The crossover between artificial intelligence (AI) and blockchain is a growing trend across various industries, similar to finance, healthcare, cybersecurity, and provide chain. In accordance with Fortune Business Insights, the worldwide AI and blockchain market value is projected to grow to $930 million by 2027, in comparison with $220.5 million in 2020. This union offers enhanced transparency, security, and decision-making, improving overall customer experience.

On this post, we’ll briefly cover the basics of AI and blockchain and discuss the important thing opportunities and challenges related to the intersection of AI with blockchain.

Understanding AI and Blockchain

AI and blockchain have distinctive frameworks, features, and use cases. Nevertheless, when combined, they’re powerful catalysts for growth and innovation.

What’s Artificial Intelligence (AI)?

Organizations and practitioners construct AI models which might be specialized algorithms to perform real-world tasks similar to image classification, object detection, and natural language processing. In consequence, AI improves productivity, reduces human error, and facilitates data-driven decision-making for all stakeholders. Some distinguished AI techniques include neural networks, convolutional neural networks, transformers, and diffusion models.

What’s Blockchain?

The blockchain concept was first realized in 2008 by an anonymous entity referred to as Satoshi Nakamoto, who introduced Bitcoin cryptocurrency in a famous research paper titled Bitcoin: A Peer-to-Peer Electronic Money System. Today, blockchain reportedly powers over 23,000 cryptocurrencies globally.

Blockchain is predicated on the principles of encryption, decentralized architecture, smart contracts – programs stored on blockchain that trigger based on predefined conditions – and digital signatures. This ensures that data can’t be tampered with and restricted to authorized users only. Blockchain framework has far-reaching applications, from handling financial transactions to cryptocurrency, supply-chain management, and digital electorates. Some distinguished examples of blockchain frameworks include Ethereum, Tezos, Stellar, and EOSIO.

The Synergy of AI and Blockchain

A merger between blockchain and AI frameworks could make safer and transparent systems for enterprises. AI’s real-time data evaluation and decision-making capabilities expand blockchain’s authenticity, augmentation, and automation capabilities. Each technologies complement one another. For example,

• Optimizing automation of supply chain processes by embedding AI in smart contracts.
• Addressing the challenges of AI ethics by ensuring the authenticity of information.
• Fostering a transparent data economy by providing actionable insights.
• Elevating the intelligence of blockchain networks by facilitating access to extensive data.
• Boosting security with intelligent threat detection in financial services.

In accordance with Moody’s Investor Service Report 2023, the interaction of AI and blockchain can potentially transform financial markets by automating manual tasks and reducing operating costs in the subsequent five years.

Major Opportunities for AI in Blockchain

AI and blockchain will converge to affect critical areas of our society. Below are some promising opportunities and use cases of blockchain and AI.

Fraud Detection

Despite various security measures, blockchain security remains to be a big concern. Cyberattacks can potentially disrupt blockchain networks completely. Hence, AI is instrumental in elevating the safety of blockchain frameworks. AI-powered fraud detection mechanisms can proactively detect and safeguard sensitive blockchain transactions from cyber threats.

AI and machine learning (ML) algorithms are able to the next:

• Analyzing transaction patterns to detect fraudulent activities made by bots.
• Trigger alerts and events in real-time to assist prepare against attacks.
• Enhance the safety of smart contracts by blocking or minimizing smart contract-based cyberattacks, similar to Reentrancy, overflow/underflow vulnerability, short address attack, and timestamp dependence.

AI-powered Smart Contracts

Smart contracts are self-fulfilling digital contracts with pre-established rules and governing principles, i.e., they robotically run actions or events when rules are met. AI could make these contracts more impactful by

• Optimizing smart contract code for reducing the price of operating blockchain, similar to Ethereum Gas.
• Improving the scalability of smart contracts using compression and parallelization.
• Analyzing & auditing smart contracts using classification and pattern recognition techniques.
• Integrating creative and cognitive capabilities in smart contracts.
• Facilitating testing and verification for smart contracts.

Furthermore, AI automation may also help save effort and time in handling complex blockchain workflows by reducing the necessity for human supervision.

AI-powered Analytics & Insights

AI enhances the capabilities of blockchain systems using data-driven insights. For example, implementing AI in a blockchain-based supply chain can improve inventory operations, transparency, sustainability, etc. ML models can run analytics on secure and trusted blockchain transaction data to:

• Predict demand variations
• Shorten supply routes
• Improve order success
• Monitor the standard of products

By maintaining snapshots of all supply-chain operations on a blockchain ledger, stakeholders can gain real-time insights and improve the traceability of their supply chains.

Decentralized Data Storage & Processing

The decentralized framework of blockchain synchronizes well with the data-handling capabilities of AI. Distributed ML models like federated learning can train on datasets stored across multiple sources. Blockchain offers an ideal framework for analyzing complex and disconnected datasets using these ML models. It maintains the privacy and security of sensitive blockchain transaction data.

Major Challenges for AI in Blockchain

If we address the next prevalent challenges, the intersection of blockchain and AI might be more seamless and quick.

Scalability Issues

Scalability is a critical technical roadblock when integrating AI and blockchain technologies as a consequence of various requirements, parameters, and limitations, similar to processing speed, data handling, and resource consumption.

AI and ML models often require high-speed processing and low latency. They favor smooth data pipelines to deliver real-time insights for timely decision-making. Conversely, the blockchain framework has slower consensus mechanisms which might be decentralized and strictly isolated in nature.

The next solutions may also help address these challenges:

• Sharding – splitting the blockchain into smaller chunks for parallel processing and scalable usage beyond the restricted domain.
• Layering – introducing dedicated layers for specific functionalities, similar to consensus mechanisms, storage partitioning, and AI-powered smart contracts. It enhances parallel processing and optimizes resource allocation.
• Sidechains – addressing the storage limitations of traditional blockchain networks by allowing smart device data to be securely stored in a separate database and mapping it to the sidechain transactions of the block.

Compatibility Issues

Making AI and blockchain work in synchronization requires ensuring compatibility aspects. Addressing this issue demands highly optimized and effective data integration strategies and data-sharing models. A few of the vital approaches on this regard include:

• Bridging the gap of information format in AI (great amount, centralized) and blockchain (small amount, decentralized) to effectively interpret blockchain data.
• Using federated learning models with blockchain may also help ensure trust and privacy while overseeing data and computation processes.

Legal & Regulatory Implications

Data privacy and protection are the first concerns when exposing sensitive data regulated by a blockchain to AI and ML models. Regulation policies, similar to GDPR, strictly force businesses to handle client data by ensuring:

• Consensual usage of information and data
• Data deletion, once processed
• Anonymization of sensitive personal or business data

The legal issues related to smart contracts are difficult. Due to this fact, it’s mandatory to create contractual terms and conditions fastidiously.

The long run of blockchain and AI are intertwined, given the rapid digital transformation across industries. Soon, we’ll witness many more advancements and opportunities, facilitating various business operations.

For more information on AI advancements and trends, visit unite.ai.