Author: YBB Capital Researcher Zeke
1. It starts with the love of novelty and boredom with the old.
In the past year, due to the lack of narrative at the application layer and the inability to match the speed of infrastructure explosion, the crypto field has gradually become a game of competing for attention resources. From Silly Dragon to Goat, from Pump.fun to Clanker, pay attention to The power of the new and old has made this battle involuted. It started with the most clichéd way of attracting attention and monetizing it, and quickly evolved into a platform model that unifies attention demanders and suppliers, and then silicon-based organisms became new content providers. Meme Among the various coin carriers, there is finally a kind of entity that can enable retail investors and VCs to reach a consensus: AI Agent.
Attention is ultimately a zero-sum game, but speculation can indeed drive things to grow wildly. In our article about UNI, we reviewed the beginning of the last golden age of blockchain. The rapid growth of DeFi was caused by the opening of Compound Finance. In the LP mining era, entering and exiting various Apy mining pools with thousands or even tens of thousands of them was the most primitive way of gambling on the chain at that time, although the final situation was that various mining pools collapsed and became a mess. But gold miners The crazy influx of money has indeed left the blockchain with unprecedented liquidity. DeFi has finally broken away from pure speculation and formed a mature track, meeting users' financial needs in payment, trading, arbitrage, pledge and other aspects. AI Agent is also going through this barbaric stage at this stage. What we are exploring is how Crypto can better integrate AI and ultimately push the application layer to new heights.
2. How can intelligent agents be autonomous?
In the previous article, we briefly introduced the origin of AI Meme: Truth Terminal, as well as the future prospects of AI Agent. This article focuses first on AI Agent itself.
Let's start with the definition of AI Agent. Agent is an old but unclear term in the field of AI. It mainly emphasizes Autonomous, that is, any AI that can perceive the environment and react. It can be called Agent. In today's definition, AI Agent is closer to intelligent body, that is, a system that imitates human decision-making is set for the big model. In academia, this system is regarded as the most promising path to AGI (General Purpose Artificial Intelligence).
In the early versions of GPT, we can clearly feel that the big model is very human-like, but when answering many complex questions, the big model can only give some specious answers. The fundamental reason is that the big model at that time was based on probability rather than causality. Secondly, it lacks the ability of humans to use tools, remember, plan, etc., and AI Agent can make up for these shortcomings. So to summarize it in a formula, AI Agent (intelligent body) = LLM (large model) + Planning (planning) +Memory+ Tools.
The large model based on prompts is more like a static person. It comes alive when we input. The goal of the intelligent agent is to be a more real person. A fine-tuned model of Meta's open-source Llama 70b or 405b (with different parameters), with the ability to memorize and use API access tools, and may require human help or input in other areas (including interactive collaboration with other agents) Therefore, we can see that the main intelligent agents in the circle are still in the form of KOLs on social networks. To make the intelligent agent more like a human, it is necessary to access planning and action capabilities, and the sub-item thinking chain in planning is particularly important. key.
3. Chain of Thought (CoT)
The concept of Chain of Thought (CoT) first appeared in the paper "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models" published by Google in 2022. The paper pointed out that the model can be enhanced by generating a series of intermediate reasoning steps. Reasoning capabilities help models better understand and solve complex problems.
A typical CoT prompt consists of three parts: a clear description of the task; a logical basis; a theoretical basis or principle that supports the task solution; an example; a specific solution presentation; this structured approach helps the model understand the task requirements and gradually approach them through logical reasoning. Answers, thereby improving the efficiency and accuracy of problem solving. CoT is particularly suitable for tasks that require in-depth analysis and multi-step reasoning, such as math problem solving, project report writing, and other simple tasks. CoT may not bring obvious advantages, but it is very useful for complex tasks. It can significantly improve the performance of the model, reduce the error rate through a step-by-step solution strategy, and improve the quality of task completion.
When building an AI agent, CoT plays a key role. The AI agent needs to understand the information it receives and make reasonable decisions based on it. CoT helps the agent to effectively process and analyze input information by providing an orderly way of thinking and converting the parsing results into This method not only enhances the reliability and efficiency of the agent's decision-making, but also improves the transparency of the decision-making process, making the agent's behavior more predictable and traceable. CoT helps The agent carefully considers each decision point to reduce wrong decisions caused by information overload. CoT makes the agent's decision process more transparent and users can more easily understand the agent's decision basis. In the interaction with the environment, CoT allows the agent to continuously learn new information and adjust its behavior. Behavioral strategy.
As an effective strategy, CoT not only improves the reasoning ability of large language models, but also plays an important role in building more intelligent and reliable AI agents. By using CoT, researchers and developers can create more adaptable AI agents in complex environments. , intelligent systems with high degree of autonomy. CoT has demonstrated its unique advantages in practical applications, especially in dealing with complex tasks. By breaking down the task into a series of small steps, it not only improves the accuracy of task solving, but also enhances This method of solving problems step by step can greatly reduce the possibility of making wrong decisions due to excessive or complex information when facing complex tasks. At the same time, this method also improves The traceability and verifiability of the entire solution.
The core function of CoT is to combine planning, action and observation to bridge the gap between reasoning and action. This thinking mode allows AI Agents to formulate effective countermeasures when predicting possible abnormal situations and interact with the external environment. At the same time, it accumulates new information, verifies pre-set predictions, and provides new reasoning basis. CoT is like a powerful accuracy and stability engine, helping AI Agents maintain high efficiency in complex environments.
4. Correct pseudo-demand
What aspects of the AI technology stack should Crypto be combined with? In last year's article, I believed that the decentralization of computing power and data is a key step to help small businesses and individual developers save costs. In the AI segment, we see a more detailed division:
(1) Computing layer (referring to the network that focuses on providing graphics processing unit (GPU) resources to AI developers);
(2) Data layer (referring to the network that supports decentralized access, orchestration, and verification of AI data pipelines);
(3) Middleware layer (referring to the platform or network that supports the development, deployment, and hosting of AI models or agents);
(4) Application layer (referring to user-oriented products that utilize on-chain AI mechanisms, whether B2B or B2C).
Each of these four layers has a grand vision, and its goal is to fight against the Silicon Valley giants who dominate the next era of the Internet. As I said last year, we really have to accept the exclusive control of Silicon Valley giants. Control computing power and data? The closed-source big model under their monopoly is a black box inside. Science is the most believed religion of mankind today. In the future, every answer given by the big model will be regarded as the truth by a large number of people. , but how can this truth be verified? According to the vision of Silicon Valley giants, the rights that intelligent entities will eventually have will be beyond imagination, such as the right to pay in your wallet and the right to use the terminal. How can we ensure that people have no evil intentions?
Decentralization is the only answer, but sometimes we need to reasonably consider how many people will pay for these grand visions? In the past, we could use tokens to make up for the idealistic situation without considering the closed loop of business. The current situation is very serious. Crypto x AI needs to be designed in combination with the actual situation. For example, how to balance the supply of both ends when the computing power layer loses performance and is unstable? In order to achieve matching centralization How many real users will there be for the data layer project? How to verify the authenticity and validity of the data provided? What kind of customers need this data? The same is true for the other two layers. In this era, we don’t need So many false demands that seem correct.
5. Meme went beyond SocialFi
As I said in the first paragraph, Meme has already taken the form of SocialFi that conforms to Web3 in an extremely fast way. Friend.tech is the Dapp that fired the first shot of this round of social applications, but unfortunately failed due to the hasty token design. Pump.fun has verified the feasibility of pure platformization, without any tokens or rules. The demanders and suppliers of attention are unified, and you can post memes, do live broadcasts, issue coins, leave messages on the platform. , transactions, everything is free, Pump.fun only charges service fees. This is basically the same as the attention economy model of social media such as YouTube and Instagram, except that the charging objects are different, and Pupm.fun is more Web3 in terms of gameplay.
Base's Clanker is a culmination of all. Thanks to the integrated ecosystem that the ecosystem itself has created, Base has its own social Dapp as an auxiliary to form a complete internal closed loop. Intelligent Meme is the 2.0 form of Meme Coin. People always seek novelty. , and Pump.fun happens to be at the center of the storm now. Judging from the trend, it is only a matter of time before the fanciful ideas of silicon-based organisms replace the vulgar jokes of carbon-based organisms.
I have mentioned Base for the umpteenth time, but the content is different each time. From the timeline, Base has never been a starter, but it is always the winner.
6. What else can an intelligent agent be?
From a pragmatic point of view, it is impossible for intelligent agents to be decentralized for a long time in the future. Judging from the construction of intelligent agents in the traditional AI field, it is not a simple decentralization of the reasoning process and open source that can be solved. The problem is that it needs to access various APIs to access Web2 content, and its operating costs are very expensive. The design of thought chains and the collaboration of multiple intelligent agents usually still rely on a human as a medium. We will go through a very long transition period. , until a suitable fusion form appears, perhaps like UNI. But as in the previous article, I still think that intelligent agents will have a great impact on our industry, just like the existence of Cex in our industry, which is incorrect but very important.
Stanford & Microsoft published an article titled "AI Agent Overview" last month, which describes the application of intelligent agents in the medical industry, intelligent machines, and virtual worlds. In the appendix of this article, there are many examples of GPT-4V as an intelligent agent. A test case for top 3A game development.
There is no need to demand too much speed in combining it with decentralization. I hope that the first puzzle piece that the intelligent body will fill is the bottom-up ability and speed. We have so many narrative ruins and blank metaverses that need to be filled. At the right stage, we will consider how to make it the next UNI.
References
What kind of ability is the chain of thinking that emerges from the big model? Author: Brain Pole
Understanding Agent in one article, the next stop for big models Author: LinguaMind
