How enterprise-level AI agents can be implemented in the automotive industry, this is the first domestic white paper to systematically explain

In recent years, with the rapid development of large model technology, the upper limit of model capabilities has been continuously challenged. However, from the perspective of industrial change, the implementation of large models may be in its infancy. How to unleash the huge potential of large models and promote faster and better changes in productivity is still a topic full of room for exploration.

Every industry is concerned about one question: What is the optimal methodology for applying large models to the business level?

When we talk about this issue, we naturally cannot avoid "AI Agent" (intelligent body).

As AI leaps from academic research to practical applications, large model-driven agents are becoming the core driving force for innovation. Even Bill Gates predicted that AI Agent will be the future of artificial intelligence. By then, the AI ​​Agent will have planning, execution, perception, memory and tool usage, and can complete the work autonomously. Humans will need to assist in setting business goals, provide necessary data and computing resources, and supervise and optimize the work results.

So, where has the application of AI Agent gone in various industries? How to maximize its value?

A recently released white paper provides comprehensive and in-depth answers to the issues discussed above.

The first systematic explanation in China

How does AI Agent land in the automotive industry?

On April 12, Tsinghua University Natural Language Processing Laboratory, Yihui Intelligence, and Face Wall Intelligence jointly released the "White Paper on Group Intelligence Technology for the Automotive Industry Driven by Large Models."

How to download the white paper: Follow the WeChat public account [Yi Hui Intelligent] and reply with the keyword "white paper" to download

In recent years, automobiles Slowing market demand and improving supply efficiency have led to fierce "price wars." This is certainly good for consumers, but it is a double-edged sword for companies in the automotive field. While quickly seizing market share, profit margins are also compressed. How to break the situation is a difficult problem.

The rise of large model technology is a new opportunity for the intelligent transformation of automobile companies. The automotive industry has the characteristics of rich data, clear scenarios, mature technology, high market demand and fierce industry competition. It happens to be one of the most suitable fields for the implementation of AI Agent.

When the strong demand for transformation met the historic breakthrough of large model technology, Tsinghua University’s Natural Language Processing Laboratory, Yihui Intelligence and Face Wall Intelligence hit it off and decided to work together to promote the transformation of the automotive industry. "big thing".

This white paper is the result of the in-depth "industry-university-research" cooperation among the three parties.

Based on Yihui Intelligent’s in-depth understanding of the automotive industry application scenarios and resource advantages, combined with Tsinghua University’s NLP laboratory to handle advanced groups Intelligent theoretical framework , and wall-facing intelligence’s basic technology in the field of large language models and agents, the three parties hope to build a technical application covering "Know-How of the large model AI agent industry" closed loop.

Specifically, the white paper introduces large model-driven swarm intelligence technology in a simple and easy-to-understand manner, and systematically explains the application prospects and practical paths of this technology in the automotive industry for the first time in the industry, especially for the automotive industry. A systematic solution was proposed.

The first chapter first comprehensively observes the current market status, opportunities and challenges of the automotive industry; the second chapter deeply discusses the large-model swarm intelligence technology system, including large-scale Language model, AI Agent, swarm intelligence and organizational twins; Chapter 3 focuses on analyzing the application value and practical cases of large model swarm intelligence technology in the automotive industry; Chapter 4 describes in detail the automotive industry’s swarm intelligence ecological matrix and its win-win logic ; and concludes with an outlook on the future in Chapter 5, emphasizing the importance of these technologies for the transformation and upgrading of the automotive industry.

Analysis of large model swarm intelligence technology system

In this white paper, we see a keyword that runs throughout the text: swarm intelligence.

The core of AI Agent lies in the linkage between LLM and perception and action. LLM understands the user's tasks, infers the tools or actions that need to be called, and gives feedback to the user based on the results of the calls or actions.

Most AI Agent applications are implemented in the form of Workflow, including a variety of nodes, such as large model nodes, code nodes, retrieval nodes, knowledge base nodes, tool nodes, dialogue strategy nodes, etc., and then based on different scenarios Different node combinations will be selected to become available Workflows.

The concept of AI Agent that most people are more familiar with is single intelligence - consisting of only one agent that interacts with the environment independently and optimizes its behavioral strategy based on feedback from the environment. But for a large number of complex scenarios, the capabilities of a single agent are still limited. On the one hand, the more knowledge and capabilities required of the AI ​​Agent, the more words in the prompt that call the underlying large model. The limited context length of the model cannot carry infinitely long prompts; on the other hand, the more content is input, the larger the number of prompts. The more likely the model is to "forget", that is, it is more likely to follow tail instructions and ignore head instructions.

When the number of agents increases, the collaboration capabilities between agents improve, and a complex and powerful group intelligence system is formed, it can handle more complex tasks and scene modeling, and produce higher-level The "emergence of intelligence". The swarm intelligence collaboration platform will be able to break down a task, with each link being responsible for professionals, and use multiple expert agents to collaboratively complete work goals in complex scenarios, greatly expanding the upper limit of the capabilities of intelligent applications and opening up the world. The model empowers the last mile of industry applications to improve quality and efficiency.

However, overall, the development of swarm intelligence technology is still in its early stages, and a large number of implementation paths still need to be explored, including how to enhance the adaptability of large models in tool use and reasoning and planning capabilities, so that they can Able to better adapt to different tasks and scenarios.

Among them, "Industry Know-How" has become the key to the realization of the value of Agent implementation. Today, artificial intelligence has surpassed human experts in many fields, but after delving into different fields, understanding industry terminology, business processes and requirements is still the focus of AI Agent "tutorial", and these often rely on industry vertical experience.

How does swarm intelligence transform the productivity of the automotive industry?

After a revolutionary iteration of technology, the physical industry usually undergoes a profound change. However, regarding the direction of AI Agent, there is a consensus among industry, academia and research circles: only by deeply understanding the needs and pain points of the industry can we develop an AI Agent that truly meets the needs of users. This is also the original intention of this white paper.

In the past few years, the "intelligent" topic in the automotive field has focused more on small-scale exploration in the field of autonomous driving. Now, large-model swarm intelligence technology is rewriting the automotive industry in a revolutionary way, turning intelligence into The hope of transformation extends to all aspects of vehicle manufacturing, supply chain, R&D and engineering, sales and distribution, marketing, after-sales service, trade and logistics, leasing and financial services, recycling and recycling.

How to change? The white paper points out five directions: improving corporate operational efficiency, accelerating process management, improving marketing experience, enhancing service experience, and improving corporate planning capabilities.

For example, from the perspective of enterprise operations, the topic center with the implementation of Agent gradually transitioned from "single intelligence" to "group intelligence", and the concept of "organizational twin" was born, including three Key parts: job twin, architecture twin and business twin. When different roles in each department have intelligent agents, they can fully analyze and transmit information, collaborate and execute each other, thereby breaking down departmental communication barriers and fully realizing data sharing and business integration.

In addition, the intelligent transformation needs of the automotive industry are also different from those of other industries.

An obvious feature is that the marketing of automobiles has sales difficulties and sales cycles that are difficult to match for other consumer products. This is manifested in high customer unit prices, low transaction rates and long sales life cycles. After a long period of development, the automotive marketing field has settled down to a standardized, fully closed-loop methodology. However, under the technological wave of electrification and intelligence, the speed of new products being put on the market and the speed of replacement are accelerating. Terminal price competition is fierce. The profit pressure of traditional channel dealers has increased sharply. Automobile companies need to more quickly gain insight into user needs and update new products. Fast product development speed, more agile response and meet users' service needs.

# It is precisely such scene characteristics that provide extremely valuable application space for large model swarm intelligence technology.

In the white paper, the three parties combined their unique understanding of the application of AI Agent, realized the organizational twin of the automobile marketing business based on swarm intelligence technology, and proposed five major solutions based on the growth needs of the core scenarios of automobile marketing. , respectively, are the Digital Intelligence Research Institute scenario solution, the new media operation scenario solution, the user operation scenario solution, the intensive DDC scenario solution, and the situational operation scenario solution.

For example, in the intensive DCC group intelligence collaboration platform, group intelligence technology realizes the organizational twin of call center customer service. The human-like understanding ability and instant feedback ability of the large language model make it an ideal tool to solve the problem of efficiency loss in the traditional outbound customer acquisition process. By accurately analyzing human language and intentions, the model can effectively reduce errors and delays caused by human instability, and build a full-process iteration mechanism through digital communication processes and other methods.

For another example, Yihui Intelligence discovered through extensive research and interviews that a group intelligence collaboration platform based on large models can also support companies in building digitally intelligent automotive research centers, and develop and deploy data collection, data cleaning, A team of digital employees with roles in data analysis, data reporting and other roles can efficiently scan, identify, classify, analyze and report data from multiple sources of user behavior, providing more efficient user insights and trend tracking.

The prelude to an intelligent transformation

Although the implementation of AI Agent in various industries is still in the early exploratory stage, it still takes time and technology to mature, but now The preliminary exploration of swarm intelligence technology has shown many advantages compared with traditional AI: stronger collaboration capabilities, higher flexibility, and the ability to provide customers with more accurate and personalized services.

Liu Zhiyuan, associate professor of the Department of Computer Science and Technology at Tsinghua University, pointed out that when it comes to exploring the implementation of AI Agent, China has strong advantages in rich scenarios and a broad market. Both companies and individuals are actively exploring various possible implementation methodologies to maximize value.

In this unprecedented transformation, Yihui Intelligence itself is one of the pioneers. Based on matrix products such as the YI CPM automotive industry large model, YI Agents digital employee platform, and YI Scene business scenario solutions, Yihui Intelligence is committed to providing leading digital solutions for automotive industry customers through large model-driven group intelligence and organizational twin solutions. The one-stop solution for employee management and operation platform solves the common problems in the current automotive industry, such as the difficulty in applying leading AI technology and the difficulty in implementing best business practices, and helps companies in the automotive industry achieve the last mile of intelligent implementation.

Li Wei, President of Yihui Intelligence, said that after AI Agent is implemented in the automotive industry, it will fully embody the core value of "improving quality and efficiency". The joint release of the white paper by the three parties not only means that new research directions and cooperation opportunities have emerged in the automotive industry, but also demonstrates to other industries the possibility and potential of the widespread application of large model technology.

Li Dahai, CEO of Wall-facing Intelligence, pointed out that the experience of implementing swarm intelligence in the automotive industry can be replicated to a certain extent, especially for those with rich data accumulation, room for fault tolerance, and efficient implementation. industry.

It is foreseeable that in the future where the reasoning, memory, planning, multi-modal interaction and tool usage capabilities of AI Agents continue to evolve, there will be great room for imagination in the intelligent transformation of all walks of life. broad.

The above is the detailed content of How enterprise-level AI agents can be implemented in the automotive industry, this is the first domestic white paper to systematically explain. For more information, please follow other related articles on the PHP Chinese website!