Original title: "How does AI affect Web3?"
Original source: veDAO Research Institute
In the context of AI, the only certainty is uncertain. People like certain things, but the uncertainty brought by AI is irreversible under the tide of technological development. Optimists believe that the emergence of AI will bring unimaginable cost reduction and efficiency improvement to the whole world. Pessimists believe that AI will have a profound impact on the current rules of the game in various industries, and thus will lead to a large number of unemployment.
But in any case, from the emergence of ChatGPT to now, people's views on AI have gradually changed from surprise and worry to acceptance. People seem to realize that whether they welcome or reject it, AI will undoubtedly penetrate into all areas of people's lives and bring subversion to various industries with its own mechanisms 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 view is not an isolated case. Many people in the Web3 industry believe that AI has a huge impact on Web3, especially in the fields of NFT and GameFi. The emergence of the concept of AIGC means that there is a new paradigm in content creation. From PGC (Professionally Generated 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 the content of Web3, in fact, the impact of AI on Web3 is more profound than we imagined.
AI is “reorganizing” Web3
AI's "reorganization" of Web3 comes from two aspects: on the one hand, the emergence of AI technology has distracted capital's attention from Web3.
Before the emergence of AI, Web3 was once a hot topic in the eyes of VCs and institutions, and various industries also launched various Web3-like concepts (such as digital collections and metaverse) as gimmicks. But after the emergence of AI, this situation has 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. The interest of institutions is shifting, coupled with other reasons such as the bear market and regulation. According to statistics from 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%.
Funds are leaving the Web3 field and entering AI.
The other side of the “rectification” is that the emergence of AI is changing the mechanism and logic of the Web3 field. Web3 projects are beginning to focus on adding AI elements to their own ecosystems. Some projects are beginning to evolve to at least have AI concepts or at least have GPT interfaces to be considered good. We can view this phenomenon as AI’s “rectification” of the Web3 world, or as a self-response measure for the Web3 world to respond to the strong “invasion” of AI.
Thus, the concept of AI Web3 emerged. In the process of integrating AI and Web3, many different products have emerged in the market. These products can be roughly divided into two categories: one is based on the direction of the project itself and adds AI elements. This type of product often involves some AI tool interfaces on the basis of its own product, and emphasizes the empowerment and promotion of AI on the product during external PR. For example, AIGOGE.
Another combination of AI+Web3 is aimed at reducing costs and increasing efficiency, such as Pionex, which focuses on AI+trading strategies; Getch, Cortex, and SingularityNET, which focus on AI+infrastructure construction; and Numerai, which focuses on AI+financial forecasting.
The emergence of Web3 products with different AI concepts reflects the market and capital's favor for this type of products. For example, the AIDOGE currency launched on April 18 rose by 218.50% in 2 days. The tokens of projects such as (Fetch.ai) FET, SingularityNET (AGIX), and Ocean Protocol (Ocean) increased by 110%, 61.53%, and 66.67% respectively in 90 days.
While the secondary market of AI Web3 concept is hot, the primary market performance is even more gratifying. Since the beginning of this year, AI Web3 concept products have also won 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 be a major trend in the future. Here, 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 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, volume, liquidity provided and attracted of trading pairs.
Data preprocessing: Clarify, transform and standardize the collected data for subsequent analysis and modeling.
Build ChatGPT model: Use the trained ChatGPT model to analyze historical data and predict current and future liquidity mining trends and benefits.
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: Based on the prediction results of the ChatGPT model, formulate trading strategies, such as selecting trading pairs, deciding trading timing, setting trading prices, etc.
Transaction execution: Execute transactions according to trading strategies. 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 strategies
The strategy is based on ChatGPT's natural language processing capabilities, and performs sentiment analysis on market sentiment by analyzing text data such as news reports and social media posts. When the sentiment in most texts is "positive" or "buy", the trading strategy may choose to buy; vice versa.
The implementation of this strategy requires the collection of market-related text data, and the cleaning, analysis and modeling of this data. The supervised learning algorithm can be used to model the sentiment analysis model, and the labeled training data can be used for training to predict the sentiment tendency of the text. The formulation of the trading strategy can be adjusted based on the prediction results of the model and 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, the backtest results and historical yields of trading strategies are analyzed to evaluate the effectiveness and reliability of the strategies and formulate trading strategies accordingly. Machine learning algorithms can be used to analyze and evaluate trading strategies, and the yield and risk of strategies can be predicted through model training and optimization. The formulation of trading strategies can be adjusted based on the prediction results of the model and factors such as trial production trends.
AI-based portfolio management
The asset portfolio management tool based on ChatGPT can use natural language processing technology to help users better manage asset portfolios, optimize asset allocation and risk control, and provide more accurate predictions and suggestions in investment decision-making plans. It can do the following:
Automated asset analysis and coin selection: ChatGPT’s natural language processing is used to analyze and evaluate the fundamentals, market conditions, and macroeconomic factors of various assets, thereby automatically selecting suitable investment targets and reducing the risk of wrong decisions.
Asset portfolio optimization: Use ChatGPT to predict market trends and risks, and provide users with asset portfolio optimization suggestions to achieve risk diversification and maximize returns.
Automated transaction execution: Based on ChatGPT's transaction decision model, it automatically executes buy and sell 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, formulate trading strategies and conduct simulated transactions on the platform without taking the risks of real transactions, allowing more users to experience AI functions while also improving their investment level.
DEX+AI feasible direction:
Decision support: Analysis and mining of trading data provide more accurate and comprehensive market analysis and forecasts, 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 by analyzing information such as users' investment preferences, risk tolerance, and historical transaction data.
Improve user experience: AI technology can provide users with a smarter, faster, and more considerate transaction service experience through intelligent customer service, intelligent recommendations, and intelligent Q&A, thereby improving user satisfaction and loyalty.
Investment information collection: AI 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: AI 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 trading.
AI Safety:
Fraud Analysis: AI technology can monitor and analyze network traffic, identify and prevent cyber attacks and fraud through artificial intelligence, and improve the security and credibility of Dex.
Contract Auditing: AI technology can help optimize the writing and deployment of smart contracts, improve the quality and reliability of their codes; it can also help monitor and prevent malicious behavior, and reduce the risks and vulnerabilities of Dex.
Credit analysis: Using technologies such as big data and machine learning, AI can analyze multi-dimensional information such as a customer's credit history, financial status, social network, behavioral data, etc. 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 the customer's risk level and predict the customer's default risk.
Fraud detection: AI can use natural language processing and image recognition technology to analyze customer transaction records and other behavioral data to detect potential fraud.
Transaction monitoring: AI can monitor trading activity using real-time data analysis techniques to identify potentially abnormal trading behavior.
Risk management: The risk management system based on ChatGPT is a system that uses natural language processing technology to analyze and assess financial market risks. It can generate market risk forecasts and warnings through the analysis of financial data and real-time market news, helping investors better manage risks.
Improve transaction speed and efficiency: Optimizing transaction processes through AI technology (such as optimal routing selection) can reduce transaction congestion, reduce transaction costs, and speed up transaction completion time.
Solve several major problems of current DEX:
Insufficient liquidity: DEX has a smaller trading volume than CEX, resulting in insufficient liquidity, and transaction prices are 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 security monitoring, and prevent risk events from occurring.
Poor user experience: DEX’s user interface is relatively simple compared to CEX, and the user experience is poor. The use of AI technology can improve user personalized service capabilities, realize intelligent customer relationship and recommendation systems, and improve user experience.
High transaction costs: Compared with the low-cost handling fees of CEX, the transaction costs of DEX are relatively high due to mining fees, etc. The use of AI technology can optimize the trading strategies of trading robots, reduce transaction costs and risks, and improve profitability.
Summarize:
In general, the emergence of AI is not just a new technology, but a new concept and a new field. It 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, it will go directly into the underlying logic of Web3, so that all behaviors in Web3 are given 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 provides the necessary conditions for the emergence and popularization of corresponding production relations. If Web3 with blockchain as the underlying 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 will inevitably play a decisive role in the popularization and promotion of the concept of Web3 in the future.
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