Looking at "AI+Blockchain" from another perspective: How does AI revolutionize Ethereum?

This article introduces the technical implementation of Ethereum, and then proposes a solution to apply machine learning, a basic AI algorithm, to the Ethereum network to improve its security, efficiency and scalability.

Author: Mirror Tang, ZEROBASE; Lingzhi Shi, ZEROBASE; Jiangyue Wang, Salus, ZEROBASE

Cover: Photo by Shubham Dhage on Unsplash

In the past year, as generative AI has repeatedly broken public expectations, the wave of AI productivity revolution has swept the cryptocurrency circle. We have seen that many AI concept projects have brought 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 these projects are highly homogenized, and most of them only focus on improving “production relations”, such as organizing computing power through decentralized networks, or creating “decentralized Hugging Faces”. Few projects attempt to truly integrate and innovate from the underlying technology. We believe that this phenomenon is caused by a “domain bias” between the fields of AI and blockchain. Despite the wide overlap between the two, few people have a deep understanding of the two fields. For example, it is difficult for AI developers to understand Ethereum’s technical implementation and historical infrastructure status, and it is even more difficult to come up with in-depth optimization solutions.

Take machine learning (ML), the most basic branch of AI, for 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 mechanism have not yet used machine learning as an effective tool for solving complex problems.

"Great innovations are often born in intersections." Our original intention in writing this article is to help AI developers 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 throw out some different angles from the market and inspire more innovative cross-combinations of "AI+Blockchain" in the developer ecosystem.

Ethereum’s technical implementation

• Basic data structures

The essence of blockchain is a chain of connected blocks. The key to distinguishing chains is chain configuration, which is also an indispensable part of a blockchain in creation. For Ethereum, chain configuration is used to distinguish different chains in Ethereum, and to identify some important upgrade protocols and landmark events. For example, DAOForkBlock marks the hard fork height of Ethereum after the DAO attack, and ConstantinopleBlock marks the block height of the Constantinople upgrade. For larger upgrades that include many improvement proposals, special fields are set to identify the corresponding block height. In addition, Ethereum includes various test networks and main networks, and the corresponding network ecosystem 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 at the beginning of the node startup, and it cannot 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 changed from a proof-of-work mining mechanism to a proof-of-stake mining mechanism.

Ethereum accounts are divided into external accounts and contract accounts. External accounts are uniquely controlled by private keys, while contract accounts are not controlled by private keys and can only be operated by calling contracts and executing contract codes through external accounts. They all contain a unique address. The Ethereum world state is an Ethereum account tree, where each account corresponds to a leaf node, which stores the status of the account (various account information and code information).

Transaction: Ethereum is a decentralized platform, and its essence is for transactions and contracts. Ethereum blocks are packaged transactions, with some other related information attached. The specific blocks are divided into two parts, namely the block header and the block body. The block header data contains evidence of connecting all blocks into a chain, which can be understood as the previous block hash, as well as the state root, transaction root, receipt root, and other additional data such as identification difficulty and counting nonce that prove the state of the entire Ethereum world. The block body stores the transaction list and the list of uncle block headers (since Ethereum has switched to proof of stake, uncle block references no longer exist).

The transaction receipt provides the results and additional information after the transaction is executed, which cannot be directly obtained by simply viewing the transaction itself. Specifically, the information contained in it can be divided into: consensus content, transaction information and block information, including whether the transaction processing is successful, transaction logs and gas consumption. By analyzing the information in the receipt, the smart contract code can be debugged and gas consumption can be optimized. It also provides a 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 tokens, execute contracts, transfer ether, or perform various operations on this block, all operations in these transactions require gas fees. The Ethereum computer needs to calculate and consume network resources when processing this transaction, so you must pay the gas fee to let the computer work for you. The final fuel fee is paid to the miners as a handling fee. The specific fee calculation formula can be understood as Fee = Gas Used * Gas ​​Price, which is the actual consumption multiplied by the unit price. The unit price is set by the initiator of the transaction, and its amount often determines the speed of the transaction on the chain. If it is set too low, the transaction may not be executed. At the same time, the gas limit consumption limit of the fee needs to be set to avoid errors in the contract causing unpredictable gas consumption.

• Trading Pool

In Ethereum, there are a large number of transactions. Compared with the centralized system, the number of transactions processed per second in 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 the 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 be 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 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 and will become executable later. Executable transactions and non-executable transactions are recorded in the pending container and 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 limits, and can be immediately reloaded into the transaction pool when the node is restarted. The local persistent storage of local transactions is achieved through the journal (reloading when restarting the node). Its purpose is to avoid losing unfinished local transactions and will be updated regularly.

Before entering the queue, the transaction will be checked for legitimacy, including various types of checks, such as: anti-DOS attack, anti-negative transaction, transaction gas limit, etc. The transaction pool can be simply divided into: queue+pending (two components all transactions). After completing the legitimacy check, subsequent checks will be carried out, including checking whether the transaction queue has reached the upper limit, and then judging whether the remote transaction (remote transaction is non-local transaction) is the lowest in the transaction pool, and replacing the lowest price transaction in the transaction pool. For the replacement of executable transactions, by default, only transactions with a 10% increase in handling fees are allowed to replace transactions that are already waiting for execution, and after replacement, they are stored as non-executable transactions. In addition, during the maintenance of the transaction pool, invalid and over-limit transactions will be deleted, and transactions that meet the conditions will be replaced.

• Consensus Mechanism

The consensus theory of Ethereum in the early stage was still based on the method of difficulty value hash calculation, that is, the hash value of the block needs to be calculated to meet the conditions of the target difficulty value, and the block is legal. Since the current consensus algorithm of Ethereum has been changed from POW to POS, the theory related to mining will not be repeated. 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, based on POS, the block time of each block of Ethereum is stable at 12s. Users pledge their own ether to obtain the right to become a validator. After that, users who participate in the pledge are randomly selected to obtain a batch of validators, and validators will be selected in each slot of 32 slots in each round of the cycle. One of the validators is selected as the proposer, and the proposer realizes the block, and the remaining validators corresponding to the slot serve as the committee to verify the legitimacy of the proposer's block and make a judgment on the legitimacy of the block in the previous round. The POS algorithm significantly stabilizes and improves the block speed, while greatly avoiding the waste of computing resources.

• Signature Algorithm

Ethereum follows the signature algorithm standard of Bitcoin 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 entire signature is simply composed of R+S+V. Each calculation introduces a random number, 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 that meet the requirements in the elliptic curve according to the R value.

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

Ethereum's data structure not only relies on traditional blockchain technology, but also introduces the Merkle Patricia Tree, also known as the Merkle Compressed Prefix Tree, for efficient storage and verification of large amounts of data. MPT combines the cryptographic hashing function of the Merkle Tree with the key path compression feature of the Patricia Tree, providing a solution that both ensures data integrity and supports fast lookup.

• Merkle Compressed Prefix Trie

In Ethereum, MPT is used to store all status 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, the integrity and accuracy of the data can be proved without checking the entire database. MPT consists of four types of nodes: leaf nodes, extension nodes, branch nodes, and empty nodes, which together form a tree that can adapt to dynamic data changes. Every time the data is updated, MPT reflects these changes by adding, deleting, or modifying nodes, while updating the root hash value of the tree. Since each node is encrypted by a hash function, any minor change to the data will result in a huge change in the root hash, thereby ensuring the security and consistency of the data. In addition, the design of MPT supports "light client" verification, allowing nodes to verify the existence or status of specific information by only 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 fast access to data, but also ensures the security and decentralization of the network, supporting the operation and development of the entire Ethereum network.

• State Machine

The core architecture of Ethereum incorporates the concept of a state machine, where the Ethereum Virtual Machine (EVM) is the runtime environment for executing all smart contract codes, and Ethereum itself can be seen as a globally shared, state transition system. The execution of each block can be seen as a state transition process, 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 tamper-proof.

In Ethereum, state refers to the current information of all accounts, including the balance of each account, storage data, and the code of smart contracts. Whenever a transaction occurs, EVM calculates and transforms the state according to the transaction content, and this process is efficiently and securely recorded through MPT. Each state transition not only changes the account data, but also leads to the update of MPT, which is reflected in the change of the root hash value of the tree.

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

• EVM

The EVM virtual machine is the foundation for the entire Ethereum to build smart contract execution state transitions. Thanks to the EVM, Ethereum can be imagined as a world computer in the true sense. The EVM virtual machine is Turing complete, which means that smart contracts on Ethereum can execute arbitrarily complex logical calculations, and the introduction of the gas mechanism successfully prevents infinite loops in contracts and ensures the stability and security of the network. From a more in-depth technical perspective, the 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 bytecodes that the EVM can understand for execution and call. EVM is the key to the innovation ability 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.

Historical Overview

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 critical for these applications. These applications are directly responsible for processing and controlling cryptocurrencies. Any vulnerabilities in smart contracts or malicious attacks on them will pose a direct threat to the security of funds and even lead to huge financial losses. For example, on February 26, 2024, the DeFi lending protocol Blueberry Protocol was attacked due to a smart contract logic flaw, resulting in a loss of approximately $1,400,000.

Smart contracts have many vulnerabilities, including unreasonable business logic, improper access control, insufficient data verification, reentrancy attacks, and DOS (Denial of Service) attacks. These vulnerabilities may cause problems in the execution of contracts and affect the effective operation of smart contracts. Take DOS attacks as an example. This attack method consumes network resources by sending a large number of transactions by attackers. Then, transactions initiated by normal users cannot be processed in time, 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 that threaten the security of their funds. For example, the shit coin" is used to describe cryptocurrencies that are believed to have little value or no long-term growth potential. Shit coin are often used as a tool for scams or pump and dump strategies for price manipulation. Shit coin investment risks are high and can result in significant financial losses. Due to their low prices and low market capitalizations, they are extremely susceptible to manipulation and volatility. Such coins are often used in pump and dump schemes and honeypot scams, which use fake projects to lure investors and steal their funds. Another common risk of shit coin is the Rug Pull, where the creator suddenly removes all liquidity from the project, causing the token value to plummet. These scams are usually marketed with fake partnerships and endorsements, and once the token price rises, the scammers will sell their tokens, disappear at a profit, and leave investors with worthless tokens. At the same time, investing in shit coin also diverts people's attention and resources from legitimate cryptocurrencies with real applications and growth potential.

In addition to shit coin, air coins and pyramid scheme coins are also ways to make quick profits. It is particularly difficult for users who lack professional knowledge and experience to distinguish them from legitimate cryptocurrencies.

efficiency

Two very direct indicators 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 time. This indicator directly reflects the processing power of the Ethereum network, and the faster the speed, the higher the efficiency. Every transaction in Ethereum requires a certain gas fee to compensate the miners who verify the transaction. The lower the gas fee, the higher the efficiency of Ethereum.

Slower transaction speeds lead to higher gas fees. Generally speaking, when transaction processing speeds slow down, there may be more transactions competing to enter the next block due to limited block space. In order to stand out from the competition, traders usually increase gas fees because miners tend to give priority to transactions with higher gas fees when verifying transactions. Then, higher gas fees will reduce the user experience.

Trading is only a basic activity in Ethereum. In this ecosystem, users can also conduct various activities such as lending, staking, investment, insurance, etc. These can be completed 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 applications and products that are suitable for them. This situation will lead to a decline 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 platforms, some DeFi lending platforms use an over-collateralization mechanism. This means that borrowers need to put up more assets as collateral, and these assets cannot be used by borrowers for other activities during the loan period. This will lead to a decrease in the utilization rate of borrowers' funds, thereby reducing market liquidity.

Application of machine learning in Ethereum

Machine learning models, such as the RFM 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, achieve user stratification to provide more personalized services, and help maintain the stable operation of the network.

Algorithm Introduction

Machine learning algorithms are 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 provided without explicit programming instructions from humans. Machine learning models, such as the RFM 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, achieve user stratification to provide more personalized services, and help maintain the stable operation of the network.

• Bayesian Classifier

The Bayesian classifier is an efficient classifier that aims to minimize the probability of classification errors or minimize the average risk under a specific cost framework in various statistical classification methods. Its design philosophy is deeply rooted in Bayes' theorem, which enables it to make decisions by calculating the posterior probability of the object based on the probability that the object belongs to a certain category under the condition that certain features are known. Specifically, the Bayesian classifier first considers the prior probability of the object, and then applies the Bayesian formula to comprehensively consider the observed data to update the belief in the classification of the object. Among all possible classifications, the Bayesian classifier selects the category with the largest posterior probability and classifies the object into this category. The core advantage of this method is that it can naturally handle uncertainty and incomplete information, making it a powerful and flexible tool suitable for a wide range of application scenarios.

As shown in Figure 2, in supervised machine learning, classification decisions are made using data and a probabilistic model based on the Bayesian theorem. Using the likelihood and the prior probabilities of the categories and features, the Bayesian classifier calculates the posterior probability that the data point belongs to each category and assigns the data point to the category with the largest posterior probability. In the scatter plot on the right, the classifier will try to find a curve that best separates the points of different colors, thereby minimizing the classification error.

• Decision Tree

The decision tree algorithm is commonly used in classification and regression tasks. It adopts a hierarchical judgment idea. According to the known data, it selects features with a large information gain rate and splits them into trees to train the decision tree. In simple terms, the entire algorithm can self-learn a decision rule from the data to judge the value of the variable. In terms of implementation, it can decompose the complex decision process into several simple sub-decision processes. Through such a derivative method, each simpler decision judgment is derived from the parent decision criterion, thus forming a tree structure.

As can be seen from Figure 3, each node represents a decision, defines a criterion for a certain attribute, and the branch represents the result of the decision. Each leaf node represents the final predicted result and category. From the perspective of algorithm composition, the decision tree model is more intuitive, easy to understand and has strong interpretability.

• 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. The algorithm can find clusters of any shape 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.

• KNN algorithm

The KNN (K-Nearest Neighbors) algorithm can be used for both classification and regression. In classification problems, the category of the item to be classified is determined based on a voting mechanism; in regression problems, 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 the new data point is simply assigned to the category of its nearest neighbor. If K>1, then voting is usually used to determine the category of the new data point, that is, the new data point will be assigned to the category 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 output values ​​of the K nearest neighbors.

• Generative AI

Generative AI is an AI technology that can generate new content (such as text, images, music, etc.) based on demand input. Its background is based on the progress of machine learning and deep learning, especially in the fields of 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 AI 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 the key points while taking a panoramic view. This unique ability makes Transformer shine in the field of text generation. Using the latest natural language processing models, such as GPT (Generative Pre-trained Transformer), to understand the application requirements expressed by users in natural language and automatically convert them into executable code can reduce the difficulty of development and significantly improve efficiency.

As shown in Figure 6, by introducing the multi-head attention mechanism and the self-attention mechanism, combined with residual connections and fully connected neural networks, and with the help of previous word embedding technology, the performance of generative models related to natural language processing has been greatly improved.

• RFM Model

The RFM model is an analysis model based on user purchasing behavior. By analyzing user transaction behavior, it can identify user groups of different values. The model stratifies users according to their most recent 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 the scores to identify the most valuable user groups. Moreover, the model can effectively divide customers into different groups to achieve the function of user stratification.

Possible applications

When applying machine learning techniques to address Ethereum’s security challenges, we conducted research from four main perspectives:

• 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, 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 and transaction frequency.

• Generate smart contract code that is secure and meets specific requirements

Both Generative Adversarial Networks (GANs) and Transformer-based Generative Networks can be used to generate smart contract code that meets specific requirements and ensures the security of the code as much as possible. However, there are differences between the two in the type of data that the training model relies on. The training process of the former mainly relies on unsafe code samples, while the latter is the opposite.

By training GAN to learn existing security contract patterns and building a self-adversarial model to generate potentially unsafe code, and then using the model to learn to identify these insecurities, we can ultimately achieve the ability to automatically generate high-quality, more secure smart contract code. By using a Transformer-based generative network model and learning from a large number of secure contract examples, we can generate contract code that meets specific needs and optimizes Gas consumption, which will undoubtedly further improve the efficiency and security of smart contract development.

• Smart contract risk analysis based on decision tree

By using decision trees to analyze the characteristics of smart contracts, such as function call frequency, transaction value, source code complexity, etc., the potential risk level of the contract can be effectively identified. By analyzing the operation mode and code structure of the contract, possible vulnerabilities and risk points can be predicted, thereby providing developers and users with a safety assessment. This method is expected to significantly improve the security of smart contracts in the Ethereum ecosystem, thereby reducing losses caused by vulnerabilities or malicious code.

• Building a cryptocurrency valuation model to reduce investment risks

By analyzing the transaction data, social media activities, market performance and other multi-dimensional information of cryptocurrencies through machine learning algorithms, an evaluation model that can predict the possibility of shit coin is constructed. This model can provide valuable references for investors, help them avoid investment risks, and thus promote the healthy development of the cryptocurrency market.

In addition, the application of machine learning has the potential to further improve the efficiency of Ethereum. We can explore it in depth from the following three key dimensions:

• Application of decision tree to optimize trading pool queuing model

The decision tree can effectively optimize the queuing mechanism of the Ethereum transaction pool. By analyzing transaction characteristics, such as gas price and transaction size, the decision tree can optimize the selection and queuing order of transactions. 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 effectively stratify users by evaluating the user's most recent transaction time (Recency), transaction frequency (Frequency) and transaction amount (Monetary value). 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 user transaction behaviors, 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, improve customer satisfaction and service efficiency.

• Credit scoring based on KNN

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

Future Directions

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

As the on-chain computing power 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.

In the future, AI/agent-driven governance mechanisms may also become a major innovation in the Ethereum ecosystem. This mechanism will bring a more efficient, transparent, and automated decision-making process, and 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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