Blockchain for AI or AI for Blockchain 

Are blockchain and AI passé? Was the tech romance just a flirtation of two powerful characters that head in different directions?

Maybe for now. But we believe we haven’t seen the last step in this mating dance. Our extensive report, AI and Blockchain Disruption unveils some perfect synergy use cases worth keeping your eye on. This article presents a brief overview of some research findings.

Unlocking business potential: The impact of blockchain and AI

Could AI technology, with its many successful broad applications, open the floodgates further by bringing blockchain into the mix? Or will it end up making blockchain better?

From life-saving medical analytics tools to multi-modal AI and self-improving AI agents, artificial intelligence can help elevate blockchain applications, from enhancing workflows to automated decision-making.

Whether blockchain for AI or AI for blockchain, we give you the inside story to help you get a clearer picture. 

The powerful potential of how AI and blockchain work together

In a nutshell, this co-evolution will allow AI algorithms to enhance their decision-making process and, due to blockchain-specific capabilities, improve the accuracy and security of data. At the same time, AI can boost the efficiency of blockchains and support operations with quick analytics and auditing.

A good example of a working blockchain AI project is Ocean Protocol, which offers a digital platform that utilizes the technology to secure and streamline data distribution. It creates a decentralized connection between data providers and consumers.

“Blockchain helps at every step of the AI model development pipeline. Every industry will be affected because both blockchain and AI are general-purpose technologies, and by extension, so is AI combined with blockchain,” said Trent McConaghy, the founder of Ocean Protocol, when interviewed by the Onchain research team.

How does blockchain support AI?

• Secure tamper-proof transactions 
  Blockchain offers a secure and tamper-proof ledger to safeguard sensitive AI-related data.
• Greater transparency
  Each transaction on the blockchain is recorded and time-stamped. Therefore, the data source is transparent and can be verified efficiently.
• Authenticity
  Decentralized blockchain networks enable data traceability and authenticity. Due to their immutable nature, data cannot be manipulated and can be traced back to its origin, restoring trust in AI.
• Distributed computing power
  Each blockchain network node can contribute computational resources. This enables AI GPU-hungry models to operate faster without any single point of failure.
• Privacy
  Blockchain is decentralized, enhancing user privacy by eliminating the need for a central authority.

How is AI used in blockchain use cases? Let’s look at some real-world examples.

Use case example: Advancing healthcare analysis

Thermaiscan operates specifically in the breast cancer risk assessment field. It has developed a non-invasive, scalable AI pre-screening tool that can detect even the smallest medical abnormalities in breast tissue. This is coupled with using blockchain to secure sensitive healthcare information, manage patient data, and leverage artificial intelligence for risk assessments and data analysis. 

This is a good example of a real-world use case achieved by fusing Thermaiscans’ AI algorithm with a thermal technology model running on the blockchain.

“Combining blockchain and AI makes achieving a good level of healthcare data security way more possible.” – Arby Leonian, CEO & Co-founder of Thermaiscan.

Other examples are projects such as Akash Network or the Golem Network, which offer decentralized computational power to AI-based companies that require GPU support and accept payment for these services in crypto-based tokens. 

Another successful blockchain AI project is Fetch.ai, which integrates AI-crypto agents with a decentralized infrastructure.

How can AI help blockchain?

• Improved scalability
  AI tools can optimize the performance of blockchain networks by effectively managing the distribution of computational resources. 
• Transaction efficiency
  P2P blockchain transactions are costly due to redundant nodes. AI can identify the fastest node, allowing others to cease efforts. This reduces costs and increases transaction speed and throughput.
• Security measures
  AI-driven solutions can enhance blockchain security by detecting deviations, identifying anomalies, and predicting potential threats.
• Automated smart contract auditing 
  AI can automatically audit smart contracts to identify and rectify vulnerabilities, increasing security and reducing the risk of fraud.
• Improved data management
  AI streamlines data management and analysis in blockchain networks by monitoring data usage, managing access, and handling related tasks, as AI can act as data gatekeepers.

How does this work in a real-world example? Let’s find out.

Use case example –  Financial security and risk management

Smart contract technology is one area in the financial services sector poised to benefit from this fusion.  

Anchain.AI operates in the cryptocurrency and financial sector and specializes in onchain anti-money laundering checks, fraud detection mechanisms, and detailed smart contract security audits. 

Anchain.AI integrates blockchain audits into AI-based smart contracts, enhancing security, contributing to safer Web3 AI implementation, and increasing trust.

Another example where AI bolsters a blockchain project is CertiK, which utilizes AI-powered tools to enhance blockchain security, smart contracts, and Web3 applications. 

Projects, such as The Graph and Ocean Protocol, deploy AI to boost efficiency, enhance data indexing and querying, and improve user experience in managing data across decentralized blockchain networks. 

The road ahead for blockchain and AI 

Growth from this fusion of technologies in numerous  Web3 AI projects is anticipated to continue, as you can see from the market size projection calculated by our research team based on their report findings.

The future of blockchain and AI will require collaboration among technologists, policymakers, educators, and industry leaders to shape its future path forward.

Working together, they can drive innovation, tackle challenges, and build a tech-forward future that benefits us all.

Finally, ethical considerations play an integral part and hold the keys to achieving widespread adoption of AI fused with blockchain.

Want to get ready for the future? Read our in-depth AI And Blockchain Disruption report to learn more about this exciting new world.