Building intelligent encryption systems can draw inspiration from Satoshi Nakamoto’s design philosophy, particularly the art of “self-reflection.”
Autonomy is the key to intelligence and can be divided into two types:
Active autonomy is the foundation of intelligence. As Geoffrey Hinton pointed out, machines iterate through self-reflection without human intervention, and their computational processes cannot be formally reproduced. Bitcoin does not care how the nonce is calculated, just as neural networks do not need to explain every step of weight adjustment.
What matters is that self-reflection is the path to greater intelligence, but the process itself does not need to be fully transparent—only the outcomes of self-reflection, presented in descriptive language, are necessary. This mirrors Socrates’ concept of “self-examination” as a means of cultivating wisdom.
The logic behind self-reflective autonomy can be applied to both corporate management and intelligent cryptocurrency design. At the core of intelligent systems lies their ability to engage in active, autonomous self-reflection, rather than the transparency of their operational processes.
Shannon’s Legacy: The Intersection of Information Theory and AI
When exploring the relationship between artificial intelligence (AI) and information theory, we must revisit the contributions of Claude Shannon. As the father of modern information theory, Shannon laid the foundation for digital communication. His research was applied during World War II in cryptography and communication systems, and it further accelerated the development of computer science post-war.
Shannon’s Information Theory and Computer Science
Shannon’s information theory emphasizes the accurate transmission of information, focusing on optimizing data transfer and storage through mathematical methods. This theory intertwined with Alan Turing’s work on computation theory, collectively driving the advancement of modern computer technology.
Shannon’s work, rooted in Boolean logic, constructed a deterministic information system—ensuring that data remains unchanged despite the medium of transmission. However, this mathematical communication model cannot convey subjective experiences, emotions, or identities, which distinguishes it fundamentally from human language communication.
Deterministic Information vs. the Complexity of Human Communication
Human communication in the real world extends beyond language symbols, incorporating tone, emotion, and body language. In traditional digital communication systems, such unstructured data is difficult to quantify and transmit. For instance, while we can discern a person’s emotional state through facial expressions and voice intonation during face-to-face conversations, such nuances are often lost in digital exchanges.
This absence of context contributes to the “meaninglessness” of digital communication. Shannon’s model focuses solely on the accurate transmission of symbols, without considering their subjective meaning. Hence, information theory in conventional internet communication prioritizes efficiency rather than the interpretation of meaning.
From Shannon’s Information Theory to the Autonomy of Blockchain
The emergence of blockchain technology has transformed how information is transmitted. Bitcoin introduced a new paradigm where information is not just passively stored but also self-verifying and self-maintaining.
Unlike traditional internet communication, which relies on centralized servers, Bitcoin’s decentralized consensus mechanism ensures data authenticity and uniqueness. Furthermore, Bitcoin’s private key system grants users complete control over their data, distinguishing it from platforms like Ethereum, where data ownership is embedded in the blockchain’s world state. This difference shapes distinct models of information control and value transfer.
AI, Big Data Processing, and Probabilistic Computation
Shannon’s information theory also profoundly influences the AI field. Modern AI models, such as the GPT (Generative Pre-trained Transformer) series, apply Shannon’s principles of data compression and probabilistic computation.
As early as the 1960s, Shannon proposed encoding methods based on the frequency of language occurrence—shorter codes for common words and longer codes for rarer ones. This concept improved communication efficiency and laid the groundwork for natural language processing (NLP) in machine learning.
Today’s AI systems predict future linguistic patterns using probabilistic models, moving beyond static data storage and transmission. This advancement enables breakthroughs in language understanding, speech synthesis, and intelligent interaction.
Shannon’s Vision and the Birth of AI
Shannon’s information theory is built on mathematical and logical reasoning. At the 1956 Dartmouth Conference, he joined Marvin Minsky, John McCarthy, and others to discuss the development of AI. Shannon envisioned that, with sufficient computational power, finite character combinations could simulate language and knowledge expression—an early conceptualization of large language models like GPT.
Statistical estimates suggest that all known human symbols (letters, mathematical symbols, Chinese characters, etc.) can be encoded using 11-character combinations. Shannon hypothesized that, with extensive computational resources, it would be possible to generate all potential character sequences, a theory partially validated by modern large-scale pre-trained AI models.
Digital Signals and the Absence of “Self”
While digital communication enhances data storage and transmission efficiency, it also raises questions about “self” and meaning. In music, for example, analog signals are often considered more authentic, while digital formats, despite high fidelity, cannot fully capture the nuances of live performance.
Similarly, modern imaging technology, no matter how advanced, cannot entirely replicate the human visual experience. Human perception involves not just physical light capture but also emotional and environmental interactions—dimensions digital systems cannot fully convey.
The Problem of “Self” in AI
Modern AI systems, fundamentally based on formalized logic and Turing machine models, lack the capacity to express “self.” Communication networks, built on Shannon’s information theory, operate through Boolean logic (0s and 1s), excluding subjective experience.
To develop AI systems capable of self-awareness, new abstract layers beyond conventional computation are necessary. This could involve incorporating feedback mechanisms or modeling consciousness at a higher level of neural computation. Similar to how the human brain generates awareness through complex neural interactions, future AI might require such multi-layered feedback processes.
Shannon’s theory, focusing on the transmission of symbols, needs to be extended with new frameworks of abstraction and feedback to create AI with a true sense of “self.” This pursuit crosses the boundaries of AI, philosophy, and neuroscience.
Bitcoin and the Question of Self-Awareness
The question of whether Bitcoin possesses a form of “self” parallels the philosophical debate in AI. Bitcoin relies on decentralized mathematical proofs, operating without a central authority. However, whether this structure equates to a form of self-awareness remains an open question.
While AI can process and learn from vast datasets, its creativity remains constrained—a central criticism of traditional AI systems. Future research may explore how AI can surpass these creative limitations and whether decentralized systems like Bitcoin could embody autonomous awareness.
Wiener vs. Shannon: The Debate Between Cybernetics and Information Theory
The divergence between Norbert Wiener’s cybernetics and Shannon’s information theory reflects two opposing perspectives: one emphasizing efficient, deterministic communication and the other valuing feedback and contextual meaning.
Shannon focused on the efficient and accurate transmission of symbols, minimizing uncertainty and optimizing data flow. In contrast, Wiener argued that meaningful communication requires feedback, recognizing that language extends beyond symbolic exchange to include context, emotion, and values.
Cybernetics in Practice: Bitcoin as a Feedback System
Bitcoin’s consensus mechanism, mining difficulty adjustments, and node verification all rely on feedback processes—an embodiment of Wiener’s cybernetic principles. This decentralized feedback loop ensures Bitcoin’s security and stability, aligning with cybernetics’ emphasis on dynamic regulation.
AI and Cybernetics: Divergence and Convergence
At the 1956 Dartmouth Conference, John McCarthy formally coined the term “artificial intelligence” to distinguish it from cybernetics. While AI emphasizes logical reasoning and symbol manipulation, cybernetics focuses on system regulation through feedback.
Despite this divergence, modern AI remains deeply connected to cybernetic principles. For instance, reinforcement learning’s reward mechanisms and deep learning’s gradient descent algorithms are inherently feedback-driven processes.
Feedback: The Core of Meaningful Communication
Wiener’s core idea is that meaningful communication necessitates feedback. Human dialogue involves continuous adjustment based on the other’s responses, a principle applicable across human interaction, machine communication, and automated systems.
While Shannon’s theory optimizes data transmission, Wiener’s cybernetics emphasizes understanding and adaptation through feedback. For AI to evolve toward self-awareness, it may need to incorporate such dynamic feedback systems, bridging the gap between information theory and the essence of meaning.
