What are the application scenarios for the integration of AI and blockchain?
AI and blockchain are the two most disruptive technologies of this era, with the potential to spark an innovation revolution across multiple industries, fundamentally changing economic and social relationships. The combination of blockchain and AI will unlock entirely new application areas. AI will significantly boost productivity, while blockchain will ensure security and transparency, leading to the emergence of various innovative applications.
According to Spherical Insights, the integration of blockchain and AI is projected to become a billion-dollar industry in the next decade. However, the question of how these two technologies will combine is still not thoroughly researched, making it worth exploring.
This article will look at AI in the context of blockchain and explore the potential intersections and value of these two technologies.
Intersection of AI and blockchain
Deep learning models excel at handling large datasets, simulating the cognitive processes of the human brain. They utilize complex neural networks to identify patterns, make predictions, and decisions. Blockchain networks have a transparent, decentralized, and tamper-resistant transaction settlement layer, making them usable once connected. Data stored on the blockchain cannot be tampered with, and users can interact with the blockchain in a permissionless and trust-minimized manner.
The combination of blockchain and AI will unlock entirely new business models, improving operational efficiency for enterprises and automating repetitive tasks for individuals. It will enable more secure and efficient data exchange and enhance decision-making processes through AI smart contracts. Furthermore, it will enhance trust and transparency in critical infrastructure and transaction processes.
The integration of AI and blockchain will not only benefit traditional business applications but will also extend to other fields. By combining AI’s powerful analytical capabilities with blockchain’s advantages in security and decentralization, it can be applied to various sectors such as education, healthcare, energy, social services, agriculture, and urban planning. This integration will enable better decision-making based on data and improve resource management efficiency.
Ensuring Security
The secure and decentralized infrastructure of blockchain technology can provide encryption protection for AI systems. We can embed security barriers within AI systems to prevent misuse or malicious manipulation. AI developers can set specific parameters in the code to control the threshold for AI access to various critical systems. Additionally, they can utilize tamper-resistant infrastructure such as blockchain, smart contracts, and oracles to create private key mechanisms.
The original intention of blockchain system design is to prevent various malicious attacks and manipulations, and these security mechanisms can also be used to prevent attacks in the AI field. In centralized systems, a problem in any one link can threaten the security of the entire system, while decentralized infrastructure is distributed across multiple nodes and multiple independently operated private keys, making it more difficult for attackers to invade the entire system.
Blockchain can effectively enhance the security of AI applications, allowing enterprises to fully leverage the potential of AI while ensuring security through encryption technology.
Tracking Supply Chain
Smart contracts are computer programs deployed and run on a blockchain, with the code of the contract specifying the triggering conditions and resulting actions. Smart contracts can execute automatically, giving them distinct advantages when combined with AI. When an AI model is integrated with a smart contract, specific conditions can be predefined to execute tasks, such as monitoring inventory levels and automatically placing orders with external suppliers when inventory is low.
The combination of blockchain and AI can also digitize paper-based processes, providing real-time monitoring of every step from production to delivery to increase transparency and reduce fraud risks. By integrating AI’s predictive analytics capabilities with blockchain, businesses can gain better insights into demand patterns, optimize inventory management, and make data-driven decisions to lower costs.
This use case can also be impactful in other areas, such as disaster relief efforts. Combining AI analytics with on-chain supply chain tracking can assist human-centric organizations and businesses in optimizing resource allocation during disasters. It provides real-time data on the quantity and geographical locations of relief supplies, improving efficiency and better allocation of resources.
Verification of content authenticity
The emergence of deep learning models like DALL-E, Stable Diffusion, and Midjourney proves the limitless potential of generating images or other media from text.
While these models show us the disruptive innovation potential of AI for productivity and creativity, they could also be used to spread misinformation or create forged images or other media.
Blockchain technology is fundamentally based on cryptography and encryption, making it suitable for verifying the authenticity of images, videos, text, and other media. Encryption techniques can be used to verify the source and integrity of content, creating tamper-proof timestamps to validate the authenticity of messages, their source, and time.
In the future, to ensure social stability, it will be essential to distinguish between content created by AI and humans. Therefore, using encryption verification and timestamps can assist decentralized platforms in showcasing, verifying, and distributing content. These platforms can help creators and users establish trust in the content, ensuring that the media has not been tampered with, is authentic, and all historical records are transparent and verifiable.
Furthermore, on-chain tokens, especially NFTs, can effectively address the challenges of verifying the authenticity and provenance of digital content. NFTs are unique digital assets that can represent ownership and verify the source of images, videos, text, music, and other types of files.
Binding an NFT to specific content allows creators to establish a digital fingerprint, ensuring the traceability of content on the chain. When content is minted as an NFT, its source, transaction history, and any subsequent modifications become transparent and easily verifiable. Once such technologies become standardized, they can enhance the accountability mechanism of internet content. Publishers can receive more incentives to ensure the authenticity of their work, and ordinary individuals can better distinguish between authentic and tampered content.
Analyze data.
The greatest value of blockchain technology lies in its ability to efficiently safeguard the authenticity of data sources. The best way to ensure data integrity in the long term is to store data in highly secure decentralized blockchain networks. Therefore, blockchain naturally serves as an excellent platform for big data analysis.
As blockchain increasingly dominates human socio-economic activities, using complex machine learning models for big data analysis can also handle massive datasets on the chain. These machine learning models can identify major trends and provide practical insights through predictive analysis. This helps businesses and individuals make efficient, rational decisions and assess new opportunities emerging in the blockchain economy.
Furthermore, AI models can optimize blockchain consensus algorithms, including those for Bitcoin, reducing latency and performing computations for blockchain nodes.
Providing Financial Services
With decentralized finance (DeFi), anyone with internet access can now access transparent financial services, engage in peer-to-peer transactions, and interact with immutable smart contracts. The DeFi ecosystem has seen significant development, and AI models can leverage these continually evolving and maturing DeFi financial services to execute operations and tasks based on predefined instructions, and settle transactions.
If large-scale language models can securely access the internet, they can also tap into the Web3’s on-chain financial technology stack to perform tasks like payments or transactions. Due to the composability of blockchain applications, AI models can execute interconnected complex financial transactions without relying on any intermediaries or opaque traditional financial systems.
Furthermore, within DeFi applications, AI can be used to automate investment strategies and provide users with innovative financial services using secure and transparent decentralized infrastructure. AI excels in decision-making, while blockchain is adept at recording real-time transaction behaviors. Therefore, their combination allows for the establishment of automated compliance and fraud detection processes based on machine learning algorithms.
Provide Healthcare Services
Some blockchains are well-suited for storing sensitive data, and advanced AI models can leverage this to analyze health data and identify recurring patterns, making accurate diagnoses based on medical imaging and cases. Additionally, innovative encryption technologies like homomorphic encryption can perform computations on data without compromising data privacy.
AI and blockchain technology can securely store and share medical cases, research data, and other sensitive information, thereby improving the management, privacy, and security of healthcare data. Researchers in healthcare and longevity fields will be able to collaborate remotely more effectively while ensuring data security.
Blockchain technology can serve as the underlying data storage solution, upon which AI diagnostic tools can be developed and treatment plans customized, enhancing data privacy and improving the efficiency and customization of healthcare systems.
Ensuring Transparency
One major challenge facing current deep learning models is their lack of transparency in decision-making processes. Due to their complexity, with sometimes billions of parameters involved, experts find it difficult to explain why a particular model outputs a specific answer for a given problem.
Although this opacity is a fundamental characteristic of deep learning models, and developing AI models that can explain their decisions ultimately falls on AI researchers, blockchain networks can still leverage their transparency to address the issue of AI model opacity to some extent.
Blockchain can transparently record data, allowing AI models to create clear frameworks for their operations. This enables the analysis and auditing of algorithmic decision models, utilizing an immutable data ledger to view the data used by the model. Ultimately, this approach can further enhance the quality of AI model recommendation algorithms.
The Future of AI and Blockchain
The advantage of AI lies in large-scale intelligence, while the advantage of Web3 lies in large-scale coordination, value exchange, and minimized trust. Therefore, when these two are combined, it will open the door to a new world, bringing higher levels of security, transparency, and efficiency to many industries.
The combination of AI and blockchain will have a tremendous disruptive impact on various industries. As more and more companies start using software integrated with AI technology to automate workflows, improve efficiency, and optimize businesses, AI models will continue to enter more niche markets.
At the same time, with the public’s trust in institutions declining over the past few decades, users are increasingly inclined to choose applications with cryptographic security. The convergence of these two paradigm shifts is bound to reshape the way society and the economy operate.
