Artificial Intelligence (AI) and Distributed Ledger Technology (DLT) together address complex tasks by optimizing and automating business processes and creating innovative new products. Despite their shared digital nature, integrating these two technologies is a challenging process that requires sophisticated solutions. AI relies on large amounts of data and computational power, which are difficult to provide within distributed ledgers. However, the integration of DLT with AI, particularly its interaction with smart contracts, is made possible through the use of an intermediary data exchange and transfer mechanism known as an oracle. This paper analyzes various methods of smart contract interaction with distributed ledgers and hypothesizes the existence of decentralized AI technology. By exploring the methods and techniques for using oracles to facilitate AI and blockchain interaction, we can assess new opportunities for the decentralized economy arising from their combination with AI services and models, and predict the emergence of new decentralized products enhanced by AI technologies.
1.1. Smart contracts and cryptography:
ensuring security in decentralized
systems
Smart contracts are traditionally defined as programs that
operate on blockchain technology [1], requiring a
decentralized virtual machine capable of programming
and data processing [2]. The defining characteristic of
smart contracts is that, once deployed on the virtual
machine, they execute autonomously according to the
program’s instructions, without control by any user. The
execution of smart contracts can result in the creation of
new types of digital assets, allowing for full or partial
management by the user of the smart contract. These
characteristics of smart contracts enable the development
of economic and financial digital systems [3] that utilize
various other digital technologies for data collection [
Homomorphic Encryption, Zero-Knowledge Proofs, MultiParty Computation, and Quantum-Resistant Cryptography.
For users of smart contracts, cryptography represents
a commitment scheme that allows for the selection of
execution conditions in secret, with the ability to reveal
these conditions later [
The Promising and Complex Integration of AI Technologies in Smart Contracts
Among the most promising and complex technologies to be applied within smart contracts are AI technologies, which are associated with large volumes of data and advanced tools for their analysis. The concept of AI 0000-0003-4934-8377 (D. Virovets); 0000-0001-6936-955X (S. Obushnyi); 0000-0003-3990-5205 (B. Zhurakovskyi); 0000-0002-77756039 (P. Skladannyi); 0000-0002-9349-7946 (V. Sokolov) © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). encompasses a variety of data analysis and management technologies, ranging from simple machine imitations of human intelligence, as envisioned by Alan Turing, to deep learning technologies for training neural networks. Modern AI technologies can utilize models for Natural Language Processing (NLP), Computer Vision, Expert Systems, Genetic Algorithms, and Multi-agent Systems, which enhance and improve AI models.
Attempts to integrate smart contracts with AI lead to
several challenges due to their inherent complexity, which
require resolution [
AI, which relies on a vast array of tools for model training and extensive databases, cannot operate within a blockchain environment in its current form due to the resource demands needed to perform the necessary computations and model creation. To integrate the decentralized protocol environment with AI, several solutions have been proposed, which will be discussed below.
One approach to organizing the interaction between AI
and smart contracts is through the use of specific
mechanisms known as oracles. Oracles enable the
integration of AI systems with smart contracts, offering a
range of possibilities [
Oracles are third-party services with smart contracts that
act as intermediaries between blockchain and external
systems, allowing smart contracts to access off-chain data,
including data from AI models. By design, blockchains and
smart contracts cannot access data outside the blockchain
environment. The primary function of a blockchain oracle
is to send requests, verify, and authenticate external data
sources, and deliver this data to the user’s smart contract
[
To implement a smart contract, it may be necessary to
obtain information from AI based on the analysis of external
data, such as prices and exchange rates, sensor data, sports
event results, flight information, insurance claims, and so
forth. This can be achieved through an intelligent oracle,
which acts as an on-chain agent providing information in
response to queries to the AI model [
An example of such an intelligent oracle is the
Chainlink project, which represents a system of
decentralized oracles. Each oracle in this system gathers
data from independent sources and compares it to ensure its
accuracy [
AI models may involve using services to collect
meteorological, agricultural, seismic, and other real-world
data, which is then structured for subsequent monitoring
and analysis [
The literature identifies three main methods for AI interaction with traditional smart contracts: Edge AI, AIcentric smart contracts, and Swarm Intelligence. The table below presents these methods along with their characteristics, as well as other theoretically possible ways AI could interact with smart contracts. The concept of Swarm Intelligence Smart Contracts, based on swarm intelligence principles for decision-making and task execution, appears particularly interesting as it integrates well with the logic of smart contracts. 1.5. Direct integration of AI into smart contracts Another technology is the direct integration of AI into smart contracts without using an intermediary like oracles, which eliminates the trust issue. This approach involves utilizing ASIC-resistant frameworks through specialized consensus algorithms and standards for ensuring operational compatibility between infrastructure and deep learning tools. An example of such technology is the Cortex project, which employs the Material Representation Tool (MRT) technology compatibility standard and the Cuckoo Cycle consensus mechanism.
Embedding artificial intelligence directly into smart
contracts in an on-chain mode requires careful
consideration, as training AI models on the blockchain is
currently a rather cumbersome process [
The Cortex and Matrix platforms offer solutions for
integrating AI models into the blockchain and subsequently
using them in smart contracts. Users can incorporate the
proposed Cortex AI system into existing smart contracts. In
the Cortex project, this is achieved through the Cuckoo
Cycle consensus algorithm, known for its high efficiency in
handling large volumes of data. The Matrix platform, on the
other hand, employs the Proof of Intelligent Mining (PoIM)
algorithm, which leverages AI for optimizing mining
processes and consensus tasks. Matrix provides components
for semantic and syntactic analysis, security verification of
smart contracts, and identification of issues in transaction
models [
An example of an AI oracle is the GainForest project,
where a smart contract is used to distribute bets based on
data about deforestation status using artificial intelligence
[
Smart contracts utilizing federated learning for training AI models on decentralized data without centralizing it in one location. Data remains on local devices, and models are trained locally, exchanging only model updates.
Smart contracts incorporating reinforcement learning algorithms for decision-making. RL models are trained based on rewards and penalties for actions, optimizing decision-making strategies. Smart contracts incorporating reinforcement learning algorithms for decision-making. RL models are trained based on rewards and penalties for actions, optimizing decision-making strategies. Implementation of natural language processing technologies for understanding and analyzing text data. Smart contracts can interact with users through text interfaces, analyze documents, and automatically execute contract terms based on textual information.
Use of computer vision technologies for analyzing images and video. Smart contracts can make decisions based on visual data, such as automatically detecting defects on a production line. Employment of multi-agent systems where agents interact to achieve a common goal. Each agent can operate autonomously while exchanging information with other agents, facilitating the creation of complex and adaptive systems.
Application of AI for automatic negotiation and agreement formation between parties. Smart contracts can autonomously negotiate terms based on predefined criteria and the interests of the parties. Decentralized Artificial Intelligence Platform that supports AI smart contracts and AI inference.
Intelligent contracts service that combines smart contract capabilities with AI elements, allowing for handling large volumes of transactions and integrating AI services into dApps.
Decentralized AI marketplace and AI Publisher, running on blockchain with Distributed Atomspace (DAS) to represent and store primary knowledge for AI agents, and it encapsulates any computational results achieved during their execution Responsible Supply Chain based on Blockchain & Artificial Intelligence for managing the supply chain using smart contracts and AI on the Hyperledger Sawtooth platform.
Decentralized platform for creating commercial models combining AI and smart contracts based on Neo blockchain technology.
Hedge fund platform based on artificial intelligence for predicting financial markets and creating AI models.
AI-powered chatbot for asset value prediction.
Analytical platform using AI and machine learning for analyzing market trends and decision-making.
AI computational platform with access to a distributed network with unlimited scalability. Analyzing large volumes of data, modeling and forecasting, integrating AI models into business processes, and sharing computational resources.
Analysis of decentralized data for decisionmaking and process automation Using AI in smart contracts for management and interactions Data collection and analysis, process automation Organizing production and supply of goods and services Using autonomous economic agents for process automation and asset management.
AI model competitions in the Numerai Tournament, fund management, and strategic investment decisions.
Process automation for asset management, decentralized loyalty programs for communities, and data analysis for decision-making.
Developing automated investment strategies for asset management.
Analyzing large volumes of data, modeling and forecasting, integrating AI models into business processes, and sharing computational resources. Neural network algorithms for AI for forecasting, analysis, and asset management.
Forecasting future events, decision-making, risk management, and business process optimization The platform for simplifying the collection, evaluation, and analysis of data with a consensus mechanism at the storage level.
Managing medical data, storing confidential participant information, providing decentralized medical services, and managing medical records. Machine learning platform for generating relational maps of verified data elements collected from “consent contracts”.
Collecting, monitoring, and analyzing mental health and behavioral data, solutions for personnel, and resource management.
Decentralized database of clinical and non-clinical data managed by AI.
Real-time biometric monitoring of participants, and personalized medical solutions.
Ecosystem for creating AI models using distributed computing and incentive models.
Machine learning model training platform and creation of autonomous agents with data sharing service.
The platform for generating and managing digital art using AI and Generative Adversarial Networks (GAN). NFT Art Generation platform using GAN neural network architecture, combining two generators and discriminators to generate texts, images, and sounds. Providing computational resources for training AI models.
Decentralized data exchange and management, creating autonomous agents for process optimization.
Automated solutions for profit distribution, trend prediction NFT management, and art generation.
Creating and managing NFTs, content monetization, and participant engagement, and managing intellectual property rights.
DanKu
AI platform to enhance public perception of artworks, train artists, and gather information for AI models. Service for training large AI models using distributed resources, reducing costs, and democratizing AI development.
Incentive system using tokens for participation in AI model creation.
Service for creating and accessing machine learning models with an incentive system and evaluation using smart contracts.
Intelligent chatbots for participants, organizing communications with communities, investment decisions, and creating recommendations for project participation.
Tools for forecasting and analyzing market data, asset and risk management strategies, and business process automation.
Tools for asset management and large data volumes, cybersecurity protection.
Collaborative development of AI models for token-based rewards.
The interaction between AI and smart contracts raises
several issues, including questions about AI’s
responsibility regarding the consequences of executing
a contract by an autonomous agent or providing false or
incorrect information for the execution of a smart
contract [
Standardization of smart contracts is crucial for uniform task typing for AI models to handle various data classes, analyze their performance, and create necessary integration modules for AI. Through these modules, smart contracts can interact with each other and organize intelligent decentralized systems with selflearning and environmental adaptation capabilities. Additionally, the standardization of intelligent smart contracts will ensure the automated and autonomous development of intelligent systems. Inter-network integration and data exchange with the physical world should be managed by oracles adapted to intelligent networks, which presents new challenges. The use of different types of AI, with varying structures and complexities, requires finding new integration solutions.
The integration of smart contracts and AI undoubtedly offers significant advantages, as blockchain provides the potential for cryptographic storage of large volumes of data and its use for training AI models, securely transmitting data between digital systems, and implementing economic model logic. Despite the complexities involved in integrating smart contracts and AI, methods and solutions for such integration already exist. In this context, the need for standardizing the methods of linking smart contracts and AI arises to enable the mutual integration of various solutions (services). The assertion that “If Smart Contracts Are the Body of the Digital Deal Era, Artificial Intelligence is the Mind” aptly describes the importance of intelligent oracles and cryptography in ensuring the interaction between systems.