The corporate world and bespoke software development services will see significant adoption of these two technologies over the next five to ten years. Industry executives that are both forward-thinking and tech-savvy still see the immense potential of combining blockchains with AI. Let's take a look at how you may use AI and blockchain for your business.

However advanced AI may be, it will never replace human judgment, and hence will never be widely adopted by the public. The inability to account for the computer's actions is one of the problems that has slowed the widespread use of AI. The public will quickly come to trust AI if its decision-making processes can be recorded.

Incorporating blockchain technology with AI has the potential to reveal previously hidden processes inside computers. Every AI decision may be recorded and made accessible in a distributed ledger. The information on a blockchain cannot be altered once it has been recorded, making it ideal for auditing and other security-sensitive applications.

4.2. Security Improvement

Blockchains include built-in encryption that makes the data very secure. Storing private and sensitive information like medical records or individual recommendations on a blockchain makes a lot of sense. Continuous and massive amounts of data are essential for AI. AI algorithms that can safely process encrypted data are now the focus of intensive research and development.

In any case, there is a supplementary viewpoint regarding the enhancement of security. There is a high level of security in the blockchain itself, but any extra layers or applications are not bulletproof. In the banking sector, machine learning will speed up the rollout of blockchain applications and allow for the prediction of potential system breaches.

4.3. Gaining Entry to and Control Over the Data Market

This is inextricably linked to better safety measures. With the ability to store massive quantities of encrypted data on a distributed ledger and have AI efficiently manage it, fresh use cases emerge. Blockchain technology makes it possible to keep sensitive information, such as medical records, and even benefit from providing others with access to it. That's why there are now markets for data, models, and AI.

Enhancing the data-management processes is another advantage of integrating AI with the blockchain. To decipher encrypted data, computers go through possible character permutations until they find the one that matches the original message. A hacking AI is like a person in that it improves with practice. AI, however, will not need a human lifetime to achieve the same level of expertise. This may be accomplished fairly rapidly with sufficient training data.

4.4. Smart Contracts Enhancement

Certain vulnerabilities in the blockchain's underlying technology provide a risk for malicious actors. This was very recently shown. To put it another way: smart contracts are not that smart. Yet. Once certain triggers are reached, they will automatically release and transfer the monies. This can only be accomplished after the blockchain community has reached a unified decision. Since the code for a smart contract is openly available, anybody may take their time to carefully examine it for vulnerabilities. The use of AI aids in the validation of smart contracts and the forecasting of exploitable flaws. Li et al. [41] create Astraea, a private smart contract-based, secure, anonymous, and decentralized auditing platform for contribution systems. In particular, they combine a Distribute Smart Contract (DiSC) with an SGX Enclave to distribute contributions, demonstrate the accuracy of the gift number (intention), and protect the anonymity of donors. They create a donation smart contract using DiSC to reimburse deposits and protect against theft and collusion attacks from nefarious collector and transponder. They use security reduction to explicitly describe and demonstrate Astraea's security and privacy. To carry out in-depth performance study, they construct a prototype of Astraea. Astraea is efficient in terms of both computing and communication, according to experimental data.

4.5. Maximizing Energy Efficiency

Energy consumption is high while data mining. This is a huge problem in the contemporary world, but Google has shown that machine learning can solve it. By feeding the DeepMind AI historical data from hundreds of sensors inside a data center, Google has enabled to cut down on the amount of energy needed to keep its data center at a comfortable temperature by 40 percent. Using this similar concept, mining hardware costs may be reduced.

5. Challenges

What are the challenges with combining blockchain with AI?

5.1. Privacy and Security

Among the obstacles of blockchain application, privacy, security, and landing protection are major concerns [76]. Due to its role as the backbone of the Internet of Value, the blockchain's inter-node communications are public and transparent, but they may also include sensitive data that users would like to keep secret. So, the key to whether or not blockchain applications can be deployed on a big scale is how to safeguard user privacy. Typical blockchain privacy protection strategies include information concealing and identity confusion. Using privacy-protecting signature technologies like ring signatures and group signatures to muddle the identities of both participants in a transaction, identity obfuscation technology makes it hard to match the true user to their blockchain transaction. The supervisor's private key allows the supervisor to access user data as required, protecting users' identities.

The user's transaction privacy is successfully protected by information concealing, which employs technologies like secure multiparty computing and zero-knowledge proof to complete transactions without disclosing any private information and to guarantee the credibility of the findings. The increased complexity of the calculations, however, results in a less effective system, therefore more work has to be done to boost its usefulness in real-world contexts. It's not easy to figure out how to apply AI algorithms sensibly to boost inefficient performance. Furthermore, the current AI algorithm has to be redesigned to be applied to a distributed context.

Private AI, which combines AI and encryption methods to solve the data security problem, was recently developed, howeverbut prior research has demonstrated that model inversion attacks may be used to reverse-engineer the model parameters to create pictures [77]. In this context, Khowaja et al. [78] suggested an industrial IoT environment-specific federated learning and encryption-based private (FLEP) AI system that offers two-tier security for data and model parameters. They provided a hypothetical approach to protect the model parameters together with a three-layer encryption mechanism for data security. The suggested approach, according to experimental data, produces improved encryption quality at the cost of a somewhat longer execution time. By applying a trust-based protection mechanism, Corradini et al. [77] suggested a two-tier blockchain architecture to improve the security and independence of smart items in the IoT. Smart items are appropriately categorized into communities in this architecture. The first-tier blockchain is local and is only used to record probing transactions carried out to assess the confidence of an item in another one of thea different community or of a same community, which reduces the complexity of the solution. These transactions are periodically aggregated after a time interval, and the resulting values are kept on the secondary blockchain. PIn particularly, , the stored values are each object'’s standing within its community and each community'’s confidence in the other communities inside the framework.

Moreover, the blockchain and Ffederated Llearning integration method has drawn a lot of interest as a new trustworthy data -sharing pattern with privacy protection. Generally speaking, this approach bypasses the supervision of the computing process and Ffederated Llearning model in favor of using blockchain technology to oversee the original data and computation outcomes [14]. In order to create a new data privacy sharing paradigm using Bblockchain and Fs and federated Llearning, Guo et al. [14] presented the ideas of the sandbox and state channel. They primarily addressed issues with data privacy sharing in federated learning and the deterioration of system performance brought on by poor data quality. The simulation results demonstrate that the suggested strategy outperforms and is more effective than the conventional data exchange method.

5.2. Credible Oracles

Blockchain players may trigger the execution of a smart contract by triggering an external event or calling a third-party function. Event or data retrieval automation is not a primary focus of smart contracts' design. To rephrase, the contracts are unable to get information from the real world. The contracts need to be "pushed" data and events. To address these issues, it is recommended to employ trustworthy oracles, which are essentially trusted external parties or nodes, to transmit events and data to smart contracts. When it comes to maintaining trust, oracles provide a new layer of complexity and potential security risks, as a previously decentralized system becomes centered on a set of oracles that must be relied upon. Usually, the agreement is reached by a vote amongst reliable oracles [79].

5.3. Concerning the Security of Smart Contracts and the Implications of Their Deterministic Execution

The success of a smart contract relies on its implementation being safe against hacks and errors. Code and data on the network should be protected against intrusion wherever possible. For instance, in 2016, hackers exploited a critical flaw in the coding of the Ethereum platform used to create the smart contract for the DAO. There was a loss of 3.6 million Ethers as a consequence of this. This problem, introduced by smart contract programming and other blockchain-based applications, calls for blockchain engineering [80]. Problems with security in smart contracts may be traced back to careless coding in the languages used to create them. The relevance of vulnerability testing for smart contracts has grown, and as a result, several tools have been created to evaluate the safety of a contract'’s source code [81, 8282]. Moreover, as it stands right now, there is no such thing as a probabilistic result for the execution of a smart contract. When AI and machine learning-based decision-making algorithms are implemented as smart contracts by the mining nodes, the execution output is typically not deterministic but rather random, unpredictable, and approximative [83]. This may be a significant difficulty for decentralized AI. With data input that might be rapidly changing as much as that of IoT and sensory readings, this calls for a unique approach to deal with approximation computation and to design consensus protocols for mining nodes for agreeing on outputs with a certain degree of confidence, accuracy, or precision.

5.4. Scalability

The key to the successful rollout of smart blockchain applications is in solving the scalability problem [84]. Blockchain decentralized applications need the underlying blockchain platform to function. If the scalability and performance of the system are inadequate, it cannot be deployed as a large-scale application. The Bblockchain'’s scaling concerns may be broken down into three primary categories: consistency problems, network latency, and performance constraints. Most nodes need to agree on the transaction data to guarantee the blockchain'’s security. The blockchain will split if the need for consistency in the distributed network is neglected in favor of faster growth. Due to its decentralized nature, blockchain'’s scalability is limited by the time it takes for data to travel between nodes in the network. This is particularly true for longer delays. The key problem that prevents the widespread the use of blockchain applications is the impact of transaction performance on scalability [85]. To maintain security and ultimate consistency, blockchain transactions cannot be completed in parallel, which makes it impossible to boost transaction throughput.

5.5. Off-Chain and On-Chain Storage Data Cooperation

Blockchain technologies and conventional information storage methods both have advantages and disadvantages. Both conventional information systems and blockchains require off-chain storage and compute infrastructure to boost performance. To doaccomplish this, it is neededcessary to combine blockchain technology with conventional information systems, with the most important consideration being to guarantee the accuracy and consistency of both the data on the chain and the data that isare stored in conventional databases. More importantly, data isare essential to the advancement of AI. There are still several obstacles to the widespread use of AI, such as issues with data quality, data monopolization, and data abuse, and so on. The introduction of blockchain technology opens up new avenues for solving these issues. The marriage of blockchains and AI is only useful in the real economy if the data on the chain isare properly combined with the data off the chain [86].

In order to enable model sharing and ensure a fair model-money exchanging process between independent developers and ML-as-a-service (MLaaS) providers, Weng et al. [87] developed a model marketplace dubbed Golden Grain. To encourage the loyal contributions of well-trained models, they implemented the swapping process on the blockchain and subsequently created a blockchain-enabled model benchmarking procedure for openly deciding the model values in accordance with their real-world performances. Their marketplace carefully offloads the laborious computation and designs a protected off-chain on-chain interaction protocol based on a trusted execution environment (TEE), for guaranteeing both the integrity and authenticity of benchmarking, in particularly to reduce the blockchain overhead for model benchmarking. In order to show the realistically inexpensive performance of their architecture, they deployed a prototype of the Golden Grain on the Ethereum blockchain and carry out comprehensive testing using common benchmark datasets