How can AI be fully integrated with Web3?
How can AI be integrated with Web3?
In the context of AI, the only certainty is uncertainty. People like certainty, but this uncertainty brought about by AI, under the tide of technological development, is irreversible. Optimists believe that the emergence of AI will bring unimaginable cost savings and efficiency gains to the world. Pessimists believe that AI will have a profound impact on the rules of the current industry, and therefore will bring about a large amount of unemployment.
But no matter what, from the appearance of ChatGPT to now, people’s views on AI have gradually been accepted from surprise and concern. People seem to realize that whether welcoming or rejecting, AI is uncontroversially going to penetrate into various fields of people and disrupt various industries with its mechanism and potential.
Now, AI is entering Web3 and exerting its influence on the entire industry.
Wang Yishi, the former founder of OneKey, said on Twitter: The narrative of Web3 has shifted from cryptocurrency to AI. Wang Yishi’s view is not unique. Many people in the Web3 industry believe that AI’s impact on Web3 is huge, especially in the NFT and GameFi fields. The emergence of the AIGC concept means that there is a new paradigm for content creation. From PGC (Professionally Generated Content) to UGC (User Generated Content), and now to AIGC, the work of content creation is handed over to the program.
In addition to the impact of AIGC on Web3 content, in fact, AI’s impact on Web3 is more profound than we think.
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AI is “rectifying” Web3
AI’s “rectification” of Web3 comes from two aspects: On the one hand, the emergence of AI technology has dispersed capital’s attention to Web3.
Before the emergence of AI, Web3 was once a sweet spot in the eyes of VCs and institutions, and various industries also launched various Web3 concepts (such as digital collections, metaverse, etc.) as gimmicks. But after the emergence of AI, this situation changed.
In the eyes of institutions, AIGC is at least more reliable than Web3, at least it is a practical thing, rather than a concept that needs to be foreseen. The interest of institutions is shifting, coupled with other reasons such as the bear market and regulation. According to the Gyro Research Institute, the global financing events in the Web3 field in March this year amounted to 5.676 billion yuan, a year-on-year decrease of 47.98%.
Capital is leaving the Web3 field and entering AI. On the other hand, the emergence of AI is changing the mechanisms and logic of the Web3 field. Web3 projects are starting to focus on adding elements of AI to their own ecosystems. Some projects are evolving to at least have an AI concept or at least a GPT interface to show off. We can view this phenomenon as AI’s “rectification” of the Web3 world, or as a self-defense mechanism of the Web3 world based on AI’s strong “invasion”.
This leads to the emergence of the AI Web3 concept. In the process of integrating AI and Web3, many different products have emerged in the market. These products can generally be divided into two categories: one is to add AI elements based on the direction of the project itself. These products often intervene in some AI tool interfaces based on their own product basis, and emphasize the empowerment and promotion of AI on the product when promoting it to the outside world, like AIGOGE.
Another way to combine AI and Web3 is to pursue cost reduction and efficiency enhancement. Pionex is a major player in AI+trading strategy, while Getch, Cortex, SingularityNET focus on AI+infrastructure construction. Numerai focuses more on AI+financial forecasting, and so on.
The emergence of Web3 products with different AI concepts reflects the market’s and capital’s favor for this type of product. For example, the AIDOGE currency, launched on April 18th, rose 218.50% within two days. Tokens of projects such as Fetch.ai (FET), SingularityNET (AGIX), and Ocean Protocol (Ocean) have grown by 110%, 61.53%, and 66.67% respectively in 90 days.
While the secondary market of the AI Web3 concept is hot, the primary market is even more impressive. AI Web3 concept products have also won one financing after another this year. On March 29th of this year, Fetch.ai received a $40 million investment from SWF Labs.
Currently, the AI+Web3 concept seems to be a major trend in the future. Therefore, the veDAO Research Institute has sorted out the different tracks in which AI may bring changes to Web3 for reference.
AI Empowers Different Tracks of Web3
AI-based trading strategies
The general idea of liquidity mining strategy based on ChatGPT is to use the ChatGPT model to predict the market situation, to determine whether to participate in liquidity mining and to choose the best timing.
The effect of AI on trading strategies:
Data collection: Use APIs to obtain data required for liquidity mining from exchanges, such as the price, trading volume, liquidity provided and attracted, etc. of trading pairs.
Data preprocessing: Clean, transform and standardize the collected data for subsequent analysis and modeling.
Build ChatGPT model: Analyze historical data and predict current and future liquidity mining trends and returns using a trained ChatGPT model.
Risk control: Develop risk control strategies based on the prediction results of ChatGPT, such as setting stop loss and take profit conditions, controlling trading volume, etc., to protect investors’ interests.
Implement trading strategies: Based on the prediction results of ChatGPT model, develop trading strategies, such as selecting trading pairs, determining trading timing, setting trading prices, etc.
Trading execution: Execute trades according to trading strategies, and the AI system automatically invests funds in mining and obtains expected returns.
Monitoring and optimization: Regularly monitor trading results and model performance, optimize and adjust strategies to maintain good investment returns and risk control effects.
AI-based sentiment analysis strategy
This strategy is based on the natural language processing ability of ChatGPT, which analyzes textual data such as news reports and social media posts to perform sentiment analysis on market sentiment. When the sentiment tendency in most of the text is “positive” or “buy,” the trading strategy may choose to buy; otherwise, it may choose to sell.
Implementing this strategy requires collecting relevant textual data related to the market and cleaning, analyzing, and modeling the data. For modeling the sentiment analysis model, supervised learning algorithms can be used to train on labeled training data to predict the sentiment tendency of text. The development of trading strategies can be adjusted based on the predicted results of the model, combined with market trends and other factors.
AI-based trading strategy analysis
This strategy analyzes and evaluates trading strategies based on ChatGPT’s ability to understand the textual description of trading strategies. For example, analyze the backtesting results and historical return rates of trading strategies to evaluate their effectiveness and reliability, and use this to develop trading strategies. Machine learning algorithms can be used for the analysis and evaluation of trading strategies to predict their returns and risks through model training and optimization. The development of trading strategies can be adjusted based on the predicted results of the model, combined with market trends and other factors.
AI-Based Portfolio Management
The AI-based portfolio management tool using natural language processing technology to help users manage their portfolio better, optimize asset allocation and risk control, and provide more accurate predictions and recommendations for investment decision-making. The tool can:
Automate asset analysis and selection: Utilizing ChatGPT’s natural language processing capabilities, it analyzes and evaluates various assets’ fundamentals, market conditions, and macroeconomic factors, among other things, to automatically select suitable investment targets and reduce the risk of incorrect decisions.
Portfolio optimization: By predicting market trends and risks with ChatGPT, it provides users with portfolio optimization advice, achieves risk diversification and maximizes returns.
Automated trading execution: Based on ChatGPT’s trading decision-making model, it automatically executes buy and sell trades, realizes real-time adjustment and optimization of assets, and reduces the risk of human intervention.
AI-Based Demo Trading Tool (AI Demo Account)
The AI-based demo cryptocurrency trading tool is a virtual trading platform that simulates the real cryptocurrency market environment based on AI algorithms and provides virtual funds for users to conduct simulated trades. Users can learn about cryptocurrency trading on the platform, develop trading strategies, and conduct simulated trades without bearing the risks of real trading, allowing more users to experience AI functions and advance their investment level.
Feasible Directions for DEX+AI:
Assist decision-making: By analyzing and mining trading data, it provides more accurate and comprehensive market analysis and predictions, helps traders make wiser investment decisions.
Optimize asset portfolio management: AI technology can provide users with more personalized and efficient asset portfolio management services by analyzing users’ investment preferences, risk tolerance, historical trading data, and other information.
Improve user experience: AI technology can provide users with more intelligent, fast, and considerate transaction service experience through intelligent customer service, intelligent recommendation, intelligent Q&A, and other methods, thus improving user satisfaction and loyalty.
Investment information collection: AI can help provide public opinion, sentiment, and risk information.
Price prediction: AI can use big data and machine learning technologies to analyze market data to predict the trend of cryptocurrency prices, helping users make wiser investment decisions.
Trading decision-making: Artificial intelligence can use automated trading systems to execute trading decisions, such as conducting trades based on preset rules and strategies, thus reducing the impact of human factors on trading.
Fraud Analysis: AI technology can monitor and analyze network traffic through artificial intelligence, identify and prevent network attacks and fraudulent behavior, and improve the security and credibility of Dex.
Contract Audit: AI technology can help optimize the writing and deployment of smart contracts, improve the quality and reliability of their code, and help monitor and prevent malicious behavior, reducing the risks and vulnerabilities of Dex.
Credit Analysis: By using big data and machine learning technologies, artificial intelligence can analyze multidimensional information such as a customer’s credit history, financial status, social network, and behavioral data to evaluate their credit risk level. Artificial intelligence can use big data and machine learning algorithms to analyze a customer’s credit history, financial status, and other relevant data to predict their default risk.
Fraud Detection: Artificial intelligence can use natural language processing and image recognition technology to analyze a customer’s transaction records and other behavioral data to detect potential fraudulent behavior.
Transaction Monitoring: Artificial intelligence can use real-time data analysis technology to monitor transaction activity and identify potential abnormal trading behavior.
Risk Management: ChatGPT-based risk management system is a system that uses natural language processing technology to analyze and evaluate financial market risks. By analyzing financial data and real-time market news, it generates predictions and alerts for market risks, helping investors better manage risks.
Improve Trading Speed and Efficiency: By optimizing the trading process (such as selecting the best route) through AI technology, trading congestion can be reduced, trading costs can be lowered, and transaction completion time can be accelerated.
Solve Current Issues with DEX:
Lack of Liquidity: DEX’s transaction volume is relatively small compared to CEX, resulting in insufficient liquidity and easily affected trading prices due to market fluctuations. Using AI technology can improve the intelligence level of trading robots, thereby improving trading efficiency and profitability, increasing trading volume and liquidity.
Security Issues: Due to the decentralized nature of DEX, there are security risks in the trading process, such as asset theft, contract vulnerabilities, etc. Using AI technology can improve risk control capabilities, achieve intelligent risk control and security monitoring, and prevent risk events from occurring.
Poor User Experience: DEX’s user interface is relatively simple compared to CEX, resulting in poor user experience. Using AI technology can improve personalized service capabilities, achieve intelligent customer relationships and recommendation systems, and enhance user experience.
High Transaction Costs: Compared to the low-cost fees of CEX, DEX currently has relatively high transaction costs due to miner fees, etc. Using AI technology can optimize the trading strategies of trading robots, reduce transaction costs and risks, and improve profitability.
Overall, the emergence of AI is not just a new technology, but a new concept and field that will bring a series of iterations and even subversion to the underlying operating logic of the entire society. This is also true for the Web3 world. The relationship between AI and Web3 will not be limited to the fusion of concepts or the simple addition of AI tools to a project. Instead, it will directly penetrate into the underlying logic of Web3, so that all behaviors in Web3 are endowed with the meaning of AI existence, making Web3 more efficient and intelligent.
It is just like the philosophy association between production tools and production relations. The two cannot be viewed independently. What kind of production tools are available determines what kind of productivity is available, and what kind of productivity is available provides the necessary conditions for the emergence and popularization of corresponding production relations. If Web3 based on blockchain represents the updated production relations, then AI is undoubtedly the most advanced production tool of this era. Therefore, we have reason to believe that the emergence, popularization, and integration of AI technology as a production tool will inevitably play a decisive role in the popularization and promotion of the Web3 concept in the future.