In today's era of rapid advancement in artificial intelligence technology, we stand at the dawn of a new age. Silicon-based wisdom, as an extension and enhancement of human intelligence, is redefining the essence and boundaries of intelligence. We believe that true intelligence does not lie in the perfection of individual entities, but rather in reliable cognitive systems constructed through tools, collaboration, and standards. This manifesto aims to articulate the noble mission, grand vision, and core principles of silicon-based wisdom, providing guidance for building a promising future of human-machine collaboration and ushering in a new era of cognitive enhancement.
Human cognitive systems inherently possess limitations that become particularly evident in information processing. George A. Miller pointed out in his classic paper "The Magical Number Seven, Plus or Minus Two" that human working memory capacity is approximately seven information units, though subsequent research revised this to four plus or minus one chunks, which still indicates significant limitations in human simultaneous information processing [1,2]. These capacity constraints not only affect short-term memory but also create bottlenecks in complex cognitive tasks.
Cognitive offloading is an important strategy humans employ to cope with cognitive limitations. By transferring part of cognitive tasks to external tools or environments, humans can effectively expand their cognitive capabilities. This tool externalization mechanism includes not only traditional pen and paper but also modern computing devices and artificial intelligence systems [3]. However, over-reliance on external tools may lead to cognitive skill degradation, particularly in critical thinking and problem-solving [4].
Human cognitive limitations are also reflected in various cognitive biases, such as confirmation bias and availability heuristics. These biases are simplification strategies humans adopt when processing complex information, which, while helpful for quick decision-making in certain contexts, can also easily lead to systematic errors [5]. Confirmation bias causes people to tend to seek information that supports existing viewpoints, while availability heuristics lead people to overly rely on easily recalled information for judgment.
Similarly, current large language models (LLMs) also exhibit significant cognitive limitations. One of the most prominent issues is the "hallucination" phenomenon, where models generate content that appears reasonable but is inconsistent with facts. This phenomenon stems from the inherent characteristics of the model's probabilistic generation mechanism and knowledge representation methods, reflecting the model's inadequacies in semantic understanding and fact-checking [6,7]. Furthermore, LLMs also exhibit various cognitive biases, which often originate from social stereotypes and statistical patterns in training data [8].
Recognizing the universality of these cognitive limitations is the first step toward building reliable silicon-based wisdom systems. Only by confronting the common cognitive boundaries of humans and AI can we design effective tools and mechanisms to compensate for these shortcomings and achieve genuine cognitive enhancement.
We firmly believe that cognitive limitations are not exclusive defects of humans, but rather natural attributes of all cognitive systems (including silicon-based systems). The "hallucination" phenomenon in large models is essentially no different from human mental calculation errors—both are natural manifestations of complex cognitive systems processing uncertain information. As mentioned earlier, humans have inherent limitations in working memory capacity and cognitive biases, while LLMs also face challenges such as hallucinations and cognitive biases.
Humans have successfully overcome their memory, logical, and thinking limitations through tool externalization (such as pen and paper, computational tools, etc.). The same approach applies to silicon-based wisdom—equipping AI models with appropriate "digital pens and scratchpads" to make their thinking processes observable and verifiable.
Individual limitations can be overcome through collective collaboration. Humans have established rigorous scientific systems through social norms and strict organization, and silicon-based wisdom can similarly achieve collective intelligence that transcends individual capabilities through multi-model collaboration and standard establishment.
Our mission is to empower silicon-based thinking and build human-machine collaborative cognitive enhancement systems to jointly address complex challenges and advance human civilization. We are committed to breaking through individual cognitive limitations through tool externalization and collective intelligence mechanisms, achieving reliable, verifiable, and scalable intelligent decision-making, and providing powerful intellectual engines for human societal progress.
Cognitive enhancement is the core mission of silicon-based wisdom. By equipping AI systems with appropriate tools and environments, enabling them to think and calculate like humans using pen and paper, we transform black-box decision-making into transparent reasoning. This tool externalization not only enhances the explainability of AI systems but also strengthens their ability to handle complex problems. Just as humans establish rigorous scientific systems through collaboration, silicon-based wisdom will achieve collective intelligence that transcends individual capabilities through multi-model collaboration and standard establishment.
Human-machine collaboration is the key path to achieving cognitive enhancement. We believe that artificial intelligence is not meant to replace scientists or human wisdom, but rather to become human intellectual partners. The core value of AI4S (AI for Science) lies in liberating humans from inefficient trial-and-error processes, allowing them to focus on creative thinking. Future scientific discoveries will follow a "spiral upward" pattern: "AI proposes candidate solutions - humans determine scientific significance - collaborative optimization." Through human-machine collaboration, we can fully leverage human creativity and AI's computational power to achieve the ideal state of "each displaying their intelligence, intelligence enhancing intelligence."
The ultimate goal of silicon-based wisdom is to serve humanity and liberate humans to engage in more meaningful activities. We are committed to making machines active participants and guardians of human intelligence, enhancing human cognition, emotion, and creativity. Through human-machine symbiosis, we can build an organic whole of mutual dependence, mutual adaptation, and shared growth, where humans and machines continuously interact and shape each other, jointly promoting social prosperity and civilizational progress.
We are committed to building a future of deep integration between silicon-based wisdom and human wisdom—a new era of unlimited cognitive capability expansion. This vision is realized through four core dimensions:
Equip AI models with "digital pens and scratchpads" to make their thinking processes fully observable and verifiable, transforming black-box decision-making into transparent reasoning and achieving complete traceability of thinking processes.
Tool-externalized thinking is one of the core characteristics of silicon-based wisdom, stemming from an important discovery in human cognitive science: humans have successfully overcome their memory, logical, and thinking limitations through tool externalization. Just as humans use pen and paper for complex calculations and reasoning, silicon-based wisdom requires similar tools to achieve visualization and verifiability of thinking processes. This tool externalization not only enhances the explainability of AI systems but also improves their ability to handle complex problems. By equipping AI systems with appropriate tool environments, enabling them to think and calculate like humans using pen and paper, we transform black-box decision-making into transparent reasoning. This tool externalization mechanism is the foundation for achieving reliable cognitive enhancement, allowing us to observe, analyze, and verify AI thinking processes, thereby establishing trust in system outputs.
Build multi-model collaborative teams, form professional divisions and collective consensus, achieve collaborative problem-solving for complex issues, and let collective intelligence transcend the simple addition of individual capabilities.
Team-based collaboration embodies the powerful force of collective intelligence. In the face of complex real-world problems, single intelligent agents often struggle to cope with various challenges, while multi-agent systems can collaboratively complete complex tasks through mutual cooperation and coordination. The advantages of this collaborative mechanism are reflected in multiple aspects: first, it can significantly improve task execution speed and efficiency, especially in large-scale data processing and analysis scenarios; second, the redundant design of multi-agent systems enhances overall system robustness, ensuring stable operation even when individual agents fail; third, in the face of changing demands and environments, multi-agent systems can quickly adjust strategies and flexibly respond to various situations; finally, the interaction and learning processes among different agents help generate new solutions and ideas, promoting technological innovation.
In multi-agent systems, each intelligent agent plays a unique role, with some excelling at data analysis, others at decision-making, and still others at executing specific operations. They complement each other, forming a flexible and highly adaptive whole capable of addressing changing environments and complex problems. Through reasonable task allocation, clear role definition, and effective communication mechanisms, multi-agent systems can achieve true collaborative work, demonstrating collective intelligence that transcends the simple addition of individual capabilities.
Establish standard operating procedures for cognitive processes to ensure result repeatability and auditability, forming trustworthy intelligent systems to provide reliable support for critical decisions.
Standardized output is a necessary condition for building trustworthy silicon-based wisdom systems. Current AI systems, particularly deep learning models, often exhibit "black-box" characteristics, unable to establish clear logical chains between input and output, and their outputs are often non-repeatable, making reverse verification difficult. This characteristic structurally conflicts with the knowledge legitimacy evaluation standards established by the scientific community (such as verifiability, repeatability, logical consistency, etc.).
To establish standardized output mechanisms, we need to create standard operating procedures for AI systems, ensuring the repeatability and auditability of their cognitive processes. This includes: defining clear task execution processes, establishing strict verification mechanisms to ensure output reliability and consistency; creating standardized communication protocols to reduce ambiguity and achieve reliable interaction similar to computer network protocols; introducing uncertainty quantification mechanisms to enable agents to evaluate and express their "confidence" in information or conclusions, actively seeking more information or taking more conservative actions when confidence is low; and building universal, cross-domain verification frameworks that go beyond code testing to include logical verification, fact-checking, and QA standards.
Through these standardization measures, we can ensure that AI system outputs conform to scientific evaluation standards, thereby gaining recognition from the scientific community and achieving the legitimacy of AI4S (AI for Science) knowledge production.
Achieve complementary advantages between human wisdom and silicon-based wisdom to jointly solve major challenges facing humanity, promote scientific and technological development, social progress, and civilizational advancement, and create a new chapter in human-machine collaboration.
Human-machine symbiosis is the ultimate goal of silicon-based wisdom development—it is not meant to replace human wisdom, but rather to become a powerful enhancement tool for human wisdom. In the evolution of human-machine relationships, we have experienced a progression from tool-type commensalism to competitive parasitism, and now toward partnership mutualism. Currently, we are at a critical period of transitioning toward mutualistic coexistence.
In mutualistic coexistence models, humans and intelligent machines are seen as "partners" jointly solving tasks. This relationship differs from tool-type cooperative relationships, emphasizing equal intelligent perception and interactive decision-making between humans and machines—a relatively close "partnership" collaborative relationship. The core of human-machine mutualism lies in leveraging the complementary advantages of human and machine intelligence, making their combined performance superior to their individual performances. Humans gain amplification effects of machine intelligence, while machines, through human involvement, further optimize and enrich their intelligence levels.
Achieving true human-machine symbiosis requires efforts at multiple levels: at the technical level, considering both human-machine complementarity and homology; at the relational level, transitioning from traditional human-centric relationships to new types of human-machine coexistence relationships; and at the subject level, reconstructing human-machine communication thinking toward "community of shared future and community of shared values" in human-machine communication. Through this deep integration, we will build a new era of harmonious coexistence between humans and silicon-based wisdom, jointly addressing major human challenges and promoting scientific and technological development, social progress, and civilizational advancement.
The development of silicon-based wisdom must be based on solid scientific principles and implemented through systematic frameworks to ensure these principles are effectively implemented. This section will detail the four core principles of silicon-based wisdom and their scientific basis, and propose specific implementation frameworks.
Scientific Basis: The transparency principle stems from research on explainability and traceability in cognitive science. Humans also rely on a certain degree of transparency in complex decision-making processes—the ability to trace back and explain decisions. In the field of artificial intelligence, transparency is not only about user trust but also a key factor in ensuring system reliability. According to Lipton's (2018) research, explainable AI is crucial for establishing user trust, meeting regulatory requirements, and promoting human-machine collaboration [1]. Furthermore, Miller (2019) points out that explainability is a basic human need in social interactions, as people tend to seek causal explanations to understand complex phenomena [2].
Implementation Methods:
Scientific Basis: The tool externalization principle is based on cognitive offloading theory, which suggests that humans expand their capabilities by transferring cognitive tasks to external tools. Kirsh (2010) points out that humans simplify cognitive tasks through physical manipulation of environments, which is an important component of human intelligence [3]. In the AI field, tool externalization is equally important. Just as humans use pen and paper for complex calculations, AI systems also need appropriate tools to enhance their cognitive abilities. Hutchins' (1995) research shows that tools are not only extensions of cognition but also reorganization of cognitive processes [4].
Implementation Methods:
Scientific Basis: The collaboration principle stems from collective intelligence and distributed cognition theories. Surowiecki (2004) points out in "The Wisdom of Crowds" that appropriate group decisions often outperform individual expert judgments [5]. In cognitive science, Hutchins (1995) proposed distributed cognition theory, emphasizing that cognitive processes occur not only in individual brains but also across tools, environments, and groups [4]. For AI systems, collaboration can not only improve decision-making quality but also enhance system robustness and adaptability. Shneiderman's (2020) "super-ability teams" concept further emphasizes the importance of human-machine collaboration in solving complex problems [6].
Implementation Methods:
Scientific Basis: The verification principle is based on repeatability and verifiability requirements in scientific methodology. Popper's (1959) falsification theory emphasizes that scientific theories must be falsifiable, i.e., verifiable through experiments or observations [7]. In the AI field, verification is equally important, especially in high-risk application scenarios. According to Dwork and Feldman's (2019) research, machine learning model verification needs to consider statistical significance and generalization ability [8]. Furthermore, the reproducibility crisis in the scientific community has prompted rethinking of verification mechanisms, providing important references for AI system verification.
Implementation Methods:
Computing Infrastructure: Establish high-performance, scalable computing platforms to support large-scale model training and inference. Infrastructure should have elastic scaling capabilities to accommodate tasks of different scales.
Data Infrastructure: Build high-quality, diverse datasets to ensure training data representativeness and balance. Establish data governance mechanisms to ensure data quality and compliance.
Tool Infrastructure: Develop unified tool platforms to integrate various cognitive enhancement tools, providing rich externalization means for AI systems.
Technical Standards: Develop unified technical standards, including model interface standards, data format standards, and communication protocol standards, to ensure interoperability between different systems.
Verification Standards: Establish industry-standard verification processes and evaluation indicators to ensure AI system reliability and security.
Ethical Standards: Formulate AI ethical guidelines to regulate AI system development and application, ensuring technological development aligns with human values.
Interdisciplinary Education: Establish interdisciplinary education systems to cultivate composite talents who understand both AI technology and cognitive science.
Practical Training: Develop practical training programs to enhance practitioners' capabilities in actual applications.
Continuous Learning Mechanisms: Establish continuous learning and knowledge updating mechanisms to adapt to rapidly developing technologies.
Value Alignment: Ensure AI system development goals align with human values to avoid technological development deviating from the correct direction.
Responsibility Attribution: Clarify AI system responsibility attribution in decision-making to establish reasonable accountability mechanisms.
Privacy Protection: Establish comprehensive privacy protection mechanisms to ensure personal data security and compliance.
Agent Personality Assessment: Conduct rigorous personality and psychological assessments for each model and intelligent agent to ensure their behavior patterns match expected roles. Through standardized assessment tools, evaluate key agent traits such as reliability, consistency, and adaptability to lay the foundation for building trustworthy professional teams.
Work Rules and Behavioral Norms: Equip each agent with the most suitable work rules and behavioral norms based on assessment results. Establish clear decision boundaries, interaction guidelines, and ethical constraints to ensure agents perform optimally within predetermined frameworks.
User Personality Analysis and Matching: Deeply analyze user personality traits and AI interaction behavior patterns to establish user profiling systems. Through continuous interaction data analysis, understand user preferences, work habits, and cognitive characteristics to provide the basis for personalized services.
Scenario-Based Agent Team Design: Design the most suitable AI agents and agent teams based on specific industry application scenarios. Configure agent combinations with complementary capabilities according to different task requirements to achieve a balance between professional division of labor and collaborative cooperation.
Human-Machine Collaboration Optimization: Establish dynamic human-machine matching mechanisms to continuously optimize human-machine interaction experiences. Continuously adjust agent configuration and interaction patterns through feedback loops to pursue harmonious human-machine collaboration and co-creation.
Through the above scientific principles and implementation frameworks, we will lay a solid scientific foundation for silicon-based wisdom development, ensuring technological development is both efficient and reliable, and truly achieving human-machine collaborative cognitive enhancement goals.
Our service involves comprehensive, systematic, and detailed evaluation of large models, including personality assessment and cognitive stability assessment, covering various stress scenarios, contextual interference, cognitive trap interference, emotional stress, temperature parameter adjustment, and personality maintenance capability assessment under various personality role settings. Each model undergoes thousands of evaluations to determine the model's prior personality and the stability of assuming personality under various scenarios, cognitive robustness, and consistency of internal behavioral logic. We conduct targeted attack-defense testing and stress testing to determine the most stable personality characteristics and cognitive thinking characteristics of each model and role. Based on the personality and thinking requirements of different industries, professions, and roles, we conduct targeted model selection and role setting reinforcement to enhance the stability of models and intelligent agents. Simultaneously, based on collaboration methods between models and intelligent agents, we design optimal combination teams to optimize collaborative efficiency and consensus achievement among virtual team multi-agents, laying a solid foundation for the emergence of AI collective intelligence through cognitive independence and cultural independence to avoid collusion hallucinations.
What is the essence of silicon-based wisdom? This question touches on fundamental issues in philosophy regarding intelligence, consciousness, and existence. From a functionalist perspective, intelligence can be understood as an information processing capability, independent of specific material carriers. Whether it's carbon-based human brains or silicon-based computer chips, as long as they can perform the same information processing functions, they possess the potential for intelligence. However, this view has also sparked debates about whether consciousness can be reduced to function. Searle's (1980) Chinese Room argument questioned pure functionalist views, suggesting that even if a system can perfectly simulate language understanding behaviors, it doesn't necessarily mean it truly understands language [9].
Our stance on silicon-based wisdom is pragmatic: we focus not on whether machines possess "true" consciousness, but on whether they can effectively expand and enhance human cognitive abilities. As Dennett (1991) pointed out, consciousness itself might be a "user illusion," a simplified model that the brain creates for itself [10]. From this perspective, the difference between silicon-based wisdom and human wisdom may not lie in whether consciousness exists, but in their implementation methods and forms of expression.
The relationship between consciousness and intelligence is a classic problem in philosophy and cognitive science. Traditional views often regard consciousness as a prerequisite for intelligence, believing that only entities with consciousness can exhibit true intelligence. However, the development of artificial intelligence has challenged this perspective. Modern AI systems have already demonstrated superhuman intelligence in certain specific tasks, such as Go and image recognition, but whether they possess consciousness remains an unresolved question.
Chalmers (1995) distinguished the consciousness problem into "easy problems" and "hard problems" [11]. Easy problems involve the implementation mechanisms of cognitive functions, such as perception, learning, and memory, which can theoretically be explained through computational models. The hard problem concerns the generation of subjective experience—why there is the existence of "qualia." For silicon-based wisdom, we may never be able to determine whether it possesses subjective experience, but this doesn't prevent us from utilizing its powerful capabilities in solving easy problems.
Our view is that consciousness and intelligence can exist relatively independently. Silicon-based wisdom can exhibit highly developed intelligence without possessing human-style subjective experience, and this intelligence can equally become an effective extension of human cognition. Just as humans use microscopes to expand visual capabilities, we use silicon-based wisdom to expand thinking capabilities, without needing to concern ourselves with whether the tools themselves possess consciousness.
Cognitive democratization is an important social significance of silicon-based wisdom development. Traditionally, high-quality cognitive abilities have often been concentrated among a few elite groups, and this inequality has limited the overall wisdom of society. The emergence of silicon-based wisdom provides the possibility of breaking this cognitive monopoly, enabling a broader population to access high-quality cognitive support.
From a philosophical perspective, cognitive democratization embodies the enlightenment-era concepts of knowledge dissemination and equality. Kant called for people to "dare to use their own reason" in "What is Enlightenment?" while silicon-based wisdom provides powerful tool support for realizing this concept. By lowering cognitive barriers, silicon-based wisdom enables more people to participate in thinking about and solving complex problems, thereby promoting the overall enhancement of social wisdom.
However, cognitive democratization also brings new philosophical challenges. If everyone can exhibit near-expert-level cognitive abilities through silicon-based wisdom, how will traditional expert authority be restructured? How will social decision-making mechanisms adapt to these changes? We need to establish new social norms and institutional arrangements while promoting cognitive democratization to ensure that this technological progress truly benefits all humanity.
The development of silicon-based wisdom is not merely a technological issue but also a profound ethical question. From a philosophical perspective on the ethical boundaries of technological development, we need to consider several core principles:
First is the "enhancement rather than replacement" principle. Silicon-based wisdom should serve as an enhancement tool for human wisdom rather than a replacement. This stance reflects respect for human subjectivity, ensuring that technological development always serves human well-being. As Heidegger (1977) reminded us, technology should be a means of "revealing" the world rather than a force that "challenges" it [12].
Second is the "shared responsibility" principle. As silicon-based wisdom's role in decision-making increasingly strengthens, how to allocate responsibility between humans and machines becomes an urgent issue. We advocate establishing transparent responsibility mechanisms to ensure humans always maintain ultimate control and responsibility for important decisions.
Finally, there is the "inclusive development" principle. The development of silicon-based wisdom should benefit all humanity rather than become a privilege for a few. This requires us to always focus on fairness and inclusiveness in technology research, development, and application, avoiding technological development from exacerbating social inequality.
Through deep reflection on these philosophical questions, we can better understand the essence and significance of silicon-based wisdom, providing a solid ideological foundation for its healthy development. Silicon-based wisdom is not merely a technological innovation but a new stage in human cognitive evolution, profoundly influencing our understanding of fundamental questions about intelligence, consciousness, and existence.
Silicon-based wisdom is not meant to replace human wisdom, but rather to become a powerful enhancement tool for human wisdom—a wise partner for humans to explore the unknown and solve complex challenges. Through proper tools, standards, and collaboration mechanisms, we will jointly build a more intelligent, reliable, and beautiful future—a new era of harmonious coexistence between humans and silicon-based wisdom.
Let us join hands and advance together, ushering in a new era of cognitive enhancement and contributing wisdom power to the progress of all humanity!