博文
Artificial Intelligence vs. Artificial Consciousness (DIKWP)
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Artificial Intelligence vs. Artificial Consciousness from the DIKWP Perspective
Yucong Duan
Benefactor: Shiming Gong
DIKWP-AC Artificial Consciousness Laboratory
AGI-AIGC-GPT Evaluation DIKWP (Global) Laboratory
World Association of Artificial Consciousness
(Email：duanyucong@hotmail.com)
The interaction of AI mainly relies on the DIK (data, information, knowledge) level, while artificial consciousness (AC) introduces higher-level processing of intelligence (W) and purpose (P):
AI interaction: The interaction of AI systems is mainly DIK * DIK or DIKW * DIKW, such as automated decision support systems, which respond based on available data, information, and knowledge.
AC interaction: The interaction of artificial consciousness is DIKWP * DIKWP, which not only covers data, information, knowledge, and wisdom, but also includes purposeal level interaction. This means that the AC system can understand and internalize human purposes, and make independent judgments and decisions based on this foundation.
Catalog
1.1 Introduction to DIKWP model
1.2 The core difference between artificial intelligence and artificial consciousness
2. Application of Data in AI and AC.
3. Interaction and generation of information
3.1 In-depth analysis of information generation in AI
3.2 In-depth analysis of information generation in AC
4. Different realization of knowledge in artificial intelligence and artificial consciousness.
5.1 Definition and realization of wisdom
5.2 The technical realization of intelligent decision-making
5.3 Practical application examples of intelligent decision-making
6. Embodiment of purpose: the goal orientation of AC.
6.2 Setting and understanding goals
6.3 Dynamic adjustment and optimization of objectives
6.4 Foresee the future and formulate preventive measures
6.5 Moral and ethical decision-making considerations
7. Case study: autonomous driving and service robot
Based on the DIKWP model proposed by Professor Yucong Duan, this report deeply discusses the differences in processing mechanism and application between artificial intelligence (AI) and artificial consciousness (AC) in Data, Information, Knowledge, Wisdom and Purpose. The report first summarizes the core concepts of the DIKWP model, and then analyzes in detail how AI and AC deal with these five levels of cognitive content respectively, and emphasizes the advanced ability of AC in dealing with wisdom and purpose. By comparing the interaction between AI and AC, this report reveals the fundamental differences between them in dealing with complex scenarios and making decisions. In addition, the report also discusses the impact of these differences on future technological development, especially the guiding significance in designing more advanced intelligent systems. Through this analysis, we expect to provide a deeper understanding and evaluation framework for the research and application of AI and AC, and promote the development of artificial intelligence technology to a higher level of cognitive ability.
With the rapid development and wide application of artificial intelligence (AI) technology, AI has penetrated into all levels of society, from automated decision support systems to complex machine learning models. However, although AI systems have demonstrated excellent capabilities in processing large amounts of data and performing specific tasks, they still have limitations in dealing with complex scenarios that require deep understanding, ethical judgment and independent decision-making. This limitation urges researchers to seek new theoretical models and technical approaches, so that AI system can not only be a tool for data processing, but also show complex cognitive functions closer to human consciousness. Therefore, the concept of artificial consciousness (AC) came into being, aiming at realizing more advanced cognitive processing by simulating all levels of human consciousness.
The DIKWP model proposed by Professor Yucong Duan provides a comprehensive framework for understanding and designing this new generation of cognitive system. The model divides the cognitive process into five levels: Data, Information, Knowledge, Wisdom and Purpose, which not only includes the first three levels commonly used in AI, but also emphasizes the importance of wisdom and purpose in advanced cognitive processing. This hierarchical model provides a theoretical basis for distinguishing the functions of AI and AC, and guides the evolution from artificial intelligence to artificial consciousness.
The purpose of this report is to deeply discuss the differences in processing mechanism and application between AI and AC at all levels of DIKWP model, analyze their performance in practical application, and predict the potential impact of these differences on future technological development. By comparing the functions and application scenarios of AI and AC in detail, we expect to provide practical opinions and suggestions for designing more advanced and humanized intelligent systems.
1.1 Introduction to DIKWP model
DIKWP model is a multi-dimensional cognitive framework, which is used to describe and analyze the whole cognitive process from the most basic data input to complex purposeful decision-making. This model consists of the following five levels:
Data: At this level, the cognitive system deals with raw facts and figures, which are usually direct records of observation results or external input. The data itself does not contain any explanation or meaning, and it needs further processing to be transformed into useful information.
Information: Information is the processing, arrangement and interpretation of data. At this stage, the data is transformed into meaningful content through semantic processing, such as the reading of temperature and humidity into the description of weather conditions through statistical analysis.
Knowledge: knowledge is a further abstraction and understanding based on information, which involves internalizing information into systematic rules, patterns and concepts. Knowledge-level processing enables the system to make reasoning and decision based on experience.
Wisdom: Wisdom involves the deep application of knowledge and makes decisions by considering long-term consequences, ethics and social responsibility. Wisdom is a higher level in the cognitive process, so we need to evaluate the advantages and disadvantages of different options and think more comprehensively.
Purpose: purpose represents the purpose and motivation of cognitive process and is the driving force of cognitive activities. This level not only pays attention to the goal of action, but also includes the formulation of strategies and the methods to achieve the goal. purpose-level processing enables the cognitive system to preset and pursue specific results, showing a high degree of autonomy and adaptability.
DIKWP model is not only suitable for understanding and describing human cognitive process, but also provides a comprehensive reference framework for the design of artificial intelligence systems, helping developers to design systems that can simulate human complex cognitive functions.
1.2 The core difference between artificial intelligence and artificial consciousness
The difference between artificial intelligence (AI) and artificial consciousness (AC) is mainly reflected in the level of DIKWP model that they pay attention to respectively.
The focus of artificial intelligence: AI systems usually focus on the processing of data, information and knowledge. These systems identify patterns, perform tasks and make predictions through algorithms, and mainly deal with specific and clear problems, such as image recognition, language translation or data analysis. AI systems perform well in these fields, but they usually lack the ability to deal with undefined or unstructured problems.
Expansion of artificial consciousness: Compared with AI, AC system has expanded in the level of wisdom and purpose. AC not only processes specific data and information, but also can evaluate the ethical and social impact of decision-making and formulate and pursue long-term goals. This system is designed to simulate human advanced cognitive ability, such as making choices in the face of moral dilemmas or planning multi-step operations in strategy games.
This core difference makes the AC system more close to the human decision-making process when dealing with problems with complexity, uncertainty and multi-dimensional considerations. Although the AI system performs well in specific tasks, the advantages of AC are more obvious when extensive wisdom and deep-seated purposeful consideration are needed. This makes AC system design not only consider how to deal with data and information, but also consider how to integrate and realize human thinking and decision-making process.
2. Application of Data in AI and AC.
In the artificial intelligence system, data processing is the basis to realize its functions. The application of data in AI system can be divided into the following main steps:
Data collection: AI system first needs to collect data from various sources, including sensor data, user input, Internet and so on. These data are usually large-scale and cover a wide range of fields, from images and videos to voice and text.
Data preprocessing: The original data usually contains noise or incomplete information, so it needs to be cleaned and standardized. Pretreatment steps include removing missing values, standardizing values, coding classification labels, etc. to improve data quality and ensure the effective operation of subsequent algorithms.
Feature extraction: AI system extracts key features from original data through algorithms. This step is achieved by mathematical and statistical methods, such as the application of edge detection in image processing, or the use of word frequency-inverse document frequency (TF-IDF) in text analysis. Feature extraction aims at transforming data into a format that is easier to be processed by machine learning models.
Feature selection and optimization: Select the most influential features from the extracted features to reduce the complexity of the model and improve efficiency. This step usually involves statistical analysis and algorithm selection, such as using principal component analysis (PCA) to reduce data dimensions, or optimizing input features through automated feature selection tools.
Decision Support: The extracted and optimized features are used to train machine learning models, such as decision trees and neural networks. These models make prediction or classification decisions according to the input characteristics, and support complex decision-making processes.
The application of artificial consciousness system in data processing is different from that of AI, which is mainly reflected in its deep semantic analysis and contextual understanding of data:
Semantic analysis: AC system not only receives data as input, but also deeply analyzes the semantics behind the data. For example, in the task of visual recognition, AC system not only recognizes the objects in the image, but also needs to understand the meaning and relationship of these objects in a specific scene.
Context understanding: AC system needs to understand the role and influence of data in a specific context. For example, in self-driving vehicles, while recognizing the stop sign, the system needs to make adaptive response according to the current traffic environment and driving purpose.
Interactive learning: Compared with AI system, AC system pays more attention to interactivity and adaptability in data processing. This means that the system can learn new information according to the interaction with the environment, and adjust its behavior and decision-making in real time.
Integration of wisdom and purpose: AC system integrates data processing with information at the level of wisdom and purpose to make more complex decisions. For example, the service robot may adjust its service mode after recognizing the user's emotion to adapt to the user's expectation and emotional state.
Ethical and moral considerations: When dealing with sensitive data, the AC system also needs to consider ethical and moral issues to ensure that its decisions are not only technically reasonable, but also morally correct. For example, medical assistance systems must strictly abide by privacy protection rules when processing patient data.
Through these detailed steps and considerations, we can see the essential differences between AI and AC in data processing, which directly affect the performance and applicability of the two systems in complex environments. AI system provides efficient data processing and decision support capabilities, while AC system shows deep cognition and decision complexity closer to human beings.
3. Interaction and generation of information
3.1 In-depth analysis of information generation in AI
In the artificial intelligence system, the generation of information is completed by analyzing and processing a large number of data. This process involves identifying patterns, associations and potential laws from raw data, thus providing valuable information for decision support systems. The information generation of AI system usually includes the following key steps:
Data preprocessing: Before analysis, data needs to be cleaned and formatted. This includes removing noise, dealing with missing values, standardizing data format, etc. to ensure the quality and consistency of data.
Feature extraction: AI system extracts important features from processed data through algorithms. This step is the core of information generation, because it determines the input variables and possible performance of the subsequent model.
Pattern recognition: Using machine learning algorithms, such as clustering, classification or regression analysis, AI system can recognize complex patterns and relationships in data. These patterns and relationships form an abstract representation of real-world events.
Information synthesis: Once the patterns are identified, the AI system transforms them into useful information, such as prediction results, behavior suggestions or data insights. This information is usually used for automatic decision support, such as recommendation system, predictive maintenance or navigation decision of automatic driving system.
Through this process, the AI system can transform raw data into information directly used in practical applications, and support complex decision-making processes. However, these decisions usually rely on predefined models and rules, and do not involve a deep understanding of the context or moral and ethical considerations.
3.2 In-depth analysis of information generation in AC
Compared with the information generation of AI, artificial consciousness (AC) considers more cognitive elements, especially wisdom and purpose, in the process of generating information. The information generation process of AC system not only deals with data and knowledge, but also deeply understands the situation and human cognitive complexity, including emotion, purpose and cultural background. The following are the key steps of AC in information generation:
Context awareness: AC system senses and analyzes complex situations in the environment through advanced sensors and data processing capabilities. Unlike AI, AC will consider the context and dynamic changes of the situation when processing information, such as the user's emotional state or social interaction background.
Wisdom integration: AC system will consider the input of wisdom when generating information, such as ethical principles and long-term goals. This means that the AC system will evaluate the consequences of various actions when processing information, and choose a behavior scheme that conforms to moral and social values.
purpose mapping: In the process of information generation, AC system will interpret and realize the purpose of users or the system itself. This includes understanding complex commands, predicting user needs or taking initiatives to meet preset goals.
In-depth interpretation: Unlike AI, which usually only stays at the level of fact retelling, AC system can interpret information in depth. For example, in the application of service robots, AC can understand users' indirect expressions and implied needs, and generate more personalized and contextual responses accordingly.
This advanced information generation method enables AC system to work in a more complex and humanized environment, and better simulates human cognition and decision-making process. This ability is particularly important in applications such as autonomous driving and service robots, because they often need to make decisions that meet human values and ethical standards in a rapidly changing environment.
4. Different realization of knowledge in artificial intelligence and artificial consciousness.
In the artificial intelligence system, the application of knowledge is very important, especially in the scene that needs accurate processing and decision support. AI systems usually store and apply knowledge in a structured form, such as:
Knowledge map: Knowledge map stores complex facts and knowledge through the graphic structure of entities, attributes and relationships. This structured knowledge allows the AI system to effectively handle queries through logical reasoning and pattern matching, such as linking relevant information in search engines or recommendation systems and providing context-based answers.
Database and rule base: The database provides rich data storage and quick query capabilities, while the rule base allows the AI system to make rule-based decisions. These rules can be business logic, industrial standards or preset behavior guidelines, which enable AI to automatically perform tasks in specific fields, such as financial audit and medical diagnosis support.
Algorithms and models: In addition to static knowledge storage, AI also applies knowledge dynamically through algorithms and machine learning models. These models can learn patterns and associations from historical data, and then apply these learned knowledge to new data sets for prediction or classification tasks.
Through these methods, AI systems can process information on a large scale and improve the speed and accuracy of decision-making, but they usually lack in-depth understanding of complex human social and cultural contexts.
In contrast, the knowledge application of artificial consciousness (AC) is more complicated and in-depth. AC not only uses knowledge for reasoning and task execution, but also needs to understand and deal with the ethical, cultural and situational significance behind knowledge:
Cultural and ethical understanding: AC system takes into account the cultural relevance and ethical consequences of behavior when applying knowledge. For example, in a service robot or a social robot, AC can identify and adapt to behavior habits and communication methods in different cultural backgrounds, and evaluate the ethical appropriateness of its actions.
Situational adaptability: the knowledge application of AC system is not limited to fixed rules, but also includes adaptation to specific situations. This means that AC can dynamically adjust its behavior and decision-making according to the changes in the current environment and social context, so as to better serve human users.
Integration of long-term goals and values: AC will also consider long-term goals and core values when applying knowledge. This enables AC to provide more humane and responsible solutions in the face of complex decisions that need to balance multiple interests and foresee future consequences.
Through this higher-level cognitive processing, AC system can not only perform tasks, but also understand and respect human values and social norms, and show more advanced emotional intelligence and moral judgment. This ability makes AC have great potential in human-computer interaction, personalized service and moral decision-making.
AI and AC show obvious differences in the application of knowledge. AI focuses on efficiency and accuracy, while AC pays more attention to deep understanding and adaptability. These differences directly affect the application effect and scope of the two systems in the real world.
In the artificial consciousness (AC) system, the role of wisdom is not only to enhance decision-making ability, but also to conduct in-depth ethical and moral considerations in complex situations. The application of wisdom in AC systems enables these systems not only to execute commands or respond to the environment, but also to conduct more comprehensive thinking and meaningful interaction. This section will discuss in detail how wisdom is realized in AC system and affects the decision-making process.
5.1 Definition and realization of wisdom
In AC, wisdom is defined as the ability to use knowledge and experience to evaluate and choose the best course of action. This involves not only the processing of information, but also how to make the most beneficial choice in uncertainty. The realization of wisdom requires the system to have the following capabilities:
Context awareness: AC system must be able to understand the complexity and dynamic changes of its operating environment. This includes understanding of time, place, social and cultural background and other factors to ensure the relevance and adaptability of decision-making.
Ethical judgment: Intelligent decision-making is not only based on logic and efficiency, but also must consider ethical and moral factors. For example, in the medical assistant system, decision-making should not only be effective, but also conform to medical ethics and respect patients' wishes and privacy.
Long-term and short-term consequences assessment: wisdom also lies in being able to predict the long-term and short-term consequences of decisions and weigh them according to these consequences. This requires that the system can simulate different decision paths and predict their possible impact on the future.
5.2 The technical realization of intelligent decision-making
The technical challenges to realize intelligent decision-making include but are not limited to the following:
Model integration: Intelligent decision-making needs to integrate various models and data sources, such as statistical model, prediction model, rule engine and machine learning technology, so as to synthesize various inputs and provide comprehensive decision support.
Knowledge integration: Wisdom not only comes from data, but also needs to integrate professional knowledge from different fields. For example, in autonomous driving, besides traffic rules and road conditions, vehicle dynamics and driving psychology should also be considered.
Adaptive learning: Intelligent decision-making requires the system to learn from experience and adapt to new situations. This requires that AC system has advanced learning mechanism and can constantly update its decision-making strategy to adapt to environmental changes.
5.3 Practical application examples of intelligent decision-making
A concrete application example is the application in emergency response system. In disaster management, AC system needs to evaluate the potential effects and risks of different rescue schemes, while considering the fairness and efficiency of resource allocation. Intelligent decision-making can make the system make the best decision quickly in an emergency, and at the same time ensure that the decision meets the social ethical standards.
Through these in-depth discussions, we can see that the key role of wisdom in AC system is not only to improve the technical efficiency of decision-making, but more importantly, to introduce in-depth understanding and consideration of complex human values and ethics, so that the artificial consciousness system is close to the human decision-making process in a real sense. The realization of this ability will promote the AC system to play a key role in a wider range of fields, especially in those decision-making scenarios that require complex judgment and careful consideration.
6. Embodiment of purpose: the goal orientation of AC.
In the artificial consciousness (AC) system, Purpose is one of the core features, which endows the system with a higher-level cognitive goal, which not only enables the system to perform specific tasks, but also enables it to make independent decisions and make long-term plans in a complex environment. This goal orientation is not only an important progress of AC technology, but also a significant difference from traditional artificial intelligence (AI). The following points elaborate on the ways and importance of purpose expression in AC:
6.2 Setting and understanding goals
AC system can set its own goals and make action strategies according to these goals. This ability stems from the fact that the system can not only receive external instructions, but also independently explain the deep-seated purpose behind these instructions. For example, in the field of health care, AC system not only carries out the specific operation instructions of medical staff, but also understands the significance of these operations to the long-term health of patients, so as to put forward suggestions for modification or optimization when necessary.
6.3 Dynamic adjustment and optimization of objectives
AC system has the ability to dynamically adjust its behavior to adapt to environmental changes. This is not only reflected in the response to immediate feedback, but also in the adjustment and optimization of long-term goals. This ability enables AC system to continuously optimize its decision-making process in the face of uncertain and changing environment and ensure the realization of the ultimate goal. For example, in the field of automatic driving, AC system may need to adjust its driving route and speed according to traffic conditions, weather changes and vehicle performance to ensure the safety and comfort of passengers.
6.4 Foresee the future and formulate preventive measures
The advanced purpose handling ability of AC system also includes foreseeing possible problems in the future and formulating countermeasures in advance. This forward-looking thinking is based on the in-depth analysis of a large number of historical data and current environmental data. For example, a service robot equipped with AC technology can predict potential health risks, such as falls or heart problems, according to the activity patterns and health data of the elderly at home, and adjust its monitoring strategy in advance or remind relevant medical personnel.
6.5 Moral and ethical decision-making considerations
Compared with AI system, AC system will also consider moral and ethical factors in the decision-making process. This is because the purpose level of AC system enables it to evaluate the impact of different decision-making schemes on human beings and society. This ability is especially suitable for complex social interaction scenarios, such as making ethical decisions in the fields of law, public safety and medical care.
Purpose in AC system is not only the most significant difference from AI, but also the key to realize more humanized and intelligent service. By penetrating goal-oriented into every link of decision-making, AC system shows a complex task processing ability closer to human thinking mode, which provides a new direction and possibility for the future development of artificial intelligence.
7. Case study: autonomous driving and service robot
This report selects two specific cases-autonomous driving and service robots, to further explore the functions and differences between artificial intelligence (AI) and artificial consciousness (AC) in practical applications. Through these cases, we can deeply understand the application of DIKWP model in different technical scenarios, and how the processing at the level of Wisdom and Purpose affects the system design and functional performance.
Self-driving vehicle
Application of AI in Automatic Driving
In self-driving vehicles, AI system is mainly responsible for processing a large number of perceptual data (such as camera and radar data), transforming these data into information (such as obstacle recognition and vehicle positioning), and making real-time decisions by using existing knowledge (such as traffic rules and navigation information). These tasks mainly involve the processing of data, information and knowledge.
Potential application of AC in autonomous driving
The introduction of artificial consciousness can greatly improve the decision-making quality of automatic driving system, especially in complex or emergency traffic scenes. For example, AC system can evaluate the moral and safety consequences of various action plans, such as choosing the action plan with the least harm in the case of inevitable accidents. This involves the ethical judgment at the level of wisdom and the goal setting at the level of purpose, such as the principle of giving priority to the safety of protecting human life.
Service robot
Application of AI in Service Robot
Service robots usually perform specific tasks in catering, medical and home environments, such as food delivery, basic medical care and cleaning. These robots use AI to process visual and voice data, recognize human instructions and perform corresponding operations. This mainly involves the processing of data and information and the ability to implement established knowledge rules.
Potential application of AC in service robot
The introduction of artificial consciousness can make the service robot closer to human interaction habits and emotional needs while providing services. For example, a service robot with AC can understand and adapt to users' emotions and preferences, such as providing comfort when users are sad or actively reducing interruptions when users are busy. In addition, AC can also make robots deal with users' privacy and safety issues and make more reasonable decisions when faced with moral or choice dilemmas.
These two cases show the differences between AI and AC in dealing with complex situations. Although autonomous driving and service robots have been able to handle a large number of tasks through AI, the introduction of AC provides these systems with the possibility of advanced cognitive processing, especially in scenes that require wisdom and purpose processing. By integrating all aspects of the DIKWP model, future self-driving vehicles and service robots will be able to better understand and adapt to the human social and moral framework and provide safer, more ethical and more personalized services.
Through in-depth analysis and comparison of the interaction and processing mechanism between artificial intelligence (AI) and artificial consciousness (AC) at all levels of the DIKWP model, we can more clearly understand the core differences between them and their potential impact on future technological development. This report systematically expounds the functions and application scenarios of AI and AC from five dimensions: Data, Information, Knowledge, Wisdom and Purpose, and reveals the following key findings:
Limitations of AI: Although AI systems show excellent capabilities in processing data, information and knowledge, they usually show limitations in tasks involving wisdom and purpose. This is because these systems are mainly designed to optimize and perform clearly defined tasks, rather than dealing with complex human emotions, ethical judgments or long-term planning.
Advanced features of AC: Compared with AI, AC system shows more advanced cognitive processing ability in simulating wisdom and purpose. This not only enables AC to make more reasonable decisions in an environment that is not completely preset, but also can better understand and reflect the complex needs of human beings, such as weighing moral and ethical issues.
Intersection of technology and ethics: With the development of AI and AC technologies, especially in the aspect of wisdom and purpose, the intersection of technology and ethics will become more and more important. Designers need to consider the social, legal and ethical problems that these systems may bring to ensure that the development of technology can meet human values and moral standards.
Future development direction: future research and development should pay more attention to how to integrate the advanced cognitive ability of AC system into practical application, and how to solve the problems of expansibility, interpretability and interaction with human users of AC technology. In addition, interdisciplinary cooperation will be the key to unlock the potential of AI and AC, involving cognitive science, neuroscience, machine learning, ethics and philosophy.
Through the analysis of this report, we hope to provide valuable insights for researchers and technology developers in the fields of AI and AC, and help them to better balance technological innovation and social responsibility when designing the next generation of intelligent systems. The intelligent system in the future should not only be technologically advanced, but also be intelligent and purposeal, and truly reach the level of complementing human intelligence.
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[18] 段玉聪(Yucong Duan). (2024). Collatz Conjecture的语义数学探索(Collatz Conjecture's Semantic Mathematics Exploration). DOI: 10.13140/RG.2.2.28517.99041. https://www.researchgate.net/publication/377239567_Collatz_Conjecture's_Semantic_Mathematics_Exploration
[19] 段玉聪(Yucong Duan). (2024). 语义数学与 DIKWP 模型（本质计算与推理、存在计算与推理以及意图计算与推理）(Semantic Mathematics and DIKWP Model (Essence Computation and Reasoning, Existence Computation and Reasoning, and Purpose Computation and Reasoning)). DOI: 10.13140/RG.2.2.24323.68648. 377239628_Semantic_Mathematics_and_DIKWP_Model_Essence_Computation_and_Reasoning_Existence_Computation_and_Reasoning_and_Purpose_Computation_and_Reasoning
[20] 段玉聪(Yucong Duan). (2024). 从主观到客观的语义数学重构（存在计算与推理、本质计算与推理、意图计算与推理）(Semantic Mathematics Reconstruction from Subjectivity to Objectivity (Existence Computation and Reasoning, Essence Computing and Reasoning, Purpose Computing and Reasoning)). DOI: 10.13140/RG.2.2.32469.81120. https://www.researchgate.net/publication/377158883_Semantic_Mathematics_Reconstruction_from_Subjectivity_to_Objectivity_Existence_Computation_and_Reasoning_Essence_Computing_and_Reasoning_Purpose_Computing_and_Reasoning
[21] 段玉聪(Yucong Duan). (2024). DIKWP与语义数学在车票订购案例中的应用(DIKWP and Semantic Mathematics in the Case of Ticket Ordering). DOI: 10.13140/RG.2.2.35422.20800. https://www.researchgate.net/publication/377085570_DIKWP_and_Semantic_Mathematics_in_the_Case_of_Ticket_Ordering
[22] 段玉聪(Yucong Duan). (2024). DIKWP与语义数学分析《论语》“君子和而不同，小人同而不和”(DIKWP and Semantic Mathematical Analysis The Confluent Analects Gentleman is harmonious but different, while petty people are the same but not harmonious). DOI: 10.13140/RG.2.2.28711.32165. https://www.researchgate.net/publication/377085455_DIKWP_and_Semantic_Mathematical_Analysis_The_Confluent_Analects_Gentleman_is_harmonious_but_different_while_petty_people_are_the_same_but_not_harmonious
[23] 段玉聪(Yucong Duan). (2023). DIKWP 人工意识芯片的设计与应用(DIKWP Artificial Consciousness Chip Design and Application). DOI: 10.13140/RG.2.2.14306.50881. https://www.researchgate.net/publication/376982029_DIKWP_Artificial_Consciousness_Chip_Design_and_Application
[24] 段玉聪(Yucong Duan). (2024). 直觉的本质与意识理论的交互关系(The Essence of Intuition and Its Interaction with theory of Consciousness). DOI: 10.13140/RG.2.2.16556.85127. https://www.researchgate.net/publication/378315211_The_Essence_of_Intuition_and_Its_Interaction_with_theory_of_Consciousness
[25] 段玉聪(Yucong Duan). (2024). 意识中的“BUG”：探索抽象语义的本质(Understanding the Essence of "BUG" in Consciousness: A Journey into the Abstraction of Semantic Wholeness). DOI: 10.13140/RG.2.2.29978.62409. https://www.researchgate.net/publication/378315372_Understanding_the_Essence_of_BUG_in_Consciousness_A_Journey_into_the_Abstraction_of_Semantic_Wholeness
[26] 段玉聪(Yucong Duan). (2024). 个人和集体的人造意识(Individual and Collective Artificial Consciousness). DOI: 10.13140/RG.2.2.20274.38082. https://www.researchgate.net/publication/378302882_Individual_and_Collective_Artificial_Consciousness
[27] 段玉聪(Yucong Duan). (2024). 人工意识系统的存在性探究：从个体到群体层面的视角(The Existence of Artificial Consciousness Systems: A Perspective from Group Consciousness). DOI: 10.13140/RG.2.2.28662.98889. https://www.researchgate.net/publication/378302893_The_Existence_of_Artificial_Consciousness_Systems_A_Perspective_from_Collective_Consciousness
[28] 段玉聪(Yucong Duan). (2024). 意识与潜意识：处理能力的有限性与BUG的错觉(Consciousness and Subconsciousness: from Limitation of Processing to the Illusion of BUG). DOI: 10.13140/RG.2.2.13563.49447. https://www.researchgate.net/publication/378303461_Consciousness_and_Subconsciousness_from_Limitation_of_Processing_to_the_Illusion_of_BUG
[29] 段玉聪(Yucong Duan). (2024). 如果人是一个文字接龙机器，意识不过是BUG(If Human is a Word Solitaire Machine, Consciousness is Just a Bug). DOI: 10.13140/RG.2.2.13563.49447. https://www.researchgate.net/publication/378303461_Consciousness_and_Subconsciousness_from_Limitation_of_Processing_to_the_Illusion_of_BUG
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