inventory to develop predictive analysis. This analysis, in turn, can help optimise inventory management, minimize costs, and facilitate data-driven decision-making.
Tracking Authenticity: Another use case where AI-driven blockchain records can be helpful is proving the authenticity of the products. Blockchain can track products from the point of origin to the point of consumption, helping prove provenance and reduce counterfeiting.
ESG-compliance: ESG practices are another area where AI and blockchain can help prove authenticity, track the carbon footprint, and determine whether the sources from which the product is derived are free from, say, animal cruelty or child labour.
Source: X | Uses cases for AI+ Blockchain in SCM
Examples of AI-blockchain use cases in Supply Chain Management
LangChain’s medical supply chain AI is transforming medical supply chain management in low-income countries through intelligent monitoring and fraud prevention.
Oracle Fusion uses agentic app capabilities and blockchain for inventory management, maintenance, logistics, and more.
IBM Food Trust has partnered with Walmart to use blockchain to track fresh products.
De Beers Group deploys the TracrTM blockchain platform to provide an immutable record of its diamond provenance.
Cybersecurity
AI models are based on highly centralised data centres. Blockchain can help AI remove a single point of failure by provisioning a decentralised storage database, updated in real-time via oracles and governed by smart contracts. Blockchain networks are essentially tamper-proof once the block becomes part of the chain. AI systems can benefit from the decentralised infrastructure which provides encryption and built-in safeguards against bad actors.
The blockchain-led AI systems can set parameters to control various aspects of the systems. The data can be encrypted and secured using tokenisation. Tokenised data is safe against manipulation, private, and can be easily monetised, giving the original data holders the opportunity to participate in the AI-blockchain economy.
AI integration with blockchain can help unravel the full possibilities while enabling the highest level of security against attacks and adversarial practices. Some cybersecurity use cases currently being explored include digital identity management, tamper-proof audit trails, tokenisation, decentralised threat intelligence, and distributed denial of service protection (DDoS).
Challenges in Blockchain Technology & AI Integration
Technical Challenges
Blockchains are decentralized peer-to-peer networks that rely heavily on node consensus and block validation. These two processes require heavy data storage and computation capacity. While the idea of AI and blockchain together is a true innovation in technology, the strain that equally data-intensive AI models bring on the already strained networks hampers scalability.
The complexity of AI models also brings difficulty in integration with DeFi networks. Blockchains are trustless networks but they are siloed entities, each with its own set of rules and governance models. Syncing them with AI models would require another level of interoperability and seamlessness. DeFi protocols also need to find a way to escape algorithmic biases that often crop up in these AI models.
Ethical & Regulatory Concerns
There are concerns around data sourcing and copyright infringements by AI models. Since AI models may be trained on sensitive data without any acknowledgement to the original creator or individual, even blockchain might be incapable of helping with data tracking and privacy. Quite a few ethical concerns have emerged lately.
For instance, there was the recent Ghibli Art controversy, in which ChatGPT created cartoon images from actual photos within seconds. However, the original creator of the cartoon style was not given any acknowledgement. It’s a clear case of copyright infringement and Intellectual property theft. Regulations are barely there, given the pace at which AI projects—and now, AI and blockchain projects—are growing.
Here’s an article by Harvard Business Review talking about the rampant IP theft issues in AI projects.
Data Privacy Issues
AI models are trained on large lakes of data in a ‘black box-like’ environment where data is sourced from multiple channels without the owner’s consent or knowledge. There's a complete lack of transparency. This source agnosticism is against the data privacy rights of individuals and organisations. There’s a need for strong regulations and legal frameworks to mitigate any data privacy risks and security threats.
AI and blockchain together can unlock functionalities and use cases across sectors and verticals–there’s no debating that. While Web3 is all about global coordination efforts, secure data sharing, and trust minimisation, AI brings human-like intelligence to machines and efficiency via algorithmic predictions.
More and more companies are attempting to break the jinx and using AI-blockchain utilities to unlock never-explored opportunities such as automation, intelligent systems, efficient processes, and more.
AI is making a large number of software products outdated, and blockchain is disrupting traditional processes. Their synergy can subvert trusted entities to replace cryptographic guarantees and automate processes for better productivity and cost efficiency.
There’s no looking back from here. Any company looking forward needs to have the two in their tech stack or fear obsolescence.
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