How does AI fully integrate with Web3?

In the context of AI, the only certainty is uncertain. People like certain things, but the uncertainty brought about by AI is irreversible under the tide of technological development. Optimists believe that the emergence of AI will bring unimaginable help in reducing costs and increasing efficiency to the whole world. Pessimists believe that AI will have a profound impact on the rules of the game in the current industry, and therefore will bring a lot of unemployment.

But in any case, from the emergence of ChatGPT to the present, people's views on AI have gradually accepted from surprise and concern. People seem to realize that, no matter whether they welcome it or reject it, AI will undoubtedly go deep into all fields of people and bring about disruption to various industries with its own mechanism and potential.

Now, AI is beginning to enter Web3 and have an impact 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 point of view is not alone. Many people in the Web3 industry believe that AI has a huge impact on Web3, especially in the NFT and GameFi fields. The emergence of the concept of AIGC means that there is a new paradigm in content creation. From PGC (Professional Generated Content, professionally produced content) to UGC (User Generated Content), and now AIGC, the work of content creation is handed over to the program.

In addition to the impact of AIGC on Web3 content, in fact, the impact of AI on Web3 is more profound than we imagined.

AI is "rectifying" Web3

AI's "rectification" of Web3 comes from two aspects: on the one hand, the emergence of AI technology has distracted capital from Web3.

Before the emergence of AI, Web3 once became the sweet pastry in the eyes of VCs and institutions, and all walks of life also launched various Web3 concepts (such as digital collections, metaverses) as gimmicks. But after the advent of AI, this situation changed.

In the eyes of institutions, AIGC at least looks more reliable than Web3, at least it is a practical thing, not a concept that needs to be foreseen. Institutional interest is shifting, plus bear markets and regulation for other reasons. According to the statistics of Gyro Research Institute, there were 86 global financing events in the Web3 field in March this year, with an amount of 5.676 billion yuan, a year-on-year decrease of 47.98%.

Funding, is leaving the Web3 space and going into AI.

Another aspect of the "rectification" is: the emergence of AI is changing the mechanism and logic of the Web3 domain. The Web3 project began to focus on adding elements of AI to its own ecology. Some projects have begun to evolve. At least the AI concept or at least the GPT interface can be used. We can regard this phenomenon as AI's "rectification" of the Web3 world, or as a self-response method based on the powerful "invasion" of AI in the Web3 world.

So there is the emergence of the concept of AI Web3. During the integration process of AI and Web3, many different products have emerged on the market. These products can be roughly divided into two categories: one is based on the direction of the project itself, adding AI elements. Such products often intervene in the interface of some AI tools on the basis of their own products, and emphasize the empowerment and promotion of AI to the product when publicizing externally. Such as AIGOGE.

Another combination of AI+Web3 is to reduce costs and increase efficiency. Pionex focuses on AI+trading strategies; Getch, Cortex, and SingularityNET focus on AI+infrastructure construction; Numerai focuses on AI+financial forecasting, etc.

The emergence of Web3 products with different AI concepts reflects the market and capital's favor for this type of product. For example, the AIDOGE currency launched on April 18 rose 218.50% within 2 days. (Fetch.ai) FET, SingularityNET (AGIX), Ocean Protocol (Ocean) and other project tokens increased by 110%, 61.53%, and 66.67% respectively within 90 days.

While the secondary market for the concept of AI Web3 is hot, the performance of the primary market is even more gratifying. Since the beginning of this year, AI Web3 concept products have also received financing one after another. On March 29 this year, Fetch.ai received a $40 million investment from SWF Labs.

At present, the concept of AI+Web3 seems to become a major trend in the future, so here, veDAO Research Institute sorts out the different tracks that AI may bring about changes to Web3 for reference.

AI empowers different tracks of Web3

AI-based trading strategy

The general idea of the ChatGPT-based liquidity mining strategy is to use the ChatGPT model to predict market conditions to decide whether to participate in liquidity mining and choose the best time.

The role of AI in trading strategies:

• Data collection: Use API to obtain the data required for liquidity mining from exchanges, such as the price of trading pairs, trading volume, liquidity supply and attraction, etc.
• Data preprocessing: clear, transform and standardize the collected data for subsequent analysis and modeling.
• Establish ChatGPT model: Use the trained ChatGPT model to analyze historical data and predict current and future liquidity mining trends and earnings.
• Risk control: Based on the prediction results of ChatGPT, formulate risk control strategies, such as setting stop loss and take profit conditions, controlling trading volume, etc., to protect the interests of investors.
• Implement trading strategies: formulate trading strategies based on the prediction results of the ChatGPT model, such as selecting trading pairs, deciding on trading opportunities, setting trading prices, etc.
• Transaction execution: Execute the transaction according to the transaction strategy, and the AI system automatically executes the investment of funds into mining and obtains the expected income.
• Monitoring and optimization: Regularly monitor transaction results and model performance, optimize and adjust strategies to maintain good investment returns and risk control effects.

AI-based sentiment analysis strategy

Based on ChatGPT's natural language processing capabilities, the strategy conducts sentiment analysis on market sentiment by analyzing text data such as news reports and social media posts. A trading strategy may choose to buy when the sentiment in the majority of the text is "positive" or "buy"; and vice versa.

The realization of this strategy requires the collection of market-related text data, and the cleaning, analysis and modeling of these data. For the modeling of the sentiment analysis model, a supervised learning algorithm can be used to train the labeled training data to predict the emotional tendency of the text. The formulation of trading strategies can be adjusted according to the prediction 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 text descriptions of trading strategies. For example, analyze the backtest results and historical returns of trading strategies to evaluate the effectiveness and reliability of the strategies, and formulate trading strategies accordingly. For the analysis and evaluation of trading strategies, machine learning algorithms can be used to predict the rate of return and risk of strategies through model training and optimization. The formulation of trading strategies can be adjusted according to the prediction results of the model, combined with factors such as trial production trends.

AI-Based Asset Portfolio Management

ChatGPT-based asset portfolio management tools can use natural language processing technology to help users better manage asset portfolios, optimize asset allocation and risk control, and provide more accurate forecasts and suggestions in investment decision-making. can do:

Automated asset analysis and currency selection: use ChatGPT's natural language processing to analyze and evaluate the fundamentals, market conditions, and macroeconomic factors of various assets, so as to automatically select suitable investment targets and reduce the risk of wrong decisions.

Asset Portfolio Optimization: Predict market trends and risks through ChatGPT, provide users with asset portfolio optimization suggestions, and achieve risk diversification and return maximization.

Automated transaction execution: Based on ChatGPT's transaction decision-making model, it automatically executes buying and selling transactions, realizes real-time adjustment and optimization of assets, and reduces the risk of human intervention.

AI-based simulated trading tool (AI Demo Account)

The AI-based simulated 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 transactions. Users can learn cryptocurrency transactions on the platform, formulate trading strategies and conduct simulated transactions without taking risks in real transactions, allowing more users to experience AI functions while also realizing the advancement of self-investment levels.

The feasible direction of DEX+AI:

Auxiliary decision-making: analysis and mining of trading data, providing more accurate and comprehensive market analysis and forecasting, and helping traders make more informed investment decisions.

• Optimize asset portfolio management: AI technology can provide users with more personalized and efficient asset portfolio management services through the analysis of users' investment preferences, risk tolerance, historical transaction data and other information.
• Improve user experience: AI technology can provide users with a more intelligent, fast and considerate transaction service experience through intelligent customer service, intelligent recommendation, intelligent question and answer, etc., and improve user satisfaction and loyalty.
• Investment information collection: Al can help provide public opinion, sentiment, and risk information.
• Price prediction: AI can use technologies such as big data and machine learning to analyze market data to predict the trend of cryptocurrency prices and help users make more informed investment decisions.
• Trading decisions: Artificial intelligence can use automated trading systems to execute trading decisions, such as trading based on preset rules and strategies, thereby reducing the impact of human factors on transactions.

AI safety:

• Fraud analysis: AI technology can monitor and analyze network traffic through artificial intelligence, identify and prevent network attacks and fraudulent behaviors, 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 its code; it can also help monitor and prevent malicious behaviors, and reduce the risks and vulnerabilities of Dex.
• Credit analysis: Using technologies such as big data and machine learning, artificial intelligence can analyze multi-dimensional information such as customer credit history, financial status, social network, and behavioral data to assess the customer's credit risk level. AI can use big data and machine learning algorithms to analyze a customer's credit history, financial status and other relevant data to assess a customer's risk level. To predict the default risk of customers.
• Fraud detection: AI can use natural language processing and image recognition techniques to analyze customer transaction records and other behavioral data to detect potential fraud.
• Transaction monitoring: Artificial intelligence can use real-time data analysis technology to monitor transaction activities to identify potential abnormal transaction behaviors.
• Risk management: ChatGPT-based risk management system is a system that uses natural language processing technology to analyze deterioration and assess financial market risks. Through the analysis of financial data and real-time market news, forecasts and warnings of market risks can be generated to help investors better manage risks.

Improve transaction speed and efficiency: Optimizing the transaction process (such as optimal routing selection) through AI technology can reduce transaction congestion, reduce transaction costs, and accelerate transaction completion time.

Solve several major problems of current DEX:

• Insufficient liquidity: Compared with CEX, the trading volume of DEX is relatively small, resulting in insufficient liquidity, and the transaction price is easily affected by market fluctuations. The use of AI technology can improve the intelligence of trading robots, thereby improving trading efficiency and profitability, and increasing trading volume and liquidity.
• Security issues: Due to the decentralized nature of DEX, there are security risks in the transaction process, such as asset theft, contract loopholes, etc. The use of AI technology can improve risk control capabilities, realize intelligent risk control and safety monitoring, and prevent risk events from occurring.
• Poor user experience: The user interface of DEX is simpler than that of CEX, and the user experience is not good. The use of AI technology can improve user personalized service capabilities, realize intelligent customer relationship and recommendation system, and improve user experience.
• High transaction costs: Compared with the low-cost handling fees of CEX, DEX currently has relatively high transaction costs due to miner fees and other reasons. The use of AI technology can optimize the trading strategy of trading robots, reduce transaction costs and risks, and improve profitability.

Summarize:

Generally speaking, the emergence of AI is not just a simple new technology, but a new concept and new field, which will bring a series of iterations and even subversions to the underlying operating logic of the entire society. The same is true for the Web3 world. The relationship between AI and Web3 will not be limited to the integration of concepts, or the simple addition of AI tools to a certain project. Instead, go directly to the underlying logic of Web3, so that all behaviors in Web3 are endowed with the meaning of AI existence, making Web3 more efficient and smarter.

Just like the philosophical connection between production tools and production relations. The two cannot be viewed independently. What kind of production tools have what kind of productivity, and what kind of productivity has provided the necessary conditions for the emergence and popularization of corresponding production relations. If Web3 with the blockchain as the bottom layer represents a newer production relationship, 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 is bound to play a decisive role in the popularization and promotion of the Web3 concept.