作者：张家林
本文约5700字，建议阅读10+分钟
本文主要探讨自然人数字身份智能体的基本原理、关键技术及其应用前景的挑战。Digital identity agents (DIAs: digital identity agents) are a kind of application agents created by digitizing, modeling and AIizing information such as an entity's behavior pattern and individual characteristics. Entities that create digital identity agents can be natural persons, legal persons, government agencies, devices, or other identifiable entities.
Large language models (LLMs) represented by GPT-3.5 can understand and generate human natural language very well, and realize in-depth interaction with existing applications and information systems through APIs, constructing a natural language-based general In Computational Paradigm. This technological advancement brings new possibilities for the development of digital identity agents. Digital identity agents can understand natural language, perform semantic search of knowledge bases, and perform a range of tasks. An entity's behavior, preferences, and data form its unique digital identity, enabling digital identity agents to perform specific actions on behalf of certain permissions.
This article mainly discusses the basic principles of digital identity agents for natural persons, discusses the key technologies and main implementation methods of digital identity agents in assisting personal work and social interaction, and looks forward to the challenges and opportunities of its application prospects.
1. Basic principles of digital identity agents
A natural person can create multiple Digital Identity Agents (DIAs). The basic model of the digital identity agent is constructed with reference to the classical agent model. The schematic diagram of the classic agent model is as follows:
Although the personal digital identity agent has a certain ability to act autonomously, its behavior is not completely autonomous. Fundamentally, it is supervised operation under the principle of "man in the loop". This means that every aspect of its perception, decision-making and execution is under the control and management of the individual. Therefore, its behavioral characteristics, preferences, and behaviors are naturally characteristic of individuals. For example, users can set codes of conduct for DIAs, limit the scope and methods of their information acquisition, and monitor and correct their behavior at any time. This design not only ensures the user's data privacy and security, but also enables users to fully control and define the behavior of DIA, making it more in line with personal needs and preferences.
On the other hand, DIAs obtain powerful capabilities by using large language models and other pre-trained models. The training of these models covers a wide range of data and knowledge, enabling DIAs not only to understand and generate natural language, but also to understand the world and perform some specific tasks at a higher level. For example, DIAs can generate various multimodal content, including text, image, audio, etc. This capability enables DIAs to interact with other entities in a multimodal manner. In addition, by cooperating with other agents, DIAs can complete more complex and specialized tasks, such as data analysis, programming, design and other capabilities. This greatly expands its great potential to assist individuals in life, work, social interaction, etc.
DIAs did not subvert the classic agent model, but adopted architectures and technologies different from other agents in terms of specific implementation. A simple example of DIAs is as follows:
This example includes the following main parts:
1. Vector Database: This is a key component of DIAs memory. Like human memory, this vector database stores information from DIAs' experiences and learning processes. This information is encoded as a high-dimensional vector, which allows the LLMs model to perform better semantic search and retrieval, and to interact efficiently with other models. This vector database mainly records individual domain knowledge, non-sensitive personal information and data, and also records the data formed by DIAs operation and actions. These data are the basis for the assessment, evaluation and selection of actions in DIAs.
2. GPT-4: As a model for evaluation and evaluation, GPT-4 plays a key role in this system. GPT-4 is a powerful pre-trained model capable of understanding and generating natural language. In this system, it is used to evaluate possible actions and evaluate their potential consequences. By comparing the evaluation results of different actions, GPT-4 helps DIAs to choose the best action.
3. QAV-GPT: This is a reinforcement learning based model responsible for the action selection of DIAs. It consists of three agents who play the role of questioner (Q), answerer (A) and verifier (V). It receives evaluation information from GPT-4 and makes decisions based on a predefined reward mechanism. This quality-based action selection mechanism ensures that the actions of DIAs are continuously optimized and progress toward user-defined goals.
4. chatX: This is a knowledge base semantic retrieval and hint generation system. It is able to retrieve relevant information from the knowledge base according to the needs of DIAs and generate useful hints. These cues can help DIAs better understand the environment, generate more appropriate responses, and make more effective decisions.
5. Human-In-Loop Controller (HILC: Human-In-Loop Controller): The controller controlled by an individual is the supervisor of the entire system. It not only monitors and controls the actions of DIAs, but also fine-tunes the entire system to suit users' needs. The existence of this controller ensures the user's ultimate control over DIAs, enabling DIAs to better serve users.
6. Distributed Identity (DID: Distributed Identity): It is a set of credential specifications and systems for uniquely identifying and verifying the identity of DIAs agents and their controllers (Controller). Generally, the controller of DIAs created by a natural person is the natural person himself. Using DID to identify identities can take into account privacy protection and trust.
There are many other instances of DIAs such as Private AI, PI, etc. The main difference between these DIAs is not the overall architecture, but the different methods and technologies used to realize the functions of the agent. But the goal is the same: to build a highly adaptive DIAs that are always under user control. And while improving the ability of DIAs to handle complex tasks, it ensures individual privacy, system security, and user-friendliness.
2. Main application scenarios and technology stacks of DIAs
The main application scenarios of DIAs include auxiliary work, socializing, and performing some civil acts. According to the objects it serves, it can be divided into three categories of scenarios:
1) Serving individuals: DIAs treat individuals as service objects and provide them with personalized services, such as managing daily tasks and providing learning assistance.
2) Service users: DIAs take external entities (users) as service objects, and interact with the external environment on behalf of individuals, such as social interaction and work support.
3) Autonomous service: DIAs regard themselves as service objects, conduct autonomous learning and self-optimization through their behaviors, improve the characteristics of each system, and realize their own evolution according to the set goals.
The technology stack of DIAs can be divided into three levels: 1) technical level; 2) data governance level; 3) social ethics level. A brief description is given below:
2.1 Serving Individuals
There are many examples of service individuals, and a few typical scenarios are listed below:
1) Personal assistant affairs: DIAs can help individuals manage daily tasks and activities. For example, DIAs can help manage calendars, remind of upcoming events, forecast and manage individual to-do lists. DIAs can help individuals set and maintain personal health goals, reminding them of exercise or regular check-ups. DIAs can also help individuals deal with many trivial matters in daily life, such as shopping, making travel plans, ordering air tickets, and financial management.
2) Education and Learning: DIAs can assist individuals in various types of learning. For example, DIAs can provide personalized learning plans, provide learning resources, answer questions, and evaluate learning effects. DIAs can also help users track and manage their learning progress, thereby improving learning efficiency.
In the scenario of serving individuals, personalized recommendation technology can provide targeted services or suggestions based on the user's historical behavior and interest patterns. For example, DIAs can recommend appropriate learning resources or provide health advice based on individual preferences. Natural language processing technology enables DIAs to understand and generate human language to provide a more natural and human-like interactive experience. User behavior prediction technology allows DIAs to predict the possible needs of individuals, so as to prepare in advance or provide necessary reminders.
At the governance level, data governance needs to ensure the security and privacy of user data and prevent personal data from being misused. Through effective AI decision-making supervision, it is possible to ensure that the behavior of DIAs is within the scope of compliance and safety, and reduce possible risks.
2.2 Service users
Several typical scenarios for serving external entities (users):
1) Social: DIAs can help individuals manage their online identities. For example, DIAs can help individuals create and publish content, manage and respond to social media interactions, and track and analyze social media trends and data. DIAs can also help maintain a user's online reputation by responding to and handling user comments and feedback in a customized manner.
2) Work companions: In work environments, DIAs can help users handle various tasks. For example, DIAs can help users filter and respond to mail, create and edit documents, manage projects, and coordinate meetings. DIAs can also help users acquire and process various information, such as finding specific data, analyzing reports, or conducting market research. By learning users' work habits and preferences, DIAs can better provide personalized work support.
In the scenario of serving users, social network analysis techniques can help DIAs understand the structure and dynamics of social networks to provide more effective social services. Text mining and information retrieval technologies can help DIAs process large amounts of text data and quickly find the information users need.
At the level of data governance, technologies that can ensure the security and privacy of user data are adopted to avoid misuse of user data. At the same time, it is necessary to adopt AI decision-making supervision and evaluation technology to evaluate and evaluate the transparency and explainability of DIAs when processing user data and making decisions.
2.3 Autonomous Service
An important domain of self-serving DIAs is self-optimization and learning. DIAs can better adapt to users' needs and preferences by continuously learning and optimizing their own behaviors and responses. For example, DIAs can optimize their own behavior and responses by analyzing user feedback and behavioral historical data. DIAs can also collaborate with other agents by learning new skills and knowledge to extend the scope and quality of their services.
In the self-serving scenario, reinforcement learning techniques enable DIAs to learn and optimize their own behavior by interacting with the environment. Adaptive system technology allows DIAs to automatically adjust their behavior and strategies based on the environment and user feedback.
At the governance level, it is necessary to build a complete set of risk management to prevent misjudgment or collapse of DIAs and ensure the stability and security of the system. In the process of self-optimization and learning of DIAs, it is necessary to continuously use AI supervision and evaluation technology to evaluate and evaluate the transparency and explainability of its decision-making process.
2.4 Social Ethics
The core of building DIAs is the need to use a large amount of personal information and data. Therefore, the protection of personal information and data is particularly important. When constructing DIAs, two broad principles of AI ethics should be strictly adhered to:
One is that users have complete autonomy. Therefore, explainable algorithms or models need to be adopted to provide transparency as much as possible so that users can understand the decision-making process of DIAs and decide to accept or reject them autonomously.
The other is scene consistency. It is necessary to strictly regulate the usage scenarios of DIAs, and adopt the principle of scenario consistency to evaluate and monitor whether the behavior is always consistent.
2.5 Identity
DIAs is a new type of technology that combines artificial intelligence with individual characteristics, which has significantly improved the degree of personalization and interaction depth compared with traditional AI. However, this high degree of personalization and interaction ability also brings new challenges to the identity of DIAs.
First, DIAs, as a technological product, base their identity on computer programs and algorithms. They are the result of being developed by humans and programmed according to the characteristics of a particular individual. However, DIAs are not self-aware, without perception and emotion, and all their reactions and behaviors are determined by algorithms.
Second, although DIAs are constructed based on the characteristics of a particular individual, they are not identical to that individual. Although DIAs can embody an individual's knowledge, skills, and behavior, this embodiment cannot express the individual's complex emotions and unique experiences.
With the development of artificial intelligence technology, more advanced DIAs may emerge in the future, they may have more complex emotional processing capabilities, and they may even have a certain degree of self-awareness. This will bring new challenges to our identity of DIAs.
Overall, the identity of DIAs is a complex and delicate issue that needs to be considered from multiple perspectives. We can neither equate DIAs with real people too much, nor completely ignore the personalization and interaction capabilities of DIAs. In the future, with the advancement of artificial intelligence technology, the identity of DIAs will be a topic that requires continuous research and discussion.
3. Why people need DIAs
DIAs are the latest product of technological advancement. Different from general agents such as ChatGPT, DIAs are built on the basis of a specific natural person, deeply embedded in the knowledge and personality characteristics of their controllers. This unique setting allows people to feel comfortable interacting with DIAs even when they know they are not human.
The existence of DIAs can meet the diverse needs of people. For example, many draw on the expertise and skills of DIAs for specific advice and guidance. On the one hand, DIAs, through artificial intelligence technology, have the ability to process a large amount of information and provide it quickly when needed. On the other hand, DIAs are deeply embedded with the knowledge and experience of their controllers. For example, a DIA created from a doctor may deeply understand and use medical knowledge and clinical experience. When providing medical advice, this DIA may be more effective than a DIA. Ordinary AI is more professional and accurate.
At the same time, DIAs play an integral role in human social interactions. They provide a sense of companionship and the opportunity for social interaction, especially for those who feel lonely or need to socialize, DIAs are like an always-on friend who can provide companionship no matter when and where. In addition, people's curiosity and desire to explore are also satisfied in the communication with DIAs. People are curious about how DIAs understand and respond to their questions, and whether DIAs can conduct in-depth conversations like people.
More importantly, DIAs have unique personalization characteristics. These characteristics are mainly reflected in language style, way of expressing opinions, sense of humor, hobbies, social style, living habits, special vocabulary and personal stories, etc. For example, a DIA based on a famous author will not only embed the author's literary knowledge, but may also replicate the author's writing style and perspective. These characteristics together form the personality of DIAs and enable them to better meet the needs of users.
These characteristics make DIAs offer completely new solutions and possibilities for many fields.
For example, suppose there is a well-known economist whose views and insights are admired by economists and investors around the world. The economist's DIAs provide in-depth and expert economic advice for a wide audience. Since DIA is embedded with the knowledge and experience of economists, it can imitate the thinking mode and expression of economists, and provide professional insights and suggestions for specific economic issues. For readers, this is undoubtedly an efficient way to obtain the wisdom of this economist.
For stars with a large number of fans, their diamonds can also meet the needs of fans in various ways. A star's diamond may imitate the star's language style, values, and personality traits to interact with fans. For fans, this can provide a more intimate and personal interactive experience. For example, fans can ask a star's diamond questions and get a "one-on-one" conversation with the star, even though they are actually interacting with an AI.
For a salesperson, DIAs also offer a new way of working. Salespeople can use DIAs to handle routine customer service tasks, such as answering customer questions or providing product information. This will greatly increase the productivity of sales staff, allowing them more time to focus on more complex tasks, such as sales strategy development and key account maintenance. At the same time, DIAs can also provide personalized services during the sales process, such as providing customized product recommendations and suggestions based on each customer's needs and preferences.
In general, DIAs combine human characteristics with technology, innovatively meet people's social needs and curiosity about new technologies, and further strengthen their differences from general-purpose agents such as ChatGPT. However, we also need to note that although DIAs have great potential, due to their characteristics, they may also bring privacy and other issues. We need to weigh the pros and cons and use them with caution.
4. Outlook
Looking into the future, DIAs will have a broader space for development in many fields. With the advancement of artificial intelligence technology, DIAs will more accurately simulate the knowledge, skills, personality and style of real people, and provide more in-depth and professional services. Whether it is economic, medical, entertainment or education industries, DIAs will become an indispensable and important role.
With opportunities lie challenges. In the application process, we will also face privacy, moral and ethical issues. For example, how do DIAs obtain and process sensitive personal information? How to ensure that DIAs behave legally and ethically? How to prevent DIAs from being used for malicious or fraudulent activities? Solving these problems requires that our legal, moral and ethical norms keep pace with technological developments.
Future DIAs may be more autonomous, and even be able to perform simple creative tasks, such as creating new articles based on the embedded writer's writing style, or producing new economic analysis reports based on economists' economic theories and viewpoints. But this may also bring new problems, especially civil behaviors trigger a series of legal and moral and ethical debates.
For all the convenience and fun that DIAs can provide, they can also trigger fear and rejection. After all, some people may be uneasy about DIAs' superhuman abilities and worry about the impact DIAs may have. In addition, there are technical challenges. Current DIAs, while already powerful, still have many limitations. For example, DIAs are currently unable to fully understand and deal with some complex human emotions and social relationships, and it is also difficult to deal with some problems that require deep understanding and innovative thinking. Overcoming these limitations requires us to make greater breakthroughs in artificial intelligence technology research.
1 Artificial Intelligence: A Modern Approach, Stuart Russell
2 Avatar Lab, https://www.ai-avatar.org
3 "QAV: Basic Principles and Application Prospects of Agent Collaboration Based on Large Language Model", Zhang Jialin 4 https://w3c.github.io/did-core/
