博文
The Post-publication and Post-verbal Communication Era
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Traditional Invention and Innovation Theory 1946-TRIZ Does Not Adapt to the Digital Era
-Innovative problem-solving methods combining DIKWP model and classic TRIZ
Purpose driven Integration of data, information, knowledge, and wisdom Invention and creation methods: DIKWP-TRIZ
(Chinese people's own original invention and creation methods:DIKWP - TRIZ)
The Post-publication and Post-verbal Communication Era - The Future Paradigm of Scientific Research
Yucong Duan, 
Shiming Gong
DIKWP-AC Artificial Consciousness Laboratory
AGI-AIGC-GPT Evaluation DIKWP (Global) Laboratory
World Association of Artificial Consciousness
(Email：duanyucong@hotmail.com)
Catalogue
1 Future paradigm of scientific research
2 Entropy of this process increases and decreases.
2.2 Entropy increasing process
3. Stage prediction of future changes in scientific research and academic exchanges
3.1 Short-term stage (1-3 years): Preliminary application of technology.
3.2 Mid-term (3-5 years): the change of cooperation mode and platform.
3.3 Long-term stage (5-10 years): comprehensive change and maturity.
3.4 Longer-term future (more than 10 years): the arrival of the post-language exchange era.
4 Beyond the language of brain-computer interface (BMI) interaction
With the rapid progress of artificial intelligence (AI) and digital technology, the traditional academic research and communication methods are undergoing profound changes. This paper discusses several key reform directions that may be brought about during this transition period, including the rise of multimodal communication, the popularization of real-time collaboration and cloud labs, the application of research assistants based on AI, the development of decentralized knowledge sharing platform, dynamically updated academic achievements and personalized and customized scientific communication. In addition, we also discussed the increase of public participation in scientific research and the challenges and opportunities brought by these changes to academic publishing and intellectual property management. These changes foreshadow the emergence of a more efficient, open and collaborative scientific research ecosystem, and at the same time require the joint efforts of researchers, academic institutions and policy makers to adapt to these changes and ensure that the quality and moral standards of scientific research are maintained.
1 Future paradigm of scientific research
The current scientific research and academic exchange system is in an unprecedented period of change. With the rapid development of artificial intelligence (AI) and advanced computing technology, the traditional academic publishing mode and language communication mode are gradually being replaced by new paradigms. In this post-paper publishing and post-language communication era, the paradigm of scientific research will undergo fundamental changes, from text-based academic communication to more intuitive, interactive and multi-dimensional communication mode. Here are a few possible directions for change:
Multi-modal communication has become the mainstream: in the post-language communication era, academic communication will no longer be limited to words and charts, but will integrate video, audio, virtual reality (VR) and augmented reality (AR) and other media. This multi-modal communication mode can provide richer and more intuitive information transmission, and make complex scientific concepts and research results more easily understood and accepted by the public.
Real-time collaboration and cloud lab: Using cloud computing and AI technology, researchers can collaborate in real time and share data and research resources around the world. Virtual laboratory and cloud platform will make scientific research work no longer limited by geographical location, and greatly improve research efficiency and innovation speed.
Research assistant based on AI: AI technology will become an important assistant for researchers, which can not only help with literature search and data analysis, but also provide support in research design, hypothesis verification and experimental simulation. The application of AI assistant will profoundly change the research methods and make the scientific exploration process more efficient and accurate.
Decentralized knowledge sharing: the decentralized knowledge sharing platform based on blockchain technology will change the traditional academic publishing and intellectual property management methods. By providing transparent and tamper-proof records, these platforms can guarantee the originality and copyright of research results and promote the free flow and sharing of knowledge.
Dynamically updated academic achievements: In the post-publication era, academic achievements will no longer be static documents, but dynamic contents that can be constantly updated and iterated. Researchers can update their research data and Conclusions in real time, and peers and the public can also access the latest research results in real time.
Personalized and customized scientific communication: With the help of big data and machine learning technology, scientific communication can be more personalized and customized. Research results and scientific knowledge can be displayed optimally according to the interests and needs of different audiences, so as to improve the effect of science popularization.
Public participation in scientific research: Through crowdsourcing and citizen science projects, non-professionals will be able to participate in scientific research more actively. This kind of participation can not only expand the data sources and perspectives of research, but also enhance the public's understanding and support for scientific research.
2 Entropy of this process increases and decreases.
Analyzing the future changes of the above-mentioned scientific research and academic exchanges from the angle of entropy increase and entropy decrease of human cognitive activities, we can regard this process as an entropy change in the process of knowledge and information processing. In physics, entropy is a measure of system disorder, while in information theory, entropy is used to describe the uncertainty or complexity of information. From this perspective, we can regard human cognitive activities as a process aimed at reducing uncertainty (that is, entropy reduction) and increasing order (that is, entropy increase).
Multi-modal communication: expanding academic communication to video, audio, VR and AR can improve the acceptability and understanding of information. This diversified way of presenting information is helpful to reduce the cognitive uncertainty of recipients on complex scientific concepts, that is, to realize entropy reduction in the cognitive process.
AI-based research assistants: AI assists research design, hypothesis verification and experimental simulation, which can improve the accuracy and efficiency of research. AI technology reduces errors and deviations when dealing with a large number of data and complex analysis, which helps to reduce the information entropy in the research process and increase the reliability of the research results.
Decentralized knowledge sharing: The decentralized knowledge sharing platform realized by blockchain and other technologies provides a transparent and tamper-proof knowledge management mechanism, which effectively reduces the uncertainty and complexity in intellectual property management.
2.2 Entropy increasing process
Real-time collaboration and cloud lab: Although these technologies improve the efficiency and convenience of research, they also bring in researchers from a wider range of resources and backgrounds, which increases the complexity and diversity of the research process, thus increasing the information entropy in the research process to some extent.
Dynamically updated academic achievements: Although the dynamic updating mechanism of academic achievements improves the adaptability and timeliness of research achievements, it also increases the information burden of recipients when acquiring and processing the latest research achievements, thus generating more entropy in the cognitive process.
Public participation in scientific research: The introduction of crowdsourcing and citizen science has expanded the data sources and perspectives of research, but at the same time it has also introduced the participation of non-professionals, which may increase the uncertainty and complexity in the research process.
The reform of future scientific research and academic exchange includes not only the process of reducing information entropy (i.e. reducing uncertainty and complexity) and realizing more efficient and accurate cognitive activities through the innovation of technology and cooperation mode, but also the process of increasing entropy due to the new uncertainty and complexity introduced by the diversification of technology application and participants. The dynamic balance and interaction of these two directions will determine the shape and efficiency of the future academic research ecosystem.
3. Stage prediction of future changes in scientific research and academic exchanges
In view of the stage prediction of future changes in scientific research and academic exchange, we can divide the whole process into several key stages, each of which embodies different aspects and depths of the transformation from the current academic exchange mode to the post-language exchange era.
3.1 Short-term stage (1-3 years): Preliminary application of technology.
Popularization of multi-modal communication tools: With the maturity and popularization of VR and AR technologies, academic communication began to try multi-modal expression, and video and audio became auxiliary materials in papers and academic reports.
Preliminary integration of AI-aided research tools: AI technology has been applied to literature search and preliminary data analysis to help researchers improve their work efficiency.
3.2 Mid-term (3-5 years): the change of cooperation mode and platform.
Development of real-time collaboration platform: Cloud computing and online collaboration tools make cross-regional and interdisciplinary real-time collaboration possible, and promote rapid sharing and feedback of research methods and achievements.
The rise of decentralized knowledge sharing platform: decentralized knowledge sharing platform based on blockchain began to appear, providing safer and more transparent storage and access methods for research results.
3.3 Long-term stage (5-10 years): comprehensive change and maturity.
Dynamic updating of academic achievements has become the norm: the publishing and updating mechanism of academic achievements has become more flexible, and research achievements can be updated and iterated in real time according to the latest data and feedback.
Popularization of comprehensive research assistants based on AI: The application of AI technology in research design, experimental simulation and hypothesis verification has become mature, and AI has become an indispensable assistant for researchers.
The establishment of the mechanism of public participation in scientific research: crowdsourcing and citizen science projects have become an important part of scientific research, and the mode of public participation has been widely recognized and applied.
3.4 Longer-term future (more than 10 years): the arrival of the post-language exchange era.
All-round multimodal communication has become the mainstream: academic communication completely transcends the limitations of words and charts, and multimodal and interactive information transmission has become the standard of scientific research and academic reports.
Personalized and customized scientific communication: Scientific communication and knowledge transfer are highly personalized and customized through big data and machine learning technology, and the popularization and education of scientific knowledge become more efficient.
In this process, we will witness the fundamental changes in the way of academic communication, thus promoting the paradigm of scientific research to develop in a more efficient, interactive and inclusive direction.
4 Beyond the language of brain-computer interface (BMI) interaction
The development of brain-computer interface (BMI) technology provides an unprecedented new perspective and opportunity for scientific research and academic exchange. With the maturity of this technology, we can foresee the following subversive changes in the scientific research paradigm:
Direct thinking communication: brain-computer interface technology enables scientists to study ideas and achievements directly through thinking communication, and is no longer limited by traditional language or writing forms. This kind of direct thinking communication will greatly improve the efficiency and accuracy of academic communication and make complex concepts and theories more clearly conveyed and understood.
Enhanced cognitive ability: Through brain-computer interface, researchers can use external computing resources to expand their cognitive ability and improve their ability to process information and solve problems. This can not only accelerate the process of scientific discovery, but also open up new research fields and promote interdisciplinary integration and innovation.
Virtual laboratory and simulation experiment: Brain-computer interface technology combined with virtual reality (VR) and augmented reality (AR) technology can create a highly realistic virtual laboratory, enabling researchers to conduct experiments and explore in a completely controlled environment. In addition, through the simulation experiment, researchers can predict and analyze the experimental results without actually carrying out the experiment, which greatly reduces the research cost and risk.
Intuitive perception of data: brain-computer interface technology allows researchers to directly perceive and analyze a large number of data without traditional data analysis methods. This intuitive data processing method can reveal the deep connections and patterns between data and provide a new perspective for scientific research.
Personalized learning and knowledge dissemination: Brain-computer interface technology can customize the contents and methods of learning and knowledge dissemination according to individual cognitive state and learning habits. This personalized learning experience can not only improve learning efficiency, but also stimulate learners' interest and creativity.
Expansion of public participation: Brain-computer interface technology enables non-professionals to participate in scientific research more directly. Through simplified interface and intuitive operation, the public can contribute their own observations and ideas, and even directly participate in data analysis and experimental design. This extensive public participation will increase the diversity and innovation of scientific research, and at the same time improve the popularization and acceptance of scientific knowledge in society.
Sharing of emotion and perception: The development of brain-computer interface technology will enable researchers to share not only thinking and knowledge, but also emotional and perceptual experiences. This new way of communication will deepen the understanding and cooperation among researchers and promote closer and more efficient teamwork.
The new challenge of ethics and privacy: With the application of brain-computer interface technology, ethics and privacy issues will become an important issue that cannot be ignored in scientific research. How to ensure the freedom and privacy of personal thinking and prevent the abuse of technology will be a problem that the scientific community and society need to face and solve together.
Cross-species scientific research: brain-computer interface technology may also open a new field of cross-species scientific research. By understanding and decoding the neural activities of other creatures, scientists can gain knowledge and inspiration directly from animal models, bringing revolutionary progress to biology, neuroscience and artificial intelligence.
Philosophical reflection on scientific research: finally, brain-computer interface technology will prompt the scientific community to make in-depth philosophical reflection on the essence and goal of scientific research. What is the ultimate purpose of scientific research? How can we use this technology to serve the well-being of mankind? These problems will stimulate the scientific community to think about the scientific responsibility and goal.
In a word, the development of brain-computer interface technology will bring unprecedented possibilities and challenges to scientific research, prompting us to rethink the future of scientific research and academic exchanges. In this new era, scientific research will become more intuitive, interactive and multidimensional, and the creation, dissemination and application of scientific knowledge will be more efficient and extensive. At the same time, it also requires our joint efforts to ensure that the development of technology can promote scientific progress and improve the quality of human life, rather than becoming a new source of division and inequality.
To sum up, the paradigm of scientific research will be more open, interactive and diversified in the post-paper publishing and post-language exchange era. These changes will promote the continuous expansion of the boundaries of scientific research, promote the innovation and integration of knowledge, and at the same time pose new challenges and opportunities. Researchers need to adapt to these changes and master new technologies and tools in order to find new opportunities in the change. In addition, academic circles and research institutions also need to update the evaluation system in order to evaluate the research results and the contributions of researchers more fairly and comprehensively. Extensive public participation will make scientific research more democratic and transparent, and enhance the sense of social responsibility and public trust in scientific research activities. In the end, these changes will promote the formation of a more open, collaborative and innovative scientific research ecosystem and provide a strong impetus for the progress of human society.
随着人工智能（AI）和数字化技术的快速进步，传统的学术研究和交流方式正在经历深刻的变革。本文探讨了这一转型期可能带来的几个关键变革方向，包括多模态交流的兴起、实时协作和云端实验室的普及、基于AI的研究助手的应用、去中心化知识共享平台的发展、动态更新的学术成果以及个性化和定制化的科学传播。此外，我们还探讨了公众参与科学研究的增加以及这些变化对学术出版和知识产权管理带来的挑战和机遇。这些变革预示着一个更加高效、开放和协作的科学研究生态系统的出现，同时也要求科研人员、学术机构和政策制定者共同努力，以适应这些变化，确保科学研究的质量和道德标准得以维持。
当前的科学研究和学术交流体系正处于一个前所未有的变革时期。随着人工智能（AI）和高级计算技术的迅速发展，传统的学术发表模式和语言交流方式正逐步被新的范式所取代。在这个后论文发表和后语言交流时代，科学研究的范式将经历根本性的变化，从基于文本的学术交流转向更加直观、互动和多维的交流模式。以下是几个可能出现的变革方向：
多模态交流成为主流：在后语言交流时代，学术交流将不再局限于文字和图表，而是融合视频、音频、虚拟现实（VR）和增强现实（AR）等多种媒介。这种多模态交流方式可以提供更为丰富和直观的信息传递，使得复杂的科学概念和研究成果更容易被公众理解和接受。
实时协作和云端实验室：利用云计算和AI技术，研究人员可以在全球范围内实时协作，共享数据和研究资源。虚拟实验室和云端平台将使得科研工作不再受地理位置的限制，极大地提高研究效率和创新速度。
基于AI的研究助手：AI技术将成为研究人员的重要助手，不仅能够帮助进行文献搜索和数据分析，还能在研究设计、假设验证和实验模拟等方面提供支持。AI助手的应用将深刻改变研究方法，使科学探索过程更加高效和精确。
去中心化的知识共享：基于区块链技术的去中心化知识共享平台将改变传统的学术发表和知识产权管理方式。这些平台通过提供透明、不可篡改的记录，可以保障研究成果的原创性和版权，同时促进知识的自由流动和共享。
动态更新的学术成果：在后论文发表时代，学术成果将不再是静态的文档，而是可以不断更新和迭代的动态内容。研究人员可以实时更新他们的研究数据和结论，而同行和公众也可以即时访问最新的研究成果。
个性化和定制化的科学传播：借助大数据和机器学习技术，科学传播可以更加个性化和定制化。研究成果和科学知识可以根据不同受众的兴趣和需求进行优化展示，提高科学普及的效果。
公众参与的科学研究：通过众包和公民科学项目，非专业人士将能够更加积极地参与到科学研究中。这种参与不仅可以扩大研究的数据来源和视角，还能增强公众对科学研究的理解和支持。
从人类认知活动的熵增熵减角度分析上述科学研究和学术交流的未来变革，我们可以将这一过程视为知识和信息处理过程中的熵变动。在物理学中，熵是一个衡量系统无序程度的量，而在信息论中，熵则用来描述信息的不确定性或复杂性。从这一视角出发，我们可以将人类认知活动视为一种旨在减少不确定性（即熵减）和增加有序性（即熵增）的过程。
多模态交流：将学术交流扩展到视频、音频、VR和AR等多种媒介，可以提高信息的可接受性和理解度。这种多样化的信息呈现方式有助于减少接收者对复杂科学概念的认知不确定性，即在认知过程中实现熵减。
基于AI的研究助手：AI辅助研究设计、假设验证和实验模拟等，可以提高研究的精确度和效率。AI技术在处理大量数据和复杂分析时减少了错误和偏差，这有助于降低研究过程中的信息熵，增加研究结果的可靠性。
去中心化的知识共享：通过区块链等技术实现的去中心化知识共享平台，提供了透明、不可篡改的知识管理机制，有效减少了知识产权管理中的不确定性和复杂性。
实时协作和云端实验室：虽然这些技术提高了研究的效率和协作的便捷性，但同时也引入了来自更广泛资源和背景的研究人员，增加了研究过程的复杂性和多样性，从而在一定程度上增加了研究过程中的信息熵。
动态更新的学术成果：学术成果的动态更新机制虽然提高了研究成果的适应性和及时性，但也增加了接收者在获取和处理最新研究成果时的信息负担，从而在认知过程中产生了更多的熵。
公众参与的科学研究：众包和公民科学项意图引入，虽然扩大了研究的数据来源和视角，但同时也引入了非专业人士的参与，这可能增加了研究过程中的不确定性和复杂性。
未来科学研究和学术交流的变革，既包含了通过技术和协作模式的创新来减少信息熵（即减少不确定性和复杂性），实现更高效、精确的认知活动的过程，也包含了因技术应用和参与主体的多样化而引入的新的不确定性和复杂性，即熵增的过程。这两个方向的动态平衡和相互作用将决定未来学术研究生态系统的形态和效能。
针对科学研究和学术交流未来变革的阶段性预测，我们可以将整个过程划分为几个关键阶段，每个阶段都体现了从当前的学术交流模式向后语言交流时代转变的不同方面和深度。
多模态交流工具的普及：随着VR、AR技术的成熟和普及，学术交流开始尝试多模态表达方式，视频和音频成为论文和学术报告中的辅助材料。
AI辅助研究工具的初步集成：AI技术开始被应用于文献搜索和初步的数据分析中，帮助研究人员提高工作效率。
实时协作平台的发展：云计算和在线协作工具使得跨地域、跨学科的实时协作成为可能，促进了研究方法和成果的迅速共享和反馈。
去中心化知识共享平台的兴起：基于区块链的去中心化知识共享平台开始出现，为研究成果提供更安全、透明的存储和访问方式。
动态更新的学术成果成为常态：学术成果的发布和更新机制变得更加灵活，研究成果可以根据最新数据和反馈进行实时更新和迭代。
基于AI的全面研究助手的普及：AI技术在研究设计、实验模拟、假设验证等方面的应用变得成熟，AI成为研究人员不可或缺的助手。
公众参与科学研究的机制建立：众包和公民科学项目成为科学研究的重要组成部分，公众参与的模式得到广泛认可和应用。
全面的多模态交流成为主流：学术交流彻底超越文字和图表的局限，多模态、交互式的信息传递方式成为科学研究和学术报告的标准。
个性化和定制化的科学传播：科学传播和知识传递通过大数据和机器学习技术实现高度个性化和定制化，科学知识的普及和教育变得更加高效。
这一过程中，我们将见证学术交流方式的根本性变革，从而推动科学研究的范式向更加高效、互动和包容的方向发展。
脑机接口（BMI）技术的发展为科学研究和学术交流提供了前所未有的新视角和机遇。随着这项技术的成熟，我们可以预见科学研究范式将发生以下几个颠覆性的变化：
直接的思维交流：脑机接口技术使得科学家能够直接通过思维交流研究想法和成果，不再受限于传统的语言或书写形式。这种直接的思维交流将极大地提高学术交流的效率和精确性，使复杂的概念和理论得以更清晰地传达和理解。
增强的认知能力：通过脑机接口，研究人员可以利用外部计算资源扩展自己的认知能力，提高处理信息和解决问题的能力。这不仅可以加速科学发现的过程，还可以开拓新的研究领域，促进跨学科的融合和创新。
虚拟实验室和模拟实验：脑机接口技术结合虚拟现实（VR）和增强现实（AR）技术，可以创建高度逼真的虚拟实验室，使研究人员能够在完全控制的环境中进行实验和探索。此外，通过模拟实验，研究人员可以在没有实际执行实验的情况下，预测和分析实验结果，大大降低研究成本和风险。
数据直觉感知：脑机接口技术允许研究人员直接感知和解析大量数据，而不需要通过传统的数据分析方法。这种直觉式的数据处理方式可以揭示数据之间的深层次联系和模式，为科学研究提供全新的视角。
个性化学习和知识传播：脑机接口技术可以根据个人的认知状态和学习习惯，定制化学习和知识传播的内容和方式。这种个性化的学习体验不仅可以提高学习效率，还可以激发学习者的兴趣和创造力。
公众参与的扩展：脑机接口技术使得非专业人士能够更直接地参与到科学研究中。通过简化的界面和直观的操作，公众可以贡献自己的观察和想法，甚至直接参与到数据分析和实验设计中。这种广泛的公众参与将增加科学研究的多样性和创新性，同时提高科学知识在社会中的普及和接受度。
情感和感知的共享：脑机接口技术的发展将使得研究人员不仅能够共享思维和知识，还能够共享情感和感知体验。这种全新的交流方式将深化研究人员之间的理解和协作，促进更为紧密和高效的团队工作。
伦理和隐私的新挑战：随着脑机接口技术的应用，伦理和隐私问题将成为科学研究中不可忽视的重要议题。如何确保个人思维的自由和隐私，防止技术滥用，将是科学界和社会需要共同面对和解决的问题。
跨物种的科学研究：脑机接口技术还可能开启跨物种的科学研究新领域。通过理解和解码其他生物的神经活动，科学家可以直接从动物模型中获取知识和灵感，为生物学、神经科学和人工智能等领域带来革命性的进展。
科学研究的哲学反思：最后，脑机接口技术将促使科学界对科学研究的本质和目标进行深入的哲学反思。科学研究的最终意图是什么？我们如何利用这项技术服务于人类的福祉？这些问题将激发科学界对科学责任和目标的新思考。
总之，脑机接口技术的发展将为科学研究带来前所未有的可能性和挑战，促使我们重新思考科学研究和学术交流的未来。在这个新的时代，科学研究将变得更加直观、互动和多维，科学知识的创造、传播和应用将更加高效和广泛。同时，这也需要我们共同努力，确保技术的发展能够促进科学进步，提高人类生活质量，而不是成为新的分裂和不平等的源泉。
综上所述，在后论文发表和后语言交流时代，科学研究的范式将更加开放、互动和多元化。这些变革将推动科学研究的边界不断扩展，促进知识的创新和融合，同时也提出了新的挑战和机遇。科研人员需要适应这些变化，掌握新的技术和工具，以在变革中找到新的机遇。此外，学术界和研究机构也需要更新评价体系，以更公正和全面地评估研究成果和科研人员的贡献。公众的广泛参与将使科学研究更加民主化和透明化，增强科研活动的社会责任感和公众信任。最终，这些变革将促进一个更加开放、协作和创新的科学研究生态系统的形成，为人类社会的进步提供强大动力。
[1] Duan Y. Which characteristic does GPT-4 belong to? An analysis through DIKWP model. DOI: 10.13140/RG.2.2.25042.53447. https://www.researchgate.net/publication/375597900_Which_characteristic_does_GPT-4_belong_to_An_analysis_through_DIKWP_model_GPT-4_shishenmexinggeDIKWP_moxingfenxibaogao. 2023.
[2] Duan Y. DIKWP Processing Report on Five Personality Traits. DOI: 10.13140/RG.2.2.35738.00965. https://www.researchgate.net/publication/375597092_wudaxinggetezhide_DIKWP_chulibaogao_duanyucongYucong_Duan. 2023.
[3] Duan Y. Research on the Application of DIKWP Model in Automatic Classification of Five Personality Traits. DOI: 10.13140/RG.2.2.15605.35047. https://www.researchgate.net/publication/375597087_DIKWP_moxingzaiwudaxinggetezhizidongfenleizhongdeyingyongyanjiu_duanyucongYucong_Duan. 2023.
[4] Duan Y, Gong S. DIKWP-TRIZ method: an innovative problem-solving method that combines the DIKWP model and classic TRIZ. DOI: 10.13140/RG.2.2.12020.53120. https://www.researchgate.net/publication/375380084_DIKWP-TRIZfangfazongheDIKWPmoxinghejingdianTRIZdechuangxinwentijiejuefangfa. 2023.
[5] Duan Y. The Technological Prospects of Natural Language Programming in Large-scale AI Models: Implementation Based on DIKWP. DOI: 10.13140/RG.2.2.19207.57762. https://www.researchgate.net/publication/374585374_The_Technological_Prospects_of_Natural_Language_Programming_in_Large-scale_AI_Models_Implementation_Based_on_DIKWP_duanyucongYucong_Duan. 2023.
[6] Duan Y. The Technological Prospects of Natural Language Programming in Large-scale AI Models: Implementation Based on DIKWP. DOI: 10.13140/RG.2.2.19207.57762. https://www.researchgate.net/publication/374585374_The_Technological_Prospects_of_Natural_Language_Programming_in_Large-scale_AI_Models_Implementation_Based_on_DIKWP_duanyucongYucong_Duan. 2023.
[7] Duan Y. Exploring GPT-4, Bias, and its Association with the DIKWP Model. DOI: 10.13140/RG.2.2.11687.32161. https://www.researchgate.net/publication/374420003_tantaoGPT-4pianjianjiqiyuDIKWPmoxingdeguanlian_Exploring_GPT-4_Bias_and_its_Association_with_the_DIKWP_Model. 2023.
[8] Duan Y. DIKWP language: a semantic bridge connecting humans and AI. DOI: 10.13140/RG.2.2.16464.89602. https://www.researchgate.net/publication/374385889_DIKWP_yuyanlianjierenleiyu_AI_deyuyiqiaoliang. 2023.
[9] Duan Y. The DIKWP artificial consciousness of the DIKWP automaton method displays the corresponding processing process at the level of word and word granularity. DOI: 10.13140/RG.2.2.13773.00483. https://www.researchgate.net/publication/374267176_DIKWP_rengongyishide_DIKWP_zidongjifangshiyiziciliducengjizhanxianduiyingdechuliguocheng. 2023.
[10] Duan Y. Implementation and Application of Artificial wisdom in DIKWP Model: Exploring a Deep Framework from Data to Decision Making. DOI: 10.13140/RG.2.2.33276.51847. https://www.researchgate.net/publication/374266065_rengongzhinengzai_DIKWP_moxingzhongdeshixianyuyingyongtansuocongshujudaojuecedeshendukuangjia_duanyucongYucong_Duan. 2023.
Data can be regarded as a concrete manifestation of the same semantics in our cognition. Often, Data represents the semantic confirmation of the existence of a specific fact or observation, and is recognised as the same object or concept by corresponding to some of the same semantic correspondences contained in the existential nature of the cognitive subject's pre-existing cognitive objects. When dealing with data, we often seek and extract the particular identical semantics that labels that data, and then unify them as an identical concept based on the corresponding identical semantics. For example, when we see a flock of sheep, although each sheep may be slightly different in terms of size, colour, gender, etc., we will classify them into the concept of "sheep" because they share our semantic understanding of the concept of "sheep". The same semantics can be specific, for example, when identifying an arm, we can confirm that a silicone arm is an arm based on the same semantics as a human arm, such as the same number of fingers, the same colour, the same arm shape, etc., or we can determine that the silicone arm is not an arm because it doesn't have the same semantics as a real arm, which is defined by the definition of "can be rotated". It is also possible to determine that the silicone arm is not an arm because it does not have the same semantics as a real arm, such as "rotatable".
Information, on the other hand, corresponds to the expression of different semantics in cognition. Typically, Information refers to the creation of new semantic associations by linking cognitive DIKWP objects with data, information, knowledge, wisdom, or purposes already cognised by the cognising subject through a specific purpose. When processing information, we identify the differences in the DIKWP objects they are cognised with, corresponding to different semantics, and classify the information according to the input data, information, knowledge, wisdom or purpose. For example, in a car park, although all cars can be classified under the notion of 'car', each car's parking location, time of parking, wear and tear, owner, functionality, payment history and experience all represent different semantics in the information. The different semantics of the information are often present in the cognition of the cognitive subject and are often not explicitly expressed. For example, a depressed person may use the term "depressed" to express the decline of his current mood relative to his previous mood, but this "depressed" is not the same as the corresponding information because its contrasting state is not the same as the corresponding information. However, the corresponding information cannot be objectively perceived by the listener because the contrasting state is not known to the listener, and thus becomes the patient's own subjective cognitive information.
Knowledge corresponds to the complete semantics in cognition. Knowledge is the understanding and explanation of the world acquired through observation and learning. In processing knowledge, we abstract at least one concept or schema that corresponds to a complete semantics through observation and learning. For example, we learn that all swans are white through observation, which is a complete knowledge of the concept "all swans are white" that we have gathered through a large amount of information.
Wisdom corresponds to information in the perspective of ethics, social morality, human nature, etc., a kind of extreme values from the culture, human social groups relative to the current era fixed or individual cognitive values. When dealing with Wisdom, we integrate this data, information, knowledge, and wisdom and use them to guide decision-making. For example, when faced with a decision-making problem, we integrate various perspectives such as ethics, morality, and feasibility, not just technology or efficiency.
Purpose can be viewed as a dichotomy (input, output), where both input and output are elements of data, information, knowledge, wisdom, or purpose. Purpose represents our understanding of a phenomenon or problem (input) and the goal we wish to achieve by processing and solving that phenomenon or problem (output). When processing purposes, the AI system processes the inputs according to its predefined goals (outputs), and gradually brings the outputs closer to the predefined goals by learning and adapting.
Yucong Duan, male, currently serves as a member of the Academic Committee of the School of Computer Science and Technology at Hainan University. He is a professor and doctoral supervisor and is one of the first batch of talents selected into the South China Sea Masters Program of Hainan Province and the leading talents in Hainan Province. He graduated from the Software Research Institute of the Chinese Academy of Sciences in 2006, and has successively worked and visited Tsinghua University, Capital Medical University, POSCO University of Technology in South Korea, National Academy of Sciences of France, Charles University in Prague, Czech Republic, Milan Bicka University in Italy, Missouri State University in the United States, etc. He is currently a member of the Academic Committee of the School of Computer Science and Technology at Hainan University and he is the leader of the DIKWP (Data, Information, Knowledge, Wisdom, Purpose) Innovation Team at Hainan University, Distinguished Researcher at Chongqing Police College, Leader of Hainan Provincial Committee's "Double Hundred Talent" Team, Vice President of Hainan Invention Association, Vice President of Hainan Intellectual Property Association, Vice President of Hainan Low Carbon Economy Development Promotion Association, Vice President of Hainan Agricultural Products Processing Enterprises Association, Director of Network Security and Informatization Association of Hainan Province, Director of Artificial Intelligence Society of Hainan Province, Visiting Fellow, Central Michigan University, Member of the Doctoral Steering Committee of the University of Modena. Since being introduced to Hainan University as a D-class talent in 2012, He has published over 260 papers, included more than 120 SCI citations, and 11 ESI citations, with a citation count of over 4300. He has designed 241 serialized Chinese national and international invention patents (including 15 PCT invention patents) for multiple industries and fields and has been granted 85 Chinese national and international invention patents as the first inventor. Received the third prize for Wu Wenjun's artificial intelligence technology invention in 2020; In 2021, as the Chairman of the Program Committee, independently initiated the first International Conference on Data, Information, Knowledge and Wisdom - IEEE DIKW 2021; Served as the Chairman of the IEEE DIKW 2022 Conference Steering Committee in 2022; Served as the Chairman of the IEEE DIKW 2023 Conference in 2023. He was named the most beautiful technology worker in Hainan Province in 2022 (and was promoted nationwide); In 2022 and 2023, he was consecutively selected for the "Lifetime Scientific Influence Ranking" of the top 2% of global scientists released by Stanford University in the United States. Participated in the development of 2 international standards for IEEE financial knowledge graph and 4 industry knowledge graph standards. Initiated and co hosted the first International Congress on Artificial Consciousness (AC2023) in 2023.
Prof. Yucong Duan
DIKWP-AC Artificial Consciousness Laboratory
AGI-AIGC-GPT Evaluation DIKWP (Global) Laboratory
DIKWP research group, Hainan University
duanyucong@hotmail.com
https://blog.sciencenet.cn/blog-3429562-1421917.html
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