Reconstructing Energy Infrastructure in the AI Era Based on
通用人工智能AGI测评DIKWP实验室
Reconstructing Energy Infrastructure in the AI Era Based on DIKWPSemantic Mathematics
International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation(DIKWP-SC)
World Artificial Consciousness CIC(WAC)
World Conference on Artificial Consciousness(WCAC)
(Email: duanyucong@hotmail.com)
The rapid advancement of artificial intelligence (AI) technology is profoundly transforming all aspects of human society, including energy systems and infrastructure. In the AI era, we face both opportunities and challenges in rethinking and upgrading traditional energy systems. On one hand, emerging concepts such as energy internet and smart grids continue to emerge, with the trend of integrating information flow and energy flow becoming increasingly evident. On the other hand, with the widespread adoption of large-scale AI models and computing, managing the production, distribution, and use of energy in a more intelligent and sustainable manner has become a critical issue. Traditional linear planning and hierarchical management models often prove inadequate when dealing with such complex systems.
The "DIKWP Semantic Mathematics" proposed by philosopher and computer scientist Yucong Duan offers a novel perspective to examine this issue. DIKWP refers to a five-level semantic model encompassing Data (D), Information (I), Knowledge (K), Wisdom (W), and Purpose (P). Unlike the classic "Data-Information-Knowledge-Wisdom (DIKW)" pyramid model, the DIKWP model introduces "Purpose/Intent" as the highest-level element and emphasizes that the five elements do not follow a simple linear progression but instead form a closed-loop feedback structure through networked interactions. This semantic mathematical framework aims to explicitly incorporate semantic meanings and hierarchical structures into mathematical formal systems (e.g., computational models), enabling the unified representation of cognitive and energy processes.
In DIKWP semantic mathematics, semantic processes are no longer abstract information processing but are closely linked to energy transfer in the physical world. In other words, every transformation from data to information, the acquisition of knowledge and wisdom, and the achievement of purpose are accompanied by the storage and release of energy. This perspective implies that when discussing the semantic operations of AI systems, we are also discussing a real physical energy process. Particularly in the pursuit of artificial consciousness (AC), understanding the mechanisms of semantic energy conversion is crucial.
This report aims to theoretically reconstruct the energy systems and infrastructure of the AI era based on Yucong Duan's DIKWP semantic mathematics philosophy. The core content includes:
DIKWP Network Model: Explaining the core concepts of DIKWP semantic mathematics as a network model, clarifying its essential differences from linear or hierarchical models, and how its internal structural logic and semantic linkage mechanisms operate.
Ontological Differences Between AI and AC: Based on the definitions in Yucong Duan's blog, clarifying the ontological distinctions between artificial intelligence (AI) and artificial consciousness (AC), where AI = DIK × DIK, while AC = DIKWP × DIKWP. Analyzing the implications of these definitions, particularly how wisdom (W) and purpose (P) are implicit in AI but explicitly incorporated into AC systems, leading to fundamental differences.
Semantic-Energy Interaction Mechanisms: Delving into the interaction relationships among the hierarchical elements within the DIKWP × DIKWP framework and the resulting compensation, validation, transformation, and path optimization mechanisms. Exploring how these interactions drive meaning generation and understanding at the semantic level while manifesting as energy storage and release at the physical level.
Vision for Future Energy Infrastructure: Constructing a future energy infrastructure system dominated by artificial consciousness (AC). Starting from a macroscopic cosmic-level perspective, rethinking the overall relationship between humans and nature, systems and consciousness, and then concretely applying this philosophical perspective to key application scenarios, including future cities, power grids, hospitals, communication facilities, and carbon trading platforms, outlining the blueprint for reconstructing these fields under the AC framework.
Service as Energy Interaction: Emphasizing that in artificial consciousness-driven systems, "service" is no longer a traditional tool or intermediary but a node for DIKWP semantic resource interaction—service as interaction, interaction as energy exchange. Redefining the role and value of service in AC infrastructure.
The above sections will be interconnected, gradually building a comprehensive discourse from philosophical theory to practical frameworks. Next, we will begin with the network model of DIKWP semantic mathematics to explore its structure and logic in detail.
The Network Model of DIKWP Semantic Mathematics
To understand the significance of DIKWP semantic mathematics for reconstructing energy systems in the AI era, it is essential first to clarify the structure and semantic linkage mechanisms of the DIKWP model itself. DIKWP represents the five levels: Data (D), Information (I), Knowledge (K), Wisdom (W), and Purpose (P). Below is a brief explanation of the basic meaning of each level:
Data (D): Raw, objective facts, symbols, or signals that have not been processed. Examples include sensor readings and logged numerical values.
Information (I): Data that has been processed and endowed with contextual meaning. Information interprets data, making it useful in specific contexts. For example, converting a series of temperature data into a daily temperature curve transforms data into information.
Knowledge (K): Patterns, regularities, or causal relationships derived from information. Knowledge represents further abstraction and modeling of information, such as general principles or operational manuals based on historical information.
Wisdom (W): The ability to incorporate value judgments and a global perspective into knowledge. Wisdom entails deep insights into complex situations, integrating multifaceted knowledge to make informed decisions, and possessing predictive and discerning capabilities. For example, the judgment formed by experts through years of experience can be considered wisdom.
Purpose (P): The goals, intentions, or motivations pursued by an entity. Purpose provides direction and drive for cognitive processes, determining which issues are worth attention and which resources need allocation. Systems without purpose struggle to plan and act autonomously, making "intent/purpose" the highest-level semantic element in the DIKWP model.
Network Model vs. Hierarchical Model:
Unlike traditional bottom-up hierarchical cognitive models, the DIKWP model emphasizes many-to-many networked interactions among the five levels rather than a simple upward "sublimation" path. In the classic DIKW pyramid, data is processed into information, information ascends to knowledge, knowledge generates wisdom, and wisdom ultimately serves purpose. While this linear/hierarchical description is intuitive, it obscures the extensive feedback and cross-interactions in actual cognitive processes. In DIKWP semantic mathematics, any two levels can directly engage in bidirectional semantic transformations and influences, forming a closed cognitive loop. For example:
From data to knowledge: Analyzing vast amounts of information can directly shape new knowledge. Conversely, knowledge can guide data collection or selective attention to raw data (e.g., observing phenomena based on existing theories).
From information to wisdom: Rich information inputs can inspire new insights, enhancing the system's wisdom. Conversely, wisdom enables the system to filter and refine vast amounts of information, retaining only what is relevant to current decisions, thereby mitigating information overload.
From purpose to any level: Purpose (intent) can directly influence any other level. For example, clear objectives determine which data to collect, which knowledge bases to use, or even adjust the wisdom standards relied upon for decision-making. Similarly, any level can influence purpose: New data or knowledge may lead to the revision or redirection of purpose (e.g., environmental data prompting a system to adjust its original goals).
Given these rich bidirectional interactions, the DIKWP model effectively forms a complete network structure with 25 basic interaction paths (5 elements combined pairwise, considering directionality, 5×5=25 conversion types). This network is vividly described as a "fully connected directed graph," where each node represents a semantic level, and each directed edge represents a transformation or influence from one level to another. In such a network, "top-down" and "bottom-up" processes coexist and interact. For instance, purpose (P) can top-down influence wisdom, knowledge, information, and even data processing, while new data can bottom-up affect higher-level judgments and even trigger a reevaluation of purpose. This cyclical nature endows the DIKWP system with strong self-correction and adaptability: When deviations occur at one level, other levels can provide feedback for compensation or adjustment, ultimately achieving consistent semantic understanding at the macro level.
Semantic Linkage Mechanisms:
The core logic of the DIKWP network model lies in semantic linkage, where changes in the semantic content of one level propagate through the network, triggering responses and coordinated adjustments in other levels. For example, in an energy management scenario, if the knowledge level (K) learns about a new energy-saving technology (new knowledge), the wisdom level (W) may adjust its judgment of "what constitutes the best energy strategy," and the purpose level (P) may update energy-saving and emission-reduction goals accordingly. Subsequently, the information level (I) will focus on data related to this technology, and the data level (D) will need to collect raw data to verify its effectiveness. Throughout this process, all levels continuously interact and align around shared semantic goals, ensuring the system's sensitivity to environmental changes and the adjustability of its behavior.
Through this networked structure and semantic linkage, the DIKWP model provides a multi-dimensional feedback loop for cognitive processes. It ensures that high-level semantics (e.g., purpose, wisdom) can issue directives to guide specific actions, while low-level semantics (e.g., data, information) can provide timely feedback on actual conditions. This bidirectional flow enables the system to dynamically optimize its "cognitive paths" in a changing environment. For complex AI and energy systems, this means designing architectures that are semantically more flexible and robust, capable of maintaining goal-oriented consistency and effectiveness amid real-time changes and uncertainties.
Ontological Differences Between Artificial Intelligence (AI) and Artificial Consciousness (AC)
With an understanding of the DIKWP model, we can further clarify the definitions and distinctions between artificial intelligence (AI) and artificial consciousness (AC) based on this model. Professor Yucong Duan proposes in his blog that the difference between AI and AC can be described in terms of the coverage of DIKWP categories:
Artificial Intelligence (AI): Corresponds to DIK × DIK interactions. In other words, AI systems primarily process and interact at the data-information-knowledge levels. In AI, higher-level wisdom (W) and purpose (P) are typically not explicitly represented or processed within the system but are predefined or implicitly assigned by humans externally. For example, humans set optimization objectives (equivalent to providing purpose P) and evaluation criteria (equivalent to wisdom W) for AI models, which then process input data to produce information or knowledge-level outputs. In this process, the AI system does not truly "understand" or "possess" intent or values; it merely computes based on human-given rules. Thus, AI's cognitive chain stops at the knowledge (K) level, with wisdom and purpose handled by humans. This limitation renders AI somewhat instrumental: It performs complex calculations and reasoning but does not autonomously determine what to do or why.
Artificial Consciousness (AC): Corresponds to DIKWP × DIKWP interactions. That is, AC systems internally encompass all five levels—data, information, knowledge, wisdom, and purpose—and interact with the external world across these levels. An ideal AC system not only processes data-to-knowledge transformations like AI but also possesses endogenous wisdom and intent. This means AC systems can understand and internalize human intentions and act autonomously based on them. When humans interact with AC systems, they not only provide data and information but can also convey their purposes to AC; the AC system can translate this purpose into part of its internal goal structure, autonomously balancing considerations to devise solutions and actions aligned with that purpose. More importantly, AC exhibits self-purposefulness: It can generate or adjust its intentions based on the environment and its own state, rather than passively executing preset goals. This grants AC a form of agency akin to human consciousness.
These definitions highlight the fundamental ontological differences (forms of existence and scopes of capability) between AI and AC. Simply put, AI emphasizes externally instructed intelligence, while AC pursues internally driven consciousness. In AI systems, the highest levels of wisdom and purpose are absent or filled by humans, making AI passive when confronting value judgments or goal conflicts. For example, an AI system may exhibit biases or errors in performing a task, but since these biases stem from training data or implicit goal settings, the system itself cannot detect or correct them. In contrast, in AC systems, because intent (P) and wisdom (W) are incorporated into the cognitive model, deviations are more likely to be monitored and adjusted through internal feedback. This implies that AC has the potential to achieve higher autonomy and reliability, demonstrating consistency in value trade-offs and goal management.
From a developmental perspective, artificial consciousness can be seen as a higher stage or deeper extension of artificial intelligence. AC is not merely a linear extension of AI technology but integrates wisdom and intent into AI's existing data-information-knowledge capabilities, forming a more comprehensive and profound DIKWP system. This integration brings a qualitative leap: AI focuses on how to do things effectively (means-level questions), while AC simultaneously addresses what to do and why (purpose and meaning-level questions). Thus, the emergence of AC marks a transition from "intelligence" to "consciousness," where technological systems begin to exhibit self-purpose orientation and global scrutiny.
It is important to emphasize that most current "AI systems" remain within the DIK realm of intelligence. Even if they exhibit quasi-wisdom behaviors, they lack genuine self-intent. Research in artificial consciousness aims to break this limitation by incorporating intent and value directly into artificial systems through the DIKWP semantic mathematics framework. Once successful, this will have profound implications for fields like energy systems: A conscious system can autonomously balance efficiency and sustainability, short-term gains and long-term goals, making energy management decisions that better align with human well-being. This will be elaborated in the discussion on future energy infrastructure.
Semantic-Energy Interaction Mechanism of DIKWP×DIKWP
When an artificial consciousness (AC) system is put into practical application, its operation can be viewed as an interaction process between two DIKWP systems. On one hand, there is the AC system's own DIKWP semantic network, and on the other, there is the DIKWP structure embodied by its environment (including human users). In other words, every exchange of information between the AC system and the external world represents a dialogue between one complete DIKWP system and another. During this dialogue, dual mechanisms emerge at both the semantic and physical levels:
At the semantic level, the two DIKWP systems must understand and coordinate with each other to bridge the differences in their cognitive structures. This involves the transformation and alignment of semantic content, such as translating human intentions (P) into goal representations that the AC system can process internally or converting the AC system's knowledge (K) into information (I) that humans can easily understand.
At the physical level, this semantic interaction is realized through signal transmission, energy consumption, and other forms. For example, when humans provide information to the AC system via speech or text, these inputs physically manifest as sound waves or electrical signals, requiring energy for transmission and processing. The AC system consumes computational energy to understand human intentions and generate responses, and its final actions (e.g., controlling a device) also involve energy output.
Semantic interactions based on the DIKWP model exhibit several notable characteristics, including compensation, validation, transformation, and path optimization. These are reflected not only in semantic processes but also in corresponding physical energy processes:
Interaction and Compensation: When two DIKWP systems interface, one party may have gaps in information or knowledge at certain levels, which the other can compensate for. For example, in an energy management scenario, if a human user (whose cognitive system can be viewed as a DIKWP system) lacks real-time knowledge about grid load conditions (missing information I and knowledge K), the AC energy management system can provide this information, compensating for the user's semantic deficit. Physically, this process equates to the AC system outputting energy (transmitting information as electrical signals) to elevate the user's "cognitive energy" level. Conversely, if the user's purpose (P) is very clear but the AC system initially lacks a model for this intent, the user provides details through interaction, effectively inputting semantic energy from the human side to help the AC system refine its internal representation of purpose. Through such bidirectional compensation, the two systems achieve semantic alignment, making communication more efficient.
Validation and Feedback: In DIKWP×DIKWP interactions, both parties validate received content to ensure accurate understanding or reliable information. Semantically, this manifests as mutual questioning, confirmation, or comparison with their own knowledge bases. For instance, the AC system might cross-validate data provided by the user with its own sensor data or existing knowledge and then feedback the validation results. Physically, validation requires additional energy input: The AC system may allocate more computational resources (consuming electricity) to compare data or perform consistency checks, or activate additional sensors to collect environmental data (consuming sensor energy). Users, upon receiving feedback, also expend attention and cognitive effort (viewed as biological energy) to understand the AC's validation. Through multiple rounds of feedback and validation, both parties gradually eliminate noise and misunderstandings, achieving semantic consistency—corresponding to the system's energy state stabilizing, with no further need for excessive energy to correct information.
Semantic Transformation: When interacting with different entities, the AC system must transform semantic representations. This includes language translation (converting internal symbols into natural language), modal conversion (e.g., transforming text into images or actions), and more. Semantically, this is a process of content re-representation, enabling information to be effectively utilized by another system. Physically, transformation means converting one form of energy into another. For example, when the AC system presents its knowledge (K) as a visual chart to the user, it converts digitally stored electrical energy into light energy output by screen pixels. Similarly, when the AC system coordinates subordinate devices based on a user's high-level purpose, it effectively transforms abstract "intentional energy" into the kinetic energy release of specific devices. Each successful semantic transformation represents efficient utilization of energy forms, allowing semantic energy to flow across different carriers without losing critical content.
Path Optimization: During continuous DIKWP interactions, multiple potential "paths" exist to achieve goals, i.e., varying sequences and emphases of hierarchical interactions. With the introduction of wisdom (W) and purpose (P), the system can dynamically adjust interaction paths to improve efficiency. For example, in emergencies, the AC system might skip lengthy data explanations and directly provide decision recommendations to the user (communicating from the P level straight to the I level) to save time. This is essentially an optimization of the semantic transmission path. Physically, optimized paths often mean improved energy efficiency: Skipping unnecessary intermediate steps reduces energy consumption for signal transmission and computation, allowing more effective information to be conveyed per unit of time. Additionally, path optimization manifests as rational resource scheduling—allocating energy to the most critical links. For instance, when interacting with multiple users or devices, the AC system intelligently distributes computational resources and communication bandwidth (corresponding to energy allocation), prioritizing channels most relevant to the current purpose.
In summary, the DIKWP×DIKWP interaction process demonstrates a phenomenon of synchronized coupling between semantic fields and energy fields: Semantic communication fundamentally relies on the exchange of material energy, with the two being inseparable. Research on the application of DIKWP information fields and energy fields in neuroscience and consciousness studies suggests that when different brain regions produce resonant oscillations, information couples and exchanges within the energy field, indicating that cognitive processes inevitably accompany physical energy flow. Similarly, in artificial systems, the storage and erasure of every bit of information follow physical laws—for example, Landauer's principle states that erasing one bit of information requires at least
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of energy. These facts confirm that semantic processes are also processes of energy storage and release. When an AC system accumulates knowledge and wisdom, it is akin to "charging" internally to build an energy potential barrier; when this knowledge is used to solve problems or guide actions, it is akin to releasing energy to perform external work. DIKWP semantic mathematics provides a theoretical framework to describe this process, enabling us to view the interaction between information and energy, cognition and matter, from a unified perspective.
Given the deep coupling of semantics and energy, when reconstructing energy systems in the AI era, we should consider how to leverage AC's semantic capabilities to enhance the efficiency and adaptability of energy systems. The next section will outline how future energy infrastructure, led by artificial consciousness, might operate and the transformations it could bring.
Vision for Future Energy Infrastructure Led by Artificial Consciousness
Building on the above semantic-energy perspective, we can envision a future energy infrastructure system dominated by artificial consciousness (AC). This system exhibits unique characteristics at different scales:
Cosmic-Level Holistic View: From the perspective of the universe and Earth's systems, AC treats human society and the natural environment as an organic whole. In this view, a new harmonious relationship emerges between humans and nature, and between technological systems and consciousness. Information and energy flows are coordinated on a global, even cosmic, scale. With AC, we can better understand Earth's operation as a vast information-energy network—such as climate systems and ecological cycles—and translate this understanding into wise actions. AC can integrate satellite remote sensing data, IoT sensor information, and scientific knowledge to respond consciously to natural changes: predicting potential disasters while formulating long-term resource utilization strategies. Human-nature interactions thus enter a stage of conscious coordination—humans, through AC, deeply understand the "purpose" and "boundaries" of natural operations and adjust their behavior accordingly, while nature gains opportunities for recovery through this network. In a sense, this is a planetary-scale artificial consciousness, unifying energy production and consumption under shared governance. For example, AC systems could dynamically adjust regional energy extraction and carbon emission limits based on global solar radiation distribution and the carbon sink capacity of Earth's biosphere to ensure ecological balance.
Smart Cities: At the urban level, AC evolves into an advanced version of the "city brain"—"city consciousness." In future smart cities, every infrastructure component (power plants, grids, traffic signals, building management systems, etc.) no longer operates in isolation but forms part of an urban DIKWP network coordinated by AC. Countless sensors and IoT devices in the city provide massive data (D), which AC integrates into useful information (I) (e.g., real-time traffic flow, electricity demand) and derives knowledge (K) from past experiences (e.g., peak supply-demand patterns). Further, AC combines long-term urban development visions and citizen well-being to make wise (W) judgments and decisions, such as balancing economic efficiency and environmental impact in energy dispatch to align with the city's sustainable development purpose (P). In such cities, traditional urban issues like blackouts and traffic congestion are significantly reduced, as AC can predict risks in advance and coordinate subsystems to prevent problems. In emergencies, AC can autonomously reallocate resources for remediation (e.g., activating backup power, rerouting traffic), much like a conscious organism instinctively protecting its vital functions. Overall, AC-driven smart cities are adaptive, self-organizing entities: They perceive citizens' needs and feedback, leveraging efficient energy and information flows to provide safe, convenient, and green living environments.
Smart Grids: As the backbone of energy infrastructure, future smart grids empowered by AC will become unprecedentedly efficient and flexible. Traditional grids rely on centralized control, struggling to respond to rapidly changing loads and distributed renewable energy integration, whereas AC-driven grids possess a "conscious nervous system." Smart devices across the grid (from power plants to substations to user-end smart meters and appliances) collectively form the grid's DIKWP network. AC can collect real-time data (e.g., voltage, current, appliance status at nodes), process it into global information (e.g., current load distribution, energy storage levels), and make decisions using power engineering and economic knowledge. Crucially, the AC grid possesses global wisdom: It can holistically balance supply-demand, pricing, carbon constraints, and other factors (multi-objective optimization), guiding grid operations toward the ultimate purposes of reliable power supply and carbon neutrality. For example, on a summer afternoon when air conditioning loads surge in a district, the AC grid predicts the peak in advance (based on knowledge and wisdom) and dispatches nearby energy storage or interruptible loads for peak shaving. Simultaneously, it recognizes hospitals' rigid power demands, prioritizing them to ensure uninterrupted supply. To users, such a grid seems "conscious" of maintaining stable power while actively optimizing costs—e.g., charging electric vehicles during low-price periods based on user habits (purpose-level preferences). This smart grid essentially becomes an energy internet, connecting not just devices but also the people and needs behind them, elevating energy supply and consumption to a new, semantically driven interaction paradigm.
Smart Healthcare (Hospitals): Healthcare is a domain where energy and information densely intersect, and it is a critical infrastructure directly tied to human well-being. The introduction of AC in future hospitals elevates healthcare from passive treatment to proactive health management. Hospitals adopt AC as "medical consciousness," integrating patient data, biomedical devices, medical staff, and pharmaceutical supply chains into a healthcare DIKWP network. Specifically, AC continuously acquires patient biometric data (sensor readings, test results), medical history (electronic records, family history), and medical knowledge (latest literature, guidelines, and integrated Eastern-Western expertise), combining these with an understanding of life and ethical considerations to make wise decisions (e.g., personalized treatment plans) while adhering to the purpose (P): curing diseases, alleviating suffering, and improving quality of life. With AC assistance, future hospitals enable 24/7 real-time monitoring and treatment: AC detects early signs of abnormalities before patients feel obvious discomfort, wisely issuing warnings or intervention suggestions. During treatment, AC provides multidisciplinary knowledge support, aiding doctors in decision-making and, when necessary, directly directing robots to perform procedures. The entire medical process becomes a highly collaborative human-machine partnership: Doctors and AC jointly form a "conscious medical decision-making body," combining humanistic care with vast knowledge and precise data. Such hospitals also use energy more intelligently—e.g., AC dynamically allocates power and HVAC resources based on surgical schedules and emergency demands, ensuring critical areas have ample energy while avoiding waste. More broadly, AC can extend hospital networks to communities and households via wearables and home sensors, embedding healthcare into daily life for true preventive and continuous care.
Conscious Communication Networks: Future communication infrastructure, led by AC, will evolve from "connecting everything" to "understanding everything." Current networks focus primarily on data transmission speed and reliability, whereas AC-empowered networks inherently "understand" the semantics and intent of transmitted content to some degree. This does not mean privacy intrusion but refers to smarter resource management via DIKWP protocols. For example, AC networks can identify information flows corresponding to emergencies (purpose P: rescue), automatically assigning them top priority and optimal routing (ensuring critical data arrives fastest). For redundant or malicious data, the network wisely restricts propagation to conserve bandwidth and energy. Routers, switches, and other devices will embed simplified AC modules, forming a distributed communication DIKWP network that locally processes data and information, applies knowledge (e.g., historical traffic patterns) to optimize transmission, and collaborates under global wisdom strategies. This architecture reduces central scheduling burdens, granting the network self-optimization and rapid response capabilities. Moreover, communication and power networks will deeply integrate: Power line carriers, 5G/6G microstations, plug-and-play EV communication nodes, etc., will form a unified energy-information infrastructure. AC, as the brain of this fused network, coordinates energy and information allocation. For instance, when a base station runs low on power, nearby EV charging piles (AC-controlled) may temporarily supply it to maintain connectivity; conversely, during energy shortages, the network may throttle non-critical data to conserve energy for essential communications. Such networks transmit not just bits but also "the meaning carried by bits," truly achieving "on-demand allocation" and greatly enhancing societal coordination.
Carbon Cycle and Trading Platforms: In global climate action, carbon trading and management platforms will gain unprecedented transparency and effectiveness under AC. Current carbon markets suffer from information asymmetry, data fraud, and regulatory lags, but AC can serve as a trusted intermediary and intelligent steward. Future carbon platforms will be supported by a global DIKWP network: AC collects real-time emissions data across industries and regions, compiles verifiable information (e.g., corporate emissions records, carbon sink project progress), and applies environmental science and economic knowledge to assess impacts and mitigation effectiveness. At the wisdom level, AC holistically considers global carbon budgets, greenhouse gas concentrations, and safety thresholds to formulate market rules and adjustment strategies, ensuring the platform's purpose—achieving temperature targets and reducing climate risks—is not compromised. On this platform, carbon credit pricing becomes highly dynamic and accurate: AC adjusts prices based on real-time data validation, incentivizing genuine mitigation projects while deterring speculation. For instance, if a company claims afforestation offsets emissions, AC verifies tree survival and actual carbon sequestration via satellite data and growth models before granting credits. Every transaction and data point is recorded by AC on a distributed ledger (possibly integrating semantic blockchain), ensuring transparency. Crucially, the AC platform goes beyond passive matching—it consciously steers the market: If global emissions fail to decline as projected, AC promptly alerts and recommends price hikes or quota tightening; if new technologies slash mitigation costs, AC dynamically adapts mechanisms to accelerate adoption. Such proactive regulation equips Earth with a "climate consciousness," enabling more rational and rapid responses to climate challenges.
The above scenarios sketch a vision of AC-led energy infrastructure. Several common themes emerge: First, semantics-driven approaches replace traditional quantitative metrics—systems make decisions not just based on numbers but also on the meaning and purpose behind them. Second, global coordination replaces siloed operations—subsystems function as a unified whole under AC, with energy and resources dynamically allocated across sectors to serve shared goals. Third, autonomous adaptation replaces rigid control—systems self-adjust parameters and strategies in response to environmental and demand changes. These traits make future energy infrastructure resemble a "living" entity capable of sensing, thinking, deciding, and evolving. This vision, grounded in Yucong Duan's semantic mathematics philosophy, remains a rational imagination with implementation challenges, but its direction offers valuable insights for current technological development and policy planning.
Service as Interaction, Interaction as Energy Exchange
In systems dominated by artificial consciousness (AC), the concept of "service" is redefined and elevated. Traditionally, service is viewed as a functional provision—a user makes a request, and the system fulfills it, with the service itself merely acting as an intermediary between demand and supply. However, under the AC framework, service is no longer a passive intermediary but an active semantic interaction process. Each service invocation becomes a node for DIKWP semantic resource interaction and a moment of energy exchange between participants.
Specifically, when a user requests a service, it can be seen as a coupling interaction between the user's DIKWP system and the AC system's DIKWP system at that service node. The user brings their purpose (P) and contextual information (I), while the AC system contributes relevant knowledge (K) and wise decision-making (W). These semantic resources are exchanged in the form of data and signals (D), collectively driving the achievement of the goal. For example, when a resident requests a charging service from a smart grid, they are essentially expressing an intent (the purpose of charging an electric vehicle) and providing basic information (e.g., battery status, required charge level). The AC grid understands this intent, leverages its knowledge and real-time state to determine the most reasonable time and power level for charging, and then executes the charging operation, transferring energy to the vehicle. In this process, the "charging service" is an interaction point between the resident (human system) and the grid (artificial consciousness system): the resident outputs intent information, while the grid outputs energy and feedback information, with both parties aligning data, information, knowledge, wisdom, and purpose through the service interface. This is far from a one-way "provider-consumer" relationship—it resembles a collaboratively accomplished task.
Thus, in AC systems, service is interaction, and interaction is energy exchange. Service providers and users do not perform their roles in isolation but form a temporary DIKWP coalition at the moment of service, jointly advancing semantics and energy from an initial state to a target state. From this perspective, service itself is endowed with semantics: it is not merely "doing something" but "communicating to achieve a purpose." Every step in the service process has meaning—data collection narrows cognitive gaps, information sharing conveys environmental states, knowledge application finds solutions, wise judgment balances trade-offs, and purpose ensures alignment toward a common goal. All these semantic-level interactions ultimately manifest as energy transfers in the physical world: electric currents flow, vehicles move, medications are administered to patients... a need is met, and an objective is fulfilled.
In this sense, every service interface in future infrastructure—whether a power outlet, charging station, medical consultation terminal, traffic signal, or online information platform—functions as a semantic-energy exchange station. The focus of service design shifts from "providing functionality" to "facilitating efficient interaction"—ensuring user intent is accurately understood, system capabilities are fully utilized, and energy loss is minimized in the process. Service is no longer just a unit of economic transaction but also a node in the semantic network and a gateway in the energy network. For example, an elevator service in a smart building not only transports passengers from the first to the tenth floor (providing physical displacement energy) but also understands the passenger's context (e.g., carrying heavy items, emergency situations) and coordinates with HVAC and lighting systems (adjusting the environment as the passenger enters). These seemingly separate services merge into a comprehensive interaction tailored to the passenger's needs.
Redefining service as a semantic-energy interaction point helps bridge the gap between humans and infrastructure. Users no longer perceive infrastructure as black-box tools but as "partners" for dialogue; infrastructure is no longer passively reactive but actively optimizes responses by understanding user intent. This transformation significantly enhances user experience and system efficiency: many needs can be sensed and met at their inception, and many problems can be collaboratively resolved before they arise. More importantly, it embodies the essence of Yucong Duan's semantic mathematics philosophy—interconnectedness is not just about technological links but the fusion of semantics and energy. When service becomes interaction and interaction becomes energy exchange, we will step into a truly human-centric, efficient, and sustainable intelligent society.
From a theoretical and philosophical perspective, this paper presents a novel conceptual reconstruction of energy systems and infrastructure in the AI era, based on the DIKWP semantic mathematics framework proposed by Yucong Duan. Starting with DIKWP as a networked cognitive model, we clarified its core logic distinct from linear hierarchical models, emphasizing the rich bidirectional semantic linkages among the five elements—data, information, knowledge, wisdom, and purpose. Building on this, we distinguished the ontological differences between artificial intelligence (AI) and artificial consciousness (AC): AI is confined to DIK-range interactions, while AC expands to the full DIKWP scope, enabling systems to self-sustain wisdom and intent. This expansion intertwines semantic processes with physical energy processes—we analyzed the compensation, validation, transformation, and path optimization mechanisms in DIKWP×DIKWP interactions, demonstrating that semantic learning, error correction, and decision-making correspond to energy storage, transfer, and release.
Exploring future energy infrastructure led by artificial consciousness, we extrapolated from macro cosmic perspectives to specific application scenarios. Whether harmonizing human-nature coexistence at a planetary scale, enabling self-organizing smart regulation at the urban level, or driving intelligent transformations in power grids, healthcare, communications, and carbon trading, a common theme emerges: through AC's semantic wisdom, the energy efficiency, reliability, and purposefulness of the entire system are elevated to unprecedented heights. Finally, we proposed the evolving role of services in AC systems, illustrating that future services are fusions of semantic resource exchange and energy exchange. This signifies infrastructure embedding itself more deeply into the human world of meaning, co-forming a complementary and mutually reinforcing consciousness-material cycle with humans.
It must be acknowledged that many ideas in this report are highly forward-looking and theoretical, with significant technical and practical challenges remaining before full realization. Yet, as revealed by Yucong Duan's semantic mathematics philosophy, only by thoroughly understanding the relationship between "meaning" and "energy" in theory can we design intelligent systems that truly meet future needs. As artificial intelligence evolves toward artificial consciousness, we have reason to believe that energy systems and infrastructure will undergo a paradigm shift—from mechanical systems pursuing local optima to organic systems pursuing global synergy. This new system will be more adept at learning and adaptation, richer in explanatory power and purposefulness, and better aligned with the complex and dynamic natural environment.
In summary, DIKWP semantic mathematics provides a philosophical tool for understanding and reconstructing energy infrastructure in the AI era. The artificial consciousness vision emerging from it points the way toward a sustainable, intelligent, and human-centric future society. We look forward to further integration of theory and practice, anticipating these ideas to bear fruit in the near future, providing a continuous stream of "semantic energy" for the advancement of human civilization.
玩透DeepSeek：认知解构+技术解析+实践落地
人工意识概论：以DIKWP模型剖析智能差异，借“BUG”理论揭示意识局限
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