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Large language models (LLMs) and other artificial intelligence systems are trained using extensive DIKWP resources (data, information, knowledge, wisdom, purpose). These introduce uncertainties when applied to individual users in a collective semantic space. Traditional methods often lead to introducing new concepts rather than a proper understanding based on the semantic space. When dealing with complex problems or insufficient context, the limitations in conceptual cognition become even more evident. To address this, we take pediatric consultation as a scenario, using case simulations to specifically discuss unidirectional communication impairments between doctors and infant patients and the bidirectional communication biases between doctors and infant parents. We propose a human–machine interaction model based on DIKWP artificial consciousness. For the unidirectional communication impairment, we use the example of an infant’s perspective in recognizing and distinguishing objects, simulating the cognitive process of the brain from non-existence to existence, transitioning from cognitive space to semantic space, and generating corresponding semantics for DIKWP, abstracting concepts, and labels. For the bidirectional communication bias, we use the interaction between infant parents and doctors as an example, mapping the interaction process to the DIKWP transformation space and addressing the DIKWP 3-No problem (incompleteness, inconsistency, and imprecision) for both parties. We employ a purpose-driven DIKWP transformation model to solve part of the 3-No problem. Finally, we comprehensively validate the proposed method (DIKWP-AC). We first analyze, evaluate, and compare the DIKWP transformation calculations and processing capabilities, and then compare it with seven mainstream large models. The results show that DIKWP-AC performs well. Constructing a novel cognitive model reduces the information gap in human–machine interactions, promotes mutual understanding and communication, and provides a new pathway for achieving more efficient and accurate artificial consciousness interactions.
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Artificial intelligence systems are often accompanied by risks such as uncontrollability and lack of explainability. To mitigate these risks, there is a necessity to develop artificial intelligence systems that are explainable, trustworthy, responsible, and demonstrate consistency in thought and action, which we term Artificial Consciousness (AC) systems. Therefore, grounded in the DIKWP model which integrates fundamental data, information, knowledge, wisdom, and purpose along with the principles of conceptual, cognitive, and semantic spaces, we propose and define the computer architectures, chips, runtime environments, and DIKWP language concepts and their implementations under the DIKWP framework. Furthermore, in the construction of AC systems, we have surmounted the limitations of traditional programming languages, computer architectures, and hardware-software implementations. The hardware-software integrated platform we propose will facilitate more convenient construction, development, and operation of software systems based on the DIKWP theory.
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We propose the DIKWP-TRIZ framework, an innovative extension of the traditional Theory of Inventive Problem Solving (TRIZ) designed to address the complexities of cognitive processes and artificial consciousness. By integrating the elements of Data, Information, Knowledge, Wisdom, and Purpose (DIKWP) into the TRIZ methodology, the proposed framework emphasizes a value-oriented approach to innovation, enhancing the ability to tackle problems characterized by incompleteness, inconsistency, and imprecision. Through a systematic mapping of TRIZ principles to DIKWP transformations, we identify potential overlaps and redundancies, providing a refined set of guidelines that optimize the application of TRIZ principles in complex scenarios. The study further demonstrates the framework’s capacity to support advanced decision-making and cognitive processes, paving the way for the development of AI systems capable of sophisticated, human-like reasoning. Future research will focus on comparing the implementation paths of DIKWP-TRIZ and traditional TRIZ, analyzing the complexities inherent in DIKWP-TRIZ-based innovation, and exploring its potential in constructing artificial consciousness systems.
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AI systems that do what they say, are reliable, trustworthy, and explainable are what people want. We propose a DIKWP (Data, Information, Knowledge, Wisdom, and Purpose) artificial consciousness white box evaluation standard and method for AI systems. We categorize AI system output resources into deterministic and uncertain resources, which include incomplete, inconsistent, and imprecise data. We then map these resources to the DIKWP framework for testing. For deterministic resources, we evaluate their transformation into different resource types based on purpose. For uncertain resources, we evaluate their potential conversion into other deterministic resources through purpose-driven. We construct an AI diagnostic scenario using a 2S-dimensional (SxS) framework to evaluate both deterministic and uncertain DIKWP resources. The experimental results show that the DIKWP artificial consciousness white box evaluation standard and method effectively assess the cognition capabilities of AI systems and demonstrate a certain level of interpretability, thus contributing to AI system improvement and evaluation.
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Artificial intelligence systems are associated with inherent risks, such as uncontrollability and lack of interpretability. To address these risks, we need to develop artificial intelligence systems that are interpretable, trustworthy, responsible, and thinking and behavior consistent, which we refer to as artificial consciousness (AC) systems. Consequently, we propose and define the concepts and implementation of a computer architecture, chips, runtime environment, and the DIKWP language. Furthermore, we have overcome the limitations of traditional programming languages, computer architectures, and software-hardware implementations when creating AC systems. Our proposed software and hardware integration platform will make it easier to build and operate AC software systems based on DIKWP theories.