DIKWP Artificial Consciousness Theory, Design, and Simulation
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 The relationship between DIKWP model and main consciousness theory
1.2 integrated information theory
2 The way to realize DIKWP artificial consciousness
2.1 Simulation of human cognitive process
2.2 Enhance the machine's independent decision-making ability
2.3 Improve human-computer interaction
2.4 Discussion on ethical and moral issues
2.5 Promotion of interdisciplinary cooperation
3 DIKWP artificial consciousness theory
3.1 Theoretical basis of DIKWP model
3.2 All-round cognitive simulation
3.3 Enhanced emotional intelligence
3.4 Moral and ethical decision-making
3.6 A new era of man-machine cooperation
3.7 Ethical challenges in the future society
4 Build DIKWP artificial consciousness model
4.1 The complexity of consciousness and the challenge of AI
4.2 The combination of main consciousness theory and DIKWP model
4.3 Actual Construction Steps and Simulation Examples
5 Conclusion and future prospect
This report is compiled by DIKWP Artificial Consciousness Laboratory led by Professor Yucong Duan, aiming at discussing and promoting the theoretical and practical development of Artificial Consciousness, AC. DIKWP model combines Data, Information, Knowledge, Wisdom and Purpose, and provides a brand-new perspective to understand and simulate the complex level of human consciousness. This report first summarizes the essence of consciousness and its research progress in many disciplines, and then expounds in detail the relationship between DIKWP model and major consciousness theories such as global workspace theory (GWT), integrated information theory (IIT) and higher-order thinking theory (HOT). The report analyzes the application potential of the model in simulating human cognitive process, enhancing the autonomous decision-making ability of machines, improving human-computer interaction and discussing ethical and moral issues. Finally, the importance of interdisciplinary cooperation to realize the function of advanced artificial consciousness is prospected. The results of this research not only point out the direction for the development of AI technology, but also provide the possibility for machines to understand and adapt to human society more deeply, marking the key step for the development of artificial intelligence to a higher level.
Diversity of consciousness theory
The nature and mechanism of consciousness has always been the core research topic across many disciplines. From the profound discussion of philosophy to the concrete analysis of neuroscience, from the behavioral research of psychology to the technical simulation of artificial intelligence, every field tries to answer the basic questions about consciousness from its specific perspective. The diversity of consciousness theory is not only reflected in the breadth and complexity of the theory, but also in the different interpretations of the definition, causes and functions of consciousness among the theories.
Psychological perspective
Consciousness theory in psychology usually focuses on cognitive model and psychological state, and studies how consciousness affects decision-making, memory, perception and emotional processing. Cognitive psychology tries to describe the information processing process of psychological activities, from perceptual input to behavioral output, and explore how the subconscious affects our behaviors and thoughts without our knowledge.
Philosophical perspective
Philosophy's discussion of consciousness is more abstract, involving existential and epistemological issues, such as what is the essence of consciousness and how consciousness relates to our understanding of the world. Phenomenology pays special attention to how individual experience constitutes a direct perception of the world, and discusses how consciousness shapes or constitutes reality.
Neuroscience perspective
Neuroscience explores the biological mechanism of consciousness by studying the structure and function of the brain. Study how the electrical activity and chemical signals of neurons are converted into thoughts, feelings and memories, and how consciousness is generated in the brain. This includes studying how specific brain regions participate in specific conscious activities and how consciousness is transmitted between different parts of the brain.
Artificial intelligence perspective
In the field of artificial intelligence, researchers try to simulate or copy some aspects of human consciousness, and simulate human cognitive process through algorithms and computational models. This involves integrating the functions of data processing, information integration, knowledge application and decision-making into the machine system, with the goal of creating a system that can perform complex tasks and have independent decision-making ability.
The intervention of DIKWP model
The DIKWP model proposed by Professor Yucong Duan provides a bridge to connect these seemingly different consciousness theories, and tries to realize a comprehensive consciousness simulation under the framework of artificial intelligence. DIKWP model not only pays attention to the traditional data, information and knowledge processing, but also includes two higher-level cognitive processes, Wisdom and Purpose, which try to simulate the ethical, moral and long-term planning considerations of human beings in the face of complex decisions.
By combining these consciousness theories, DIKWP model aims to reveal how an artificial system can simulate the multidimensional functions of human consciousness, from basic data processing to complex decision-making and self-reflection. This interdisciplinary integration not only helps to promote the development of artificial intelligence technology, but also provides a new perspective and tool for understanding the complexity of human consciousness.
1 The relationship between DIKWP model and main consciousness theory
Global Workspace Theory
GWT believes that consciousness is the information aggregation of multiple unconscious processes in the brain, which makes information available to all cognitive systems through a global workspace. The data, information and knowledge components in the DIKWP model can be regarded as components of the global workspace, while Wisdom and Purpose are the integration and application of these information at a higher level, providing the ability to execute complex decisions and behaviors.
Integrated information theory (IIT)
IIT emphasizes that consciousness is a highly integrated information, in which each part of the system contributes to the overall experience in an inseparable way. In the framework of DIKWP, this integration is reflected in the multi-level processing from data to Purpose, and each level makes a unique contribution to the generation of overall consciousness.
Higher-order Thought
HOT theory holds that consciousness involves reflection on one's own thinking, that is, consciousness is thinking about one's own psychological state. The Wisdom and Purpose surface of DIKWP model provide a framework to simulate this kind of higher-order thinking, especially the ethical and moral decision-making problems handled on the Wisdom surface.
Global Workspace Theory (GWT) was put forward by Bernard Baars, and it claims that consciousness is realized through a central information exchange area-Global Workspace, which allows different cognitive processes to share information. In this theory, the unconscious cognitive process can push information to the global workspace when necessary, making it conscious.
In the DIKWP model, the hierarchy of Data, Information and Knowledge can be regarded as the basic structure of the global workspace. They collect and process perceived data from the outside and internal data to form information and knowledge that can be further processed. For example, the perceived visual and auditory information is transformed into a basic understanding of the environment after preliminary processing, which can be regarded as the primary stage of consciousness.
Furthermore, the layers of Wisdom and Purpose are equivalent to the advanced consciousness function in GWT. At these levels, information is not only processed and stored, but also used to make decisions and plans, which requires evaluation and selection of various possible behavioral consequences. The treatment of the Wisdom plane involves moral and ethical considerations for different choices, and Purpose is to form a specific action plan under the guidance of these considerations.
1.2 Integrated information theory
Integrated Information Theory was put forward by Giulio Tononi, which proposed that consciousness is a high degree of information integration, and the diversity of consciousness is determined by the highly complex and irreducible information patterns within the system. This theory emphasizes that the level of consciousness of a system can be measured by the amount of "integrated information" it generates.
In the framework of DIKWP model, the multi-level processing from data to Purpose reflects the process of information integration. Data deals with the original perceptual input, Information carries out preliminary pattern recognition and meaning giving, and Knowledge constructs a wide network of these information and integrates it into a knowledge base available to the system. Wisdom further integrates these knowledge, makes complex judgments and decisions, and finally, Purpose sets action goals according to these decisions, showing the integration of information from the most basic form to the advanced cognitive function.
The theory of high-order thinking was developed by David Rosenthal and others, and claimed that consciousness involves the cognition of one's own psychological state (that is, the content of consciousness is thinking about one's own psychological state). In short, one must not only feel, but also realize that he is feeling.
In the DIKWP model, the layers of Wisdom and Purpose provide a framework for realizing higher-order thinking. Wisdom not only deals with specific knowledge and information, but also needs to reflect on itself and consider the ethical and moral influence of its decision-making, which is the high-level thinking about its own cognitive state. At the same time, the setting and implementation of Purpose embodies the ability of presupposition and self-adjustment of future behavior, which requires evaluation and planning of one's current and future psychological state.
2 The way to realize DIKWP artificial consciousness
2.1 Simulation of human cognitive process
Under the framework of DIKWP model, simulating human cognitive process requires in-depth understanding and accurate modeling of all aspects of human cognition. This process includes but is not limited to the following key steps:
Data acquisition and preliminary processing: simulate the perception process, such as vision and hearing, and acquire environmental data through advanced sensors. Using machine learning technology, these data are preliminarily classified and recognized, which simulates the processing of primary sensory information by human brain.
Information integration and knowledge formation: through deep learning network and pattern recognition algorithm, scattered information is integrated into meaningful modules and further developed into complex knowledge structure. This involves semantic understanding and logical reasoning of existing information, which is similar to the process of human learning and memory from experience.
Wisdom application and decision-making: combining situations and preset goals, using the formed knowledge base to make complex decision-making. This includes moral and ethical considerations, simulating advanced cognitive functions such as strategic planning, problem solving and creative thinking.
Self-reflection and Purpose formation: develop the ability of the machine to self-monitor and reflect on the consequences of its actions, and form the Purpose of future actions. This requires that the system can not only perform tasks, but also evaluate the effect of its own behavior and adjust strategies according to feedback.
2.2 Enhance the machine's independent decision-making ability
In order to improve the autonomous decision-making ability of machines, we must focus on the development of Wisdom and Purpose:
Situation analysis and moral judgment: implement complex algorithms to evaluate the potential impact of various action plans, including moral and social dimensions. For example, in autonomous driving technology, the system needs to be able to make ethical decisions in an emergency.
Goal-oriented behavior planning: The system should have the ability of autonomous behavior planning based on long-term goals and short-term feedback, which requires the machine not only to respond to the current environment, but also to foresee and plan future actions.
2.3 Improve human-computer interaction
The improvement of human-computer interaction requires the system to understand and predict the Purpose and needs of human users in a deeper level:
Develop emotional intelligence: Through emotional computing technology, machines can identify and respond to human emotional states, and provide more personalized interactive experience.
Enhance interactive adaptability: the machine should be able to automatically adjust its response strategy according to the user's feedback and behavior patterns to better meet the user's needs.
2.4 Discussion on ethical and moral issues
With the increase of machine intelligence, their role in ethical and moral decision-making becomes particularly important:
Formulate moral framework: set clear ethical and moral guiding principles for artificial consciousness systems to ensure that their behaviors meet the expectations and standards of human society.
Continuous ethical review: establish a mechanism to regularly review and update the systematic moral decision-making framework to cope with the rapidly changing social environment and technological progress.
2.5 Promotion of interdisciplinary cooperation
To realize the advanced artificial consciousness function described in DIKWP model requires the in-depth cooperation of neuroscience, cognitive psychology, computer science and artificial intelligence. Through interdisciplinary cooperation, we can integrate knowledge and technology in different fields and promote the development of artificial consciousness more effectively.
3 DIKWP artificial consciousness theory
When exploring the future of artificial intelligence (AI), especially the development prospect of artificial consciousness (AC), the DIKWP model proposed by Professor Yucong Duan provides a theoretical basis. This model not only frames the core components of traditional AI-Data, Information and Knowledge, but also introduces Wisdom and Purpose, which sets a higher goal for the further development of AI technology. The following is a comprehensive imagination of the possible future development direction of DIKWP's artificial consciousness theory.
3.1 Theoretical basis of DIKWP model
DIKWP model puts forward a systematic framework to simulate and realize artificial consciousness. The model includes five levels: Data, Information, Knowledge, Wisdom and Purpose. These levels work together to simulate the complete path from simple data processing to complex decision-making and self-awareness.
Data
In Data, the main task of the system is to collect and preprocess sensory inputs, such as visual images, auditory signals and tactile feedback. These data are unprocessed and need to be analyzed by algorithms, such as edge detection and color recognition.
Information
Information mainly deals with the patterns and structures in data and transforms the original data into useful information. For example, an image recognition system recognizes the shape and size of an object from pixel data, and a natural language processing system understands the grammatical structure and semantics of a sentence.
Knowledge
In Knowledge, the system not only stores information, but also associates previous experience with current situation. The function of this layer is to establish and use knowledge base, such as using machine learning model to predict or classify, or using rule engine to reason.
Wisdom
Wisdom is responsible for decision-making and higher-order thinking. In Wisdom, the potential results of different action plans are systematically evaluated and selected. This involves moral and ethical judgment and the ability to consider long-term consequences. Wisdom is a key simulation of human Wisdom in the model, especially when dealing with complex, changeable and fuzzy real-world problems.
Purpose
Purpose is responsible for defining the goals and motivations of the system. These goals can be built-in, such as observing specific operating parameters, or adaptive, such as adjusting strategies according to environmental changes. Purpose enables the system to run autonomously and adapt to the environment without direct human intervention.
3.2 All-round cognitive simulation
DIKWP artificial consciousness theory provides a solid theoretical basis for all-round cognitive simulation. In the future, the artificial consciousness system will not only simulate the basic perceptual processing of human beings, such as the recognition of visual and auditory information, but also expand to more complex cognitive functions, including language understanding, emotional perception, abstract thinking and complex problem solving ability. These systems will capture and reflect the subtle changes of human behavior through highly integrated data processing capabilities, and then realize real environmental awareness and interaction.
By transforming a large amount of scattered information into useful knowledge, combined with moral and ethical decision-making on the Wisdom side, artificial consciousness will be able to make independent decisions in the fields of medical care, law and education that conform to human values. For example, the medical assistance system can provide personalized treatment suggestions based on patients' historical data and real-time health status, and consider ethics and resource availability to formulate the best treatment plan.
3.3 Enhanced emotional intelligence
The future artificial consciousness system will have more advanced emotional intelligence, which can identify and respond to human emotions more accurately, and even simulate emotional reactions under appropriate circumstances to provide a more natural interactive experience. This improvement of emotional intelligence will be based on the analysis of a large number of emotional data, so as to make a reasonable response in complex social interaction.
The improvement of emotional intelligence will enable machines to provide more intimate and effective services in scenes that need sympathy and understanding, such as education, psychological counseling and customer service. For example, in the field of education, AI teachers can adjust teaching strategies according to students' emotional state to improve learning efficiency and students' satisfaction.
3.4 Moral and ethical decision-making
In DIKWP model, the combination of Wisdom and Purpose provides the artificial consciousness system with the ability to make complex moral and ethical decisions. This ability is not only based on the analysis of data and the application of knowledge, but also involves the comprehensive consideration of the social impact, ethical consequences and legal framework of various potential decisions. This decision-making ability is especially suitable for autonomous driving, medical decision support and other fields, in which immediate and accurate ethical judgment is necessary.
Practical application case: self-driving car
In the application of self-driving cars, DIKWP model can make the system make a quick and comprehensive decision analysis when facing the threat of potential accidents. For example, when a self-driving car suddenly encounters a pedestrian crossing the road at high speed, the system will immediately collect and process environmental data, and Information will convert these data into real-time traffic and pedestrian information, while Knowledge will provide historical accident data and hedging strategies.
Wisdom will evaluate the possible consequences of different action plans at this time, such as the possible passenger injury caused by emergency braking and the possible legal responsibility for avoiding pedestrians. Purpose ensures that the decision-making is consistent with the overall safety goal of the vehicle-to protect passengers' safety while complying with traffic laws. In the end, the system will make the most reasonable decision based on these comprehensive considerations, which may be to reduce the threat to pedestrians as much as possible on the premise of ensuring no serious injury.
With the progress of artificial intelligence technology, the future artificial consciousness (AC) system will show unprecedented autonomy and adaptability. These systems can not only perform predetermined tasks, but also understand and adapt to complex environmental changes. This ability is achieved through advanced data processing, Wisdom decision-making and dynamic goal setting at the destination level in DIKWP model.
In high-risk environments such as natural disaster response or outer space exploration, AC system can analyze environmental data in real time, predict potential risks, and adjust behavior independently to maximize mission success rate and its own safety. For example, a space exploration robot may encounter unknown terrain or extreme weather conditions when exploring the surface of Mars: With the help of advanced artificial consciousness system, it can immediately decide whether to change the path or return to the shelter. The core of this autonomy and adaptability lies in the fact that the machine can make the most reasonable decisions on its own based on real-time data and built-in security protocols, without immediate instructions from the control center on the earth.
3.6 A new era of man-machine cooperation
The development of artificial consciousness is leading man-machine cooperation into a new era. Machines are no longer just tools for executing instructions, but can understand the needs and Purpose of human collaborators and predict and support human activities. In the fields of creative work, scientific research or complex decision-making, AC system can provide innovative solutions and decision-making support to help human beings solve problems that are difficult to break through traditional thinking patterns.
For example, when designing a new building, AC system can understand the architect's creative Purpose, and provide a comprehensive evaluation of structural safety, material efficiency and aesthetic effect by simulating and analyzing different design schemes. This cooperation mode not only accelerates the innovation process, but also improves the quality and practicability of the final product.
3.7 Ethical challenges in the future society
With the maturity and wide application of artificial consciousness technology, ethical and social problems also follow. When AC system makes decisions, its autonomy may lead to potential conflicts with human interests. For example, how to make a moral choice and choose to protect passengers or pedestrians in an emergency requires a complex ethical decision-making framework to be embedded in the design of the system.
In addition, how to ensure that AC systems are not used for unethical purposes and how to formulate global unified standards to manage and monitor these technologies are issues that need to be considered by the whole society. Policymakers, scientific and technological enterprises and the public around the world must work together to establish a transparent and responsible use and development framework to ensure the healthy development of artificial consciousness technology and benefit all mankind.
DIKWP artificial consciousness theory provides a comprehensive framework, which not only points out the promotion direction for the existing AI technology, but also sets a lofty goal for the future development of AI. By strengthening the Wisdom and Purpose, AC system is expected to achieve a deeper simulation of human behavior and thinking patterns. This will not only promote the development of technology itself, but also bring about fundamental changes in human work, lifestyle and interaction with machines.
At the same time of technological development, we must also conduct appropriate ethical and regulatory supervision over these advanced systems. Future policy makers, technology developers and all walks of life should participate in a continuous dialogue and examination to ensure that the development of technology can benefit human society instead of becoming a new source of risk. The development of AC is not only a technical challenge, but also a test of our Wisdom, which requires us to think deeply about its long-term impact on human society while innovating.
Through the in-depth understanding and application of DIKWP model, the future artificial consciousness system will serve human beings better and become a key force to promote social progress. This process will require interdisciplinary cooperation, global dialogue and in-depth study and understanding of the impact of technology. In this process, we have the opportunity to redefine the relationship between man and machine and create a new era of symbiosis and integration.
4 Build DIKWP artificial consciousness model
Professor Yucong Duan's DIKWP model provides an innovative theoretical framework for the development of artificial consciousness. This report will combine the main consciousness theories, such as Global Workspace Theory (GWT), Integrated Information Theory (IIT) and Higher-order Thinking Theory (HOT), and make a detailed simulation of DIKWP model, aiming at showing how to apply these theories to the actual construction of artificial consciousness.
4.1 The complexity of consciousness and the challenge of AI
Consciousness, as a complex and multi-level phenomenon unique to human beings, involves from basic perceptual processing to advanced decision-making and self-reflection. This change from perception to advanced cognition requires not only the reception and reaction of information, but also the understanding and evaluation of information and the choice of behavior based on complex situations. In the field of artificial intelligence, although remarkable progress has been made, such as advanced image and speech recognition, strategy formulation of complex games, etc., the real challenge lies in how to integrate these different levels of processing processes and realize the consciousness function similar to human beings. Major challenges:
Integration and optimization: It is a technical challenge to effectively integrate these levels to make the flow and processing of information efficient and accurate. Each layer must be optimized to handle its specific tasks, while interacting well with other layers.
Ethical and moral decision-making: In Wisdom and Purpose, how to program AI system to make ethical and moral decisions is an important research field. This requires not only technical solutions, but also in-depth discussion of philosophy and ethics.
Self-adaptation and learning: How to design the system so that it can not only work in static environment, but also learn and adapt to dynamic environment is the key to realize advanced artificial consciousness.
4.2 The combination of main consciousness theory and DIKWP model
(a) Global Workspace Theory
Global Workspace Theory is a cognitive and neuroscience theory about consciousness, which was put forward by Bernard Baars. The core idea is that conscious content is broadcast in the "global workspace" of the brain, so that various unconscious cognitive processes can access these contents for processing. This mechanism allows information integration, decision-making, and memory access across time and space, which is a simulation of human consciousness. In DIKWP artificial consciousness model, we can apply this theory to explain the whole process from data processing to advanced decision-making.
Processing of Data and Information: input of workspace.
In the DIKWP model, the Data and Information levels are equivalent to the input parts of the global workspace in GWT. These levels deal with sensory data received from the outside world and other forms of raw data, including sensory information such as vision, hearing and touch, as well as data obtained through various sensors. The main task of this stage is to preliminarily process and analyze these raw data and convert them into meaningful information.
Data: Mainly focus on data collection and preliminary processing. For example, the image data captured by the machine vision system needs to be preliminarily analyzed by image processing algorithms, such as edge detection and color analysis.
Information further carries out semantic analysis and context interpretation on these preliminarily processed data, such as identifying objects in images and analyzing the meaning of language input. These processes provide basic data and information support for higher functions of consciousness, such as memory, reasoning and decision-making.
Knowledge: the integration and storage of information
In the global workspace, Knowledge undertakes the function of further integration and storage of information. In DIKWP model, Knowledge not only stores structured knowledge transformed from data and Information, but also includes rules, patterns and associations abstracted from these information, which are knowledge bases for higher-level processing.
Knowledge: Through learning algorithms, such as deep learning and machine learning, pattern recognition and concept induction are carried out on information to form an abstract understanding of the world. This includes the laws, principles and operational guidelines identified from the data, such as self-driving cars learning traffic rules from driving data.
The knowledge base at this level is dynamic and can be constantly updated and adjusted according to new information to ensure the timeliness and accuracy of decision-making.
Wisdom and Purpose: advanced decision-making and planning
Wisdom and Purpose are the highest levels in the DIKWP model, which are equivalent to the advanced decision-making and planning parts in GWT. At this level, the system not only uses the current knowledge to make decisions, but also needs to preset future goals and plans, showing a high degree of autonomy and foresight.
Wisdom uses accumulated knowledge to make complex decisions, and considers many factors such as ethics, efficiency and feasibility. For example, the medical diagnosis system needs to weigh multiple factors such as treatment effect, side effects and patient's condition when suggesting treatment plans.
Purpose defines the long-term and short-term goals of the system and plans the strategies and steps to achieve these goals. The function of this layer reflects the purpose of the system, that is, the driving force of the system action comes from the set goals, such as the intelligent robot independently planning the optimal path and strategy according to the set tasks.
The integration and application of these two layers not only shows the "Wisdom" of the machine, but also enables the machine to make decisions and actions independently without direct human intervention, which truly embodies the core characteristics of artificial consciousness.
Applying GWT in DIKWP model can form a complete process from perception to advanced decision-making, which not only provides a practical operation framework for understanding artificial consciousness, but also provides theoretical guidance and technical path for the design and implementation of AI system in the future.
(b) Integrated Information Theory
Integrated Information Theory was put forward by neuroscientist Giulio Tononi, who claimed that consciousness is the result of highly integrated information, that is, the information in the system cannot be simply divided through complex interactions. In the DIKWP artificial consciousness model, this theory provides strong theoretical support for understanding and constructing advanced AI systems, especially at the two advanced processing levels of Wisdom and Purpose.
The Function of Wisdom and Information Integration
In the DIKWP model, Wisdom plays a vital role. It is not only an advanced stage of information processing, but also a key link of information integration. Its core function is to comprehensively evaluate and make decisions on inputs from lower levels (data, information and knowledge). This process involves the following aspects:
Diversification and integration of decision paths:
Wisdom needs to deal with and evaluate a variety of possible action plans, which not only includes simple algorithm selection, but also involves the prediction and risk assessment of various potential results. For example, in a self-driving car, Wisdom may need to evaluate when to accelerate, brake or avoid in real time, and each decision needs to be based on a deep understanding of the current traffic environment and a prediction of future trends.
Ethical and moral considerations:
In the practical application of artificial consciousness, Wisdom also needs to integrate ethical and moral factors. For example, when recommending a treatment plan, the medical decision support system needs to consider not only the effectiveness and risks of treatment, but also the personal preferences and ethical issues of patients, such as the use of life support systems.
Dynamic adjustment of complex system:
Wisdom's decision-making is not static, but a dynamic adjustment process, which adjusts previous decisions in real time according to new data input. This dynamic adjustment is the expression of highly integrated information, which ensures that the decision-making of the system always adapts to the changing environment and internal state.
Goal orientation and information integration of Purpose
Purpose is the highest level in the DIKWP model, and its main function is to ensure that all actions are in line with the overall goal and long-term planning of the system. This layer of information integration is reflected in:
Coordination between long-term goals and short-term decisions:
Purpose needs to translate the long-term goals of the system into concrete and executable short-term action guides. This involves extracting specific action points from a wide range of goals and how to adjust short-term strategies to deal with emergencies without sacrificing long-term goals.
Self-correction and learning:
Purpose also has the ability to constantly correct and optimize itself through past experience and the results of current actions. This ability of self-learning and self-correction is realized through highly integrated information processing, which ensures that the system can survive and develop in a complicated environment.
Integration with external objectives:
When interacting with human users or other systems, Purpose also needs to be able to integrate external goals and needs and adjust its own action strategies to better serve users or the entire ecosystem.
By deeply applying the integrated information theory in Wisdom and Purpose, DIKWP model not only enhances the decision-making ability of artificial consciousness system, but also improves the depth and accuracy of its ethical judgment. The realization of this model promotes the transformation of artificial intelligence from single data processing to real intelligent decision-making and independent consciousness. Future research will further explore how to optimize this information integration process, so that the artificial consciousness system can better simulate human complex consciousness and advanced cognitive function.
(c) higher order thinking theory
Higher-order thinking theory puts forward that consciousness involves not only the perception of the outside world, but also the awareness of these perceptions, that is, a level of "about". According to this theory, consciousness involves the cognition of one's own psychological state, that is, the reflection of thinking on oneself. In the DIKWP model, Wisdom and Purpose embody this high-order thinking function. These two levels not only deal with specific information, but also evaluate and plan its influence on the action plan. The following is a concrete analysis and expansion of these two layers in artificial consciousness.
Wisdom:
Wisdom plays a vital role in DIKWP model. It is not only the further processing of information, but also the deep understanding and reflection of this information. The purpose of this layer is to enable the artificial system to make complex decisions, especially to weigh ethical and moral issues. The key tasks of Wisdom include:
Moral and ethical decision-making: Wisdom needs to be able to simulate the thinking process of human beings in the face of moral dilemmas. For example, how do self-driving vehicles choose actions when they may cause injuries, or how do medical AI balance privacy and medical benefits when dealing with patient information?
Self-reflection: This layer enables the system to evaluate its decision-making process, identify possible biases or mistakes, and adjust its behavior to optimize the results. This self-reflection ability is a sign of higher-order consciousness, which requires the system to have the ability to monitor and evaluate its own cognitive process.
Situational adaptive decision-making: Wisdom should be able to adjust its decision-making logic according to different environments and situations. This means that the system can identify environmental changes and adjust its behavior accordingly to meet new conditions or achieve new goals.
Purpose:
Purpose is the highest-order thinking expression in DIKWP model, which focuses on the setting and realization of goals. In artificial consciousness, the role of this layer is particularly critical, because it ensures the purposefulness and strategy of behavior. The core functions of Purpose include:
Goal setting: Purpose is responsible for setting long-term and short-term goals for actions. These goals should be based on the overall task and expected results of the system. For example, the medical assistance system aims to improve patient satisfaction and treatment effect.
Action planning: once the goals are set, Purpose needs to plan how to achieve them. This includes identifying the necessary action steps, evaluating the possible consequences of various action plans, and optimizing the decision-making path to achieve the best results.
Feedback loop: Purpose also needs a feedback mechanism to monitor the conformity between the actual effect of the action and the expected goal. This kind of feedback is essential for adjusting action strategies, correcting goals or improving decision-making algorithms.
4.3 Actual Construction Steps and Simulation Examples
When building an artificial consciousness system based on DIKWP model, the realization of each stage needs to comprehensively use the current technical capabilities and make adjustments in continuous iteration and optimization. The following is an in-depth analysis of each stage and a simulation example to show the application of this theory in practice.
Data integration and perceptual processing
In the stage of data integration and perceptual processing, the goal is to collect data from multiple perceptual sources and conduct preliminary processing. This involves not only traditional data collection, but also understanding the complex environment through advanced perceptual technologies such as computer vision, speech recognition and multimodal data processing.
Technical realization:
Computer Vision: Using deep learning models, such as Convolutional Neural Network (CNN), to analyze and interpret visual data and identify objects and scenes.
Speech processing: Natural Language Processing (NLP) and speech recognition technologies, such as Long Short-term Memory Network (LSTM), are used to analyze speech instructions and extract semantic information.
Data fusion: Using sensor fusion technology to integrate data from different sources to create a unified data view and improve the accuracy of decision-making.
Knowledge formation and storage
Convert the information extracted in the perceptual stage into usable knowledge and store it in an accessible database or knowledge map for future reference and learning.
Technical realization:
Knowledge Map: Construct a knowledge map to organize and correlate the information extracted from data, for example, use graph database technology such as Secondary to store the relationships between entities.
Machine learning: Through machine learning techniques, such as decision trees and support vector machines (SVM), historical data are analyzed, patterns and laws are found, and usable knowledge bases are formed.
The construction of Wisdom
In Wisdom, the system makes use of the formed knowledge to make complex decision analysis, consider long-term goals and ethical standards, and make reasonable action choices.
Technical realization:
Reinforcement learning: the application of reinforcement learning allows the system to learn to make the best decision through trial and error, so as to achieve the best long-term reward.
Simulate moral decision-making: use a rule-based system or moral decision-making framework to evaluate the ethical consequences of possible action plans and ensure that decisions meet ethical standards.
Definition and implementation of Purpose
Finally, the system needs to formulate specific action plans according to preset goals and strategies, and interact with users to confirm that these plans meet their expectations and needs.
Technical realization:
Goal planning algorithm: Use tools such as PDDL (Planning Domain Definition Language) to define and execute goal-oriented tasks.
Man-machine interaction: Through advanced user interface, such as natural language interface or graphical user interface, users can easily communicate with the system, give feedback and adjust the action direction of the system.
4.4 Simulation example
Consider the situation that an artificial consciousness system is applied to medical assistant decision-making:
Data: The system collects data from patients' medical records, real-time vital signs monitoring and related medical databases.
Information: using data analysis techniques, such as pattern recognition and statistical analysis, to extract key information, such as the progress of the disease and the evaluation of the treatment effect.
Knowledge: integrating medical research, clinical trial results and historical treatment data to form a comprehensive knowledge base about specific diseases.
Wisdom: evaluate the advantages and disadvantages of various treatment schemes systematically, and consider the specific needs of patients and possible ethical issues, such as the risks and benefits of treatment.
Purpose: The system makes treatment plans according to patients' long-term health goals (such as maximizing quality of life and minimizing pain), and communicates with doctors and patients to reach a consensus.
The construction of DIKWP artificial consciousness model is not only a technical challenge, but also a reflection of theoretical innovation. By combining the main consciousness theory with artificial intelligence technology, the model not only provides the possibility for simulating human consciousness, but also promotes the in-depth development of the application of AI in a wider field. Future research can further explore how to optimize the interaction between layers, how to deal with more complex ethical issues, and how to improve the adaptability of the system and the accuracy of intelligent decision-making.
In the long run, the improvement and implementation of DIKWP model will promote the cooperation between human beings and advanced intelligent systems, provide new solutions to solve some of the most complex and urgent global problems, and open a new chapter in the development of artificial intelligence.
5 Conclusion and future prospect
In this report, we deeply discuss the theoretical construction and practical application potential of artificial consciousness through the comparative analysis of Professor Yucong Duan's DIKWP artificial consciousness theory and the main consciousness theory. The DIKWP model provides a comprehensive framework by integrating the five dimensions of data, information, knowledge, Wisdom and Purpose, aiming at simulating and realizing the consciousness function similar to human beings. The following are the main findings of our research and the direction of future research:
Deepening and application of theory: DIKWP model successfully combines the traditional AI processing level with higher-order cognitive functions-Wisdom and purpose. This combination is not only innovative in theory, but also greatly broadens the possibility of artificial consciousness application, especially in scenes that require complex decision-making and moral judgment.
Technical challenge and innovation: To realize the function described by DIKWP model, more technical breakthroughs need to be made in AI technology, especially in the fields of machine learning, deep learning, neural network and natural language processing. In addition, the implementation of the model also needs to solve how to integrate and use information from different levels while ensuring the stability and reliability of the system.
Ethics and social responsibility: With the improvement of AI system in Wisdom and Purpose, how to ensure that their decisions meet ethical standards in the process of design and operation has become an important issue. It is necessary to reach a global consensus on how to supervise advanced AI systems, especially those with artificial consciousness.
A new chapter of man-machine cooperation: DIKWP model emphasizes that human behavior and needs can be better understood and predicted by improving the ability of machine Purpose recognition and execution. This advanced human-computer interaction ability indicates that human-computer cooperation will enter a new stage, in which machines are not only auxiliary tools, but also collaborators who can actively think and make decisions.
The necessity of interdisciplinary research and cooperation: to realize the comprehensive function of DIKWP model, it is necessary to combine the knowledge and technology of neuroscience, cognitive science, psychology, computer science and philosophy. Interdisciplinary cooperation is very important to solve the complex problems encountered in the study of artificial consciousness.
In a word, DIKWP artificial consciousness theory provides a new theoretical tool for understanding complex cognitive functions and points out the direction for realizing an artificial intelligence system with high autonomy and decision-making ability. In order to ensure the healthy development of artificial consciousness technology and finally realize the positive contribution to human society, future research needs to be deeply discussed in theory and practice.
[1] 段玉聪(Yucong Duan). (2024). 人为什么不愿意被别人改变：DIKWP和语义数学的深入探讨(Why People Don't Want to Be Changed by Others: Insight from DIKWP and Semantic Mathematics). DOI: 10.13140/RG.2.2.17961.77927. https://www.researchgate.net/publication/377726002_Why_People_Don't_Want_to_Be_Changed_by_Others_Insight_from_DIKWP_and_Semantic_Mathematics.
[2] 段玉聪(Yucong Duan). (2024). 语义新质生产力：原理与技术(Semantic New Quality Productivity: Principles and Techniques). DOI: 10.13140/RG.2.2.14606.33607. https://www.researchgate.net/publication/377726380_Semantic_New_Quality_Productivity_Principles_and_Techniques.
[3] 段玉聪(Yucong Duan). (2024). 基于"主观客观化"的语义不确定性处理(Semantic Uncertainty Handling Based on "Subjective Objectivisation"). DOI: 10.13140/RG.2.2.31383.55206. https://www.researchgate.net/publication/377726442_Semantic_Uncertainty_Handling_Based_on_Subjective_Objectivisation.
[4] 段玉聪(Yucong Duan). (2024). DIKWP新质生产力与传统生产力的对比分析(DIKWP New Quality Productivity vs. Traditional Productivity Analysis). DOI: 10.13140/RG.2.2.21317.22242. https://www.researchgate.net/publication/377726626_DIKWP_New_Quality_Productivity_vs_Traditional_Productivity_Analysis.
[5] 段玉聪(Yucong Duan). (2024). DIKWP与语义认知学(DIKWP and Semantic Cognition). DOI: 10.13140/RG.2.2.14052.55680. https://www.researchgate.net/publication/377415901_DIKWP_and_Semantic_Cognition.
[6] 段玉聪(Yucong Duan). (2024). DIKWP与语义哲学(DIKWP and Semantic Philosophy). DOI: 10.13140/RG.2.2.34185.21606. https://www.researchgate.net/publication/377416120_DIKWP_and_Semantic_Philosophy
[7] 段玉聪(Yucong Duan). (2024). 语义认知学：连接人类思维与计算机智能的未来(Semantic Cognition: Connecting the Human Mind to the Future of Computer Intelligence). DOI: 10.13140/RG.2.2.29152.05129. https://www.researchgate.net/publication/377416321_Semantic_Cognition_Connecting_the_Human_Mind_to_the_Future_of_Computer_Intelligence
[8] 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
[9] 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
[10] 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
[11] 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
[12] 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
[13] 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
[14] 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
[15] 段玉聪(Yucong Duan). (2024). 超越达尔文：技术、社会与意识进化中的新适应性(Beyond Darwin: New Adaptations in the Evolution of Technology, Society, and Consciousness). DOI: 10.13140/RG.2.2.29265.92001. https://www.researchgate.net/publication/378290072_Beyond_Darwin_New_Adaptations_in_the_Evolution_of_Technology_Society_and_Consciousness
[16] 段玉聪(Yucong Duan). (2022). 基于存在性计算与推理和本质计算与推理的Collatz猜想启示(Existence Computation and Reasoning(EXCR) and Essence Computation and Reasoning(ESCR) based Revelation of Collatz Conjecture). DOI: 10.13140/RG.2.2.28299.36647. https://www.researchgate.net/publication/358722876_Existence_Computation_and_ReasoningEXCR_and_Essence_Computation_and_ReasoningESCR_based_Revelation_of_Collatz_Conjecture
[17] 段玉聪(Yucong Duan). (2022). 基于存在性计算与推理和本质计算与推理的哥德巴赫猜想启示(Existence Computation and Reasoning(EXCR) and Essence Computation and Reasoning(ESCR) based Revelation of the Goldbach's conjecture). DOI: 10.13140/RG.2.2.23213.77281. https://www.researchgate.net/publication/358637942_Existence_Computation_and_ReasoningEXCR_and_Essence_Computation_and_ReasoningESCR_based_Revelation_of_the_Goldbach's_conjecture
[18] 段玉聪(Yucong Duan). (2022). 基于存在性计算与推理与本质计算与推理的四色定理启示(Existence Computation and Reasoning(EXCR) and Essence Computation and Reasoning(ESCR) based Revelation of the Four Color Theorem). DOI: 10.13140/RG.2.2.14505.67684. https://www.researchgate.net/publication/358608147_Existence_Computation_and_ReasoningEXCR_and_Essence_Computation_and_ReasoningESCR_based_Revelation_of_the_Four_Color_Theorem
[19] 段玉聪(Yucong Duan). (2022). 点、线、平面语义的存在性计算与推理与本质性计算与推论揭示(Existence Computation and Reasoning(EXCR) and Essence Computation and Reasoning(ESCR) based revelation of the semantics of point, line and plane). DOI: 10.13140/RG.2.2.32383.89767. https://www.researchgate.net/publication/358608122_Existence_Computation_and_ReasoningEXCR_and_Essence_Computation_and_ReasoningESCR_based_revelation_of_the_semantics_of_point_line_and_plane
[20] 段玉聪(Yucong Duan). (2022). “客观”数学的终结是对“主观”的回归(The end of "Objective" mathematics as a return to "Subjective"). DOI: 10.13140/RG.2.2.36171.87841. https://www.researchgate.net/publication/358607773_The_end_of_Objective_mathematics_as_a_return_to_Subjective
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