Heading toward Artificial Intelligence 2.0

With the popularization of the Intemet, permeation of sensor networks, emergence of big data, increase in size of the information community, and interlinking and fusion of data and information throughout human society, physical space, and cyberspace, the information environment related to the current development of artificial intelligence （AI） has profoundly changed. AI faces important adjustments, and scientific foundations are confronted with new breakthroughs, as AI enters a new stage： AI 2.0. This paper briefly reviews the 60-year developmental history of AI, analyzes the external environment promoting the formation of AI 2.0 along with changes in goals, and describes both the beginning of the technology and the core idea behind AI 2.0 development. Furthermore, based on combined social demands and the information environment that exists in relation to Chinese development, suggestions on the develoDment of Al 2.0 are given.

Framework and Key Technologies of Human-machine Hybrid-augmented Intelligence System for Large-scale Power Grid Dispatching and Control

With integration of large-scale renewable energy,new controllable devices,and required reinforcement of power grids,modern power systems have typical characteristics such as uncertainty,vulnerability and openness,which makes operation and control of power grids face severe security challenges.Application of artificial intelligence(AI)technologies represented by machine learning in power grid regulation is limited by reliability,interpretability and generalization ability of complex modeling.Mode of hybrid-augmented intelligence(HAI)based on human-machine collaboration(HMC)is a pivotal direction for future development of AI technology in this field.Based on characteristics of applications in power grid regulation,this paper discusses system architecture and key technologies of human-machine hybrid-augmented intelligence(HHI)system for large-scale power grid dispatching and control(PGDC).First,theory and application scenarios of HHI are introduced and analyzed;then physical and functional architectures of HHI system and human-machine collaborative regulation process are proposed.Key technologies are discussed to achieve a thorough integration of human/machine intelligence.Finally,state-of-theart and future development of HHI in power grid regulation are summarized,aiming to efficiently improve the intelligent level of power grid regulation in a human-machine interactive and collaborative way.

Interaction and the Genesis of Experience: A Phenomenological Contribution for Meaningful Embodied Artificial Intelligence

In this article I will address the issue of the meaning of Embodied Artificial Intelligence(EAI)as it is configured today.My starting point is the refined interactive perspective on the semantics of EAI,as was recently suggested by Froese and colleagues.This perspective rests on the assumption that the concept of human bodily subjectivity must be extended to include meaning-making processes,which are enabled by advanced AI systems that may be incorporated in the human biological body.After having clarified the technical background,I will introduce the genetic component of the phenomenological method as a suitable tool to face the aforementioned issue.Towards this end,I will place the genetic method in the context of the so-called New Human-Machine Interaction(New HMI).I will further outline a genetic phenomenology of visual embodiment,suggesting a futuristic application based on the thesis of the“technological supplementation of phenomenological methodology”through the synthetic method.The case at stake is that of patients with a severe clinical picture characterised by the loss of corneal function,who in the near future could be treated with synthetic corneal prosthetic implants produced by a 3D bio-printing process by using an advanced EAI technique.I will conclude this article with a brief review of the main problems that still remain open.

Human-Machine Shared Lateral Control Strategy for Intelligent Vehicles Based on Human Driver Risk Perception Reliability

Intelligent vehicle(Ⅳ)technology has developed rapidly in recent years.However,achieving fully unmanned driving still presents numerous challenges,which means that human drivers will continue to play a vital role in vehicle operation for the foreseeable future.Human-machine shared driving,involving cooperation between a human driver and an automated driving system(AVS),has been widely regarded as a necessary stage for the development of IVs.Focusing onⅣdriving safety,this study proposed a human-machine shared lateral control strategy(HSLCS)based on the reliability of driver risk perception.The HSLCS starts by identifying the effective areas of driver risk perception based on eye movements.It establishes an anisotropic driving risk field,which serves as the foundation for the AVS to assess risk levels.Building upon the cumulative and diminishing effects of risk perception,the proposed approach leverages the driver's risk perception effective area and converts the risk field into a representation aligned with the driver's perspective.Subsequently,it quantifies the reliability of the driver's risk perception by using area-matching rules.Finally,based on the driver’s risk perception reliability and dif-ferences in lateral driving operation between the human driver and the AVS,the dynamic distribution of driving authority is achieved through a fuzzy rule-based system,and the human-machine shared lateral control is completed by using model predictive control.The HSLCS was tested across various scenarios on a driver-in-the-loop test platform.The results show that the HSLCS can realize the synergy and complementarity of human and machine intelligence,effectively ensuring the safety ofⅣoperation.

Mutually trustworthy human-machine knowledge automation and hybrid augmented intelligence:mechanisms and applications of cognition,management,and control for complex systems

In this paper,we aim to illustrate the concept of mutually trustworthy human-machine knowledge automation(HM-KA)as the technical mechanism of hybrid augmented intelligence(HAI)based complex system cognition,management,and control(CMC).We describe the historical development of complex system science and analyze the limitations of human intelligence and machine intelligence.The need for using human-machine HAI in complex systems is then explained in detail.The concept of“mutually trustworthy HM-KA”mechanism is proposed to tackle the CMC challenge,and its technical procedure and pathway are demonstrated using an example of corrective control in bulk power grid dispatch.It is expected that the proposed mutually trustworthy HM-KA concept can provide a novel and canonical mechanism and benefit real-world practices of complex system CMC.

Swarm Intelligence Research:From Bio-inspired Single-population Swarm Intelligence to Human-machine Hybrid Swarm Intelligence

Swarm intelligence has become a hot research field of artificial intelligence.Considering the importance of swarm intelli-gence for the future development of artificial intelligence,we discuss and analyze swarm intelligence from a broader and deeper perspect-ive.In a broader sense,we are talking about not only bio-inspired swarm intelligence,but also human-machine hybrid swarm intelli-gence.In a deeper sense,we discuss the research using a three-layer hierarchy:in the first layer,we divide the research of swarm intelli-gence into bio-inspired swarm intelligence and human-machine hybrid swarm intelligence;in the second layer,the bio-inspired swarm intelligence is divided into single-population swarm intelligence and multi-population swarm intelligence;and in the third layer,we re-view single-population,multi-population and human-machine hybrid models from different perspectives.Single-population swarm intel-ligence is inspired by biological intelligence.To further solve complex optimization problems,researchers have made preliminary explor-ations in multi-population swarm intelligence.However,it is difficult for bio-inspired swarm intelligence to realize dynamic cognitive in-telligent behavior that meets the needs of human cognition.Researchers have introduced human intelligence into computing systems and proposed human-machine hybrid swarm intelligence.In addition to single-population swarm intelligence,we thoroughly review multi-population and human-machine hybrid swarm intelligence in this paper.We also discuss the applications of swarm intelligence in optimization,big data analysis,unmanned systems and other fields.Finally,we discuss future research directions and key issues to be studied in swarm intelligence.

Implementation of Strangely Behaving Intelligent Agents to Determine Human Intervention During Reinforcement Learning

Intrinsic motivation helps autonomous exploring agents traverse a larger portion of their environments.However,simulations of different learning environments in previous research show that after millions of timesteps of successful training,an intrinsically motivated agent may learn to act in ways unintended by the designer.This potential for unintended actions of autonomous exploring agents poses threats to the environment and humans if operated in the real world.We investigated this topic by using Unity’s MachineLearningAgent Toolkit(ML-Agents)implementation of the Proximal Policy Optimization(PPO)algorithm with the Intrinsic Curiosity Module(ICM)to train autonomous exploring agents in three learning environments.We demonstrate that ICM,although designed to assist agent navigation in environments with sparse reward generation,increasing gradually as a tool for purposely training misbehaving agent in significantly less than 1 million timesteps.We present the following achievements:1)experiments designed to cause agents to act undesirably,2)a metric for gauging how well an agent achieves its goal without collisions,and 3)validation of PPO best practices.Then,we used optimized methods to improve the agent’s performance and reduce collisions within the same environments.These achievements help further our understanding of the significance of monitoring training statistics during reinforcement learning for determining how humans can intervene to improve agent safety and performance.

人智交互情境中用户对生成式人工智能的心智感知及反应研究

[目的/意义]随着AI组成技术的发展及人智协作的日趋紧密,高阶的生成式人工智能(GAI)代理具备涌现能力,通过类人化特征与用户建立社会化连接。文章旨在丰富社会技术视角下人智交互发生机理的探索,并为人本人工智能背景下GAI设计提供参考。[方法/过程]爬取豆瓣社区1275条用户与ChatGPT对话的帖子,遵循建构主义范式,基于CASA范式和心智感知理论,采用系统性主题分析方法构建框架模型。[结果/结论]构建了GAI作为社会行动者的理论框架,得到人智交互情境下用户对于GAI的心智感知及社会化反应。心智感知包括GAI主体性感知和感受性感知两种。GAI主体性感知包括创造力、可靠性、运行规则、客观性、有用性;GAI感受性感知包括共情、沟通责任、趣味性、友善、文化适应性。这两种感知会相互影响。用户对GAI的社会化反应涉及认知加工、情感唤醒、行为强化三个方面。认知加工包括知识获得、态度转变;情感唤醒包括愉悦感、共鸣、社交连接;行为强化包括持续付费行为、准社会互动。用户的社会化反应与心智感知存在双向作用关系。

从交互到共生:智慧教育生态系统中人机关系重构的逻辑进路

人工智能与教育教学的深度融合必将推动教育生态的整体性变革。随着智能技术的迭代升级,智慧教育生态系统中的人机关系逐渐从人机交互的初级形态走向人机协同、人机融合,最终实现人机共生的终极愿景。教育领域的新一代自然式人机交互,以智能机器为中介形成“物理-信息对偶空间”,并根据人与智能机器默契程度的不同从低到高分为基于有声语言的交互、基于肢体信号的交互和基于意念感知的交互。在智慧教育人机协同形态中,智能机器晋升成为认知主体,充当“代具”弥补人类的“缺陷存在”从而共同完成复杂的任务,具体可分为计算智能型协同、感知智能型协同、认知智能型协同以及社会智能型协同。在人机融合形态下,人、机器和环境系统相互作用而产生新型群体智能系统,人机关系呈现具身智能等特征。人机共生作为人机关系发展的终极形态,无论是物理形式抑或是逻辑形式的共生,师生与人工智能“宛如同类”相互依存,秉持互利原则不断优化和进步,追求教育育人价值最大化。

共生教育视域下人工智能与教育的关系探析

共生教育为研究人工智能与教育的关系提供了理论视角。人工智能与学校教育的共生关系表征在供应技术服务实现与学校教育同频共振、推动因材施教实现与学校教育赋能共生、促进深度融合实现与学校教育资源共享等方面。因此,构建人工智能与教育融合共生的良性发展路径,需要积极转变教育智能化观念、推动学校智能化环境建设、涵养教师教学智能化能力、提高学生自主学习能力以及创建协同推进教育智能化的外在机制。

拓维与共生:数智时代高校教学评价体系建设的高质量发展路径

高校教学评价是高校评判教学工作、回归教学本位、深化教学改革和提升教学质量的关键环节。在数智时代,高校教学评价体系面临“师-生”“校-社”“教-创”“学-用”等四个维度的拓展。运用共生理论的最优评价标准进行审视发现,高校教学评价体系存在教学主体缺位、教学功能异化、教学关系非对称性与教学效果非互惠性的现实困境。由此,高校教学评价体系的高质量发展路径为:建立数智型教学评价制度,抬升弱势教学主体评价;塑造高质量的教学评价观,发挥教学评价的数智功能;推动教学评价管理数智化,拓宽多元主体互评渠道;构建并喻文化的互惠模型,实现高质量的教智融合评价。

人机共生:人工智能驱动下艺术设计的发展与革新

如今,人类站在了人机共生时代历史性的转折点上,人工智能已逐渐融入艺术设计领域,过去的设计范式即将面临消解,新的设计范式悄然建立。艺术设计在人工智能面前面临着巨大挑战,未来的艺术设计如何与人工智能共存是值得思考的问题。本文通过归纳案例从人工智能与艺术设计的现状出发,探究艺术设计领域的未来发展与革新,总结出在人机共生背景下人工智能与设计双向赋能并相互引领两个行业未来的变革与潮流。

基于人工智能技术的播音主持“人机共生”实现策略

人工智能(Artificial Intelligence,AI)技术为媒介传播活动提供了强大的技术支撑,AI虚拟主播应运而生,在媒介传播中参与度越来越高,不仅改变了新闻播报的方式,而且为媒体行业升级和转型发展开辟了新的道路。播音主持工作者与AI主播之间的共生关系需要建立在彼此优势上,通过有效合作和互补,共同推动媒体行业发展。以播音主持“人机共生”实现策略作为研究对象,分析AI主播的优势及短板,探讨播音主持工作者的不可替代性,提出针对性的策略。

人机共生中的拟人化:概念溯源、谱系重建与议题拓展

拟人化,是将人类特征赋予非人能动体的过程。拟人化不仅影响了个体在人机传播中的情感及认知,更作为一种“意义生成”被卷入人机共生的谱系中。研究对拟人化的概念及对象进行溯源,并对拟人化的具体维度进行梳理。而在现有研究中,拟人化作为人机传播中常见的心理现象,多被视为被机器线索触发的无意识互动,伴随着人机之间的长期关系成为可能,机器的类人能力也对人的独特性形成潜在威胁。研究将拟人化的研究谱系基于“价值共生”与“长期关系”进行拓展,并从拟人化与情感关系调适、拟人化与共生意义建构、拟人化与技术环境适应三个方向,对相关研究议题进行具体解释。研究认为,拟人化能为人机情感关系提供往返和调适,催生新的人机传播脚本,并在共生意义层面协助个体进行自我的“分布式”建构,而不同群体的文化实践也为拟人化提供了多元的解释资源,面对持续变化且充满不确定性的技术环境,拟人化也能为个体提供适应经验。

融入与共生:ChatGPT在数字人文中的风险应对与前景展望

ChatGPT业已渗透到社会的各个层面,与数字人文的积极互动、共生合作成为大势所趋,并已成为数智时代从事数字人文研究炙手可热的辅助工具。文章通过对现阶段ChatGPT在数字人文领域应用发展概况的梳理,阐释了ChatGPT在数字人文中的现实风险与应对之策。研究发现,ChatGPT在数字人文中具有数据安全、知识产权、学术伦理、教育侵蚀、文化偏见、知识生产六大风险,由此认为ChatGPT应以人为本、以法为基、以管为核、以容为向来正向促进数字人文,未来ChatGPT在数字人文项目、数字人文教育、数字人文工具、数字学术出版等领域大有可为。

智能化教学评价的风险挑战及对策研究

数智时代,人工智能赋能教学评价迎来了可喜的变化,其主要表现在教学评价主体多元化、评价方式多样化、评价过程动态化和评价结果个性化四个方面。目前,智能化教学评价工具仍存在一些伦理性风险,如:评价安全风险、忽视复杂质性因素和忽视评价主体尊严等,因此,评价主体在评价过程中应保持理性和谨慎。同时,有关院校也应制定严格的数据安全规约、构建人本化的评价伦理秩序、发挥协同化的双向联动效应,使智能化技术与教学评价深度耦合,发挥数智技术“向善”的功用,营造人与技术协同共生的有序评价氛围,创设智能化教学评价的生态化发展新空间。

数字化时代的机器异化批判——基于历史唯物主义的视角

机器作为生产工具的历史样态,在资本主义社会正式出场,随后持续与科技革命融合,并逐步发展为智能机器。作为典型的技术物化形式,机器本身具有自然属性和社会属性的二重性。进入数字化时代,机器异化突出表现为人沦为“机器的附庸”。机器通过对“人手”“人脑”的代替导致“人手贬值”“人脑贬值”,并在此基础上对劳动者进行精神规训,使其丧失主体性。机器异化的根源在于机器的资本主义应用。为从根本上重构人与机器的关系,扬弃机器异化,就必须变革生产方式,激活机器解放人类的潜能;利用社会主义制度优势推动机器“向善”发展;坚持创新驱动发展战略,推动机器“高质量发展”;通过技术赋能提升人的数字素养,以促进“人机共生”,从而为促进人的自由而全面发展创造历史条件。

人机共生社会的韧性系统研究:嬗变、机制与根源

随着人工智能等技术的不断进步,由人—智能体—社会构成的系统正朝着协同共生的趋势发展,其引发的风险也不容忽视。智能体在与人类认知行为和社会现有结构进行信息传播和态势共融方面面临着亟待解决的问题,特别是缺乏面对风险的韧性属性的系统性分析。为了解决上述问题,本文首先辨析人机共生社会的概念及发展历程。然后,对智能体的韧性属性和人类的心理韧性进行了阐释,为人机协同共生韧性系统提供了机制分析。最后,从人类感知与认知限制、信息不确定性和模糊性、智能社会价值观与伦理模糊性以及难以中立性等层面分别探讨人与智能体关系的演变以及产生风险的根源,从而为消解风险、提高人—智能体—社会系统韧性提供了深入的分析与设计视角。总体而言,本研究旨在深化对人类与智能体在交互环境与社会价值层面韧性增强属性的理解,同时为人机共生社会中的风险消解与韧性提高提供了有价值的见解。随着人工智能技术的不断演进,我们对人—智能社会协同共生的理解和体验也将不断丰富,对于未来的智能传播交互理论和实践具有重要意义。

AI产业技术创新系统运行机制与优化对策研究

AI(人工智能)技术是新工业革命的核心驱动力之一。推动AI产业技术创新是构建自主可控AI核心技术体系、培育全球AI产业发展新高地、推进各行业智能化升级的重要举措。针对我国AI产业链发展不均衡,系统运行机制缺乏细粒度分析等问题,从驱动力、资源供给、合作模式3个维度,研究AI产业技术创新系统运行机制。结果发现:(1)AI产业技术创新系统具有自组织性和共生性特征;(2)AI产业包括硬件、系统和应用3个技术创新子系统,子系统之间既有技术关联又存在较大发展差异;(3)AI技术创新存在底层关键技术创新能力不足、创新资源供给障碍和技术创新合作思路不明等现实问题。在机制研究基础上,基于共生理论提出人工智能产业可持续发展优化对策。

人机学习共生体--论后人工智能教育时代基本学习形态之构建

人工智能与教育的融合实现了教育的规模化、个性化和智能化,然而它并未突破传统教育知识传授模式的局限,未来的人工智能教育应用应当从"机器教人"向"人机共生"的知识创造性发展。"人机学习共生体"这一后人工智能教育时代的学习形态,是在分析人工智能发展历程和学习者角色演变的基础上,以经验之塔理论和知识创造螺旋理论为基础提出的。后人工智能教育时代人机共生的学习形态,即在学习者的客体观、主体观、主体间、共生观和他者观等基础上,形成的具有连续统样态的人工智能教育模式。人机学习共生体围绕隐性知识和显性知识之间的转化,形成了学习者与智能体的共生关系,通过共同化、表出化、联结化和内在化等知识转化过程,持继促进知识创造。人机共生学习是学习者、智能体和教师所构成的以知识共生为核心的学习过程。未来人机学习共生体的实现,需要解决智能体的主体性技术、学习绩效支持以及学习模式创设等挑战。