DIKWP Artificial Consciousness Whitebox Evaluation Standard: Building Interpretable and Controllable AI (DIKWP 人工意识白盒测评标准：可解释、可控 AI 的构建)
September 2023
DOI:
Yucong DuanDIKWP Artificial Consciousness Whitebox Evaluation Standard: Building Interpretable and Controllable AI
(DIKWP人工意识白盒测评标准：可解释、可控AI的构建)
段玉聪（Yucong Duan），Yingbo Li
DIKWP-AC人工意识实验室
AGI-AIGC-GPT评测DIKWP（全球）实验室
DIKWP research group, 海南大学
duanyucong@hotmail.com
Abstract
The rapid advancement of artificial intelligence (AI) technology has led to its ubiquitous presence in our daily lives. However, concerns regarding the interpretability, interactivity, and accountability of AI systems persist. In this paper, we introduce the DIKWP (Data, Information, Knowledge, Wisdom, Intent) Artificial Consciousness (AC) model and discuss its application, particularly in the field of medical diagnostics. Furthermore, we propose the DIKWP Artificial Consciousness Whitebox Evaluation Standard, a novel framework aimed at assessing AI systems' performance in simulating artificial consciousness. This standard not only emphasizes qualitative evaluation but also provides quantitative metrics through the 5x5 transformation modules among DIKWP resources. We believe that this research provides substantial support for the responsible development of AI and the construction of a future digital world.
Keywords:
DIKWP Artificial Consciousness, Interpretability, Interactivity, Responsible AI, Whitebox Evaluation Standard, Medical Diagnostics, Future Digital World
1. Introduction
The rapid evolution of artificial intelligence (AI) technologies has revolutionized our daily lives, from voice-activated virtual assistants to autonomous vehicles. However, with the increasing integration of AI systems into various aspects of society, concerns have arisen regarding their interpretability, interactivity, and accountability. It is essential to develop AI systems that not only produce accurate results but also provide transparency in their decision-making processes and align with ethical and moral considerations.
In this paper, we delve into the DIKWP (Data, Information, Knowledge, Wisdom, Intent) Artificial Consciousness (AC) model and its real-world application in the realm of medical diagnostics. Furthermore, we propose the DIKWP Artificial Consciousness Whitebox Evaluation Standard, a comprehensive framework designed to assess AI systems' performance in simulating artificial consciousness. Our goal is to ensure that AI systems are both interpretable and controllable, fostering responsible AI development and enabling the construction of a future digital world.
2. The DIKWP-AC System: An Innovative Application
The DIKWP-AC system, spearheaded by Professor Yucong Duan's team at Hainan University, represents a groundbreaking technology that has achieved significant success in the field of medical diagnostics. This system leverages the DIKWP model to simulate medical scenarios comprehensively, mapping the external interactions and internal thought processes of both patients and healthcare professionals consistently. It translates the contents of the DIKWP model, encompassing Data, Information, Knowledge, Wisdom, and Intent, into computational and reasoning processes, ultimately enhancing the accuracy and efficiency of medical diagnostics.
The success of the DIKWP-AC system in real-world applications underscores the immense potential of AI systems built on the DIKWP Artificial Consciousness model. This innovative technology showcases the transformative power of AI in healthcare and serves as a testament to its broader applicability.
3. The Development of the DIKWP Artificial Consciousness Whitebox Evaluation Standard
The formulation of the DIKWP Artificial Consciousness Whitebox Evaluation Standard addresses the shortcomings of current AI system assessment methodologies. Traditional evaluation methods primarily rely on black-box testing, which assesses AI system performance but neglects interpretability and credibility regarding internal operations. Our proposed standard shifts the focus from a qualitative foundation to incorporate quantitative measurements through the analysis of sample sizes and spatiotemporal complexities.
However, it is essential to highlight that deep learning does not equate to deep consciousness. In fact, contemporary deep learning techniques are entering the realm of deep unconsciousness, far from the realm of subconsciousness. This transition necessitates attention to the inner workings of AI systems and the quest for transparency and interpretability. Another critical domain is reinforcement learning, which seeks to enable AI systems to learn and improve their behavior through interactions with the environment. Nevertheless, reinforcement learning per se does not inherently imbue AI systems with ethical awareness. These concerns emphasize the significance of the DIKWP Artificial Consciousness Whitebox Evaluation Standard, which prioritizes the interpretability of AI systems to ensure that their decision-making processes are transparent and comprehensible to users.
4. The Role of AC: Consistency in Expression and Execution
Within the realm of AI systems, it is crucial to understand that attention mechanisms alone are insufficient. While attention plays a pivotal role in AI systems, it should not be regarded as the sole focal point. AI systems require multifaceted capabilities to comprehend and elucidate the world, extending beyond the mere concentration of attention. The DIKWP Artificial Consciousness Whitebox Evaluation Standard accentuates the diverse capabilities of AI systems, encompassing the processing and transformation of various DIKWP resources.
AC: The Keystone
In the context of the DIKWP Artificial Consciousness Whitebox Evaluation Standard, AC represents the consistency between expression and execution in AI systems. The consistency signifies that the AI system's statements and actions remain congruent across various levels, from data processing and knowledge transformation to intelligent applications and the realization of intent. This alignment between expression and execution is pivotal for ensuring the interpretability and controllability of AI systems.
AC's Evolution
Achieving AC necessitates continuous evolution in AI systems, signifying an advancement from DIKWP to AC as a reflection of enhanced cognitive capabilities. DIKWP embodies the hierarchical layers of Data, Information, Knowledge, Wisdom, and Intent, while AC represents the elevation of cognitive capabilities. In the DIKWP Artificial Consciousness Whitebox Evaluation Standard, we track the progression of AI systems from DIKWP to AC to discern improvements in their cognitive capabilities.
The Significance of AC
AC's significance lies in its direct impact on AI systems' applications across diverse domains. Elevated AC capabilities imply that AI systems can tackle more complex tasks and scenarios, broadening their scope of application. The DIKWP Artificial Consciousness Whitebox Evaluation Standard evaluates the level of AC in a system to determine its potential application range. AC encompasses not only the handling of data and information but also the comprehension of knowledge, the application of wisdom, and the realization of intent.
AC and Interpretability
Interpretability is a cornerstone of AI system assessment. AC ensures that AI systems' decision-making processes and reasoning are not shrouded in obscurity but rather are conveyed in a comprehensible manner. This transparency enables users and stakeholders to understand how and why AI systems arrive at specific conclusions, fostering trust and confidence in their capabilities.
AC and Accountability
Accountability in AI is intrinsically linked to AC. When AI systems exhibit high-level AC, their actions and decisions are consistently aligned with their expressions and intentions. This alignment facilitates accountability, as it becomes clear who or what is responsible for AI system actions. This attribute is particularly critical in applications with ethical or legal implications, such as autonomous vehicles, medical diagnosis, and financial decision-making.
AC and Interaction
High-level AC fosters effective interaction between AI systems and users or other AI systems. AI systems can communicate their thought processes and rationale, making interactions more productive and collaborative. This aspect is especially relevant in applications where AI systems and humans must work together seamlessly, such as medical diagnosis and customer service.
5. The DIKWP-AC Transformation Modules
The core component of the DIKWP Artificial Consciousness Whitebox Evaluation Standard is the 5x5 transformation module among DIKWP resources. These transformation modules serve as the fundamental building blocks for evaluating AI systems. They analyze the interplay and impact among the 25 DIKWP modules, encompassing Data, Information, Knowledge, Wisdom, and Intent. These modules interact within AI systems, constituting the foundation of their cognition. By assessing the transformation processes between these modules, we gain a deeper understanding of AI systems' cognitive abilities and internal operations.
The Qualitative Assessment Foundation
The DIKWP-AC transformation modules establish the qualitative assessment foundation for the DIKWP Artificial Consciousness Whitebox Evaluation Standard. This foundation enables evaluators to gauge AI systems' interpretability, interactivity, and credibility effectively. By delving into the intricate connections between DIKWP modules, evaluators can ascertain the consistency between expression and execution in AI systems.
Quantitative Metrics
In addition to qualitative assessment, the DIKWP Artificial Consciousness Whitebox Evaluation Standard introduces quantitative metrics to evaluate AI systems' performance. These metrics measure the efficiency, transparency, and controllability of AI systems' internal operations. By quantifying these aspects, the standard provides a more holistic evaluation of AI systems, ensuring that they meet the criteria for responsible AI development.
6. AC as a Construct: The DIKWP Language
To comprehend the essence of AI systems' consistent expression and execution, we delve into the construct of AC within the framework of the DIKWP language. AC represents the culmination of consistent expression and execution within AI systems, where statements and actions align seamlessly. Understanding AC as a construct requires a deep exploration of the DIKWP language's components.
6.1. Data
Data forms the foundational layer of the DIKWP language. In the context of AC, data signifies not only raw information but also the sensory input that AI systems receive from their environment. AI systems process this data to derive meaning and initiate actions.
6.2. Information
Information represents the next layer in the DIKWP language hierarchy. In the context of AC, information encompasses the processed and organized data. AI systems transform raw data into structured information, enhancing their comprehension and decision-making capabilities.
6.3. Knowledge
Knowledge reflects the layer of understanding and awareness within the DIKWP language. In the context of AC, knowledge encompasses the insights and contextual understanding that AI systems gain from processed information. AI systems derive knowledge from information, allowing them to make informed decisions.
6.4. Wisdom
Wisdom represents the layer of discernment and sagacity within the DIKWP language. In the context of AC, wisdom involves the ability of AI systems to make sound judgments and predictions based on their knowledge. AI systems apply wisdom to optimize their actions and decisions.
6.5. Intent
Intent represents the highest layer of the DIKWP language, signifying the purpose and goal-setting capacity of AI systems. In the context of AC, intent embodies AI systems' objectives and the alignment of their actions with their goals. AI systems express their intent through consistent actions.
7. AC's Pathway: Data to Intent
The journey from data to intent within the DIKWP language represents the progression of AI systems' cognitive capabilities. Data is processed into information, which fosters the development of knowledge. Knowledge, in turn, leads to wisdom, enabling AI systems to formulate intent. AC ensures that this pathway remains unbroken, with expressions and actions consistently aligned along each step.
8. AC as the Keystone of Interpretability
Interpretability hinges on the alignment of AI systems' expressions with their actions. AC ensures that AI systems convey their reasoning and decision-making processes transparently, fostering interpretability. Users and stakeholders can trace the cognitive pathway from data to intent, understanding how AI systems arrive at specific conclusions.
9. AC's Role in Interactivity
AC significantly influences AI systems' interactivity. When AI systems consistently express their intent through actions, interactions with users or other AI systems become more effective. Users can engage with AI systems confidently, knowing that their expressions and actions align seamlessly.
10. AC and Accountability
Accountability in AI is closely tied to AC. When AI systems exhibit high-level AC, it is clear who or what is responsible for their actions and decisions. This attribute is particularly vital in applications where ethical or legal consequences may arise, such as autonomous vehicles or financial decision-making.
11. AC's Influence on AI Applications
AC's impact extends across diverse AI applications. In healthcare, high-level AC in medical diagnostic AI systems ensures that their diagnoses are both accurate and interpretable, enhancing trust between healthcare professionals and AI-driven tools. In autonomous vehicles, AC guarantees that the vehicle's actions align with its intent, minimizing the risk of accidents and enhancing passenger safety. In the financial sector, AC ensures that AI-driven investment decisions remain consistent with their expressed objectives, protecting the interests of investors.
12. Conclusion
The DIKWP Artificial Consciousness Whitebox Evaluation Standard represents a pioneering framework designed to address critical issues in current AI system assessment, including interpretability, interactivity, and accountability. AC, as a manifestation of consistent expression and execution within the DIKWP language, plays a pivotal role in realizing controllable AI systems essential for responsible AI development and the construction of a future digital world.
High-level AC ensures that AI systems maintain consistency between expression and execution, ultimately increasing their credibility and controllability. By applying the DIKWP Artificial Consciousness Whitebox Evaluation Standard, we can comprehensively evaluate AI systems' performance, not only focusing on the quality of their output but also on the transparency and efficiency of their internal operations. This helps ensure the credibility and controllability of AI systems across various domains, providing a solid foundation for responsible AI development.
In conclusion, the DIKWP Artificial Consciousness Whitebox Evaluation Standard offers substantial support for the responsible development of AI and the construction of a future digital world. By guaranteeing that AI systems are highly interpretable, interactive, and efficient, we can better address the challenges and opportunities posed by AI technology, paving the way for a more trustworthy and controllable future. We look forward to the widespread adoption of this standard, further driving the development and application of AI technology to construct a future digital world that aligns seamlessly with human needs and values.
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