Navigating the AI Agent Landscape: Insights into Advancements and Opportunities
AI Agents are gradually taking center stage in the field of AI, and they are considered to be the next focus for OpenAI. Andrej Karpathy, co-founder of OpenAI, recently mentioned in a public event, “Compared to training methods, OpenAI is currently paying more attention to the changes in the Agent domain. Whenever new AI Agents papers come out, there is excitement within the organization, and serious discussions take place.”
This article will analyze the technical principles, track trends, and application scenarios of AI Agents.
Technical Introduction
An AI Agent is an intelligent system that can perceive its environment, make decisions, and take actions. It functions as a virtual agent with autonomy and intelligence, capable of interacting with human users or other systems, and responding to changes in the environment by making decisions and performing actions.
The basic principles of an AI Agent can be divided into the following steps:
Perception and Data Acquisition: Data input or information and data acquisition by the AI Agent through perception systems (sensors, cameras, microphones, etc.), such as game states, images, sounds, etc.
State Representation: Data needs to be processed and represented in a form that the Agent can understand, such as transforming it into vectors or tensors for input into neural networks.
Neural Network Models: Deep neural network models are commonly used for decision-making and learning, such as convolutional neural networks (CNN) for image processing, recurrent neural networks (RNN) for sequence data processing, or more advanced models like self-attention mechanisms (Transformers), etc.
Reinforcement Learning: The Agent learns the optimal action strategy through interaction with the environment. In addition to these, the operation principles of an Agent also include policy networks, value networks, training and optimization, exploration and exploitation, etc. For example, in a gaming scenario, the policy network can take game states as input and output action probability distributions, the value network can estimate state values, and the Agent can continuously improve the policy and value networks through interaction with the environment using reinforcement learning algorithms to achieve better results.
In one of the intriguing experiments conducted in the realm of AI Agents, researchers from Stanford University and Google Research published a paper showcasing their successful construction of a small town in a sandbox-like game scenario. This town featured 25 generative agents, aiming to test the interactive behaviors of the agents within a human-like society. Leveraging the power of ChatGPT 3.5, a large language model, the agents were able to generate believable behaviors, simulating human lifestyles, autonomously engaging in daily activities, social interactions, and even participating in a Valentine’s Day party.
However, ensuring the coherence of their behaviors posed a challenge. Since the non-player characters (NPCs) lack true “memory,” they might forget changes in their environment beyond the basic character setup. To address this, the researchers developed a framework model called “Memory-Planning-Reflection.
In this model, “memory” refers to the process where NPCs record environmental features in a memory stream upon perceiving their surroundings. When faced with new situations, NPCs retrieve data from the memory stream, reason based on the retrieved information, and then decide how to respond.
The concept of “planning” in the model involves NPCs initially storing a rough plan for the day (e.g., waking up, attending classes, completing assignments, sleeping) in the memory stream, which is then decomposed into various refined actions to allow adjustments based on real-time circumstances.
“Reflection” represents a more advanced form of memory, prompting NPCs to engage in higher-level thinking through inference based on known data. The data for reflection can originate not only from the NPC’s own observations but also from the observations of other NPCs.
In conclusion, AI Agents hold immense potential and can be applied in various domains. Their capabilities in perception, reasoning, and decision-making make them valuable in fields such as healthcare, finance, transportation, and education. They can assist with complex tasks, analyze data, provide recommendations, and improve overall efficiency.
Technological Trends
AI Track Trends
LaoBai, an investment research partner at ABCDE, once summarized the judgments of the Silicon Valley venture capital community regarding the next steps in AI development:
- There are no vertical-specific models, only large models + vertical-specific applications.
- Edge devices, such as mobile phones, may pose a barrier, but they also present an opportunity for AI based on edge devices.
- The length of context may trigger a qualitative change in the future (currently, vector databases are used as AI memory, but the context length is still insufficient).
From an industry development perspective, large-scale general-purpose models have strong versatility, so it is unnecessary to repeatedly attempt in the field of large-scale general-purpose models. Instead, more attention should be paid to applying large-scale general-purpose models to specific vertical domains to meet industry-specific needs.
Edge devices refer to terminal devices that perform data processing and decision-making locally, without relying on cloud computing centers or remote servers. Due to the diversity of edge devices, deploying AI agents on these devices and obtaining device data in a reasonable manner is a challenge, but it also presents a new opportunity. Addressing this challenge can bring more intelligent functionality and efficient decision-making capabilities to edge devices.
The issue of Context has received significant attention. In the context of large-scale language models (LLMs), Context can be understood as the amount of information, and Context length can be understood as the dimensionality of data. Increasing the length of Context can help the model gain a more comprehensive understanding of influencing factors.
The current consensus is that although the use of vector databases as AI memory restricts the length of Context, there will be a qualitative change in Context length in the future. Subsequent LLM models can seek more advanced methods to process and understand longer and more complex Context information, thereby expanding application scenarios further.
AI Agent Trends
AI Agent as middleware: AI Agent acts as a middleware that connects large models and vertical applications, and can expand the application functions of Dapps. It can support the function expansion of Dapp by providing underlying programs, allowing it to better integrate AI technology.
Integrate AI Agent in Dapp: According to user scenarios, the Dapps most likely to integrate AI Agent are open social applications, chatbots and games. These applications usually require interaction with users and can provide more intelligent functions and experiences by integrating AI Agents.
In addition, there is another possibility to transform the existing Web2 traffic entrance into an AI+Web3 entrance to lower the user threshold of Web3.
Intense competition calls for the development of unique competitive advantages: The middleware layer where AI Agent operates will become a fiercely competitive track. This means that there are many competitors in this field, and it is challenging to establish a sustainable competitive advantage, especially since the technological barrier is not very high, making it difficult to create a product moat.
To enhance its competitive edge, AI Agent can attract more users and developers by leveraging network effects and creating user stickiness. Creating network effects involves enticing more users and developers to use AI Agent, forming a virtuous cycle that increases its influence and market share. Creating user stickiness means that AI Agent needs to continuously improve the user experience to meet B2C demands, encouraging users to use AI Agent in the long term and fostering user loyalty.
Track Mapping
Vertical Applications:Vertical applications often exist in the form of agents, which can take various forms such as bots, botkits, virtual assistants, intelligent decision support systems, and automated data processing tools. Typically, AI agents use OpenAI’s general model as the underlying framework and combine it with other open-source or proprietary technologies, such as text-to-speech (TTS). Additionally, specific data is incorporated for fine-tuning, which is a training technique in the field of machine learning and deep learning that aims to further optimize a model that has already undergone pre-training on large-scale data. Through this approach, an AI agent can be created that performs better than ChatGPT in a specific domain.
The advantage of vertical application agents lies in their focus on specific domains. By leveraging fine-tuning and incorporating domain-specific data, AI agents can demonstrate superior performance in their respective domains. Compared to general models, vertical application agents are better equipped to meet the specific needs of particular domains and provide more accurate and specialized solutions.
However, creating an AI agent that surpasses ChatGPT is not easy. The fine-tuning process requires a substantial amount of domain-specific data and a deep understanding of the model for adjustments. Additionally, vertical application agents need to be continuously updated and improved to adapt to the changes and demands of their specific domains.
Generative AI Applications: In summary, AI Agents exhibit numerous advantages and show great potential in the field of Generative AI. Their ability to enhance creativity, save time and costs, enable hyper-personalization, improve efficiency, and facilitate data synthesis opens up broad avenues for development. Additionally, they excel in creating realistic simulations, adapting to learning, enhancing knowledge organization and discovery, and elevating customer experiences. By leveraging the combination of Generative AI and Conversational AI, businesses can achieve significant breakthroughs in customer engagement and gain a competitive edge in the market.
The ongoing development of Generative AI Agents is set to reshape industries, revolutionize creative processes, and drive innovation across various sectors.
Conclusion
In summary, AI agents are transforming how organizations and people interact with technology, offering numerous advantages over traditional software tools. By leveraging advances in natural language processing, machine learning and related AI techniques, companies can automate workflows, enhance customer service and unlock new intelligent applications.
VAI LABS, a technologically advanced company, is committed to providing enterprise-level AI Agents technology solutions to assist businesses in achieving these objectives. With cutting-edge AI technology and extensive industry experience, VAI LABS delivers tailored products and services for businesses seeking AI Agents capabilities.
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