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Synergizing Artificial Intelligence and Blockchain
Abstract
This chapter provides a comprehensive examination of the confluence of Artificial Intelligence (AI) and Blockchain, which are two of the most notable technical advancements in our day. This explores the distinct methods in which the powerful data processing skills of AI and the solid security features of Blockchain might be merged to transform different sectors. The combination of AI and Blockchain is examined through real-world implementations in healthcare to enhance patient care and safeguard data, in supply chain management to enhance transparency and efficiency, in finance to detect fraud and ensure secure transactions, and in legal systems to automate and secure contract management. The chapter delves into the obstacles and potential future developments of integrating AI with Blockchain. It highlights the possibility of AI to enhance the intelligence and autonomy of Blockchain systems, while highlighting the role of Blockchain in guaranteeing the trustworthiness and consistency of AI applications. The integration of these methods has the potential to develop advanced, protected, and streamlined systems, hence enabling the emergence of inventive solutions in data administration and industrial processes.
... The deployment of the AI and blockchain in the cybersecurity approach has brought notable gains in the characterization of operations effectiveness with special emphasis on a range of times it takes to respond to occurrence. Primary data collected from real-case studies intuitionally shows an average of 68 percent time cut down in the response to the incident where it took 35.7 minutes in traditional security methodology while only took 11.4 minutes with the amalgamate of AI-blockchain according to the study by Tyagi et al., (2014). These benefits are evident in the fact that AI has the ability to automatically classify an incident, while the use of blockchain means that the data used to investigate an incident is verified and easily at hand without having to wait several hours or days to have the data investigated manually. ...
... Among the tested implementation models, it is possible to identify the trends in financial services that emphasize transactions confirmation based on the multi-level checkpoint with the help of blockchain's cryptographic and AI's pattern recognition attributes. Still, as noted by Tyagi et al. (2014), these implementations of the concept usually have a three-fold validation process, including initial perform verification of the transaction and its authorization with the help of blockchain technology The second step aims at behavioral analysis of the transaction and categorizing it based on its similarity to previous successful frauds, as well as analysis of the current characteristics and comparison of the transaction within the framework of continuously updated machine-learning models. This approach allows financial institutions to sustain the large number of transactions per second (the average of 11,221 was established by analysing the implementations) and at the same time to perform rather effective security verification to address the emerging forms of fraud against financial services that are being provided digitally. ...
... The lack of specialized expertise is viewed as the most severe organizational issue: 78% of implementing organizations struggle to find professionals with both AI and blockchain knowledge. Tyagi et al., (2014) reveal that the average time taken to recruit personnel in organizations across sectors is six months and above for the senior position that needs AI and blockchain, and the greatest shortage is felt in the financial services' sector and the government. According to Salama and Al-Turjman's study (2022), measures that can be taken by organizations to overcome this problem are internal capability building programmes (67%), partnerships with universities (54%), and strategic outsourcing relationships with consulting firms (49%). ...
Introduction: This review examines the integration of artificial intelligence (AI) and blockchain technologies for secure data management in cybersecurity across the United States. Due to the growing instances of cyber threats today, organizations and government agencies are looking at new ways of technological integration to enhance the aspect of data protection. This paper aims to establish how AI and blockchain functions together to provide secure solutions to the current cybersecurity threats. Materials and Methods: This article adopts the secondary research approach that involves the use of surveys from peer-reviewed articles, industry reports, and analytical studies on AI-blockchain frameworks. The assumptions made are based on expert opinions from the interviews and case studies that have been compiled from reputed institutions such as the Cybersecurity and Infrastructure Security Agency (CISA), the Massachusetts Institute of Technology (MIT), and other Fortune 500 firms. From previous research, quantitative data is conducted and pertains to performance, the implementation rate, and sectorial outcomes in the financial service, health and the critical infrastructure sectors. Results: Based on the findings, it can be concluded that integrated AI-blockchain systems are 37% more effective in mitigating APT attacks as opposed to antecedent security models. The organizations that have adopted such technologies have showed improved statistics of reduced data breach incidents by 42% and unauthorized access attempts by 56%. As far as the adoption rate, there are several leaders – and they have proved to be the financial institutions in New York and California with health care providers in Massachusetts and Texas not far behind. The technical pros are better out of the ordinary detection (up to 89% accuracy rates) and cryptographic audited trails, which considerably improve the response time. Discussion: The review identifies four distinct implementation models emerging across different sectors and regions in the United States. Financial institutions primarily leverage these integrated technologies for transaction verification and fraud detection, while healthcare organizations focus on secure patient data management and access control. Government agencies, particularly in Virginia and Maryland, employ these systems for critical infrastructure protection and threat intelligence sharing. Key implementation factors include regulatory compliance requirements, organizational security maturity, and industry-specific threat landscapes. Conclusion: The integration of AI and blockchain technologies provide a new lea for viewer cybersecurity strategies in the United States. But all these need some considerations in form of rules and regulations, technical difficulties, and challenges of qualified human resource. The overall recommendations for increasing the level of adoption consist of sectoral best practices to be followed in implementing the discussed concepts and frameworks, cross-organizational collaboration models, and educational programs to foster the acquisition of adequate workforce competencies in these converging technologies.
... U.S. officials must fortify regulatory frameworks to fill oversight gaps, reduce social inequality, and guarantee that AI technologies are applied responsibly and fairly in all industries as the technology continues to play a crucial role in revolutionizing supply chains. By doing this, the United States can maximize supply chain AI's potential while lowering risks and advancing social progress (Alzoubi, 2024;Tyagi, et al., 2024). ...
The incorporation of artificial intelligence (AI) into global supply chains is revolutionizing industries by increasing productivity, cutting costs, and improving decision-making. However, the adoption of AI in supply chains also presents significant ethical challenges, especially when comparing advanced economies like the United States and developing economies like Nigeria. This study examines the ethical issues that arise from AI-powered supply chains through a comparative policy lens, focusing on the U.S. and Nigeria. In the U.S., ethical concerns center on privacy, data security, algorithmic transparency, and the possibility of job displacement, while in Nigeria, additional challenges include infrastructure constraints, a lack of regulatory frameworks, and a digital divide that exacerbates the ethical implications of AI adoption in supply chains. This looks at how the policies of the two nations handle these problems, with the US highlighting the necessity of precise laws, moral standards, and corporate accountability in the application of AI. Nigeria's new AI laws, on the other hand, emphasize data governance, capacity building, and the development of an inclusive digital environment. The significance of customized policy solutions that take into account the distinct economic, social, and technical circumstances of each country is highlighted by examining these divergent approaches. The report also emphasizes the necessity of international collaboration in creating uniform ethical guidelines for artificial intelligence in global supply chains. The findings suggest that while AI holds the potential to revolutionize supply chains, it also necessitates careful policy planning and ethical oversight to ensure that its benefits are realized equitably and sustainably across different regions. Keywords: Ethical, Challenges, AI-powered, Supply chains, U.S, Nigeria, Policy perspective.
... The amalgamation of blockchain technology and artificial intelligence can provide a formidable safeguard against these assaults and security concerns [72]. The amalgamation of these methodologies could foster the development of sophisticated, secure, and efficient systems, hence facilitating the formation of innovative solutions in data management and industrial operations [73]. As a result, ICAIS will become an effective tool for niche leader to succeed in MGO. ...
The objective of this investigation is to evaluate the influence of niche market leadership (NML) on market globalization orientation (MGO) by means of an intelligent cloud-based accounting information system (ICAIS). The study also examined the moderating function of industry dynamism (IND) in the relationship between NML and ICAIS as well as the relationship between ICAIS and MGO. The data obtained from a convenience and snowball sample of respondents employed in small and medium companies (SMEs) in Vietnam was analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) with the assistance of SmartPLS 4.1.0.6. The results clearly showed that NML had a significant and beneficial effect on MGO. The findings eventually demonstrated that the association between NML and MGO was partially mediated by ICAIS. The statistical outcomes also highlighted the moderating role of IND in the relationship between NML and ICAIS as well as the relationship between ICAIS and MGO. Broadly speaking, this study offers valuable insights for managers, stakeholders, and policymakers to comprehend on how to foster MGO through leveraging the potential of NML and ICAIS.
... Artificial intelligence (AI) enhances fraud detection by utilizing sophisticated data analytics and pattern recognition techniques. On the other hand, blockchain technology strengthens security measures by preventing unauthorized modifications to financial data (Chatterjee et al., 2024;Tyagi et al., 2024;Peters & Panayi, 2016). The incorporation of these technologies is transforming the accounting field, requiring proficiency in data analytics, artificial intelligence, and blockchain technology and redirecting attention from conventional duties to more strategic positions (Qasim & Kharbat, 2020;Jackson et al., 2022;Schmidt et al., 2016). ...
This research paper uses qualitative analysis to examine the profound influence of artificial intelligence (AI) and blockchain technologies on accounting practices. The study utilizes case studies and semi-structured interviews with industry experts to identify central themes, including efficiency and automation, accuracy and data integrity, fraud detection and security, professional roles and skills, and ethical and regulatory considerations. The results demonstrate that AI increases efficiency by automating repetitive tasks and enhancing fraud detection, while blockchain guarantees the precision and reliability of financial records. Nevertheless, incorporating these technologies into existing systems poses difficulties, including technical obstacles, adherence to regulatory requirements, and ethical considerations such as safeguarding data privacy and addressing algorithmic bias. Due to these findings, accounting professionals must acquire new skills in data analytics and technology management. It is recommended that educators integrate artificial intelligence (AI) and blockchain into accounting curricula. At the same time, policymakers are advised to establish well-defined regulatory frameworks to facilitate the adoption of these technologies. The study also identifies areas for future investigation, such as the enduring effects of AI and blockchain on accounting methods, the factors that influence user adoption, and the creation of efficient regulatory structures. The research thoroughly analyzes how AI and blockchain are transforming the accounting profession, providing insights into the opportunities and challenges they bring.
This study investigates the role of automated controls and streamlined compliance in managing risk in digital finance institutions. Through a mixed-methods approach, combining quantitative analysis and qualitative insights from leading Indian digital finance companies (CRED, ZEST MONEY and PHONE PAY), the research examines the effectiveness of advanced technologies in enhancing risk management practices. The study found strong positive correlations between the adoption of automated controls and key performance metrics, including the fraud detection rate (0.68), regulatory reporting accuracy (0.72), and overall risk management effectiveness (0.75). Regression analysis revealed that automated control implementation was the strongest predictor of risk management effectiveness. A comparative analysis of different digital finance models highlights the varying challenges and benefits of implementing these technologies. This study proposes a holistic, future-proof framework that integrates AI, machine learning and blockchain for real-time risk monitoring, automated regulatory reporting and dynamic compliance management. This framework addresses the limitations of the current fragmented approaches and offers a scalable solution that is adaptable to evolving regulatory landscapes. The findings of this study have significant implications for practitioners and policymakers, emphasizing the need for investment in strong automated systems and adaptive regulatory frameworks. While acknowledging the limitations in sample size and longitudinal scope, this research provides a foundation for future studies and offers practical insights for enhancing risk management in the rapidly evolving digital finance sector. The study concludes that effective integration of automated controls and streamlined compliance is crucial for ensuring the resilience, integrity and sustainable growth of the digital finance ecosystem.
Tools based on machine learning (ML) have seen widespread application in the prediction and categorization of mathematical symbols and phrases. The purpose of this work is to conduct a comprehensive analysis of the machine learning and non-machine learning strategies that are currently in use for the recognition of mathematical expressions. (MEs). The authors collected and analyzed research studies on the recognition of MEs (and closely related models and issues as well), which are published from January 2000 to December 2022 in the SLR. The review has nominated 98 primary studies out of the extracted 202 studies after heedful filtering using inclusion/exclusion criteria and quality assessment. The pertinent data is derived from IEEE explore, Science Direct, Wiley, Scopus, ACM Digital Library, etc. For assiduously reviewing and synthesizing the data, the authors used grounded theory and other qualitative and quantitative techniques. The analysis reveals that the support vector machine as an ML model with CROHME as the dataset and expression recognition rate as an accuracy metric is frequently used in the chosen studies. Recognition is typically fragmented down into three stages—segmenting symbols, recognizing symbols, and analyzing structures—in non-ML studies. In conclusion, this work aims to synthesize the results of existing research to provide a summary of the state-of-the-art in recognizing handwritten MEs.
The state of computer network technologies has continually advanced at a rapid pace. Software Defined Networking (SDN) and Blockchain (BC) have emerged as complementary technologies providing support that facilitates greater security and greater network performance for many domains of application, including the Internet of Things (IoT) ecosystem, ideally resulting in an improvement in our collective quality of life. The proliferation of IoT devices, driven by a wide variety of use cases and its ubiquitous availability, combined with the emergence of SDN and BC, presents rich opportunities for various emerging research efforts. This paper presents a comprehensive survey of the studies in which BC and SDN have been integrated into the IoT ecosystem, referred to hereafter as BC-enabled Software- Defined IoT (BC-SDIoT). First, we discuss the motivations and drivers for integrating BC-enabled SDN and BC-SDIoT, as well as the benefits and drawbacks. Second, we categorize the relevant studies according to six key implementation objectives and ideas that combine BC, SDN, and IoT technologies to create smart, secure, and effective frameworks: Security, computing paradigms (edge and fog computing), trust management, access control & authentication, privacy, and networking. In the corresponding sections, we present the categories (i.e., problem domains) of the aforementioned novel taxonomy and discuss related studies (i.e., solutions) in depth. Finally, we outline potential major challenges, open issues, and future prospects that require further research attention and intensive endeavors for complete and ground-breaking frameworks to broaden newer research domains in BC-SDIoT. This survey paper may be a fruitful primer for a reader investigating the exploitation of BC in SDN and IoT ecosystems.
It is undeniable that the adoption of blockchain- and artificial intelligence (AI)-based paradigms is proceeding at lightning speed. Both paradigms provide something new to the market, but the degree of novelty and complexity of each is different. In the age of digital money, blockchains may automate installments to allow for the safe, decentralized exchange of personal data, information, and logs. AI and blockchains are two of the most talked about technologies right now. Using a decentralized, secure, and trustworthy system, blockchain technology can automate bitcoin payments and provide users access to a shared ledger of records, transactions, and data. Through the use of smart contracts, a blockchain may also regulate user interactions without the need for a central authority. As an alternative, AI provides robots with the ability to reason and make decisions and human-level intellect. This revelation led to a thorough assessment of the AI and blockchain combo created between 2012 and 2022. This critical review contains 121 articles from the recent decade that investigate the present situation and rationale of the AI and blockchain combination. The integration’s practical application is the emphasis of this overview’s meatiest portion. In addition, the gaps and problems of this combination in the linked literature have been studied, with a focus on the constraints.
The past decade is known as the era of integrations where multiple technologies had integrated, and new research trends were seen. The security of data and information in the digital world has been a challenge to everyone; Blockchain technology has attracted many researchers in these scenarios. This paper focuses on finding the current trends in Blockchain technology to help the researchers select an area to carry future research. The data related to Blockchain Technologies have been collected from IEEE, Springer, ACM, and other digital databases. Then, the formulated corpus is used for topic modelling, and Latent Dirichlet Allocation is deployed. The outcomes of the Latent Dirichlet Allocation model are then analyzed based on various extracted key terms and key documents found for each topic. All the topic solution has been identified from the bag of words. The extracted topics are thereafter semantically mapped. Thus, based on the analysis of more than 900 papers, the most recent research trends have been discussed in this paper, ultimately focusing on the areas that need more attention from the research community. Also, the meta data analysis has been accomplished, evaluating the year wise and publication source wise research growth. More than 15 research directions are elaborated in this paper, which can direct and guide the researchers to pursuit the research in specific trends and also, find the research gaps in various technologies associated with Blockchain Technology.
Healthcare professionals, patients, and other stakeholders have been storing medical prescriptions and other relevant reports electronically. These reports contain the personal information of the patients, which is sensitive data. Therefore, there exists a need to store these records in a decentralized model (using IPFS and Ethereum decentralized application) to provide data and identity protection. Many patients recurrently visit doctors and undergo treatments while receiving different prescriptions and reports. In case of an emergency, the doctors and attendants may need and benefit from the patients’ medical history. However, they are unable to go through medical history and a wide range of previous reports and prescriptions due to time constraints. In this paper, we propose an AI-assisted blockchain-based framework in which the stored medical records (handwritten prescriptions, printed prescriptions, and printed reports) are stored and processed using various AI techniques like optical character recognition (OCR) to form a single patient medical history report. The report concisely presents only the crucial information for convenience and perusal and is stored securely over a decentralized blockchain network for later use.
Nowadays, blockchain technology and industry has developed rapidly all over the world, which is inseparable from continuous innovation and improvement on smart contract technology. Therefore, by summarizing the working principle and application research status of blockchain smart contract, this paper analyzes the development and challenges of smart contract. Firstly, we introduce the model and operation principle of blockchain smart contract for the overall architecture, analyze the deployment process of smart contract with Ethereum, Hyperledger Fabric and EOSIO, and make a comparative analysis from the technical level. And taking Byteball, InterValue and IOTA platforms as examples, we introduce the deployment process and application potential for DAG-based blockchain smart contract. Additionally, we also summarize the application research of smart contract for international and Blockchain Oracle, and discuss its innovative application and development trend in the future. Secondly, we introduce the application status of smart contract with Ethereum and Hyperledger Fabric platforms from the aspects of financial transactions, Internet of things, medical applications, and supply chain, and further discuss EOS (enterprise operation system), Blockchain Oracle and other application fields. Furthermore, we introduce the application advantages and challenges to smart contract for industrial Internet from the fields of manufacturing, food industry, industrial Internet of things and industry 4.0. Finally, we discuss the challenges faced by smart contract with technical issues, analyzes the impact on large-scale applications and mining system on the sustainable development of smart contract, and looks forward to the future research direction of blockchain smart contract.
The information system of healthcare operates through various frameworks, like wireless body area network, telecare medical information system, and mobile or electronic healthcare. All these systems need to maintain the personal health records (PHRs) for various users (ie, patients, doctors, and nurses). In such systems, we need to process and store the health related sensitive data (ie, PHRs). In this article, we aim to provide a robust security mechanism to secure exchange and storage of healthcare data, especially PHRs. We present a generic architecture of blockchain-enabled secure communication mechanism for Internet of Things-driven personal health records (BIPHRS). We then discuss various threats and security attacks of healthcare system along with different available security mechanisms. The conducted security analysis and detailed comparative study of the state of art blockchain enabled security schemes for PHR systems show that the proposed BIPHRS provides a better security and more functionality features as compared to other similar existing approaches.
Blockchain constructs a distributed point-to-point system, which is a secure and verifiable mechanism for decentralized transaction validation and is widely used in financial economy, Internet of Things, large data, cloud computing, and edge computing. On the other hand, artificial intelligence technology is gradually promoting the intelligent development of various industries. As two promising technologies today, there is a natural advantage in the convergence between blockchain and artificial intelligence technologies. Blockchain makes artificial intelligence more autonomous and credible, and artificial intelligence can prompt blockchain toward intelligence. In this paper, we analyze the combination of blockchain and artificial intelligence from a more comprehensive and three-dimensional point of view. We first introduce the background of artificial intelligence and the concept, characteristics, and key technologies of blockchain and subsequently analyze the feasibility of combining blockchain with artificial intelligence. Next, we summarize the research work on the convergence of blockchain and artificial intelligence in home and overseas within this category. After that, we list some related application scenarios about the convergence of both technologies and also point out existing problems and challenges. Finally, we discuss the future work.
This research summarizes the implementation of blockchain technology in the food and agriculture industry in Canada. Our research indicates that blockchain solutions are an existing and proven set of technologies. We also describe how blockchain based supply chain traceability information has many more benefits than its current use for food safety and product recalls. We recommend that costs for development of blockchain based solutions should also be distributed across stakeholders, and apportioned by the relevant industry associations. Our research indicates that adoption of blockchain technology in agriculture will achieve critical mass earlier when the industry applies a consortium approach, in a regulatory environment that is supported by government. This report also makes recommendations relevant to the integration of blockchain for end consumers of food.
The early development of blockchain technology (BCT) has already demonstrated the technology's potential to serve the needs of different industries. BCT has also become established as a popular research topic in different scientific disciplines. This paper aims at introducing how several relevant scientific disciplines-supply chain management; management, economics and finance; computer science; security engineering-see the research and education perspectives for BCT. A field review is conducted to present challenges and opportunities of BCT, as well as suggestions for future research and education on the topic as seen from the selected different perspectives. The paper also presents methods for combining relevant disciplines in a modular online course to address the stated challenges and promote interdisciplinary blockchain education.
This document summarizes research supporting the implementation of blockchain technology in the food and agriculture industry in Ontario. First, our research indicates that blockchain technology based solutions are an existing and proven set of technologies, where it has already been proven that the source of food items can be identified in seconds where previous processes required days and weeks of effort. Second, we also describe how blockchain based supply chain provenance information has many more benefits than its current use for food safety and product recalls. Consumers are willing to pay premium prices for food based on reliable supply chain information that matches the consumers expectations and values. Costs related to blockchain technology based solution development and implementation will be relatively small in comparison to revenues generated through increased market share by organizations providing useful information to consumers, and other stakeholders in the food supply chain. Third, we recommend that costs for development of blockchain based solutions should also be distributed across stakeholders, and apportioned by the relevant industry associations. Implementation of smart contracts can be used to guarantee a pre-arranged distribution of revenues and costs across stakeholders across the food supply chain. A consortium approach will reduce individual organization related risks, and will ensure that complete end to end supply chains participate in blockchain based technology solutions. Our research indicates that adoption of blockchain technology in agriculture will be achieve critical mass earlier when the industry applies a consortium approach, in a regulatory environment that is supported by government. Finally, this report also makes recommendations relevant to the integration of blockchain technology with per item labelling, one of the core competencies and strengths of the project partner, Accu-Label Inc.
Instead of each organization attempting to navigate their own path through the different choices in technologies, we recommend a consortium approach where a blockchain technology based solution is selected and implemented through existing industry associations. Industry associations by design address the needs of their members, and therefore have a built-in relationship of trust with their members. This preexisting trust between industry associations and their members will enable pre-arranged distribution of costs and revenues related to implementation of blockchain technology between different stakeholders. Industry associations can pilot simpler low cost blockchain based solutions to reduce the risks developing more complex and expensive solutions. Blockchain based smart contracts provide an additional layer of assurance by pre-defining conditions of revenue/cost distribution, and automating the process.
Summary of Recommendations
1. Pilot different blockchain technologies to determine the best fit for the industry and organization.
2. Determine information that is of value to stakeholders in the supply chain, such as end consumers, and that they are willing to pay for.
3. Distribute the costs of blockchain technology development across different stakeholders in a consortium approach through industry associations.
4. Ensure that a robust mechanism exists to bridge information flows with physical food product flows.
With the vigorous development of blockchain technology represented by bitcoin, blockchain technology has gradually entered the stage of blockchain 3.0 characterized by “programmable society”. And the application of blockchain technology in all walks of life has achieved actual results. Blockchain technology has typical characteristics of decentralization, Tamper-resistant data, information openness and transparency, and natural fit with the application requirements in the field of certificate tracing, which makes the development of the applications of blockchain deposit and traceability in full swing. First, this paper describes the concept, application process, key technology of blockchain deposit and traceability, the three application architectures of blockchain deposit and traceability, and the overall architecture of its system. Then, it introduces the application of scenarios and the blockchain deposit and traceability in various fields. Next, it discusses the issues existing in the development of the application of blockchain deposit and traceability. Finally, the paper also expresses the best wishes for the future of its application.
Blockchain technology has been ushering in nothing short of a decentralized revolution. Distributed/decentralized energy is recognized the best way to ensure energy sustainability in the future. An open question is what promise the integration of blockchain and energy hold for energy future. This paper systematically reviews the theory of blockchain and explores the current status of energy blockchain research and applications using a visual bibliometric analysis method and the Scopus database from 2014 to 2020. The results show that the number of publications about blockchain technology in the energy sector have been skyrocketing, especially since 2018, which indicates the combining blockchain technology with energy sector is a new cross-cutting research area with increasing attention. At the national level, developing countries begin to move to the world stage, catching up or even surpassing several traditional developed countries in the field of energy blockchain. Cluster analysis results show that the existing energy blockchain research focuses on renewable energy, trying to solve the bottlenecks in its development process, and providing better solutions for the replacement of fossil energy by renewable energy. We therefore contend that blockchain may be fueling the renewable energy and powering our energy sustainability. Finally, the possible future development trend of energy blockchain is offered.
It is undeniable that artificial intelligence (AI) and blockchain concepts are spreading at a phenomenal rate. Both technologies have distinct degree of technological complexity and multi-dimensional business implications. However, a common misunderstanding about blockchain concept, in particular, is that blockchain is decentralized and is not controlled by anyone. But the underlying development of a blockchain system is still attributed to a cluster of core developers. Take smart contract as an example, it is essentially a collection of codes (or functions) and data (or states) that are programmed and deployed on a blockchain (say, Ethereum) by different human programmers. It is thus, unfortunately, less likely to be free of loopholes and flaws. In this article, through a brief overview about how artificial intelligence could be used to deliver bug-free smart contract so as to achieve the goal of blockchain 2.0, we to emphasize that the blockchain implementation can be assisted or enhanced via various AI techniques. The alliance of AI and blockchain is expected to create numerous possibilities.