How can AI revolutionize Ethereum? Looking at “AI+Blockchain” from another angle

Question

Author: Mirror Tang | Salus; Yixin Ren | Hongshan capital; Lingzhi Shi | Salus; Jiangyue Wang | Salus

In the past year, as generative AI has repeatedly exceeded public expectations, the wave of AI productivity revolution has swept through the cryptocurrency circle. We have seen that many AI concept projects have brought about a wave of wealth creation myths in the secondary market. At the same time, more and more developers have begun to develop their own "AI+Crypto" projects.

However, a closer look reveals that the homogeneity of these projects is very serious, and most of the projects only focus on improving "production relations", such as organizing computing power through decentralized networks, or creating "decentralized Hugging Face” etc. Very few projects attempt true integration and innovation from the underlying technology. We believe that the reason for this phenomenon is that there is a "domain bias" between the AI and blockchain fields. Despite their extensive intersection, few people have a deep understanding of both fields. For example, it is difficult for AI developers to understand the technical implementation and historical infrastructure status of Ethereum, and it is even more difficult to propose in-depth optimization solutions.

Take machine learning (ML), the most basic branch of AI, as an example. It is a technology that allows machines to make decisions based on data without explicit programming instructions. Machine learning has shown great potential in data analysis and pattern recognition, and has become commonplace in web2. However, due to the limitations of the times when it was first born, even at the forefront of blockchain technology innovation such as Ethereum, its architecture, network and governance mechanisms have not yet used machine learning as an effective tool to solve complex problems.

"Great innovations are often born in cross-fields." Our original intention in writing this article is to allow AI developers to better understand the blockchain world, and also to provide new ideas for developers in the Ethereum community. In the article, we first introduced the technical implementation of Ethereum, and then proposed a solution to apply machine learning, a basic AI algorithm, to the Ethereum network to improve its security, efficiency, and scalability. We hope to use this case as a starting point to present some perspectives that are different from those on the market and inspire more innovative cross-combinations of "AI+Blockchain" in the developer ecosystem.

Technical implementation of Ethereum

Basic data structure The essence of a blockchain is a chain connecting blocks. The key to distinguishing chains is the chain configuration, which is also an indispensable part of the creation of a blockchain. For Ethereum, chain configuration is used to distinguish different chains in Ethereum and identify some important upgrade protocols and flag events. For example, DAOForkBlock marks the height of the hard fork at which Ethereum experienced a DAO attack, and ConstantinopleBlock marks the block height at which Constantinople was upgraded. For larger upgrades that include many improvement proposals, special fields will be set to identify the corresponding block height. In addition, Ethereum includes various test networks and main networks, and the corresponding network ecology is uniquely identified through ChainID.

The genesis block is the zeroth block of the entire blockchain, and other blocks directly or indirectly reference the genesis block. Therefore, the correct genesis block information must be loaded when the node is started and must not be modified arbitrarily. The configuration information of the genesis block includes the aforementioned chain configuration, and also adds fields such as relevant mining rewards, timestamps, difficulty, and gas limits. It should be noted that the consensus mechanism of Ethereum has evolved from proof-of-work mining. The mechanism is converted to proof of stake.

Ethereum accounts are divided into external accounts and contract accounts. The external account is uniquely controlled by a private key, while the contract account has no private key control and can only be operated by calling the contract from the external account to execute the contract code. They all contain a unique address. The Ethereum world state is an Ethereum account tree. Each account corresponds to a leaf node, which stores the status of the account (various account information and code information).

Transaction: As a decentralized platform, Ethereum’s essence is for transactions and contracts. Ethereum’s blocks are packaged transactions, as well as additional relevant information. The specific blocks are divided into two parts, namely the block header and the area. Block, in which the block header data contains evidence that connects all blocks into a chain, which we can understand as the previous block hash, as well as the state root, transaction root, receipt root, and proof of the state of the entire Ethereum world. Several other indicators indicate difficulty, counting nonce and other additional data. The block body stores the transaction list and the list of uncle block headers (since Ethereum has converted to proof of stake, the uncle block reference no longer exists).

Transaction receipts provide the results and additional information after the transaction was executed that cannot be obtained directly by simply viewing the transaction itself. Specifically, the information contained in it can be divided into: consensus content, transaction information and block information, including information on whether transaction processing is successful and consumption information such as transaction logs and gas. Debug smart contract code and optimize gas consumption by analyzing the information in the receipt. and provides a form of confirmation that the transaction has been processed by the network, and the results and impact of the transaction can be viewed.

In Ethereum, gas fees can be simply understood as handling fees. When you send Token, execute a contract, transfer Ethereum, or perform various operations on this block, the operations in these transactions require gas fees. Ether When processing this transaction, the computer needs to perform calculations and consume network resources, so you have to pay gas fees to let the computer work for you. The final fuel fee is paid to the miners as a handling fee. The calculation formula for the specific fee can be understood as Fee = Gas Used * Gas Price, which is the actual consumption multiplied by the consumption unit price. The unit price is set by the initiator of the transaction, and its amount is often Determines how quickly transactions are uploaded to the chain. If the setting is too low, the transaction may not be executed. At the same time, it is also necessary to set a gas limit consumption upper limit to avoid errors in the contract causing unpredictable gas consumption. 2. Trading pool In Ethereum, there are a large number of transactions. Compared with the centralized system, the number of transactions per second processed by the decentralized system is obviously bleak. Due to the large number of transactions entering the node, the node needs to maintain a transaction pool to properly manage these transactions. The broadcast of transactions is carried out through p2p. Specifically, a node will broadcast an executable transaction to its neighboring nodes, and then the neighboring nodes will broadcast the transaction to the neighboring nodes of the node. In this way, A transaction can spread to the entire Ethereum network within 6 seconds.

Transactions in the transaction pool are divided into executable transactions and non-executable transactions. Executable transactions have a higher priority and will be executed and packaged in the block, while all transactions that have just entered the transaction pool are non-executable transactions. It will then become executable. Executable transactions and non-executable transactions are recorded in the pending container and the queue container respectively.

In addition, the transaction pool will also maintain a local transaction list. Local transactions have many advantages. They have higher priority, are not affected by transaction volume restrictions, and can be reloaded into the transaction pool immediately when the node is restarted. The local persistent storage of local transactions is implemented through journal (reloading when restarting the node). Its purpose is not to lose unfinished local transactions and will be updated regularly.

The legality of the transaction will be checked before entering the queue, including various types of checks, such as: anti-DOS attack, anti-negative transaction, transaction gas limit, etc. The simple composition of the transaction pool can be divided into: queue+pending (the two constitute all transactions). After completing the legality test, subsequent checks will be carried out, including checking whether the transaction queue has reached the upper limit, and then judging remote transactions (remote transactions are non-local transactions) ) is the lowest in the trading pool, replace the lowest price transaction in the trading pool. For the replacement of executable transactions, by default only transactions that increase the handling fee by 10% are allowed to replace transactions that are already waiting for execution, and will be stored as non-executable transactions after replacement. In addition, during the maintenance process of the transaction pool, invalid and over-limit transactions will be deleted, and transactions that meet the conditions will be replaced. 3. Consensus mechanism The early consensus theory of Ethereum was still based on the difficulty value hash calculation method. That is to say, the hash value of the block needs to be calculated to meet the conditions of the target difficulty value before the block is legal. Since the current consensus algorithm of Ethereum has been changed from POW to POS, the theories related to mining will not be discussed in detail. Here is a brief description of the POS algorithm. Ethereum completed the merger of the beacon chain in September 2022 and implemented the POS algorithm. Specifically, for POS-based Ethereum, the block time of each block is stable at 12s, and users pledge their own Ethereum coins to obtain The right to become a validator. After that, users who participate in staking will be randomly selected to obtain a batch of validators. In each round, validators will be selected from each of the 32 slots, and one of them will be selected for verification. The proposer serves as the proposer, and the proposer implements the block production, and the remaining validators corresponding to the slot serve as the committee to verify the legality of the proposer's block and at the same time make a judgment on the legality of the block in the previous cycle. The POS algorithm significantly stabilizes and improves the speed of block production, while greatly avoiding the waste of computing resources. 4. Signature algorithm Ethereum follows Bitcoin's signature algorithm standard and also uses the secp256k1 curve. Its specific signature algorithm uses ECDSA, which means that the calculated signature is calculated based on the hash of the original message. The composition of the entire signature is simple. That's R+S+V. Each calculation will introduce random numbers accordingly, where R+S is the original output of ECDSA. The last field V is called the recovery field, which indicates the number of searches required to successfully recover the public key from the content and signature, because there may be multiple coordinate points in the elliptic curve that meet the requirements based on the R value.

The whole process can be simply summarized as follows: the transaction data and signer-related information are hashed after RLP encoding, and signed with the private key through ECDSA to obtain the final signature, and the curve used in ECDSA is the secp256k1 elliptic curve. Finally, by combining the signature data with the transaction data, you can obtain a signed transaction data and broadcast it.

Ethereum's data structure does not just rely on traditional blockchain technology, but also introduces Merkle Patricia trees, also known as Merkle compressed prefix trees, for efficient storage and verification of large amounts of data. MPT combines the cryptographic hashing function of Merkle trees and the key path compression properties of Patricia trees to provide a solution that both guarantees data integrity and supports fast search. 5. Merkle compressed prefix tree In Ethereum, MPT is used to store all state and transaction data, ensuring that any data changes are reflected in the root hash of the tree. This means that by verifying the root hash, you can prove the integrity and accuracy of the data without having to check the entire database. MPT consists of four types of nodes: leaf nodes, extension nodes, branch nodes and empty nodes. These nodes together form a tree that can adapt to dynamic data changes. Each time the data is updated, MPT reflects these changes by adding, deleting, or modifying nodes, while updating the root hash of the tree. Since each node is encrypted by a hash function, any small changes to the data will lead to huge changes in the root hash, thus ensuring the security and consistency of the data. In addition, MPT is designed to support "light client" verification, allowing nodes to verify the existence or status of specific information simply by storing the root hash of the tree and the necessary path nodes, greatly reducing the need for data storage and processing.

Through MPT, Ethereum not only achieves efficient management and rapid access to data, but also ensures the security and decentralization of the network, supporting the operation and development of the entire Ethereum network. 6. State machine The core architecture of Ethereum incorporates the concept of state machines. The Ethereum Virtual Machine (EVM) is the runtime environment for executing all smart contract codes, and Ethereum itself can be regarded as a globally shared, state transition system. The execution of each block can be viewed as a state transition process, moving from one globally shared state to another. This design not only ensures the consistency and decentralization of the Ethereum network, but also makes the execution results of smart contracts predictable and non-tamperable.

In Ethereum, status refers to the current information of all accounts, including the balance of each account, storage data, and smart contract code. Whenever a transaction occurs, EVM calculates and converts the state based on the transaction content. This process is recorded efficiently and securely through MPT. Each state transition not only changes the account data, but also causes an update of the MPT, which is reflected in the change of the root hash of the tree.

The relationship between EVM and MPT is crucial because MPT provides data integrity guarantees for Ethereum’s state transitions. When the EVM executes transactions and changes account status, the relevant MPT nodes are updated to reflect these changes. Since each node of the MPT is linked by a hash, any modification to the state will cause a change in the root hash. This new root hash is then included in the new block, ensuring the stability of the entire Ethereum state. Consistency and security. Next we introduce the EVM virtual machine. 7. EVM

The EVM virtual machine is the foundation for the entire Ethereum to build smart contracts and execute state transitions. It is thanks to the EVM that Ethereum can be imagined as a world computer in a true sense. The EVM virtual machine is Turing complete, which means that smart contracts on Ethereum can perform any complex logical calculations, and the introduction of the gas mechanism successfully prevents infinite loops in the contract and ensures the stability and security of the network. . On a more technical level, EVM is a stack-based virtual machine that uses Ethereum-specific bytecode to execute smart contracts. Developers usually use high-level languages, such as Solidity, to write smart contracts, and then compile them into bytecode that the EVM can understand for execution calls by the EVM. EVM is the key to the innovation capabilities of the Ethereum blockchain. It not only supports the operation of smart contracts, but also provides a solid foundation for the development of decentralized applications. Through EVM, Ethereum is shaping a decentralized, secure and open digital future.

Ethereum History Review

Figure 1 Historical review of Ethereum

Challenges facing Ethereum Security

Smart contracts are computer programs that run on the Ethereum blockchain. They allow developers to create and publish a variety of applications, including but not limited to lending applications, decentralized exchanges, insurance, secondary financing, social networks, and NFTs. The security of smart contracts is crucial for these applications. These applications are directly responsible for processing and controlling cryptocurrencies. Any loopholes in smart contracts or malicious attacks on them will pose a direct threat to the security of funds and even lead to huge economic losses. For example, on February 26, 2024, the DeFi lending protocol Blueberry Protocol was attacked due to a logic flaw in the smart contract, resulting in a loss of approximately US$1,400,000.

The vulnerabilities of smart contracts are multi-faceted, covering unreasonable business logic (Business Logic), improper access control, insufficient data verification, re-entrancy attacks, and DOS (Denial of Service) attacks. These vulnerabilities may cause problems in the execution of the contract and affect the effective operation of the smart contract. Take the DOS attack as an example. This attack method uses the attacker to send a large number of transactions to consume network resources. Then, transactions initiated by normal users cannot be processed in a timely manner, which will lead to a decline in user experience. Moreover, this will also lead to an increase in transaction gas fees. Because when network resources are tight, users may need to pay higher fees to have their transactions prioritized.

In addition, users on Ethereum also face investment risks, and the security of funds will be threatened. For example, spamcoins are used to describe cryptocurrencies that are considered to have little value or no potential for long-term growth. Spam coins are often used as a tool for fraud or for price manipulation in pump and dump strategies. Investment in junk coins is very risky and can result in significant financial losses. Due to their low price and low market capitalization, they are highly susceptible to manipulation and volatility. The coin is often used in pump-and-dump schemes and honeypot scams, where fake projects are used to lure investors and steal their funds. Another common spamcoin risk is a rug pull, where the creator suddenly removes all liquidity from the project, causing the token's value to plummet. These scams are often marketed with false partnerships and endorsements, and once the token price increases, the scammers sell their tokens, make a profit and disappear, leaving investors with worthless tokens. At the same time, investing in shitcoins distracts attention and resources from legitimate cryptocurrencies that have real applications and growth potential. In addition to junk coins, air coins and MLM coins are also ways to make quick profits. Distinguishing them from legitimate cryptocurrencies is especially difficult for users who lack expertise and experience.

efficiency

Two very direct metrics for evaluating Ethereum's efficiency are transaction speed and gas fees. Transaction speed refers to the number of transactions that the Ethereum network can process per unit of time. This metric directly reflects the processing power of the Ethereum network, with faster speeds meaning higher efficiency. Every transaction in Ethereum requires a certain gas fee to compensate the miners who perform transaction verification. The lower the gas cost, the higher the efficiency of Ethereum.

Reduced transaction speed will lead to higher gas fees. Generally speaking, when transaction processing speeds slow down, there may be an increase in transactions competing to enter the next block due to limited block space. To stand out from the competition, traders often increase gas fees because miners tend to prioritize transactions with higher gas fees when validating transactions. Then, higher gas fees will reduce the user experience.

Transactions are just the basic activity in Ethereum. In this ecosystem, users can also carry out various activities such as lending, pledging, investment, and insurance. These can all be accomplished through specific DApps. However, given the wide variety of DApps and the lack of personalized recommendation services similar to traditional industries, users will feel confused when choosing the applications and products that suit them. This situation will lead to a decrease in user satisfaction, which will affect the efficiency of the entire Ethereum ecosystem.

Take lending as an example. In order to maintain the security and stability of their own platforms, some DeFi lending platforms will use an over-collateralization mechanism. This means that the borrower needs to put up more assets as collateral, and these assets cannot be used by the borrower for other activities during the borrowing period. This will lead to a decrease in borrowers' capital utilization, thereby reducing market liquidity.

Application of machine learning in Ethereum

Machine learning models, such as RMF model, generative adversarial network (GAN), decision tree model, K nearest neighbor algorithm (KNN), DBSCAN clustering algorithm, etc., are playing an important role in Ethereum. The application of these machine learning models in Ethereum can help optimize transaction processing efficiency, improve the security of smart contracts, realize user stratification to provide more personalized services, and help maintain the stable operation of the network.

Algorithm Introduction

A machine learning algorithm is a set of instructions or rules for parsing data, learning patterns in the data, and making predictions or decisions based on those learnings. They learn and improve automatically from the data they are provided without the need for explicit programming instructions from humans. Machine learning models, such as RMF model, generative adversarial network (GAN), decision tree model, K nearest neighbor algorithm (KNN), DBSCAN clustering algorithm, etc., are playing an important role in Ethereum. The application of these machine learning models in Ethereum can help optimize transaction processing efficiency, improve the security of smart contracts, realize user stratification to provide more personalized services, and help maintain the stable operation of the network.

Bayesian Classifier

Bayesian classifier is an efficient classifier that aims at minimizing the probability of classification error or minimizing the average risk under a specific cost framework among various statistical classification methods. Its design philosophy is deeply rooted in Bayes' theorem, which allows it to make decisions by calculating the posterior probability of an object given the probability that an object belongs to a certain class given certain characteristics. Specifically, the Bayesian classifier first considers the prior probability of the object, and then applies the Bayesian formula to comprehensively consider the observation data to update the belief about the object classification. Among all possible classifications, the Bayesian classifier selects the one with the largest posterior probability of classifying the object into this category. The core advantage of this approach is its ability to naturally handle uncertainty and incomplete information, making it a powerful and flexible tool suitable for a wide range of application scenarios.

Figure 2 Bayesian classifier

As shown in Figure 2, in supervised machine learning, data and a probability model based on Bayes' theorem are used to make classification decisions. Using the likelihood and prior probabilities of categories and features, the Bayesian classifier calculates the posterior probability that the data points belong to each category and assigns the data points to the category with the largest posterior probability. In the scatterplot on the right, the classifier will try to find a curve that best separates the points of different colors, thus minimizing the classification error.

Decision Tree

The decision tree algorithm is often used in classification and regression tasks. It adopts the idea of hierarchical judgment. Based on the known data, the decision tree is trained by selecting features with a large information gain rate and then splitting them into trees. To put it simply, the entire algorithm can self-learn a decision rule based on the data to judge the value of the variable. In terms of implementation, it can decompose the complex decision-making process into several simple sub-decision-making processes. Through such a derivative method, Each simpler decision judgment is derived from a parent decision criterion, forming a tree structure.

As can be seen from Figure 3, each node represents a decision and sets the judgment criteria for a certain attribute, and the branches represent the results of the decision. Each leaf node represents the final predicted result and category. From the perspective of algorithm composition, the decision tree model is relatively intuitive, easy to understand and has strong interpretability.

Figure 3 Decision tree model

DBSCAN algorithm

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based spatial clustering algorithm with noise, which seems to work particularly well for non-connected data sets. This algorithm can discover clusters of arbitrary shapes without specifying the number of clusters in advance, and has good robustness to outliers in the data set. The algorithm can also effectively identify outliers in noisy data sets. Noise or outliers are defined as points in low-density areas, as shown in Figure 4.

Figure 4 DBSCAN algorithm identifies noise

KNN algorithm

The KNN (K-Nearest Neighbors) algorithm can be used for both classification and regression. In a classification problem, the voting mechanism is used to determine the category of the item to be classified; in a regression problem, the average or weighted average of the k nearest neighbor samples is calculated for prediction.

As shown in Figure 5, the working principle of the KNN algorithm in classification is to find the nearest K neighbors of a new data point, and then predict the category of the new data point based on the categories of these neighbors. If K=1, then new data points are simply assigned to their nearest

Neighbor category. If K > 1, then a voting method is usually used to determine the class of the new data point, that is, the new data point will be assigned to the class to which the most neighbors belong. When the KNN algorithm is used for regression problems, the basic idea is the same, and the result is the average of the K sample output values of the nearest neighbors.

Figure 5 KNN algorithm used for classification

Generative Artificial Intelligence

Generative artificial intelligence is an AI technology that can generate new content (such as text, images, music, etc.) based on demand input. Its background is based on advances in machine learning and deep learning, particularly with applications in areas such as natural language processing and image recognition. Generative AI learns patterns and associations from large amounts of data and then generates new output content based on this learned information. The key to generative artificial intelligence lies in model training, which requires excellent data for learning and training. In this process, the model gradually improves its ability to generate new content by analyzing and understanding the structure, patterns and relationships in the data set.

Transformer As the cornerstone of generative artificial intelligence, Transformer pioneered the introduction of the attention mechanism, which enables information processing to focus on key points while also having an overview. This unique ability makes Transformer shine in the field of text generation. Utilizing the latest natural language processing language models, such as GPT (Generative Pre-trained Transformer), to understand users' application requirements expressed in natural language and automatically convert them into executable code can reduce development difficulty and significantly improve efficiency.

As shown in Figure 6, through the introduction of multi-head attention mechanism and self-attention mechanism, combined with residual connection and fully connected neural network, and with the help of previous word embedding technology, the performance of generative models related to natural language processing has been greatly improved. The earth ascends.

Figure 6 Transformer model

RFM model introduction**:**

The RFM model is an analysis model based on user purchasing behavior. By analyzing users' transaction behavior, it can identify user groups with different values. This model stratifies users based on their latest consumption time (R), consumption frequency (F), and consumption amount (M).

As shown in Figure 7. These three indicators together form the core of the RFM model. The model scores users based on these three dimensions and sorts them according to their scores to identify the most valuable user groups. Moreover, this model can effectively divide customers into different groups to realize the function of user stratification.

Figure 7 RFM layered model

Possible applications

When applying machine learning technology to address the security challenges of Ethereum, we conducted research from four main aspects:

Identify and filter malicious transactions based on Bayesian classifier By building a Bayesian classifier, possible spam transactions are identified and filtered, including but not limited to large, frequent, and small transactions that lead to DOS attacks. This method effectively maintains the health of the network and ensures the stable operation of the Ethereum network by analyzing transaction characteristics, such as Gas price, transaction frequency, etc.
Generate smart contract code that is secure and meets specific requirements Both Generative Adversarial Networks (GAN) and Transformer-based generative networks can be used to generate smart contract code that meets specific requirements and ensure the security of the code as much as possible. However, there are differences between the two in the type of data they rely on to train their models. The training process of the former relies mainly on unsafe code samples, while the opposite is true for the latter.

By training GAN to learn existing security contract patterns, build a self-confrontational model to generate potentially unsafe code, and then learn to identify these insecurities through model learning, ultimately achieving the ability to automatically generate high-quality, more secure smart contract code. . Utilizing the transformer-based generative network model and learning a large number of security contract examples to generate contract code that meets specific needs and optimizes gas consumption will undoubtedly further improve the efficiency and security of smart contract development. 3. Smart contract risk analysis based on decision tree Using decision trees to analyze the characteristics of smart contracts, such as function call frequency, transaction value, source code complexity, etc., can effectively identify the potential risk level of the contract. By analyzing the contract's operating mode and code structure, possible vulnerabilities and risk points can be predicted, thereby providing developers and users with security assessments. This approach is expected to significantly improve the security of smart contracts in the Ethereum ecosystem, thereby reducing losses due to vulnerabilities or malicious code. 4. Construct an evaluation model for cryptocurrency to reduce investment risks

Use machine learning algorithms to analyze multi-dimensional information such as cryptocurrency transaction data, social media activities, and market performance to build an evaluation model that can predict the possibility of spam coins. This model can provide investors with a valuable reference to help them avoid investment risks, thereby promoting the healthy development of the cryptocurrency market.

In addition, the use of machine learning also has the potential to further improve the efficiency of Ethereum. We can delve deeper into the following three key dimensions:

Decision tree application to optimize transaction pool queuing model Based on decision trees, the queuing mechanism of the Ethereum transaction pool can be effectively optimized. By analyzing transaction characteristics, such as gas price and transaction size, decision trees can optimize transaction selection and queuing order. This method can significantly improve transaction processing efficiency, effectively reduce network congestion, and reduce users’ transaction waiting time.
Stratify users and provide personalized services The RFM model (Recency, Monetary value, Frequency) is an analytical tool widely used in customer relationship management. It can evaluate the user's last transaction time (Recency), transaction frequency (Frequency) and transaction amount (Monetary value). Perform effective user stratification. Applying the RFM model on the Ethereum platform can help identify high-value user groups, optimize resource allocation, and provide more personalized services, thereby improving user satisfaction and the overall efficiency of the platform.

The DBSCAN algorithm can also analyze users' trading behavior, help identify different user groups on Ethereum, and further provide more customized financial services for different users. This user stratification strategy can optimize marketing strategies and improve customer satisfaction and service efficiency. 3. Credit scoring based on KNN

The K-nearest neighbor algorithm (KNN) can score users' credit by analyzing the transaction history and behavior patterns of Ethereum users, which plays an extremely important role in financial activities such as lending. Credit scores can help financial institutions and lending platforms evaluate borrowers' repayment ability and credit risk, thereby making more accurate loan decisions. This avoids over-borrowing and improves market liquidity.

Future Directions

From the perspective of macro fund allocation, Ethereum, as the world's largest distributed computer, cannot invest too much in the infra layer, and it needs to attract developers from more backgrounds to participate in co-construction. In this article, by sorting out the technical implementation and problems faced by Ethereum, we imagined a series of relatively intuitive possible applications of machine learning. We also very much look forward to AI developers in the community who can deliver these visions with real value. Landed.

As the computing power on the chain gradually increases, we can foresee that more complex models will be developed for network management, transaction monitoring, security auditing and other aspects to improve the efficiency and security of the Ethereum network.

Going further, artificial intelligence/agent-driven governance mechanisms may also become a major innovation point in the Ethereum ecosystem. The more efficient, more transparent, and more automated decision-making process brought about by this mechanism brings a more flexible and reliable governance structure to the Ethereum platform. These future development directions will not only promote the innovation of Ethereum technology, but also provide users with a better on-chain experience.

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