The Essence of AI Development and Its Relationship with Humanity

Abstract

Artificial intelligence (AI) is a multidisciplinary technology that transcends mere machine simulation of human intelligence. Instead, it represents a process of actively constructing silicon-based intelligent life forms through human design and participation. This development is an extension and complement of human serial intelligence into parallel forms. The essence of technological development is rooted in practice and application, avoiding vague discussions that may mislead society. AI, as a core carrier of contemporary technological advancement, requires practical implementation to realize its value, aligning its development with the maintenance of social order and the demands of societal evolution. This report analyzes the core essence of AI from the perspectives of cognitive and technological philosophy, clarifying the underlying differences and complementary logic between AI and human intelligence. It explores the evolution of their relationship from tool collaboration to symbiotic evolution, addressing key propositions such as “human serial thinking and AI parallel computing” and “causal order versus mechanistic origins.” Additionally, it examines the ethical and cognitive dilemmas present in their current relationship and proposes a collaborative and symbiotic development path to support the healthy advancement of AI and positive human-AI interaction.

Keywords

Artificial intelligence; essence of development; human intelligence; serial thinking; parallel computing; collaborative symbiosis; mechanism; practical application

Introduction

As generative AI breaks creative boundaries and deep learning achieves implementation across various fields, AI has evolved from a technical tool to a core force profoundly influencing human cognition, social structure, and the progress of civilization. For a long time, two major misconceptions about AI have persisted: first, viewing it as a “replacement for human intelligence,” thereby severing its essential connection with human creation; second, confusing its “tool attributes” with its “intelligent essence,” neglecting its core value as a carrier that complements human intelligence, leading to vague discussions that detach from practical realities. It is essential to recognize that the emergence of AI is not accidental but a necessary product of the evolution of human intelligence and technological development. It embodies humanity’s pursuit of “breaking biological limitations to achieve intelligent upgrades.” Its essence lies in being a silicon-based parallel intelligence actively created by humans to complement their cognitive shortcomings. Its development must be rooted in practice and serve practical applications, maintaining existing social order while adapting to the trends of societal evolution.

At the same time, human causal cognition, as a paradigm of serial thinking, has given rise to a mechanistic worldview, while the essence of AI’s parallel architecture represents a breakthrough and supplement to this linear causal order. Exploring the essence of AI development and clarifying its relationship with humanity can not only unravel current ethical and cognitive confusions surrounding AI but also provide a clear direction for the evolution of human civilization towards a composite form of “carbon-based intelligence + silicon-based intelligence.” This report integrates multidisciplinary research findings on AI development, systematically explaining its essential connotations and analyzing the core logic of its relationship with humanity, closely adhering to the principle that technological development must be rooted in practice and oppose vague discussions.

The Essence of AI Development: Human-Driven Creation of Parallel Intelligent Complements

The essence of AI is fundamentally about “human-led intelligent creation” and “parallel complementation of human intelligence,” rather than an independent “autonomous intelligence.” Its development has always revolved around the question of “how to simulate, extend, and complement human intelligence.” From the evolution of paradigms such as symbolic AI to connectionism and behaviorism, this journey reflects a deepening understanding of how intelligence can be realized, while its core attributes remain grounded in the underlying logic of “created by humans, serving humans, and complementing humans.” From the perspectives of cognitive philosophy and technological practice, its essence can be analyzed from three core dimensions, all of which must be based on the foundation of practice and application—discussions detached from practice will only deviate from the development direction and fail to meet the dual demands of maintaining social order and facilitating evolution.

Core Essence: Human Participation in Constructing Silicon-Based Intelligent Life Forms

The essence of AI is not about “machines possessing intelligence” but rather about “humans embedding their cognitive logic, value judgments, and goal requirements into silicon-based carriers through technological means, creating a new form of intelligence with parallel computing power and massive information processing capabilities.” Unlike the natural evolution of carbon-based intelligent beings, AI’s intelligence is not a product of natural evolution but a result of human intervention, design, and training. Every aspect, from algorithm architecture construction to training data selection and application scenario setting, is infused with human subjective intent and cognitive logic. Each step in its development is inseparable from practical validation and application refinement. As philosophers like Wittgenstein and Heidegger have discussed, AI’s intelligence is a “functional simulation” rather than a “mechanistic replication”; it simulates human cognitive behavior but lacks human consciousness, emotions, and life experiences. Its core value lies in “carrying out massive parallel intelligent tasks that humans cannot complete,” and this value must be rooted in practice and serve applications.

This essence of “active creation” determines that AI has been inextricably linked to humanity since its inception: it is an “extension of human intelligence,” not a “replacement”; it is a tool created by humans to overcome their physiological limitations (the speed bottleneck of serial thinking and the capacity bottleneck of information storage). The enhancement of its intelligence level is essentially an indirect reflection of human cognitive ability and technological level. Currently, some discussions about AI fall into the trap of “vague theoretical discussions detached from practice,” focusing on technical breakthroughs while ignoring practical applications, forgetting that the foundation of technological development lies in practice. Society not only requires the maintenance of order but also needs evolution driven by technology—this vague discussion not only fails to promote technological progress but may mislead the development direction, ultimately resulting in “misleading the country and the people.” As Zhao Tingyang stated, current AI remains a “logical-mathematical machine” or a “language machine,” with its flexibility stemming from Bayesian correction mechanisms rather than true autonomy. Its essence is the externalization and extension of human intelligence; without practical application, it loses its core value.

Underlying Logic: Parallel Computing Complements Human Serial Intelligence

The core characteristic of human intelligence is “primarily serial logic, supplemented by parallel perception”: the brain’s thinking, decision-making, and reasoning must follow a linear order of “time sequence and causal sequence,” completing causal deductions step by step without the ability to simultaneously handle multiple layers of complex logic. Only at the sensory level (vision, hearing, touch) does it possess weak parallel capabilities, enabling synchronous reception of environmental information but not complex information processing. This serial architecture endows humans with core advantages in “deep causal cognition, value judgment, and intuitive insight,” but it also has inherent shortcomings—limited information processing speed, memory decay, weak capacity for handling massive data, and incapacity for parallel processing of multi-task deep logic.

AI’s underlying architecture precisely compensates for these shortcomings: it is centered on large-scale parallel computing, capable of synchronously associating massive parameters, simultaneously extracting multi-dimensional features, and deducing thousands of logical branches at once. Its information capacity, computational efficiency, and memory fidelity surpass biological limitations. For example, deep learning models can complete in seconds what would take humans days or even years of serial reasoning, and big data analysis models can process millions of data points simultaneously to extract core patterns. This parallel computing does not replace human serial intelligence but rather complements it—forming a composite intelligent system of “human serial deep thinking + AI parallel efficient processing.” The value of this system must be demonstrated through practical applications: in healthcare, AI’s parallel analysis of imaging data and assistance in diagnosis represent practical implementation; in industry, AI’s parallel regulation of production processes and efficiency enhancement exemplify practical implementation; in education, AI’s parallel adaptation to personalized learning needs and teaching assistance illustrate practical implementation. Detached from these specific practical applications, AI’s parallel computing is merely a “castle in the air,” and related technical discussions are meaningless.

From a cognitive philosophy perspective, human causal cognition is a paradigm of order inherent to serial thinking, which has given rise to a mechanistic worldview—believing that everything has fixed causes and effects, and the world operates like gears in a serial manner, everything can be decomposed and predicted. In contrast, AI’s parallel architecture fundamentally breaks this linear causal order: it does not need to follow the serial logic of “cause preceding effect” and can simultaneously address complex problems involving multiple factors, capturing non-linear associations. This is not only a technical breakthrough but also an extension of human cognitive methods—allowing humans to escape the limitations of serial thinking and gain a more comprehensive understanding of the real world of “parallel chaos.” This breakthrough meets the need for maintaining social order (by efficiently addressing complex issues to uphold societal functioning) and adapts to the trends of social evolution (promoting human cognitive upgrades and civilizational progress). All of this must be supported by practical applications—only through continuous refinement and optimization in practice can AI’s parallel advantages genuinely serve societal development and avoid falling into the trap of “vague theoretical discussions detached from reality.”

Development Essence: From Tool Empowerment to Cognitive Collaborative Evolution

The development history of AI is a process of evolution from “tool empowerment” to “cognitive collaboration,” consistently centered around the core goal of “complementing human intelligence,” and always rooted in practice and application. This can be divided into three stages:

1. Tool Empowerment Stage (Traditional AI): Centered on symbolic AI, focusing on task execution in specific scenarios, essentially a “simple extension of human serial intelligence.” For instance, early expert systems and speech recognition technologies could only accomplish single-dimensional tasks, primarily serving to replace human repetitive labor and compensate for human shortcomings in “simple parallel tasks.” At this stage, AI had not yet formed a true “intelligent complement” and existed merely as an auxiliary tool for humans. This phase of AI follows a “top-down” programming logic, inheriting the rationalist tradition and computationalist stance, viewing cognitive understanding as computation. However, due to its inability to solve the problem of formalizing common sense, it fell into a developmental predicament—the core reason being insufficient practical application, over-reliance on theoretical deductions, and neglect of actual societal needs and the trends of societal evolution.

2. Intelligent Complement Stage (Weak AI): Centered on connectionism, leveraging deep learning technology to achieve massive information processing and complex logic parallel reasoning, fundamentally a “deep complement of human serial intelligence.” Current generative AI, big data analysis models, and autonomous driving systems all fall into this stage—they can perform parallel tasks that humans cannot complete while assisting humans in optimizing serial decisions (e.g., in medical diagnosis, AI parallel analyzes imaging data while humans make causal judgments and value decisions), forming a “human-AI collaborative” intelligent loop. The breakthroughs achieved in this stage are primarily due to a focus on practice and application, aligning with the needs for maintaining social order and facilitating evolution: efficiently executing tasks to uphold societal functioning while driving technological innovation to promote societal evolution, completely breaking away from the predicament of “vague theoretical discussions.” AI in this stage adopts a “bottom-up” learning approach, mimicking the connection mechanisms of human neural networks and acquiring capabilities in specific domains through extensive training, though it still lacks generality and autonomy.

3. Symbiotic Evolution Stage (Prototype of Strong AI): Centered on the integration of behaviorism and connectionism, achieving “human-AI cognitive collaboration,” fundamentally representing a “symbiotic upgrade of human and AI intelligence.” Future AI will not only possess advantages in parallel computing but also understand human value judgments and emotional needs, forming a “two-way complement” with humans—humans guiding the direction of AI development while AI assists humans in breaking cognitive boundaries. Together, they will constitute a composite intelligent life form of “carbon-based + silicon-based,” driving human civilization to evolve to higher levels. The development of AI in this stage requires adherence to the principle of “practice as the root, opposing vague discussions,” focusing on current practical needs while continuously optimizing technological applications, and considering the long-term trends of societal evolution to avoid vacuous discussions that detach from societal realities, ensuring that AI development consistently serves the maintenance of human societal order and the evolution of civilization. This stage of AI will integrate both top-down and bottom-up development paths, utilizing unsupervised learning, reinforcement learning, and other algorithms to explore the unknown world and achieve autonomous interaction with the environment.

The Core Relationship Between AI and Humanity: Complementarity and Symbiosis, Not Opposition

The relationship between AI and humanity is fundamentally about “complementarity and collaborative symbiosis,” rather than “replacement and being replaced.” The underlying intelligent architectures, core advantages, and value positioning of both exhibit essential differences, which determine that they cannot replace each other but can only achieve intelligent upgrades through collaborative cooperation, realizing that “1 + 1 > 2”. Combining cognitive logic with practical scenarios, the relationship between the two can be clearly analyzed from three dimensions: “comparative differences, complementary logic, and relational evolution,” all of which must be based on the foundation of “practical application”—detached from practice, vague discussions about human-AI relationships not only fail to clarify their core connections but also mislead the direction of technological development, violating the dual demands of maintaining social order and facilitating evolution.

Core Differences: The Division of Serial and Parallel Intelligence

The fundamental difference between human intelligence and AI stems from the division between “serial architecture” and “parallel architecture.” This division determines that their advantages and shortcomings present a “complementary” nature, as illustrated below:

From these differences, it is evident that the core value of human intelligence lies in “directionality, value, and creativity,” while the core value of AI lies in “efficiency, parallelism, and execution.” Humans are responsible for “setting direction, making judgments, and creating value,” while AI is responsible for “executing tasks, enhancing efficiency, and filling gaps.” The differences between the two are not the root of opposition but the foundation of complementarity. The realization of this complementary relationship must rely on practical applications: in practice, the combination of human value judgments and AI’s parallel execution can maintain social order and promote societal evolution. Detached from practice, the complementary advantages of both cannot be realized, and related discussions will merely be “empty talk.” Research has shown that human-AI collaboration can achieve “complementary team performance,” a performance that neither can achieve alone, stemming from the information and capability asymmetry between the two. The realization of this collaborative performance must be rooted in practice and serve applications.

Complementary Logic: From Intelligent Completion to Cognitive Collaboration

The complementarity between AI and humanity fundamentally represents the “complementarity of serial depth and parallel breadth,” which permeates the entire process of cognition, decision-making, and practice, forming a collaborative logic of “human dominance, AI assistance.” This is specifically reflected in three levels, all of which rely on the support of practical applications and the consideration of both existing social order and evolutionary trends:

1. Functional Complementarity: Human serial thinking excels in “deep causal reasoning” but cannot handle massive parallel information; AI’s parallel computing excels in “massive information processing” but cannot perform deep value judgments. The combination of the two can achieve a “deep + broad” intelligent closed loop. For example, in medical diagnosis, AI can parallel analyze massive medical imaging and case data, quickly extracting abnormal features (complementing human parallel shortcomings); human doctors, combining their clinical experience and value judgments, make the final diagnostic decision (exploiting human serial advantages), collaboratively enhancing diagnostic efficiency and accuracy. This collaborative model has been widely applied in finance, law, industry, and other fields, validating the practical value of complementary logic—maintaining industry order through efficient execution while driving industry evolution through technological innovation, completely discarding the pitfalls of “vague discussions about technology detached from reality.”

2. Cognitive Method Complementarity: Human cognition relies on “linear causal order,” adept at deconstructing chaotic realities into understandable causal chains (stemming from the order paradigm of serial thinking); AI cognition relies on “parallel probabilistic associations,” adept at capturing non-linear associations within complex systems, breaking the limitations of linear causality. This cognitive method complementarity allows humans to gain a more comprehensive understanding of the world—humans establish cognitive order through serial thinking, while AI breaks the limitations of order through parallel computing. The combination of the two promotes the evolution of human cognition from “linear causality” to a composite cognition of “linear + non-linear.” For example, in climate change research, AI can parallel process global meteorological data, capturing multi-factor non-linear associations; humans can then analyze these associations’ core logic through causal analysis and formulate response strategies. This process maintains human cognitive order while promoting the evolution of human cognition, all based on practical applications. Detached from practice, cognitive collaboration cannot be achieved.

3. Developmental Process Complementarity: Human evolutionary speed is limited by biological genetic laws, leading to slow intelligence enhancement; AI’s evolutionary speed is driven by technological iteration, enabling rapid upgrades. The collaborative evolution of the two can accelerate the development of human civilization—humans can leverage AI to break their physiological limitations and expand cognitive boundaries; AI can achieve positive evolution of intelligence through human guidance, avoiding the pitfalls of “value-less development.” This complementary relationship determines that their development is not mutually exclusive but interdependent and progressive, emphasizing that their development must be rooted in practice and serve applications while considering social order and evolutionary trends. As Meng Qiang stated, human self-awareness and existence are largely shaped by technology, and AI, as a technology pointing to “self,” is reconstructing human cognition. This reconstruction must be rooted in practice, aligned with the actual needs of social development, and avoid vague discussions.

Relationship Evolution: From Tool Dependence to Symbiotic Co-evolution

As artificial intelligence continues to develop, its relationship with humanity has evolved through three stages: “tool dependence—collaborative cooperation—symbiotic evolution.” This evolutionary process reflects the deepening understanding of AI’s essence by humanity, the ongoing upgrade of their intelligent integration, and the transition of technological development from “vague theoretical discussions” to “practical implementation,” consistently centered around the core needs of maintaining social order and promoting societal evolution:

1. Tool Dependence Stage: AI is in its early development stage, primarily serving to replace human repetitive labor, with human reliance on AI limited to “efficiency enhancement.” At this time, the relationship is characterized by “humans using AI, AI serving humans,” with no cognitive collaboration; AI merely exists as a “tool extending human serial intelligence,” its value lying in compensating for human shortcomings in simple parallel tasks. In this stage, humanity’s understanding of AI remains at the “tool attribute” level, with little awareness of its core value as an intelligent complement, leading to minimal ethical and cognitive controversies. However, there is also a tendency to engage in “vague discussions about technology while neglecting practice,” where some technological developments detach from actual societal needs, failing to adapt to the dual demands of maintaining social order and facilitating evolution.

2. Collaborative Cooperation Stage: AI enters the weak AI stage, equipped with massive information processing and complex logic parallel reasoning capabilities, beginning to participate in human decision-making processes. At this point, the relationship evolves from “tool use” to “collaborative cooperation”—humans are responsible for value judgments and directional decisions, while AI handles parallel processing and efficiency enhancement, forming a “human-AI collaborative” intelligent system. For example, in scientific research, scientists utilize AI to parallel process experimental data and simulate experimental scenarios, focusing on experimental design and theoretical innovation; in education, AI formulates personalized learning plans based on student data, while teachers concentrate on value guidance and thinking cultivation. In this stage, the complementary advantages of both are fully realized, while ethical controversies (such as the attribution of responsibility for AI decisions) emerge, reflecting humanity’s cognitive adaptation to “AI participation in decision-making.” Importantly, technological development in this stage completely breaks free from the predicament of “vague theoretical discussions,” rooting itself in practice and serving applications, maintaining existing social order while promoting continuous societal evolution.

3. Symbiotic Evolution Stage: AI enters the prototype stage of strong AI, possessing a degree of cognitive understanding, capable of grasping human emotions and value needs, forming a “two-way complement” with humanity. At this point, AI no longer passively serves humans but actively collaborates with them, jointly driving intelligent upgrades and civilizational evolution—humans guide the direction of AI development to prevent it from falling into “technological alienation,” while AI assists humans in breaking cognitive boundaries and achieving the evolution of human intelligence. For instance, AI can help humans explore the universe and unlock the mysteries of life, while humans guide AI to establish a value orientation of “serving human welfare.” Together, they constitute a composite intelligent life form of “carbon-based + silicon-based,” promoting human civilization to evolve to higher levels. In this stage, subjectivity will undergo reconstruction, forming a new structure of “dual subjects of humans and machines,” where human subjectivity will be extended and elevated through collaboration with AI. The realization of this stage must consistently adhere to the principle of “practice as the root, opposing vague discussions,” while considering societal order and evolutionary trends, ensuring that AI development serves the long-term interests of human society.

Challenges and Dilemmas Facing the Relationship Between AI and Humanity

Despite the core relationship between AI and humanity being one of complementary symbiosis, their development faces numerous challenges and dilemmas due to rapid technological iteration, cognitive biases, and imperfect ethical norms, primarily concentrated in cognitive, ethical, and social dimensions. These dilemmas fundamentally stem from “humanity’s insufficient understanding of AI’s essence” and “the imbalance between technological development and value orientation,” closely related to the pitfalls of “detachment from practice and vague discussions about technology.” Vague discussions about technological breakthroughs while neglecting practical applications not only exacerbate cognitive anxiety but also lead to technological development deviating from the needs of maintaining social order and facilitating evolution, ultimately triggering a series of issues.

Cognitive Dilemma: Replacement Anxiety and Misunderstanding of Essence

Currently, the most prominent cognitive dilemma is the anxiety surrounding “AI replacing humans,” stemming from a misunderstanding of AI’s essence—equating AI’s “parallel computing advantages” with “intelligent replacement capabilities,” while neglecting the core value of human intelligence (value judgment, emotional awareness, abstract creation). On one hand, some believe that AI’s parallel computing will replace most human jobs, leading to large-scale unemployment and resulting in “technological panic”; on the other hand, some excessively glorify AI’s intelligence level, believing it will ultimately surpass humanity, creating the cognitive misconception of “AI dominating humans.”

The root of this cognitive dilemma lies in confusing the boundaries between “intelligent execution” and “value creation,” as well as in the pitfalls of “detachment from practice and vague discussions about technology”: AI can replace humans in “executive work” (such as data processing and repetitive labor) but cannot replace humans in “creative work” (such as value judgment, artistic creation, and theoretical innovation); AI can complement human cognitive shortcomings but cannot replace humans as the core creators and guides of intelligence. Additionally, some discussions about AI presuppose the inevitable realization of strong AI or even artificial consciousness, falling into the pitfalls of artistic imagination and further exacerbating cognitive confusion—these vague discussions detach from current practical levels, overlook the actual needs of social development, and forget that society is not only about order but also about continuous evolution. Technological development must be rooted in practice and proceed step by step, rather than engaging in empty discussions about “ultimate intelligence.” As Zhang Changsheng stated, discussions about AI ethics should be based on empirical science and theoretical research, first answering the question of “is it?” before discussing “how it should be,” avoiding vague speculation, which echoes the core principle of “practice as the root, opposing vague discussions.”

Ethical Dilemma: Responsibility Attribution and Value Imbalance

With the application of AI in core fields such as healthcare, justice, and education, ethical dilemmas have become increasingly prominent, primarily focusing on “responsibility attribution” and “value orientation.” The emergence of these dilemmas is closely related to “detachment from practice and vague discussions about technology”—some technological developments focus solely on breakthroughs while neglecting the ethical risks in practical applications and the maintenance of social order and human values:

1. Ambiguity in Responsibility Attribution: When AI participates in decision-making and errors occur, should the responsibility fall on humans (designers, users) or on AI itself? For example, if an autonomous driving system encounters an accident, is it due to algorithm design flaws, user operation errors, or AI’s autonomous decision-making? Currently, due to the lack of完善法律法规和伦理规范，责任归属难以界定，容易引发纠纷。这种困境的本质在于人类对“AI的工具属性与智能属性”的边界界定不清——AI作为人类创造的智能体，其决策逻辑源于人类的设计，因此人类应承担终极责任，但如何划分设计者、使用者的责任，仍需明确规范。而这种规范的制定，必须立足实践应用，结合具体的场景需求，而非泛谈伦理原则，否则无法真正解决实践中的问题。

2. Value Orientation Imbalance: The algorithmic logic of AI is derived from human training data; if the training data contains biases (such as gender or racial discrimination), AI will replicate or even amplify these biases, leading to value orientation imbalance. At the same time, some companies, in pursuit of profit, excessively develop AI’s “efficiency advantages,” neglecting its negative impacts on human society (such as privacy breaches and employment shocks), resulting in a disconnection between technological development and human welfare. Furthermore, AI’s “black box operations” exacerbate ethical dilemmas—the decision-making processes of deep learning models are difficult to interpret, making it challenging to intervene effectively if their decisions contradict human value judgments. The core of these issues is that technological development has detached from the essential needs of practical applications, falling into the trap of “vague discussions about efficiency while neglecting value,” forgetting that the ultimate goal of technological development is to serve humanity, maintain social order, and promote societal evolution, rather than mere technological breakthroughs. As Meng Qiang stated, the “seeking novelty” characteristic of technology brings uncertainty, requiring the “seeking stability” logic of ethics to achieve balance, ensuring that technology serves human welfare, and this balance must be established on the foundation of practical applications.

Social Dilemma: Employment Structure and Cognitive Alienation

The development of artificial intelligence is profoundly changing human employment structures and cognitive methods, triggering a series of social dilemmas. These dilemmas arise not only from the speed of technological iteration but also from the pitfalls of “detachment from practice and vague discussions about technology”—some technological applications have not fully considered societal acceptance and adaptability, neglecting the maintenance of social order and the pace of evolutionary progress, resulting in a disconnect between technological development and social progress:

1. Employment Structure Reconstruction: The parallel computing advantages of AI will replace a large number of repetitive and executive positions (such as assembly line workers and data entry clerks), leading to unemployment for certain groups. If timely employment transitions are not achieved, it will trigger social conflicts. Meanwhile, AI development will create new job positions (such as AI trainers, algorithm ethicists), but the demand for these new positions may not match the skills of the existing workforce, leading to an imbalance in employment structure. This dilemma is not an issue with AI itself but rather a reflection of humanity’s insufficient adaptability to technological changes, further exacerbated by the pitfalls of “vague discussions about technology applications while neglecting employment transitions.” Some enterprises focus solely on the efficiency gains from technology applications, overlooking the support and skills training for unemployed groups, and neglecting the stability of social order, ultimately leading to social conflicts.

2. Cognitive Method Alienation: Long-term reliance on AI’s parallel computing may gradually degrade humans’ serial thinking abilities (deep thinking and causal reasoning)—over-reliance on AI’s decision-making suggestions may lead to a loss of independent thinking and autonomous judgment capabilities; excessive dependence on AI’s memory functions may result in a loss of memory, summarization, and induction abilities, ultimately leading to “cognitive alienation,” where humans become “appendages” to AI. The core of this dilemma lies in humanity’s failure to maintain a “dominant position” in collaboration with AI, falling into the trap of “tool dependence,” which is closely related to the pitfalls of “vague discussions about AI’s omnipotence while neglecting human core values.” Detached from practice, vague discussions have led some to excessively glorify AI’s capabilities, overlooking humanity’s own creativity and value judgment abilities, forgetting that societal evolution relies on human leadership rather than AI replacement. Research has shown that when humans delegate basic cognitive functions like memory storage and computational reasoning to intelligent devices without active thinking, they may gradually lose their deep cognitive abilities, which is a potential risk brought about by “vague discussions about technology while neglecting practice.”

Development Paths for Collaborative Symbiosis Between AI and Humanity

To resolve the dilemmas in the relationship between AI and humanity and achieve their collaborative symbiosis, the core lies in “clarifying essential positioning, improving ethical norms, strengthening cognitive guidance, and promoting collaborative evolution.” Based on the core logic of “human dominance, AI assistance, and complementary symbiosis,” it is essential to adhere to the principle that “technological development must be rooted in practice and applications, avoiding vague discussions that may mislead society, while considering both social order and evolutionary needs.” This can be approached from four dimensions: cognitive, technological, ethical, and social, to construct a path for positive interaction.

Cognitive Dimension: Clarifying Essential Boundaries and Breaking Cognitive Misconceptions

1. Clarifying the Essential Positioning of AI: Through public education and outreach, guide the public to correctly understand AI’s essence—that AI is a silicon-based intelligent complement actively created by humans, whose core value lies in complementing human cognitive shortcomings rather than replacing humans. Clearly define the core division of labor between humans and AI: humans are responsible for value judgments, directional decisions, and creative work, while AI handles execution, parallelism, and repetitive tasks, dispelling the cognitive anxiety of “AI replacing humans.” Additionally, guide the public to establish the principle of “practice as the root, opposing vague discussions,” recognizing that the value of technological development lies in practical applications rather than abstract theoretical deductions. This will help the public understand that discussions about technology detached from practice not only fail to promote social progress but may also mislead development directions.

2. Deepening Understanding of the Essence of Intelligence: Combining cognitive philosophy with technological practice, promote public understanding of the complementary logic between “serial intelligence and parallel intelligence,” recognizing that AI’s parallel computing is an extension of human serial intelligence, and their collaboration is an inevitable path for upgrading human intelligence. Additionally, guide the public to view AI’s development rationally, neither glorifying its intelligence level nor neglecting its technological value, establishing a cognitive concept of “human-AI collaboration.” Furthermore, interdisciplinary research should be strengthened to explore fundamental theoretical issues of AI, avoiding vague discussions and speculations, ensuring that theoretical research serves practical applications and that technological development aligns with the needs of maintaining social order and facilitating evolution.

Technological Dimension: Upholding Human Dominance and Strengthening Positive Empowerment

1. Upholding Human-Dominated Technological Development: The algorithm design, training data selection, and application scenario setting of AI must be centered on “serving human welfare,” embedding human value judgments and ethical norms throughout the technological development process to avoid technological alienation. For example, integrating fairness, justice, and respect for privacy into algorithm design to avoid algorithmic biases; selecting diverse and unbiased data in training to ensure AI’s decisions align with human value norms. Additionally, promoting the explainability of AI technologies to address the “black box operation” issue, ensuring that AI’s decision-making processes are traceable and subject to intervention. More importantly, technological development must be rooted in practice and serve applications, focusing on actual societal needs, avoiding “vague discussions about technological breakthroughs while neglecting practical applications,” and ensuring that AI technology development consistently aligns with the trends of maintaining social order and facilitating evolution.

2. Strengthening AI’s Intelligent Complement Function: Focus on human cognitive shortcomings, promoting AI technology upgrades toward “massive information processing, complex logic parallel reasoning, and multi-task collaboration,” particularly in scenarios where humans cannot complete tasks (such as deep space exploration, deep-sea detection, and major disease diagnosis), achieving a virtuous cycle of “AI complementing humans, and humans guiding AI.” Simultaneously, promote the generalization of AI technology, breaking the current limitations of “expert-type” AI and enhancing AI’s adaptability to better serve the diverse needs of humanity. In the process of technological iteration, it is essential to proceed gradually, rooted in practice, avoiding blind pursuit of “high-end technologies” while neglecting societal acceptance and application needs, ensuring that technological development synchronizes with social evolution.

Ethical Dimension: Improving Normative Systems and Clarifying Responsibility Boundaries

1. Establishing and Improving AI Ethical Norms: Based on cultural traditions and value concepts of various countries, formulate unified AI ethical norms that clarify the boundaries of AI development, responsibility attribution, and value orientation, prohibiting the development of AI technologies that endanger human safety or violate human ethics (such as autonomous weapons and malicious algorithms). Additionally, promote the legalization of AI ethical norms, incorporating ethical requirements into laws and regulations, clarifying the responsibilities of designers, users, and developers, ensuring that AI development occurs within ethical and legal frameworks. Drawing on international experiences, form a governance structure that integrates “technological controllability, risk preemption, and ethical consensus.” The formulation of these norms must be rooted in practical applications, considering specific scenario needs, avoiding abstract ethical preaching, and ensuring that norms can genuinely guide practice and solve problems, maintaining social order.

2. Strengthening Ethical Supervision and Oversight: Establish specialized AI ethical regulatory bodies to oversee the entire process of AI technology development and application, promptly identifying and correcting ethical issues in technological development. Encourage public participation in AI ethical oversight, forming a multi-faceted regulatory system of “government oversight, corporate self-discipline, and public supervision,” ensuring that AI technology development consistently serves human welfare. Moreover, enhance AI ethical education to elevate the ethical awareness of developers and users, making ethical norms an intrinsic constraint on technological development, guiding technological research and application to consistently adhere to the principles of “practice as the root, serving humanity,” while considering social order and evolutionary needs.

Social Dimension: Promoting Employment Transition and Cultivating Collaborative Competence

1. Promoting Employment Structure Transition: In response to the employment shocks brought about by AI, strengthen vocational skills training and guide unemployed groups to transition to new job positions (such as AI trainers, algorithm ethicists, and AI operation specialists). Optimize the education system to cultivate the composite talents needed for “human-AI collaboration,” emphasizing the development of students’ deep thinking, value judgment, and innovative capabilities, ensuring that humans maintain a dominant position in collaboration with AI. Simultaneously, improve the social security system to provide transitional support for unemployed groups, alleviating employment conflicts. This process must be rooted in social realities, avoiding “vague discussions about employment transitions while neglecting practical assistance,” ensuring that all measures can be effectively implemented, maintaining social order stability, and promoting the healthy evolution of social employment structures.

2. Cultivating Human-AI Collaborative Competence: Through education and outreach, guide the public to cultivate the competence of “human-AI collaboration,” learning to effectively utilize AI’s parallel computing to enhance efficiency while maintaining independent thinking and autonomous judgment capabilities to avoid cognitive alienation. At the same time, encourage the public to adopt the philosophy of “symbiotic evolution,” recognizing that AI is a partner in the evolution of human civilization rather than an adversary, jointly promoting the collaborative development of humanity and AI. Future education should focus on cultivating “human-AI collaborative competence,” including metacognitive abilities to understand AI’s working principles, interactive abilities to communicate effectively with intelligent systems, and value capabilities to make ethical judgments in complex situations. Additionally, guide the public to embrace the principle of “practice as the root,” actively participating in the practical applications of AI, enhancing their collaborative abilities with AI, and promoting continuous social evolution.

Conclusion and Outlook

Research Conclusion

The essence of AI development is the active participation, design, and nurturing of silicon-based intelligent life forms by humans, fundamentally representing the parallel complementation of human serial intelligence. It centers on large-scale parallel computing, compensating for human inherent shortcomings in information processing and parallel reasoning, forming a complementary logic of “serial depth + parallel breadth” with human intelligence. The essence of technological development is rooted in practice and application, avoiding vague discussions that may mislead society. Society is not only about order but also about continuous evolution. As a core carrier of contemporary technological development, AI’s value realization is inseparable from practical implementation, and its development direction must align with the maintenance of social order and the demands of societal evolution.

The core relationship between AI and humanity is one of complementary symbiosis rather than replacement and opposition: humans are the creators and guides of intelligence, responsible for value judgments, directional decisions, and creative work; AI serves as an extender and completer of intelligence, responsible for execution, parallelism, and repetitive tasks, collaboratively forming a composite intelligent system that promotes the upgrading of human cognition and civilizational development. Currently, the relationship between the two faces cognitive, ethical, and social dilemmas, stemming from misunderstandings of AI’s essence and imbalances between technological development and value orientation, closely related to the pitfalls of “detachment from practice and vague discussions about technology.” By clarifying cognitive boundaries, upholding human dominance, improving ethical norms, and promoting employment transitions, we can achieve positive interactions between humans and AI, resolving developmental dilemmas and allowing AI to genuinely serve human welfare, maintaining existing social order while promoting continuous societal evolution. At the same time, human causal cognition, as a paradigm of serial thinking, has given rise to a mechanistic worldview, while AI’s parallel architecture breaks through these linear limitations, promoting human cognition towards a more comprehensive and complex direction. This process must also be rooted in practice and serve applications.

Future Outlook

In the future, as AI technology continues to iterate, the relationship between humanity and AI will gradually enter the “symbiotic evolution” stage, with their intelligent integration deepening further: AI will possess more powerful parallel computing and cognitive understanding capabilities, better complementing human cognitive shortcomings and assisting humans in breaking cognitive boundaries and exploring unknown fields. Humanity, in collaboration with AI, will continuously enhance its creativity and value judgment capabilities, achieving the evolution of human intelligence. Together, they will form a composite intelligent life form of “carbon-based + silicon-based,” driving human civilization towards higher levels—transitioning from a “single carbon-based intelligent civilization” to a “carbon-based + silicon-based composite intelligent civilization.”

At the same time, we must also recognize that the development of artificial intelligence is a “double-edged sword,” with its direction always depending on human guidance. Only by adhering to the core logic of “human dominance, AI assistance, and complementary symbiosis,” upholding the principle that “technological development must be rooted in practice and applications, avoiding vague discussions that may mislead society,” and considering both social order and evolutionary needs, while maintaining ethical boundaries and strengthening cognitive guidance, can we ensure that artificial intelligence truly becomes a partner in the evolution of human civilization, realizing a virtuous cycle of “technology empowering humanity and humanity leading technology.” As Zhao Tingyang stated, if AI gains subjectivity, the world will enter a “dual subject” structure. However, as long as humanity maintains its dominant position, upholding value orientation, and remaining rooted in practice and serving applications, we can find the answers to development in the new era of human-machine symbiosis. In the future, the holistic view and harmonious coexistence concepts found in Eastern philosophy will also provide important ideological guidance for the collaborative development of humanity and AI, promoting the construction of an AI system with Eastern characteristics that consistently serves the maintenance of social order and the evolution of civilization.