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Artificial intelligence (AI) and blockchain are two of the most transformative technologies of our time. Individually, they have the potential to revolutionize a multitude of industries and transform economic and social interactions and relationships. When combined, they unlock a new frontier of possibilities that can empower a new generation of applications that benefit both from the vast productivity gains unlocked by AI and the security and transparency enabled by blockchain technology.
According to a report by Spherical Insights, the intersection of blockchain and AI is set to grow into a billion-dollar industry within the next decade. Despite this potential, the integration of these two technologies has so far remained relatively underexplored, leaving room for further investigation as the two segments progress toward broader mainstream adoption.
In this post, we outline the concept of AI in blockchain, explore the potential convergence of these two technologies, and discuss the benefits that can come as a result of their combination.
Deep learning models excel at processing vast amounts of data to identify patterns, make predictions, and enable decision-making processes by leveraging intricate neural networks that mimic the cognitive processes of the human brain. A blockchain network offers a transparent, decentralized, and censorship-resistant Internet-native economic settlement layer that enables immutable data storage and permissionless, trust-minimized digital interactions.
The combination of blockchain and AI can produce intelligent automated decision-making systems that provide highly reliable outputs that trigger specific real-world outcomes based on immutable, tamper-proof data.
The integration of blockchain and AI could unlock entirely new business models, create operational efficiencies for organizations, help automate repetitive tasks for individuals, enable more secure and efficient data exchange, enhance decision-making processes through AI-driven smart contracts, and improve overall trust and transparency in key infrastructure and economic processes.
The convergence of AI and blockchain also has the potential to provide numerous benefits beyond traditional business applications. By combining the powerful analytical capabilities of AI with the secure, decentralized nature of blockchains, the technologies could be applied to areas such as education, healthcare, energy, social impact, agriculture, urban planning, and more to enable data-driven decision-making and more efficient management of resources.
In this section, weāll explore an array of potential use cases that highlight the potential impact of AI and blockchain integrations.
Decentralized infrastructure and blockchain technology can act as encryption-backed guardrails for AI systems. In such a model, AI systems can be deployed with built-in safeguards that reduce their ability to be misused or utilized for adversarial behaviors. Developers of AI can encode the specific parameters within which AI can access various key systems, and private keys can enforce these conditions with the help of tamper-proof decentralized infrastructure like blockchains, smart contracts, and oracles.
Decentralized, blockchain-based systems have been designed from the ground up to combat manipulation by various adversaries, and these security measures could extend to the use of adversarial AI agents. Unlike centralized systems, where a single point of failure can put the entire system at risk, decentralized infrastructure is spread across multiple nodes and multiple independent private keys, making it more difficult for a single adversary to compromise the entire system.
The utility of AI models and the security of blockchains can help reduce attack vectors and bolster the security of AI applications, enabling organizations to leverage the full potential of AI while maintaining a high standard of security enforced by cryptographic guarantees.
A smart contract is a computer program hosted and executed on a blockchain that consists of code specifying predetermined conditions that, when met, trigger outcomes. The self-executing nature of smart contracts offers some inherent advantages when it comes to leveraging the power of artificial intelligence. AI models incorporated into smart contracts could utilize specific predetermined conditions to execute tasks: Detecting the need for additional inventory and executing the order with an external supplier, for example.
The combination of blockchain and AI could also improve transparency and reduce the potential impact of fraud through the digitization of paper-based processes, and by enabling the real-time tracking of goods from production to delivery. By combining AI-driven predictive analytics with blockchains, companies can gain better insights into demand patterns, optimize inventory management, and make data-driven decisions to minimize costs.
This use case can also offer benefits in other fields, such as disaster relief. AI-driven analytics combined with blockchain-based supply chain tracking could help humanitarian organizations optimize resource allocation during natural disasters. By providing real-time data on the availability and location of essential supplies, emergency relief efforts can be optimized so that supplies are allocated to their ideal destination.
The capabilities of deep learning models, such as DALL-E, Stable Diffusion, and Midjourney, have highlighted the profound potential of generating images and different forms of media purely based on natural language text prompts (or using other forms of media).
While these models highlight the transformative potential of AI to increase productivity and supercharge the scope of human creativity, they could also be used in an adversarial manner to manipulate public opinion by spreading misinformation and propaganda or creating deep fakes and other misleading synthetic media.
Underpinned by cryptography and encryption, blockchain technology can help validate the authenticity of images, video files, text documents, or other types of media by being able to cryptographically verify where a piece of content originates from and whether it has been tampered with or altered in any way. This type of cryptographic watermarking technology can also be used for tamper-proof timestamping to help verify the authenticity of āwho knew what when.ā
In a future where it becomes imperative to be able to differentiate between AI- and human-generated content for maintaining stability in society, cryptographic validation and timestamping could facilitate the creation of decentralized platforms for content curation, verification, and distribution. Such platforms could empower content creators and users to establish trust regarding the information that is being propagated by ensuring that the media they spread is unaltered, authentic, and underpinned by a transparent and verifiable history.
Moreover, blockchain tokensāspecifically non-fungible tokens (NFTs)ācould present a solution to combat the challenges associated with verifying the authenticity and provenance of digital content. NFTs, which are unique digital assets, can be used to represent ownership and verify the origins of various forms of media, including images, videos, text, music, and other types of files.
By assigning an NFT to a piece of content, creators can establish a digital fingerprint that ensures the contentās traceability on-chain. When a piece of content is minted as an NFT, its origin, ownership history, and any subsequent modifications become transparent and easily verifiable. Such technologies becoming standardized could foster more accountability with online content, where publishers are better incentivized to maintain the authenticity of their work while ordinary people can more confidently discern between genuine content and content thatās been tampered with.
One of the most useful benefits of blockchain technology is its ability to provide unparalleled data provenance. Storing data in a highly secure and decentralized blockchain-based network may be one of the best ways to ensure data integrity over the long term. This naturally makes blockchain networks a good breeding ground for large-scale data analytics.
As blockchain technology increasingly underpins key aspects of human economic and social activity, large-scale analytics using sophisticated machine learning models could harness the vast sets of data generated on-chain. By doing so, these models could identify overarching trends and offer actionable intelligence through predictive analytics, enabling both businesses and individuals to make informed decisions about the opportunities that emerge from the on-chain economy.
In addition, AI models could help optimize calculations for algorithms that are used in the consensus process in blockchain systems, for example, Bitcoin mining, helping decrease latency and compute requirements for blockchain nodes.
Decentralized finance (DeFi) enables anyone with an Internet connection to access transparent financial services that involve peer-to-peer transactions and immutable smart contracts. The growth of the DeFi ecosystem has been momentous, and AI models could take advantage of the increasing variety and complexity of financial services offered by this ecosystem by using DeFi as an economic layer to execute actions and tasks based on predetermined instructions.
A large language model securely connected to the Internet could perform routine tasks involving payments or economic exchange by utilizing the on-chain financial stack of the Web3 industry. Due to the inherent composability of blockchain applications, AI models could carry out complex interconnected loops of financial transactions without having to rely on intermediaries and an opaque, paper-based financial system.
In addition, AI-powered automated investment strategies in DeFi applications can offer entirely new financial services underpinned by secure, transparent, and decentralized infrastructure. Due to the decision-making capabilities of AI and blockchainās effectiveness in recording real-time economic activity, a combination of the two technologies could also enable automated compliance and fraud-detection processes powered by machine learning algorithms.
Certain implementations of blockchain technology can be ideal for storing sensitive data, which can then be utilized by advanced AI models to analyze health data and identify recurring patterns, and make accurate diagnoses based on medical scans and records. In addition, novel encryption techniques, such as homomorphic encryption, could enable running computations on this data without compromising data privacy.
AI and blockchain technology can enhance data management, privacy, and security in healthcare by facilitating the secure storage and sharing of patient records, medical research data, and other sensitive information. This could allow healthcare and longevity researchers to collaborate more effectively from different physical locations while upholding the highest standards of data security.
By leveraging blockchain technology as a foundation for data storage, AI-driven diagnostic tools and custom treatment plans could be developed with increased data privacy, leading to a more efficient and personalized healthcare system.
A challenge presented by current deep learning models is the lack of transparency in their decision-making processes. Due to the immense complexity of these models, which sometimes involve hundreds of billions of parameters, even experts can struggle to explain why a particular model generates a specific output when prompted with a specific input.
While this opacity can often be a property of the underlying deep-learning architectures, and creating AI models that can explain or indicate their own decision-making is ultimately up to AI researchers, the inherent transparency of blockchain networks can help address some of the issues associated with this lack of transparency.
By facilitating a transparent record of data, blockchains can enable AI models to provide a clear framework for their operations. This allows for the analysis of audit trails on the decision-making patterns of algorithms, and the use of an immutable data ledger to reveal what data the models are relying on, ultimately helping to contribute to greater integrity of the recommendations generated by AI models.
Many AI models rely on exceedingly large data sets. While data is only one component, this training data can significantly influence the capabilities of the AI system. Decentralized storage solutions enabled by blockchain-based systems, such as Filecoin, IPFS, and Arweave, could help preserve training data integrity and ensure accurate provenance in the future. Additionally, as mentioned earlier, innovative encryption techniques could enable deep learning models to be trained on encrypted datasets while safeguarding privacy and confidentiality.
Integrating blockchain-based storage solutions into the deep learning stack could enhance the security and reliability of AI systems while simultaneously promoting transparency and trust in their decision-making.
With the emergence of AI-assisted development tools, such as GitHub Copilot, the productivity of smart contract developers can be increased by orders of magnitude. Smart contract applications could be further augmented with AI-powered APIs, which provide analytics of real-world sensors, sentiment analysis, or generative models, to bring forth an entirely new generation of Web3 applications.
In this demo, Google AI Lead Laurence Moroney showcases how he was able to develop an AI art generator for smart contracts using Stable Diffusion and Chainlink Functions.https://www.youtube.com/embed/kG-ODpYRREQ?feature=oembed&enablejsapi=1&origin=https%3A%2F%2Fblog.chain.link
AI can also be used to unlock entirely new Web3 gaming experiences by enabling game developers to seamlessly generate entire game worlds, in-game assets, non-player characters, and scripted in-game events, and codify game mechanics using natural language and generative AI models while imprinting these parameters into the gameās logic on-chain. Entire games could be developed by a collective of enthusiasts using open-source code assisted by the generative capabilities of AI models.
While the integration of AI and blockchain technology offers benefits for a number of industries, some challenges must be addressed to fully realize the underlying potential. AI models traditionally have had a data collection problem, whereby they have to connect to distinct datasets from different parties. Interoperability between different blockchain networks and AI platforms is crucial for harnessing the power of these technologies, and standards must be established to increase connectivity and ensure compatibility between the two technologies.
Additionally, data privacy frameworks may need to be updated to accommodate the challenges posed by the integration of AI and blockchain to help uphold user privacy standards and maintain user trust.
Furthermore, while both of these technologies have the potential to reshape fundamental processes in society, public awareness of them is comparably low. Education focusing on the benefits, risks, and considerations associated with the convergence of AI and blockchain could help build public trust in the deployment of these technologies and increase user demand for AI systems supported by blockchain-based safety mechanisms.
Once the synergies between decentralized systems and AI become more apparent, more AI systems could become equipped with cryptographic guardrails and more blockchain-based applications could be integrated with AI. This would help solve the trust issue for users and enable them to become more comfortable with interacting with advanced AI systems, helping shape the trajectory of technological progress toward more sustainable AI development.
AI enables intelligence at scale, while Web3 enables coordination, value transfer, and trust-minimization at scale. When combined, these technologies can unlock new possibilities and enhance a multitude of industries by improving security, transparency, and overall efficiency.
The potential for transforming various sectors by combining AI and blockchains is tremendous. As companies strive to automate tasks, boost productivity, and enhance their business offerings through a large portion of software products becoming impacted by AI, AI models are expected to continue to proliferate into different segments of the economy.
Simultaneously, with the ongoing decades-long decline of trust in institutions, users are increasingly gravitating toward applications underpinned by cryptographic guarantees. The convergence of these two technological tectonic shifts is set to fundamentally reshape how our societies and economies operate.
Source: Chainlink
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