The Deep Logic of Two AI Winters#

The history of artificial intelligence is not a smooth march of triumph, but rather filled with dramatic ups and downs. Historians call the low periods in AI development “AI winters,” a term rich with metaphorical meaning—like winter in nature, it signifies a temporary dormancy of vitality, but also nurtures the hope of spring.

The First Winter (1974-1980) marked the end of AI’s first golden age. In 1966, the Automatic Language Processing Advisory Committee (ALPAC) released a harsh critique of machine translation projects, concluding that machine translation was “slower, less accurate, and more expensive than human translation.” This report was like a bucket of cold water, extinguishing enthusiasm for AI omnipotence.

A more devastating blow came from Marvin Minsky and Seymour Papert’s 1969 book “Perceptrons.” They mathematically proved that single-layer perceptrons could not solve linearly inseparable problems (such as the XOR problem), a finding that nearly destroyed the entire neural network research field. Although they mentioned in the book that multi-layer networks might solve these problems, the lack of effective training algorithms at the time made this “possibility” seem unreachable.

In 1973, British mathematician James Lighthill, commissioned by the British Science Research Council, published the famous “Lighthill Report,” which comprehensively and severely criticized AI Chronicle. The report argued that AI Chronicle had failed to achieve its promised goals, with most work being “disappointing.” This report directly led to the British government drastically cutting AI Chronicle funding.

The Second Winter (1987-2000) witnessed the collapse of the expert systems bubble. In the 1980s, expert systems were seen as the hope for AI commercialization. These systems attempted to encode human expert knowledge into rules, enabling computers to reason in specific domains. However, expert systems quickly revealed fatal weaknesses: they were extremely fragile, unable to handle uncertainty, expensive to maintain, and lacked learning capabilities.

More importantly, the collapse of the LISP machine market symbolized the predicament of the symbolic AI approach. These expensive hardware systems designed specifically for AI applications became completely uncompetitive under the impact of rapidly improving general-purpose computers. By the 1990s, most expert system projects were abandoned, and AI entered another winter.

Common Characteristics of AI Booms#

If winters reveal the limitations of AI development, then booms showcase the infinite possibilities of human imagination. Each AI boom shares striking similarities: technological breakthroughs trigger media attention, media attention brings capital influx, capital influx drives more research, followed by overly optimistic predictions and unrealistic promises.

The First Boom (1950s-1960s) began with the ambitious vision of the Dartmouth Conference. In 1956, John McCarthy, Marvin Minsky, and others gathered at Dartmouth College, formally proposing the concept of “artificial intelligence.” They believed that “every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it.”

This optimistic sentiment quickly infected the entire society. Herbert Simon predicted in 1965: “Within 20 years, machines will be capable of doing any work that humans can do.” Such prophecies seem obviously over-optimistic today, but they reflected that era’s unlimited confidence in technological progress. Governments and military were also infected by this optimism, investing substantial funds in AI Chronicle.

The Second Boom (1980s) was marked by the commercial success of expert systems. Expert systems like XCON saved Digital Equipment Corporation tens of millions of dollars, proving AI’s commercial value. The Japanese government ambitiously launched the Fifth Generation Computer Project, attempting to surpass the United States in the AI field. Knowledge engineering became a hot discipline, with people believing that as long as human expert knowledge could be effectively encoded, intelligent systems surpassing humans could be created.

The Third Boom (2010s to present) was triggered by breakthroughs in deep learning. In 2012, AlexNet’s stunning performance in the ImageNet competition marked the arrival of the deep learning era. The development of big data, cloud computing, and GPUs provided a solid technical foundation for this boom. From AlphaGo defeating Lee Sedol, to continuous breakthroughs in GPT series models, to the nationwide AI craze triggered by ChatGPT, we are experiencing the most spectacular technological explosion in AI history.

Philosophical Reflections on Cyclical Patterns#

These cyclical fluctuations are not accidental, but reflect the inherent laws of technological development. Gartner’s “Technology Maturity Curve” explains this phenomenon well: any new technology goes through technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and finally reaches the plateau of productivity.

AI’s development trajectory perfectly confirms this pattern. Each technological breakthrough triggers excessive expectations, and when reality cannot meet these expectations, disillusionment occurs. But this “disillusionment” is not true failure, but cognitive correction and technological precipitation. In each winter, truly valuable technologies and ideas are preserved and developed, laying the foundation for the next breakthrough.

At a deeper level, this cyclicity reflects humanity’s spiral ascent in understanding intelligence. Each cycle gives us deeper insights into the nature of intelligence: from early superstition about logical reasoning, to emphasis on knowledge representation, to focus on learning capabilities, and finally to dependence on large-scale data and computation. Each stage is not a simple negation of the previous stage, but a synthesis and transcendence at a higher level.

Modern Interpretations of Turing’s Legacy#

In 1950, Alan Turing proposed the famous “imitation game” in his paper “Computing Machinery and Intelligence,” later known as the Turing Test. Behind this seemingly simple test lie profound philosophical questions: What is intelligence? How do we judge whether a system possesses intelligence?

In the ChatGPT era, the Turing Test has gained new significance. When we converse with GPT-4, it’s hard not to be impressed by its fluent language expression and seemingly profound insights. In many cases, if we didn’t know our conversation partner was AI, we would likely think we were communicating with a learned human. Does this mean these systems have already passed the Turing Test?

The answer is not simple. The core of the Turing Test lies in a behaviorist view of intelligence: if a system’s behavior cannot be distinguished from humans, then we should consider it intelligent. This view sidesteps the difficult-to-verify concept of “internal understanding” and focuses instead on observable external performance.

However, modern large language models make us reconsider the limitations of this behaviorist stance. While GPT-4 can generate impressive text, does it truly “understand” the meaning of these texts? Does it possess consciousness, emotions, or subjective experience? These questions bring us back to fundamental thinking about the nature of intelligence.

The Chinese Room Argument’s LLM Challenge#

In 1980, philosopher John Searle proposed the famous “Chinese Room” argument, directly challenging the possibility of strong artificial intelligence. Searle envisioned a scenario where a person who doesn’t understand Chinese is locked in a room, answering Chinese questions by consulting detailed rule manuals. From the outside, this person appears to “understand” Chinese, but in reality, they are merely mechanically executing syntactic rules without true semantic understanding.

Searle’s argument centers on distinguishing between syntax and semantics: computer programs can only process syntactic symbols and cannot achieve true semantic understanding. This argument has gained new attention in the era of large language models, as LLMs’ working principles seem to be exactly the kind of pure syntactic operations Searle described.

In 2021, computational linguist Emily Bender and others published a paper titled “On the Dangers of Stochastic Parrots,” comparing large language models to “stochastic parrots.” They argued that just as parrots can mimic human language without understanding its meaning, LLMs only statistically mimic human text without true understanding.

However, this view also faces challenges. Philosopher David Chalmers, in his 2023 paper “Could a Large Language Model be Conscious?”, presented a different perspective. Chalmers argued that while current LLMs may not yet possess consciousness, future AI systems might acquire some form of consciousness or understanding capability as technology develops.

Levels and Boundaries of Intelligence#

The development of modern AI has led us to reexamine the distinction between “weak AI” and “strong AI.” Weak AI focuses on solving specific problems without claiming to possess true intelligence or consciousness; strong AI attempts to create systems with general intelligence and consciousness. For a long time, most AI Chronicle belonged to the weak AI category, but the emergence of large language models has blurred this boundary.

Models like GPT-4 have demonstrated surprising generality: they can perform mathematical reasoning, write code, compose poetry, and analyze philosophical problems. This multi-domain capability has led people to wonder whether we are approaching some form of Artificial General Intelligence (AGI).

However, these systems also expose obvious limitations. They lack the ability for continuous learning, cannot form long-term memory, are prone to hallucinations, and have limited understanding of the physical world. More importantly, they seem to lack some core characteristics of human intelligence: creativity, intuition, emotional understanding, and moral judgment.

Emergent phenomena provide a new perspective for understanding AI intelligence. When neural networks reach a certain scale, they suddenly exhibit capabilities not explicitly contained in the training data. This emergence is reminiscent of phase transition phenomena in complex systems science: when a system parameter exceeds a critical value, the entire system’s properties undergo qualitative change.

New Perspectives from Philosophy of Technology#

Heidegger proposed in “The Question Concerning Technology” that technology is not merely a tool, but a way of “revealing” the world. Technology changes how we understand the world and ourselves. The development of AI technology is profoundly changing our understanding of intelligence, consciousness, and humanity.

When we interact with AI systems, we are not just using a tool, but engaging in an ontological dialogue. AI becomes a mirror for understanding our own intelligence: by observing AI’s capabilities and limitations, we see more clearly the uniqueness of human intelligence.

This mirror relationship is bidirectional. On one hand, we design AI systems based on our understanding of human intelligence; on the other hand, AI systems’ performance influences our definition of intelligence. This cyclical feedback creates an evolving cognitive framework that continuously deepens our understanding of the nature of intelligence.

The Tripolar Global Landscape#

Current AI development exhibits distinct geopolitical characteristics, forming a tripolar structure among the United States, China, and the European Union. Each region has different development models, value orientations, and strategic objectives, and these differences are reshaping the global technological landscape.

The American Model embodies a market-driven innovation ecosystem. Silicon Valley tech giants—Google, Microsoft, OpenAI, Meta—have invested heavily in AI Chronicle and development, forming a complete industrial chain from basic research to commercial applications. America’s advantages lie in its strong fundamental research capabilities, abundant venture capital, open talent mobility, and mature technology transfer mechanisms.

However, the United States also faces strategic balance issues between open source and closed source approaches. On one hand, open source can promote innovation and international cooperation; on the other hand, open-sourcing core technologies might weaken America’s competitive advantages. National security considerations further complicate this issue, leading to continuously strengthened technology export controls against China.

The Chinese Model reflects a state-led concentrated development path. The Chinese government has listed AI as a national strategic priority, providing infrastructure support for AI development through major projects like “New Infrastructure” and “East Data West Computing.” China’s advantages lie in its massive data resources, rich application scenarios, strong manufacturing capabilities, and the resource integration capacity of its national system.

Tech giants like Baidu, Alibaba, Tencent, and ByteDance have rapidly developed with government support, forming a complete ecosystem from cloud computing to terminal applications. China has reached world-advanced levels in computer vision, speech recognition, and natural language processing, and even leads globally in certain application scenarios.

The European Model emphasizes a regulation-first value orientation. The AI Act passed in 2024 is the world’s first comprehensive AI regulatory law, reflecting the EU’s emphasis on AI ethics and safety. The EU attempts to promote its regulatory standards globally through the “Brussels Effect,” playing a leading role in AI governance.

Although the EU lags relatively behind in AI technological innovation, its exploration in digital sovereignty, privacy protection, and algorithmic transparency provides important reference for global AI governance. The EU’s strategic focus is not on winning the technology race, but on ensuring AI development aligns with European values and interests.

The Rise of Technological Nationalism#

As the importance of AI technology becomes increasingly prominent, countries have begun to view it as a core element of national security and economic competitiveness. This has led to the rise of technological nationalism, manifested in the protection of key technologies and restrictions on foreign technologies.

The chip war is a typical manifestation of this trend. The United States has strengthened domestic semiconductor manufacturing capabilities through the CHIPS and Science Act, while implementing strict chip export controls on China. These measures aim to maintain America’s advantage in AI computing power and prevent key technologies from flowing to potential adversaries.

The competition for algorithmic sovereignty is equally fierce. All countries hope to master the autonomous R&D capabilities of core AI algorithms, avoiding being constrained by others in key technologies. China has proposed an “autonomous and controllable” technology development path, while the EU emphasizes “digital sovereignty,” both reflecting this trend.

Data localization has also become an important issue. Data is viewed as the “new oil” of the AI era, and countries are trying to ensure through legal means that their domestic data is not abused by foreign enterprises. This trend may lead to fragmentation of global data flows, affecting international cooperation in AI technology.

The Dialectics of Cooperation and Competition#

Despite intense competition, international cooperation remains an important driving force for AI development. In 2024, the UN General Assembly successively passed China-led resolutions on “Strengthening International Cooperation in AI Capacity Building” and US-led resolutions on “Safe, Secure and Trustworthy AI Systems for Sustainable Development,” demonstrating international consensus on AI governance.

The competition over technology standardization also reflects the complex relationship between cooperation and competition. All countries hope their technical standards will become international standards, but they also recognize the importance of unified standards for the entire industry’s development. International standardization organizations like IEEE and ISO have become important platforms for countries to compete for technological influence.

Global AI governance faces multiple challenges: How to balance innovation and security? How to coordinate different values and interests? How to prevent technological fragmentation from leading to a “digital iron curtain”? These questions have no standard answers and require countries to seek cooperation amid competition and find consensus amid differences.

A New Era of Neuro-Symbolic Fusion#

An important trend in current AI development is the rise of Neuro-Symbolic AI. This approach attempts to combine the learning capabilities of neural networks with the reasoning abilities of symbolic systems, creating more powerful and interpretable AI systems.

Traditional symbolic AI excels at logical reasoning and knowledge representation but has limitations in handling uncertainty and learning from data. Neural networks perform excellently in pattern recognition and statistical learning but lack interpretability and reasoning capabilities. Neuro-symbolic fusion attempts to combine the strengths of both while compensating for their weaknesses.

This fusion has already shown potential in multiple domains. In natural language processing, researchers are beginning to combine knowledge graphs with large language models to improve the factual accuracy and reasoning capabilities of models. In computer vision, symbolic reasoning is being used to enhance the logic and consistency of visual understanding. In robotics, the combination of symbolic planning and neural perception is creating more intelligent autonomous systems.

Explainable AI is an important application direction for neuro-symbolic fusion. As AI systems are applied in critical domains such as healthcare, finance, and justice, there is an increasing need to understand AI decision-making processes. Neuro-symbolic methods make AI decision-making processes more transparent and interpretable by introducing symbolic reasoning.

The Revolutionary Potential of AI for Science#

AI is becoming a powerful tool for scientific research, ushering in a new era of “AI for Science.” DeepMind’s breakthrough achievements with AlphaFold in protein structure prediction mark the beginning of AI playing a revolutionary role in fundamental scientific research.

AlphaFold’s success lies not only in its technological innovation, but more importantly in its transformation of the entire biological research paradigm. Traditional protein structure analysis requires years of time and enormous funding, while AlphaFold can predict high-precision protein structures within minutes. This capability is accelerating drug discovery, disease research, and bioengineering development.

Automation of scientific discovery is another exciting direction. AI systems are beginning to extract knowledge from vast scientific literature, generate new hypotheses, and even design experiments to verify these hypotheses. In materials science, AI is helping discover new material combinations; in astrophysics, AI is discovering new astronomical phenomena from massive observational data.

New paradigms for interdisciplinary research are also forming. AI, as a universal tool, can connect knowledge and methods from different disciplines. Biologists can use AI to analyze genetic data, physicists can use AI to simulate complex systems, and sociologists can analyze social networks through AI. This interdisciplinary fusion is generating unprecedented scientific insights.

The Rise of Bio-Inspired Computing#

Bio-inspired computing represents another important direction in AI development. Researchers are increasingly recognizing that the working principles of biological neural systems may provide important insights for AI development.

Neuromorphic chips attempt to simulate the working methods of biological neurons, achieving more efficient computation. Unlike traditional digital chips, neuromorphic chips use analog computing and event-driven processing methods, capable of performing complex computational tasks with extremely low power consumption. Intel’s Loihi chip and IBM’s TrueNorth chip are both important explorations in this field.

Quantum-classical hybrid computing also shows enormous potential. Quantum computing has exponential advantages in certain specific problems, while classical computing excels in versatility and stability. Combining the two may create unprecedented computational capabilities. Google’s AlphaQubit project is exploring the combination of quantum error correction and AI, paving the way for practical quantum computing.

The development of Brain-Computer Interface (BCI) technology provides possibilities for direct fusion of AI and biological intelligence. Companies like Neuralink are developing high-bandwidth brain-computer interfaces, attempting to achieve seamless connection between the human brain and AI systems. Although this technology is still in its early stages, it may ultimately change the fundamental way humans interact with AI.

The Physicalization of Embodied Intelligence#

Embodied AI represents the expansion of AI from the virtual world to the physical world. This type of AI not only possesses cognitive abilities but also has the capability to perceive and manipulate the physical environment. The transition from virtual assistants to robotic companions marks a significant leap in AI applications.

Technological integration of multimodal perception is key to embodied intelligence. Modern robots need to integrate multiple perceptual modalities such as vision, hearing, touch, and smell to form a comprehensive understanding of the environment. This integration is not only a technical challenge but also an important question in cognitive science: how to fuse information from different modalities into a unified world model?

New models of human-machine collaboration are forming. Future robots are not meant to replace humans but to collaborate with them. This requires robots to have the ability to understand human intentions, predict human behavior, and adapt to human habits. Collaborative robots (Cobots) have already been applied in manufacturing and may expand to more fields such as service industries, healthcare, and education in the future.

Technological Determinism vs Humanism#

The rapid development of AI has triggered profound thinking about the relationship between technology and humanities. Technological determinists believe that technological development has its inherent logic, and humans can only adapt rather than change this trend. Humanists emphasize the importance of human values, believing that technology should serve human welfare rather than the opposite.

Technological development indeed has its inherent driving force. Once a certain technological path is proven feasible, there will be strong economic and competitive pressure to drive its development. AI technology development also follows this logic: more powerful models, higher performance, and broader applications are all natural trends in technological development.

However, the direction of technological development is not completely uncontrollable. Humans can guide the direction of technological development through laws and regulations, ethical guidelines, and social norms. The EU’s AI Act is an embodiment of such efforts, attempting to ensure that AI development aligns with human values and social interests.

The key lies in finding a balance between technological progress and humanistic care. We cannot hinder beneficial innovation due to fear of technology, nor can we ignore basic human values in pursuit of progress. This requires the joint participation of technical experts, policymakers, ethicists, and the general public.

Redefining Work and Meaning#

The impact of automation on employment is one of the most concerning issues in the AI era. Historically, every technological revolution has eliminated some jobs while creating new employment opportunities. But the special characteristic of the AI revolution is that it can replace not only physical labor but also some mental labor.

This replacement is not a simple one-to-one substitution, but a reshaping of the entire labor structure. Some jobs requiring creativity, emotional understanding, and complex judgment may become more important, while some repetitive and rule-based jobs may be automated. This requires workers to continuously learn new skills and adapt to changing work environments.

A deeper issue is the reevaluation of human labor value. If machines can complete most productive work, what is the value of humans? This may drive us to rethink the meaning of work: from a means of livelihood to a path of self-realization, from economic activity to social contribution.

The possibility of a post-scarcity society is also worth considering. If AI and automation can significantly reduce production costs and improve production efficiency, human society may enter an era of relative material abundance. This will change our basic assumptions about wealth distribution, social security, and personal development.

Transformation of Education and Cognition#

Education systems face fundamental challenges in the AI era. Traditional education focuses on knowledge transmission, but in an era where AI can quickly access and process information, pure knowledge memorization becomes less important. The focus of education may shift toward capability development: critical thinking, creative problem-solving, emotional intelligence, ethical judgment, etc.

Human-machine collaborative educational models are emerging. AI can serve as personalized learning assistants, providing customized learning content and methods based on each student’s characteristics. Teachers’ roles may shift from knowledge transmitters to learning guides and character shapers.

Lifelong learning becomes an inevitable trend. In a rapidly changing technological environment, one-time school education is no longer sufficient to meet the needs of an entire career. People need to continuously update their knowledge and skills to adapt to new work requirements and social environments.

Challenges of Ethics and Governance#

The widespread application of AI brings unprecedented ethical challenges. Algorithmic bias is one of the most prominent issues. The training data for AI systems often reflects biases and inequalities in real society, and these biases are amplified and solidified by algorithms. How to ensure the fairness and inclusiveness of AI systems is a complex technical and social problem.

Privacy protection and data rights also face new challenges. AI systems require large amounts of data for training, but this data often involves personal privacy. How to promote AI development while protecting privacy requires finding balance at multiple levels including technology, law, and ethics.

Algorithmic transparency and accountability are another important issue. When AI systems make decisions that affect people’s lives, people have the right to know how these decisions are made. However, the “black box” nature of deep learning models makes such transparency difficult to achieve. How to improve the interpretability of AI systems while maintaining their performance is an important research direction.

存在论层面的思考#

AI的发展最终将我们带到存在论的根本问题：什么是人类？什么使我们独特？在AI能够模拟甚至超越人类某些能力的时代，我们需要重新定义人类身份。

人类的独特性可能不在于我们的认知能力，而在于我们的情感体验、道德直觉、审美感受、存在焦虑等更加深层的特质。这些特质构成了人类经验的核心，也是我们与AI系统的根本区别。

智能等级制的消解也是一个重要趋势。传统上，我们习惯于将智能视为一个线性的等级系统，人类处于顶端。但AI的发展显示，智能可能是多维度的、情境依赖的。不同类型的智能适用于不同的任务和环境，没有绝对的高低之分。

共生关系的构建可能是人类与AI未来关系的最佳模式。而不是将AI视为威胁或工具，我们可以将其视为伙伴和协作者。这种关系需要相互理解、相互尊重、相互依赖，共同创造更美好的未来。

Optimistic Scenario: The Golden Age of Intelligent Collaboration#

In the most optimistic scenario, humanity successfully achieves Artificial General Intelligence (AGI), and this AGI is friendly, controllable, and aligned with human values. Human-machine collaboration reaches perfect balance: AI handles complex computational and analytical tasks, while humans focus on creative, emotional, and moral work.

Science and technology achieve exponential progress. AI scientists can rapidly discover new scientific laws, design new materials and drugs, and solve major challenges facing humanity such as climate change, disease, and poverty. All fields including education, healthcare, and transportation are fundamentally improved through AI applications.

Global governance achieves effective coordination. Countries form consensus on AI development and governance, establishing effective international cooperation mechanisms. Technological development promotes cultural exchange and mutual understanding rather than exacerbating division and conflict.

In this scenario, humanity enters a new era of material abundance, spiritual fulfillment, and social harmony. Work becomes a path to self-realization, learning becomes lifelong pleasure, and creation becomes humanity’s primary activity.

Pessimistic Scenario: Intensification of Division and Conflict#

In the pessimistic scenario, AI development exacerbates existing social problems. The technology gap widens the wealth divide: elite classes mastering AI technology gain enormous advantages, while ordinary people face risks of unemployment and marginalization. Social division intensifies and class mobility decreases.

Geopolitical competition escalates into technological cold war. Countries mutually blockade each other to compete for AI hegemony, technology development becomes fragmented, and international cooperation is interrupted. Cyberspace splits into mutually isolated “digital iron curtains,” information flow is obstructed, and cultural exchange decreases.

Employment crisis triggers social unrest. Large numbers of jobs are replaced by AI, but creation of new employment opportunities is insufficient. Social security systems cannot cope with massive unemployment, leading to social instability and political polarization.

AI arms race brings security risks. Countries compete to develop AI weapon systems, lowering the threshold for war and increasing conflict risks. Loss of control over autonomous weapon systems may lead to accidental conflicts and humanitarian disasters.

In this scenario, technological progress does not bring universal welfare, but instead intensifies division and conflict in human society.

Realistic Scenario: Gradual Adaptation and Adjustment#

The most likely scenario is a realistic one between optimism and pessimism. AI technology continues to develop rapidly, but its impact is gradual and uneven. Technology development exhibits wave-like progress characteristics: breakthroughs and setbacks alternate, booms and calm coexist.

Institutional innovation lags behind technological development, but eventually catches up. Governments, enterprises, and social organizations experience learning and adaptation processes in responding to AI challenges. Some policy measures may fail, but through trial and error and adjustment, relatively effective governance frameworks are ultimately formed.

Regionally differentiated development models become the norm. Different countries and regions choose different AI development paths based on their cultural traditions, economic conditions, and political systems. This diversity brings both competition and promotes mutual learning and borrowing.

Humans demonstrate strong adaptability. Although AI brings challenges, humans gradually adapt to new environments through educational reform, skills training, social security, and other measures. New forms of work and lifestyles continuously emerge, and social structures achieve gradual adjustment.

In this scenario, AI development is neither utopia nor doomsday, but another major technological and social transformation in human history. Humans, with their unique wisdom and resilience, seek opportunities in challenges and maintain continuity in change.

Series Summary#

Looking back at the complete panorama of AI development constructed by these nine articles, we have witnessed a complete narrative from technological history to intellectual history. From the ambitious vision of the Dartmouth Conference to the rise and fall of neural networks; from the commercial exploration of expert systems to the stunning breakthroughs of deep learning; from the architectural revolution of Transformers to the emergent miracles of large language models; finally to the new chapter of multimodal fusion and embodied intelligence.

Each technological node is not merely engineering progress, but a deepening of human understanding of intelligence. We have moved from superstition about logical reasoning to emphasis on learning capabilities; from dependence on symbolic operations to grasping statistical patterns; from focus on single modalities to exploration of multimodal fusion. This cognitive evolution reflects the maturation of human thinking and expansion of vision.

The deep patterns of AI development gradually become clear: technological progress is not linear but spiral; each breakthrough builds on previous accumulation; each setback provides lessons for the next leap. More importantly, AI development is always closely related to human understanding of our own intelligence—it is both an extension of our cognitive abilities and a mirror for self-recognition.

Philosophical Reflection#

The mirror metaphor gains its deepest meaning here. AI is not merely a technological tool, but a mirror of human intelligence. Through creating and observing AI, we see more clearly the characteristics, limitations, and possibilities of human intelligence. This mirror relationship is dynamic and interactive: we design AI according to our understanding of human intelligence, while AI’s performance in turn influences our definition of intelligence.

Self-cognition and understanding of others interweave in this process. AI, as the “other,” helps us better understand the “self.” When we discover that AI can play chess, write poetry, and program, we begin to rethink the meaning of these abilities for humans. When we discover that AI lacks emotion, intuition, and moral judgment, we cherish these uniquely human qualities even more.

Questions about the essence and boundaries of intelligence will continue to perplex us. As AI capabilities continuously improve, the definition of “intelligence” may constantly evolve. Perhaps we will ultimately discover that intelligence is not a concept that can be precisely defined, but an open, multidimensional, context-dependent phenomenon.

Future Outlook#

The next technological cycle is already brewing. Emerging technologies such as neuro-symbolic fusion, quantum computing, bio-inspired computing, and brain-computer interfaces may trigger a new round of AI revolution. But regardless of how technology develops, human exploration of intelligence will never stop. This is our fundamental characteristic and eternal mission as intelligent beings.

Human civilization is entering a new stage. In this stage, intelligence is no longer an exclusive privilege of humans, but a capability that can be created, replicated, and enhanced. This change will profoundly affect our understanding of ourselves, society, and the universe. We need to redefine human value and meaning, reconstruct social organization and governance, and rethink the direction and goals of civilization.

Humanistic care becomes even more important in the age of intelligence. The more powerful technology becomes, the more we need guidance from humanistic spirit. Science tells us what is possible, but only humanities can tell us what should be. In an era of rapid AI development, we need more philosophical thinking, ethical reflection, artistic creation, and humanistic care.

Call to Action#

Facing the opportunities and challenges of the AI era, each of us has the responsibility and obligation to participate. First, we need to view AI development rationally, neither blindly optimistic nor overly pessimistic. AI is a tool, and its value depends on how we use it.

Second, we need to actively participate in technology governance. AI development should not be just the concern of technical experts and entrepreneurs, but the common responsibility of all society. We need more public participation, democratic discussion, and social supervision to ensure AI development serves humanity’s overall interests.

Finally, we need to maintain adherence to humanistic spirit. While pursuing technological progress, we cannot forget humanity’s basic values: dignity, freedom, equality, justice, and goodness. These values are not obstacles to technological development, but its ultimate goals.

The story of mirrors within mirrors continues. Each new mirror reflects new possibilities, each reflection brings new insights. In this infinite world of mirrors, we are both observers and observed, both creators and created. Let us write a new chapter of collaborative development between humanity and AI with open minds, rational thinking, and humanistic care.

In the mirror of intelligence, we see not only the future of technology, but the future of humanity. This future is full of uncertainty, but also full of hope. As long as we maintain wisdom, courage, and goodwill, we can find humanity’s path in this challenging era and create our tomorrow.

The “AI Origins” series concludes here. From the sprouting of technology to the maturation of thought, from historical retrospection to future prospects, we have completed a deep exploration of intelligence. Thank you to every reader for your companionship. Let us continue forward on the journey of the intelligent age, using human wisdom to illuminate the path ahead.