What are the application scenarios of AI combined with blockchain?

AI and blockchain are two of the most disruptive technologies of our time, with the potential to revolutionize innovation across multiple industries and completely disrupt economic and social relationships. The combination of blockchain and AI will unlock entirely new areas of application. ai will dramatically increase productivity, while blockchain will ensure security and transparency, thus giving rise to a variety of innovative applications.

According to Spherical Insights, the combination of blockchain and AI will grow into a billion dollar industry over the next decade. However, the question of how exactly these two technologies will be combined has not been studied in depth yet, so it is worthwhile to find out.

In this article, we will look at AI in the context of blockchain and explore the potential intersection of these two technologies and their value.

The Intersection of AI and Blockchain

Deep learning models excel at processing big data, simulating the cognitive processes of the human brain and using complex neural networks to identify patterns, make predictions and make decisions. Blockchain networks have a transparent, decentralized and manipulation-resistant transaction settlement layer that is network ready, on which data is stored and cannot be tampered with, and with which users can interact in a permissionless and trust-minimized manner.

The combination of blockchain and AI will give rise to automated intelligent decision systems that output highly reliable results and trigger real-world operations based on tamper-proof data.

Blockchain combined with AI will unlock entirely new business models for businesses to improve operational efficiency, automate repetitive tasks for individuals, exchange data more securely and efficiently, enhance decision-making processes through AI smart contracts, and improve trust and transparency in critical infrastructure and transaction processes.

The combination of AI and blockchain will not only benefit traditional business applications, but will extend to other areas as well. Combining AI's powerful analytical capabilities with blockchain's advantages in security and decentralization can be applied to various fields such as education, healthcare, energy, social, agriculture and urban planning to make better data-based decisions and improve resource management efficiency.

AI and blockchain will disrupt a range of traditional industries

Use Cases for Combining AI and Blockchain

This chapter will explore a range of potential use cases for combining AI and blockchain.

Securing

Decentralized infrastructure and blockchain technology can provide cryptographic security for AI systems. We can embed security barriers in AI systems to avoid abuse or malicious manipulation of the system; AI developers can set specific parameters in the code to control the threshold for AI access to various critical systems; and private key mechanisms can be created using tamper-proof infrastructures such as blockchains, smart contracts, and prophecy machines.

Blockchain systems were originally designed to protect against various malicious attacks and manipulations, and these security mechanisms can also be used to protect against attacks in the AI domain. In a centralized system, a problem in one link may threaten the security of the entire system; whereas a decentralized infrastructure is distributed to multiple nodes and multiple mutually independent private keys, making it more difficult for an attacker to compromise the entire system.

Blockchain can effectively enhance the security of AI applications, so companies can fully utilize the potential of AI and guarantee security through encryption technology.

Tracking the supply chain

Smart contracts are computer programs that are deployed and run on the blockchain, with code in the contract specifying trigger conditions and the results of the trigger. Smart contracts can be executed automatically and therefore have special advantages when combined with AI. AI models plugged into smart contracts can predefine specific conditions to perform tasks such as: monitoring inventory and automatically placing orders with external suppliers when inventory is low.

Blockchain combined with AI can also digitize paper-based processes and monitor every step of the process from production to delivery in real time to increase transparency and reduce the risk of fraud. Companies combining AI's predictive analytics capabilities with blockchain will be able to gain better insight into demand patterns, optimize inventory management and make data-based decisions to reduce costs.

This use case could also be useful in other areas, such as disaster mitigation efforts. Combining AI analytics with on-chain supply chain tracking capabilities can help people-centric organizations and businesses optimize resource allocation during disasters by providing real-time data on the quantity and geographic location of relief supplies to improve efficiency and better distribute supplies.

Verify the authenticity of content

The emergence of deep learning models such as DALL-E, Stable Diffusion and Midjourney proves that generating images or other media with text has unlimited potential.

While these models give us a glimpse of AI's potential for disruptive innovation in productivity and creativity, they can also be used to spread false rumors or fake images or other distribution mediums.

Blockchain technology is underpinned by cryptography and encryption and can therefore be used to verify the authenticity of media such as images, videos and text, using cryptography to verify the origin of content and whether it has been tampered with. This cryptographic watermarking technology can also be used to create tamper-proof timestamps to verify the authenticity of message content, source and time.

In the future, if social stability is to be guaranteed, it will be necessary to be able to distinguish between content created by AI and humans. Cryptographic verification and timestamps can therefore be used to assist decentralized platforms in displaying, verifying and distributing content. Such platforms can also help creators and users build trust in content by ensuring that the medium of information distribution has not been tampered with, is authentic, and that all histories are transparent and verifiable.

In addition, on-chain passwords, especially NFTs, can effectively solve the problems faced in verifying the authenticity and traceability of digital content. NFTs are unique digital assets that can be used to represent asset ownership and verify the origin of various types of files such as images, videos, text and music.

Binding NFTs to a particular piece of content allows creators to create digital fingerprints and guarantee the traceability of content on the chain. When content is minted as an NFT, its origin, resale history and any subsequent modifications become transparent and easily verifiable. Such technologies, once they become standards, can enhance accountability mechanisms for Internet content. Publishers can gain more incentives to safeguard the authenticity of their work, and the average person can better distinguish between authentic and doctored content.

Analyzing Data

The greatest value of blockchain technology is its ability to safeguard the authenticity of data sources in the most efficient way. The best way to safeguard data integrity in the long term is to store it in a decentralized blockchain network that is extremely secure. Therefore, blockchain is also naturally a good platform for big data analysis.

As blockchain increasingly dominates human socio-economic activities, big data analytics using sophisticated machine learning models are equally capable of handling the massive data sets on the chain. These machine learning models can identify megatrends and output practical insights through predictive analytics. And this can help companies and individuals make efficient, rational decisions and determine emerging opportunities in the on-chain economy.

In addition, AI models can optimize blockchain consensus algorithms, including bitcoin, to reduce latency and expand computing for blockchain nodes.

Providing financial services

With decentralized finance (DeFi), anyone with an Internet connection can access transparent financial services, initiate peer-to-peer transactions, and interact with tamper-evident smart contracts. the DeFi ecosystem has come a long way, and AI models can leverage these ever-richer and more mature DeFi financial services to perform operations and tasks based on predefined instructions and to settle transactions.

Large language models with secure access to the Internet can also access Web3's on-chain fintech stack to perform routine tasks such as payments or transactions. Due to the inherently composable nature of blockchain applications, AI models can execute complex financial transactions that are interconnected and do not rely on any intermediaries or opaque traditional financial systems.

In addition, AI can be used in DeFi applications to automate investment strategies and provide innovative financial services to users using a secure and transparent decentralized infrastructure. while AI excels at decision making, blockchain excels at recording real-time transaction behavior, so the combination of the two can be used to create automated compliance and fraud monitoring processes based on machine learning algorithms.

Provide medical coverage

Some blockchains are well suited for storing sensitive data, and advanced AI models can use this to analyze health data and identify recurring patterns, and make accurate diagnoses based on medical photography and cases. In addition, innovative encryption techniques such as homomorphic encryption can also be used to compute data without compromising data privacy.

AI and blockchain technology can enhance the management, privacy and security of medical data by securely storing and sharing cases, medical research data and other sensitive data. Researchers in healthcare and longevity will be able to collaborate more effectively offsite and secure data to the greatest extent possible.

Blockchain technology can be used as the underlying data storage solution on which to develop AI diagnostic tools and customized treatment plans, while enhancing data privacy and improving the efficiency and customization of healthcare systems.

Ensuring Transparency

One of the major challenges facing deep learning models today is the opaqueness of the decision-making process. Because these models are so complex, sometimes with hundreds of billions of parameters, it can also be difficult for experts to explain why a model outputs a specific answer to a specific question.

While this opacity is a fundamental feature of deep learning models, and it is ultimately the job of AI researchers to develop AI models that can explain their decisions, blockchain networks can somehow leverage their transparency to solve the problem of opaque AI models.

Blockchains can transparently record data and therefore allow AI models to create clear frameworks for operation, algorithm-based decision models to analyze audit trails, and use tamper-proof data ledgers to view the data used by the models. Ultimately, this will further improve the quality of the AI model recommendation algorithm.

Decentralized data storage

Many AI models rely heavily on large data sets. While data is only one element, it can greatly impact the performance of AI systems. blockchains such as Filecoin, IPFS and Arweave can provide decentralized storage solutions that effectively guarantee the quality of training data and accurate data traceability. Also, as mentioned above, innovative cryptography can provide encrypted datasets for deep learning models and protect data privacy at the same time.

Combining blockchain storage solutions with deep learning technologies will improve the security and reliability of AI systems and simultaneously enhance transparency and trust in the decision-making process.

Developing Smart Contracts

With the advent of AI-assisted development tools such as Github Copilot, the efficiency of smart contract developers has been dramatically improved. In addition, it is possible to integrate AI-powered API interfaces into smart contract applications, analyze real-world sensor data or user sentiment on social media, or create generative models. And these will ultimately drive the next generation of Web3 applications. In this demo, Laurence Moroney, head of AI, shows how to use Stable Diffusion and Chainlink Functions to develop AI art generators for smart contracts.

AI can also help game developers create entire game worlds, in-game assets, NPCs, and gameplay events to unlock new Web3 game experiences. In addition, developers can develop game mechanics using natural language and generative AI models and embed these parameters into the game's on-chain logic. A group of game enthusiasts can write open source code with the aid of generative AI models to develop games together.

Challenges and Caveats of AI Combined with Blockchain

While the combination of AI and blockchain technology can bring many benefits to many industries, there are still challenges to overcome to truly unlock the potential of both. one problem AI models have always faced is collecting data, as they must access multiple disparate data sets. To perfectly combine AI and blockchain, it is necessary to address the interoperability between the two platforms and establish standards to enhance connectivity and compatibility between the two technologies.

There is also a need to upgrade the data privacy framework to address the issues encountered in the integration of AI and blockchain to safeguard user privacy and trust.

Although both technologies have the potential to reshape the roots of society, there is currently not enough public awareness of them. If the public can be educated about the benefits, risks and caveats of combining AI and blockchain technologies, it will make people more confident in the combination of blockchain and AI technologies and increase user demand.

Once more people see the synergy between decentralized systems and AI technologies, more AI systems will integrate cryptographic security mechanisms and blockchain applications. This will effectively address user trust and make them more comfortable interacting with AI and drive sustainable development of AI technology.

The Future of AI and Blockchain

AI's strength is large-scale intelligence, while Web3's strength is large-scale coordination, value exchange and trust minimization. Therefore when the two are combined, the door will be opened to bring higher security, transparency and efficiency to many industries.

The combination of AI and blockchain will have a huge disruptive impact on various industries. AI models will continue to enter more market segments as more and more enterprises begin to use software integrated with AI technology to automate workflows, improve efficiency, and optimize their businesses.

At the same time, as public trust in institutions has declined in recent decades, users are increasingly inclined to choose applications with encryption safeguards. The convergence of these two paradigm shifts will inevitably reshape the way society and the economy operate.