Artificialo Inteligence and Artifical Consciousness

11 August 2023

MAIB: Manifold learning, Artificial Intelligence, Biology Forum (MAIB)

Presentation Record(Previous Presentation will be showed here if the video is not released for this talk)

Dr. Yucong Duan

Professor Duan Yucong is the head of the AGI-AIGC-GPT evaluation DIKWP laboratory, a doctor of engineering, a third-level professor of Hainan University, and a doctoral supervisor. He is currently a member of the Academic Committee of the School of Computer Science and Technology of Hainan University, and data, information, knowledge, and wisdom of Hainan University , the head of the DIKWP innovation team, the editorial board member of the Journal of Hainan University (Natural Science Edition), the head of the big data discipline of Hainan University, and a distinguished researcher at the Chongqing Police College, and the double-hundred talent team of the Hainan Provincial Party Committee “for data, information, knowledge, technology Director of DIKW Integrated Internet Innovation Team, Vice President of Hainan Invention Association, Vice President of Hainan Intellectual Property Association, Vice President of Hainan Low Carbon Economic Development Promotion Association, Vice President of Hainan Agricultural Products Processing Enterprises Association, the United States Visiting researcher at Central Michigan University and member of the doctoral steering committee at the University of Modena, Italy. Long-term exploration of concepts-semantic interactive cognitive generation, attribution, clarification, representation, formal modeling, and dealing with practical problems. Based on the multi-dimensional, multi-modal, and multi-scale practice of the two-way full interaction process of physical and digital resources under the background of cross-border servitization, theoretical abstraction, problem essence identification, problem transformation, and specific technical generalization are carried out to form a concept-semantic Deterministic definition and analysis as the core of complex content processing and optimization strategies, engineering mapping of mixed-modal expressions to DIKW typed elements and graph structures, oriented to lack of objective semantics, inconsistent accuracy, inconsistency, redundant expressions, subjective and objective In general AI scenarios with semantic confusion, an intent-driven invention-creation method for DIKW fusion is proposed: DIKWP-TRIZ. Based on the expression of multimodal DIKW content resources and the asymmetry of processing, computing, storage, and transmission resources for conversion between multiple modalities, a cross-border, cross-border, cross-border, cross-border and cross-border oriented concept semantic fuzzy, inaccurate, missing, and modeling and processing efficiency improvement was carried out. The subject uses engineering practice to associate technology with invention and innovation practice. Gradually form a DIKW graphical modeling and processing theoretical system and technical methods with obvious innovation and outstanding advantages. In the past 5 years, 241 serialized Chinese national and international invention patents (including 15 PCT invention patents) have been designed for multi-industry and multi-fields for concept semantic ambiguity, imprecise, missing, transmission, modeling and processing efficiency improvement. The achievement “DIKW Graph Expansion and Modeling Processing” won the third prize of the 2020 Wu Wenjun Artificial Intelligence Technology Invention Award. The main participant of the project, Professor Duan Yucong, as the chairman of the program committee, has held the DIKW Mapping International Forum for many years. In 2021, he independently initiated and co-organized the first International Conference on Data, Information, Knowledge and Wisdom-IEEE DIKW 2021 with the Hainan Science and Technology Association. Co-chair of the DIKW 2022 Congress Steering Committee. Awarded as the most beautiful scientific and technological worker in Hainan Province in 2022 (and recommended as a candidate for the most beautiful scientific and technological worker in China). In October 2022, in the field of Information & Communication, he was selected into the “Lifetime Science Influence Ranking” list of the world’s top 2% top scientists (World’s Top 2% Scientists 2022) released by the team of Professor John Loannidis of Stanford University and Mendeley Data. Participated in the development of 2 IEEE financial knowledge graph international standards and 4 industry knowledge graph standards. This manual contains 77 authorized Chinese national and international invention patents, covering cases and applications in various fields. Among them, patent cases in the field of network security include semantic modeling and abstract enhancement methods based on data graphs, information graphs, and knowledge graphs, positive and negative bidirectional dynamic balance search strategies for resource environments, fault-tolerant intelligent semantic search methods, automatic security situation awareness, Analysis and alarm system, and Internet of Things resource collection and transmission optimization system, etc. The patent cases in the field of smart public security law involve the security protection of typed resources, data privacy protection, and the design of multi-functional interactive areas. The research and development of these patents aims to solve key problems in the field of cybersecurity and smart public prosecution, and provide innovative methods and systems to process data, information, knowledge, intelligence and intention. These achievements not only reflect Professor Duan Yucong’s excellent research ability and innovative thinking, but also have broad application prospects and commercial value. We believe that this manual will provide potential patent clients with an in-depth understanding of DIKWP method modeling and processing, and provide important reference and support for their innovative projects in the field of cybersecurity and smart public prosecution. If you are interested in more information or cooperation opportunities, please feel free to contact us (contact email: duanyucong@hotmail.com).

Background

Here’s a quick overview of artificial intelligence (AI) and artificial consciousness:

Artificial Intelligence (AI) refers to the ability of machines to simulate human intelligence and perform tasks that typically require human cognition. Some key aspects:

  • AI systems can learn from data, identify patterns, make predictions and decisions.

  • Common techniques used include machine learning, deep learning, neural networks, natural language processing etc.

  • AI has achieved remarkable success in areas like computer vision, speech recognition, game-playing, autonomous vehicles etc.

  • Current AI still lacks robust general intelligence and relies heavily on big data and intensive computation.

Artificial Consciousness is the hypothetical concept of developing consciousness in artificial systems. Some key points:

  • It involves creating self-awareness, subjective experience, sentience in machines.

  • This is considered the ultimate frontier for AI but remains elusive.

  • Approaches focus on replicating the architecture and neural activity patterns of the human brain.

  • Basic building blocks like artificial neural networks aim to mimic neurons.

  • Significant philosophical debates exist around machines attaining true human-like consciousness.

  • Current AI systems have minimal levels of agency, self-awareness or reasoning.

  • Achieving artificial consciousness is an extremely complex challenge.

In summary, AI focuses on replicating narrow cognitive skills while artificial consciousness involves replicating the broad concept of human awareness or sentience.

人工智能是指机器模拟人类智能,能够完成通常需要人类认知能力的任务的能力。人工智能系统可以从数据中学习、识别模式、进行预测和决策。目前的人工智能技术已经在图像识别、语音识别、游戏、自动驾驶等领域取得长足进展,但仍然缺乏健壮的通用智能,依赖大数据和强大计算能力。

人工意识是在机器中创造自我意识、主观体验、感知能力的假设概念。这被认为是人工智能的终极目标,但目前仍然难以实现。相关研究主要集中在复制人脑的架构和神经活动模式,人工神经网络就是试图模拟神经元连接。但机器是否能达到真正类人的意识,存在重大哲学争议。当前的人工智能系统具备的自主性、自我意识和推理能力都非常有限。要实现人工意识仍面临极其复杂的挑战。

简而言之,人工智能致力于模拟人类狭义的认知能力,而人工意识涉及模拟人类广泛的自我意识或感知能力,两者有明显区别。

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