=Paper=
{{Paper
|id=Vol-3829/short5
|storemode=property
|title=Integration of smart contracts and artificial intelligence using cryptographic oracles (short paper)
|pdfUrl=https://ceur-ws.org/Vol-3829/short5.pdf
|volume=Vol-3829
|authors=Denys Virovets,Sergiy Obushnyi,Bohdan Zhurakovskyi,Pavlo Skladannyi,Volodymyr Sokolov
|dblpUrl=https://dblp.org/rec/conf/cqpc/VirovetsOZSS24
}}
==Integration of smart contracts and artificial intelligence using cryptographic oracles (short paper)==
https://ceur-ws.org/Vol-3829/short5.pdf
Integration of smart contracts and artificial intelligence
using cryptographic oracles⋆
Denys Virovets1,†, Sergiy Obushnyi1,†, Bohdan Zhurakovskyi2,†, Pavlo Skladannyi1,∗,†
and Volodymyr Sokolov1,†
1
Borys Grinchenko Kyiv Metropolitan University, 18/2 Bulvarno-Kudriavska str., 04053 Kyiv, Ukraine
2
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute,” 37 Peremogy ave., 03056 Kyiv, Ukraine
Abstract
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.
Keywords
blockchain, cryptography, smart contracts, artificial intelligence, AI, web3, crypto-economy,
decentralization, oracle, decentralized artificial intelligence 1
1. Introduction Homomorphic Encryption, Zero-Knowledge Proofs, Multi-
Party Computation, and Quantum-Resistant
1.1. Smart contracts and cryptography: Cryptography.
ensuring security in decentralized For users of smart contracts, cryptography represents
systems a commitment scheme that allows for the selection of
execution conditions in secret, with the ability to reveal
Smart contracts are traditionally defined as programs that
these conditions later [7]. This is made possible by using
operate on blockchain technology [1], requiring a
Zero-Knowledge Proof (ZKP) technology, which enables
decentralized virtual machine capable of programming
transaction verification without identifying participants
and data processing [2]. The defining characteristic of
or data. Cryptographic methods used for smart contracts,
smart contracts is that, once deployed on the virtual
including Homomorphic Encryption, Zero-Knowledge
machine, they execute autonomously according to the
Proofs, Multi-Party Computation, and Quantum-Resistant
program’s instructions, without control by any user. The
Cryptography, can be employed in projects focused on
execution of smart contracts can result in the creation of
decentralized identity, decentralized finance, and medical
new types of digital assets, allowing for full or partial
research where privacy is paramount. In this way,
management by the user of the smart contract. These
cryptography ensures secure communication between
characteristics of smart contracts enable the development
parties and guarantees the security of data involved in the
of economic and financial digital systems [3] that utilize
operation of smart contracts.
various other digital technologies for data collection [4],
The Promising and Complex Integration of AI
analysis [5], and asset management [6]. Trust between
Technologies in Smart Contracts
participants in smart contracts is established through
Among the most promising and complex technologies
cryptographic algorithms and protocols that ensure
to be applied within smart contracts are AI technologies,
secure communication and data protection. This security
which are associated with large volumes of data and
is achieved through the application of various
advanced tools for their analysis. The concept of AI
cryptographic methods in smart contracts, such as
CQPC-2024: Classic, Quantum, and Post-Quantum Cryptography, August 0000-0003-4934-8377 (D. Virovets); 0000-0001-6936-955X
6, 2024, Kyiv, Ukraine (S. Obushnyi); 0000-0003-3990-5205 (B. Zhurakovskyi); 0000-0002-7775-
∗ Corresponding author. 6039 (P. Skladannyi); 0000-0002-9349-7946 (V. Sokolov)
†
These authors contributed equally.
© 2024 Copyright for this paper by its authors. Use permitted under
d.virovets.asp@kubg.edu.ua (D. Virovets); s.obushnyi@kubg.edu.ua Creative Commons License Attribution 4.0 International (CC BY 4.0).
(S. Obushnyi); zhurakovskybiyu@tk.kpi.ua (B. Zhurakovskyi);
p.skladannyi@kubg.edu.ua (P. Skladannyi); v.sokolov@kubg.edu.ua
(V. Sokolov)
CEUR
Workshop
ceur-ws.org
ISSN 1613-0073
39
Proceedings
encompasses a variety of data analysis and management 1.2. The role of distributed ledger
technologies, ranging from simple machine imitations of technology in static and dynamic
human intelligence, as envisioned by Alan Turing, to deep models
learning technologies for training neural networks.
Modern AI technologies can utilize models for Natural Distributed ledger technology (DLT) has demonstrated its
Language Processing (NLP), Computer Vision, Expert effectiveness in static models for accounting financial
Systems, Genetic Algorithms, and Multi-agent Systems, transactions, managing reputation systems, handling
which enhance and improve AI models. agreements, and confirming ownership rights. In contrast,
AI algorithms require dynamic data updates to create and
Attempts to integrate smart contracts with AI lead to
maintain training models. Training models based on smart
several challenges due to their inherent complexity, which
contracts cannot guarantee accurate predictions, partly due
require resolution [8]. The primary issue with smart
to the lack of support for fixed-point arithmetic and
contracts is the technical difficulty of storing and directly
differences in computational architectures. One potential
utilizing large amounts of data in distributed ledgers. It is
solution is to apply the Naive Bayes algorithm in smart
well known that AI can significantly contribute to the
contracts, which requires probability calculations using
development of decentralized products and asset
floating-point numbers based on Gaussian probability [11].
management by preprocessing and analyzing data AI, which relies on a vast array of tools for model
through normalization and cleansing before submitting it training and extensive databases, cannot operate within a
to the blockchain structure, thereby reducing the blockchain environment in its current form due to the
excessive load on the ledger. Additionally, machine resource demands needed to perform the necessary
learning techniques have been developed that can be used computations and model creation. To integrate the
to build and refine smart contract code, including through decentralized protocol environment with AI, several
the use of NLP [9]. AI enhances the adaptability of smart solutions have been proposed, which will be discussed
contracts by incorporating logic, neural graphs, and below.
neural networks through its integration into the smart One approach to organizing the interaction between AI
contract code or external usage to verify and ensure and smart contracts is through the use of specific
contract integrity. Tools such as deep learning mechanisms known as oracles. Oracles enable the
frameworks like TensorFlow are considered promising integration of AI systems with smart contracts, offering a
for their integration into smart contracts [10]. range of possibilities [12]. The traditional form of an oracle
that facilitates this interaction is depicted in Fig. 1
Figure 1: Traditional interaction of AI with smart contracts via an oracle
1.3. Oracles as mediators between smart contracts cannot access data outside the blockchain
blockchain and AI environment. The primary function of a blockchain oracle
is to send requests, verify, and authenticate external data
Oracles are third-party services with smart contracts that sources, and deliver this data to the user’s smart contract
act as intermediaries between blockchain and external [8]. The table below outlines the functions of oracles that
systems, allowing smart contracts to access off-chain data, facilitate the collection and provision of data for use in
including data from AI models. By design, blockchains and smart contracts and AI models.
40
1.4. Oracle functions and their decentralized oracles. Each oracle in this system gathers
characteristics data from independent sources and compares it to ensure its
accuracy [8]. These intelligent smart contracts, capable of
Table 1 responding to monitored conditions, acquire the
Oracle functions in data provision for intelligent smart characteristics of dynamic smart contracts. They can
contracts
autonomously make decisions after analyzing the
Oracle Functions Function Characteristics information and then send commands and queries to other
Data Collection for AI – Collects and processes systems [15]. In these interactions, AI can be effectively
external data for centralized used to execute logic for monitoring states and events and
and decentralized AI models. making appropriate decisions.
– Transfers external data to AI AI models may involve using services to collect
algorithms.
meteorological, agricultural, seismic, and other real-world
Data Verification – Ensures the reliability and
authenticity of data, data, which is then structured for subsequent monitoring
preventing manipulation and and analysis [16]. Distributed ledgers alone cannot perform
fraud. these tasks without special tools. On the other hand, smart
– Verifies data sources, reducing contracts can manage interactions with AI models. For
risks associated with
example, the SingularityNET project represents a
inaccurate or falsified data.
Integration with AI – Transfers and integrates decentralized platform that allows the creation and sharing
analytical predictive data from of AI models and their monetization in a marketplace based
AI into smart contracts. on the ERC20 standard within the Ethereum network.
Similar solutions are offered by projects such as Namahe,
To implement a smart contract, it may be necessary to Neuromation, TraDove, AdHive, ATN, Cortex, and NAM
obtain information from AI based on the analysis of external [17]. The table below lists some services that offer solutions
data, such as prices and exchange rates, sensor data, sports for integrating AI into smart contracts.
event results, flight information, insurance claims, and so The literature identifies three main methods for AI
forth. This can be achieved through an intelligent oracle, interaction with traditional smart contracts: Edge AI, AI-
which acts as an on-chain agent providing information in centric smart contracts, and Swarm Intelligence. The table
response to queries to the AI model [13]. The intelligent below presents these methods along with their
oracle enables smart contracts to utilize AI analytics and characteristics, as well as other theoretically possible ways
forecasts for automated decision-making and contract AI could interact with smart contracts. The concept of
execution, while also ensuring the verification of the Swarm Intelligence Smart Contracts, based on swarm
accuracy of the information provided to the AI models [14]. intelligence principles for decision-making and task
An example of such an intelligent oracle is the execution, appears particularly interesting as it integrates
Chainlink project, which represents a system of well with the logic of smart contracts.
Table 2
Classification of smart contracts by methods of interaction with AI (compiled based on the source [10])
Method of AI Interaction
Characteristics
with Smart Contracts
Edge AI Smart Contracts Smart contracts that combine AI technologies with edge computing capabilities, enabling data
processing and contract execution closer to the data source. This approach reduces latency and
enhances efficiency. Key features include:
– Local data processing.
– Reduced communication delays.
– Improved overall performance.
AI-Centric Smart Contracts Smart contracts that integrate AI capabilities with automated contract execution on the
blockchain. This allows for the creation of intelligent contracts that can analyze data, make
complex decisions, and automatically execute actions based on the results. Key features include:
– Data analysis capabilities.
– Decision-making based on AI insights.
– Automated contract execution.
Swarm Intelligence Smart Smart contracts that utilize swarm intelligence principles for decision-making and task execution.
Contracts Swarm intelligence refers to the collective behavior of decentralized, self-organizing systems,
typically observed in natural systems such as ant colonies, bee swarms, or bird flocks. In the
context of smart contracts, this means using multiple agents interacting with each other and their
environment to achieve a common goal or perform tasks. Key features include:
– Collective decision-making.
– Decentralized coordination.
– Task execution based on swarm dynamics.
41
1.5. Direct integration of AI into smart consideration, as training AI models on the blockchain is
contracts currently a rather cumbersome process [8]. However,
some projects have achieved notable success by adapting
Another technology is the direct integration of AI into networks to leverage AI capabilities, including creating
smart contracts without using an intermediary like oracles,
specialized networks with their consensus algorithms,
which eliminates the trust issue. This approach involves
such as Proof of Intelligent Mining (PoIM) in Matrix and
utilizing ASIC-resistant frameworks through specialized
Delegated Proof of Stake (DPoS) in Cortex. The PoIM
consensus algorithms and standards for ensuring
consensus algorithm, developed for intelligent mining
operational compatibility between infrastructure and deep
and operating a decentralized network based on machine
learning tools. An example of such technology is the Cortex
learning technology, increases the number of
project, which employs the Material Representation Tool
transactions per second and facilitates the use of
(MRT) technology compatibility standard and the Cuckoo
Cycle consensus mechanism. intelligent models within the network.
Embedding artificial intelligence directly into smart
contracts in an on-chain mode requires careful
Figure 2: Generalized Structure of Data Transmission in Smart Contracts Using an AI Oracle
The oracle structure for interacting with smart contracts handling large volumes of data. The Matrix platform, on the
involves the smart contract itself, a tool for retrieving data other hand, employs the Proof of Intelligent Mining (PoIM)
from an AI model, a database, and a dynamic data source for algorithm, which leverages AI for optimizing mining
the model. Upon meeting certain conditions, the smart processes and consensus tasks. Matrix provides components
contract sends a request to the oracle, which in turn returns for semantic and syntactic analysis, security verification of
data from the AI model to the smart contract. Popular smart contracts, and identification of issues in transaction
Oracle services that can work with AI models include models [8]. These platforms’ solutions enable the creation
Oraclize (Provable), Town Crier, Reality K, Witnet, and of decentralized AI models for implementation in DAO
Chainlink, among others. An example of using Oraclize is smart contracts to optimize decision-making and manage
the Etherisc project, which uses an oracle to access flight participant relationships.
delay data to automate insurance payouts in case of flight An example of an AI oracle is the GainForest project,
delays. Another project, Ethersquares, implements logic for where a smart contract is used to distribute bets based on
sports betting, where users can verify the accuracy of data about deforestation status using artificial intelligence
received data through the oracle [17]. [18]. The oracle automatically analyzes and evaluates
The Cortex and Matrix platforms offer solutions for satellite images and detects deforestation issues using
integrating AI models into the blockchain and subsequently remote sensing algorithms. Based on the collected and
using them in smart contracts. Users can incorporate the analyzed information, bets are redistributed among
proposed Cortex AI system into existing smart contracts. In participants. Fig. 3 illustrates the operation of such an
the Cortex project, this is achieved through the Cuckoo oracle.
Cycle consensus algorithm, known for its high efficiency in
42
Figure 3: Machine-powered incentive system in GainForest (compiled based on the source [19])
The blockchain-based platform for artificial intelligence smart contracts allows to make and execute decisions
systems, SingularityNET, provides the capability to automatically based on AI [8]. Other mentioned services
integrate autonomous agents into smart contracts, offer methods for integrating smart contracts with AI
connecting them to data exchange channels and AI systems models for use in operations or provide mechanisms for
that interact with modules [8]. This technology enables collaboration, where AI models are applied to manage
smart contracts to interact with real-world sensors and incentive systems, reward distribution, and evaluate
devices, obtaining information simultaneously with AI- participation in joint projects.
driven decisions on subsequent actions. The structure of
Table 3
Classification of smart contracts by AI application areas
Smart Contracts AI
Characteristics
Interaction Technology
Federated Learning Smart contracts utilizing federated learning for training AI models on decentralized data without
Smart Contracts centralizing it in one location. Data remains on local devices, and models are trained locally,
exchanging only model updates.
Reinforcement Learning Smart contracts incorporating reinforcement learning algorithms for decision-making. RL models
Smart Contracts are trained based on rewards and penalties for actions, optimizing decision-making strategies.
Predictive Analytics Smart contracts incorporating reinforcement learning algorithms for decision-making. RL models
Smart Contracts are trained based on rewards and penalties for actions, optimizing decision-making strategies.
Natural Language Implementation of natural language processing technologies for understanding and analyzing
Processing (NLP) Smart text data. Smart contracts can interact with users through text interfaces, analyze documents, and
Contracts automatically execute contract terms based on textual information.
Computer Vision Smart Use of computer vision technologies for analyzing images and video. Smart contracts can make
Contracts decisions based on visual data, such as automatically detecting defects on a production line.
Multi-agent Systems Employment of multi-agent systems where agents interact to achieve a common goal. Each agent
Smart Contracts can operate autonomously while exchanging information with other agents, facilitating the
creation of complex and adaptive systems.
Autonomous Negotiation Application of AI for automatic negotiation and agreement formation between parties. Smart
Smart Contracts contracts can autonomously negotiate terms based on predefined criteria and the interests of the
parties.
43
Table 4
AI services integrated with blockchain and their possible uses in smart contracts (compiled based on the source [8, 19–21])
Service General Description and Functions Usage in Smart Contracts
Cortex Decentralized Artificial Intelligence Platform that supports Analysis of decentralized data for decision-
AI smart contracts and AI inference. making and process automation
Matrix AI Intelligent contracts service that combines smart contract Using AI in smart contracts for management and
Network capabilities with AI elements, allowing for handling large interactions
volumes of transactions and integrating AI services into
dApps.
SingularityNET Decentralized AI marketplace and AI Publisher, running Data collection and analysis, process automation
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
Namahe Responsible Supply Chain based on Blockchain & Artificial Organizing production and supply of goods and
Intelligence for managing the supply chain using smart services
contracts and AI on the Hyperledger Sawtooth platform.
Fetch.AI Decentralized platform for creating commercial models Using autonomous economic agents for process
combining AI and smart contracts based on Neo automation and asset management.
blockchain technology.
Numerai Hedge fund platform based on artificial intelligence for AI model competitions in the Numerai
predicting financial markets and creating AI models. Tournament, fund management, and strategic
investment decisions.
Eligma AI-powered chatbot for asset value prediction. Process automation for asset management,
decentralized loyalty programs for communities,
and data analysis for decision-making.
Peculium Analytical platform using AI and machine learning for Developing automated investment strategies for
analyzing market trends and decision-making. asset management.
DeepBrain AI computational platform with access to a distributed Analyzing large volumes of data, modeling and
Chain (DBC) network with unlimited scalability. Analyzing large forecasting, integrating AI models into business
volumes of data, modeling and forecasting, integrating AI processes, and sharing computational resources.
models into business processes, and sharing
computational resources.
Neural Neural network algorithms for AI for forecasting, analysis, Forecasting future events, decision-making, risk
and asset management. management, and business process optimization
BurstIQ The platform for simplifying the collection, evaluation, and Managing medical data, storing confidential
analysis of data with a consensus mechanism at the participant information, providing decentralized
storage level. medical services, and managing medical records.
LifeGraph Machine learning platform for generating relational maps Collecting, monitoring, and analyzing mental
of verified data elements collected from “consent health and behavioral data, solutions for
contracts”. personnel, and resource management.
Vytalyx Decentralized database of clinical and non-clinical data Real-time biometric monitoring of participants,
managed by AI. and personalized medical solutions.
Neuromation Ecosystem for creating AI models using distributed Providing computational resources for training
computing and incentive models. AI models.
Synapse AI Machine learning model training platform and creation of Decentralized data exchange and management,
autonomous agents with data sharing service. creating autonomous agents for process
optimization.
StyleGAN2 The platform for generating and managing digital art Automated solutions for profit distribution,
using AI and Generative Adversarial Networks (GAN). trend prediction NFT management, and art
generation.
NFTGAN NFT Art Generation platform using GAN neural network Creating and managing NFTs, content
architecture, combining two generators and monetization, and participant engagement, and
discriminators to generate texts, images, and sounds. managing intellectual property rights.
44
Kojii.ai AI platform to enhance public perception of artworks, Intelligent chatbots for participants, organizing
train artists, and gather information for AI models. communications with communities, investment
decisions, and creating recommendations for
project participation.
PrimeIntellect Service for training large AI models using distributed Tools for forecasting and analyzing market data,
resources, reducing costs, and democratizing AI asset and risk management strategies, and
development. business process automation.
Nous Incentive system using tokens for participation in AI Tools for asset management and large data
Research model creation. volumes, cybersecurity protection.
DanKu Service for creating and accessing machine learning Collaborative development of AI models for
models with an incentive system and evaluation using token-based rewards.
smart contracts.
The interaction between AI and smart contracts raises Standardization of smart contracts is crucial for uniform
several issues, including questions about AI’s task typing for AI models to handle various data classes,
responsibility regarding the consequences of executing analyze their performance, and create necessary
a contract by an autonomous agent or providing false or integration modules for AI. Through these modules,
incorrect information for the execution of a smart smart contracts can interact with each other and
contract [22]. Due to these risks, the use of AI in DAOs organize intelligent decentralized systems with self-
brings up the need for standardization of smart contracts learning and environmental adaptation capabilities.
and crypto-assets for their compatibility with AI models. Additionally, the standardization of intelligent smart
Known token standards such as ERC-20, ERC-721, and contracts will ensure the automated and autonomous
ERC-1155 are used in economic models or reward development of intelligent systems. Inter-network
systems created by AI. AI can also create unique content integration and data exchange with the physical world
and release it as NFTs. The idea of more global use of AI should be managed by oracles adapted to intelligent
in smart contracts has led to the development of specific networks, which presents new challenges. The use of
smart contract standards designed to facilitate their different types of AI, with varying structures and
integration with AI. Below are some of these standards complexities, requires finding new integration solutions.
along with their characteristics.
2. Conclusion
Table 5
Smart contract standards in the Ethereum environment, The integration of smart contracts and AI undoubtedly
adapted for interaction with AI offers significant advantages, as blockchain provides the
potential for cryptographic storage of large volumes of
Standard Characteristics
data and its use for training AI models, securely
ERC-2362 Standard for decentralized oracles, which transmitting data between digital systems, and
can be used to obtain external data for
implementing economic model logic. Despite the
smart contracts, including data from AI or
complexities involved in integrating smart contracts and
forecasting models.
ERC-3001 Standardizes the integration of AI, methods and solutions for such integration already
decentralized oracles, allowing the exist. In this context, the need for standardizing the
incorporation of external data from AI to methods of linking smart contracts and AI arises to
provide more accurate and informed enable the mutual integration of various solutions
decisions. (services). The assertion that “If Smart Contracts Are the
ERC- Standards are used for proof of work Body of the Digital Deal Era, Artificial Intelligence is the
8000+ (PoW) and other consensus mechanisms, Mind” aptly describes the importance of intelligent
which can be utilized for managing oracles and cryptography in ensuring the interaction
resources and computations with AI. between systems.
ERC-7007 A specifically designed standard for
verified AI-generated content. Allows for
the tokenization of AI-generated content References
with verification of source and generation [1] V. Zhebka, et al., Methodology for Choosing a
parameters [23].
Consensus Algorithm for Blockchain Technology,
ERC-7231 Standard for tokens of verified AI-
in: Digital Economy Concepts and Technologies,
generated content, aimed at improving
data sovereignty and user control over vol. 3665 (2024) 106–113.
digital identities. Integrates Web2 and [2] S. Obushnyi, et al., Ensuring Data Security in the
Web3 identities into a single non-fungible Peer-to-Peer Economic System of the DAO, in:
token (NFT), enabling users to own, Cybersecurity Providing in Information and
manage, and monetize their online Telecommunication Systems II, vol. 3187 (2021)
presence across various platforms. 284–292.
[3] D. Virovets, et al., Soul Bound Token as Digital
Twins in Peer-to-Peer Economic Systems, in:
45
Digital Economy Concepts and Technologies, vol. International Joint Conference on Artificial
3665 (2024) 97–105. Intelligence. Doctoral Consortium (2022) 5851–
[4] R. Marusenko, V. Sokolov, I. Bogachuk, Method of 5852. doi: 10.24963/ijcai.2022/828.
Obtaining Data from Open Scientific Sources and [17] V. Lopes, L. A. Alexandre, An Overview of
Social Engineering Attack Simulation, Advances Blockchain Integration with Robotics and
in Artificial Systems for Logistics Engineering, vol. Artificial Intelligence (2018). doi:
135 (2022) 583–594. doi: 10.1007/978-3-031-04809- 10.48550/arXiv.1810.00329.
8_53. [18] AI Smart Contracts. The Age of A.I. Powered
[5] F. Kipchuk, et al., Assessing Approaches of IT Incentives (2017). URL:
Infrastructure Audit, in: IEEE 8th International https://medium.com/gainforest/predictive-smart-
Conference on Problems of Infocommunications, contracts-dc15b9986d8c
Science and Technology (2021) 213–217. doi: [19] From Imagination to Reality: 6 Companies
10.1109/picst54195.2021.9772181. Building the Future with AI and Blockchain (2023).
[6] Aimeta Bridging Communities, AI and Metaverse URL: https://www.linkedin.com/pulse/from-
Projects, Whitepaper, Version 1.2 (2023). imagination-reality-6-companies-building-future-
[7] M. C. Reis, Extending Smart Contracts Security ai-blockchain-yusgf
Through Cryptographic Protocols, Faculdade de [20] S. Shahriar, K. Hayawi NFTGAN: Non-Fungible
Computação da Universidade Federal de Token Art Generation Using Generative
Uberlândia, Uberlândia, Brasil (2020). Adversarial Networks. (2021). doi:
[8] I. Reshi, et al., AI-Powered Smart Contracts: The 10.48550/arXiv.2112.10577.
Dawn of Web 4, TechRxiv (2023). doi: [21] A. Anupama, S. Deekshith, S. Karthik, Crypto AI:
10.36227/techrxiv.221894 38.v1. Digital Nostalgic Art Generation Using GAN and
[9] S. Badruddoja, et al., Making Smart Contracts Creation of NFT Using Blockchain, J. Emerging
Smarter, (2021) 1–3. doi: 10.1109/ICBC51069. Technol. Innov. Res. 9(7) (2014) 217–220.
2021.9461148. [22] M. Sako, Contracting for Artificial Intelligence.
[10] M. Sai Mohan, T. Swamy, V. Chandu Reddy, Communications of the ACM 66 (2023) 20–23. doi:
Strengthening Smart Contracts: An AI-Driven 10.1145/3584707.
Security Exploration. Global Journal of Computer [23] C. So, et al., ERC-7007: Verifiable AI-Generated
Science and Technology, 23(D2) (2023) 57–67. Content Token. An ERC-721 extension for
[11] S. Badruddoja, et al., Making Smart Contracts verifiable AI-generated content tokens using Zero-
Predict and Scale, Fourth International Conference Knowledge and Optimistic Machine Learning
on Blockchain Computing and Applications Techniques (2023). URL:
(BCCA) (2022) 127–134. doi: 10.1109/BCCA55292. https://eips.ethereum.org/EIPS/eip-7007
2022.9922480.
[12] V. Papadouli, V. Papakonstantinou, A Preliminary
Study on Artificial Intelligence Oracles and Smart
Contracts: A Legal Approach to the Interaction of
Two Novel Technological Breakthroughs,
Computer Law & Security Review 51 (2023).
[13] Oracle. Gain Access to Data Stored Outside of the
Blockchain. URL:
https://github.com/fravoll/solidity-
patterns/blob/master/docs/oracle.md
[14] Blockchain Oracles: Bridging External Data
Sources with AI Smart Contracts (2024). URL:
https://medium.com/sample-dcentai-
blog/blockchain-oracles-bridging-external-data-
sources-with-ai-smart-contracts-e7fffac0c323
[15] S. Terzi, et al., Blockchain 3.0 Smart Contracts in
E-Government 3.0 Applications (2019). doi:
10.48550/arXiv.1910.06092.
[16] S. D. Facchini, Decentralized Autonomous
Organizations and Multi-agent Systems for
Artificial Intelligence Applications and Data
Analysis, Proceedings of the Thirty-First
46