Artificial intelligence for disease diagnostics still has a long way to go

Artificial intelligence(AI)can sometimes resolve difficulties that other advanced technologies and humans cannot.In medical diagnostics,AI has the advantage of processing figure recognition,especially for images with similar characteristics that are difficult to distinguish with the naked eye.However,the mechanisms of this advanced technique should be well-addressed to elucidate clinical issues.In this letter,regarding an original study presented by Takayama et al,we suggest that the authors should effectively illustrate the mechanism and detailed procedure that artificial intelligence techniques processing the acquired images,including the recognition of non-obvious difference between the normal parts and pathological ones,which were impossible to be distinguished by naked eyes,such as the basic constitutional elements of pixels and grayscale,special molecules or even some metal ions which involved into the diseases occurrence.

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.

Artificial Intelligence in Healthcare and Medicine:Promises,Ethical Challenges and Governance

Artificial intelligence (AI) is rapidly being applied to a wide range of fields,including medicine,and has been considered as an approach that may augment or substitute human professionals in primary healthcare.However,AI also raises several challenges and ethical concerns.In this article,the author investigates and discusses three aspects of AI in medicine and healthcare:the application and promises of AI,special ethical concerns pertaining to AI in some frontier fields,and suggestive ethical governance systems.Despite great potentials of frontier AI research and development in the field of medical care,the ethical challenges induced by its applications has put forward new requirements for governance.To ensure “trustworthy” AI applications in healthcare and medicine,the creation of an ethical global governance framework and system as well as special guidelines for frontier AI applications in medicine are suggested.The most important aspects include the roles of governments in ethical auditing and the responsibilities of stakeholders in the ethical governance system.

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.

Research on Multi-Dimensional Aesthetic Perception of Art Design in the Era of Artificial Intelligence

With the rapid development of artificial intelligence technology,the way we interact with products and the environment has undergone great changes compared with the past.While the technology of the artificial intelligence era affects our lives and redefines our relationship with machines,it is also changing our aesthetic and perception of art design.The article mainly starts from the perspective of art design interaction mode and experience innovation,and studies the multi-dimensional aesthetic perception and interaction mode of art design in the era of artificial intelligence in human vision,touch and hearing.

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.

Exploiting Human Pose and Scene Information for Interaction Detection

Identifying human actions and interactions finds its use in manyareas, such as security, surveillance, assisted living, patient monitoring, rehabilitation,sports, and e-learning. This wide range of applications has attractedmany researchers to this field. Inspired by the existing recognition systems,this paper proposes a new and efficient human-object interaction recognition(HOIR) model which is based on modeling human pose and scene featureinformation. There are different aspects involved in an interaction, includingthe humans, the objects, the various body parts of the human, and the backgroundscene. Themain objectives of this research include critically examiningthe importance of all these elements in determining the interaction, estimatinghuman pose through image foresting transform (IFT), and detecting the performedinteractions based on an optimizedmulti-feature vector. The proposedmethodology has six main phases. The first phase involves preprocessing theimages. During preprocessing stages, the videos are converted into imageframes. Then their contrast is adjusted, and noise is removed. In the secondphase, the human-object pair is detected and extracted from each image frame.The third phase involves the identification of key body parts of the detectedhumans using IFT. The fourth phase relates to three different kinds of featureextraction techniques. Then these features are combined and optimized duringthe fifth phase. The optimized vector is used to classify the interactions in thelast phase. TheMSRDaily Activity 3D dataset has been used to test this modeland to prove its efficiency. The proposed system obtains an average accuracyof 91.7% on this dataset.

Research and Application of AI-Based Interactive Exhibits in Wuhan Museum of Science and Technology

This article aims to explore the development and application of AI-based interactive exhibits in Wuhan Museum of Science and Technology.By utilizing computer vision,natural language processing,and machine learning technologies,an innovative exhibit development and application system is proposed.This system employs deep learning algorithms and data analysis methods to achieve real-time perception of visitor behavior and adaptive interaction.The development process involves designing user interfaces and interaction methods to effectively enhance visitor engagement and learning outcomes.Through evaluation and comparison in practical applications,the potential of this system in enhancing exhibit interaction,increasing visitor engagement,improving educational effectiveness,and expanding avenues for scientific knowledge dissemination are validated.

Grasping AI in Videogames Distinguishing Human-Computer-Interaction from Human-Human-Interaction

Videogames feature non-player-characters. Such ingame entities are controlled by artificial intelligence that is sought to match the user＇s skill. Defining and understanding the given human-computer-interaction videogame genres can be differentiated in either competitive matches or coherent plays. While artificial intelligence answers in responsive patterns only, natural intelligence holds creative qualities. A match is a game that creates suspense over the question of who is going to prevail. It can thus only be a game that features competing human players, since a computerized opponent is unaware of the game＇s significance. The nature of the match lies within the interactive trial. In a play, however, there is no battle but only cooperation. The aesthetics of such a play unfold to the audience, its purpose is showing rather than experiencing and/or competitively interacting. Artificial intelligence is then a surrogate that is often used in videogames to create an illusion of competition or battle. Its actual purpose is not to feature a worthy opponent, but to be a programmatic loser. In narrative and linear videogames, such as AI takes the role of a supporting actor whose function in the plot is, to signalize the player＇s dominance.

Research progress of artificial psychology and artificial emotion in China

Since the concept of artificial psychology and artificial emotion was first presented, it has become a topic of interest in academic circles and enterprises. In this article, we first briefly introduce the basic concepts and principles of artificial psychology and artificial emotion, analyzing the unified macro-model of the cross-disciplinary system architecture against the need-motivation-behavior framework. Second, we discuss the origin of artificial psychology and artificial emotion, its course of development, and its present situation in China. We also present a review of the published papers and research endeavors of Chinese universities and research institutions and the technical engineering applications of artificial psychology and artificial emotion. Finally, we summarize the challenges to the further development of artificial psychology and artificial emotion and our recommendations for improving the cognitive computing model of psychological states and developing reliable and accurate humanoid interaction and cooperation technology, robot platforms with emotions and humanoid interaction and cooperation capabilities, and humanoid robots for the elderly and the disabled in smart homes. We believe that, with intensive research, artificial psychology and artificial emotion may be developed further and may eventually reach maturity.

交互失误情境下交互式人工智能拟人化的负面影响——基于ChatGPT和搜索引擎的实验证据

[目的/意义]对人机交互失误情境下用户产生的情绪和态度展开研究,旨在验证人工智能拟人化理论的负面效应并探索交互式人工智能拟人化产生负面影响的缓解机制。[方法/过程]以人机交互失误为情境,基于期望一致性理论和拟人化理论,通过实验法分别从交互的结果失误和调用失误两个角度,探索了交互式人工智能不同的拟人化程度对用户产生负面态度的影响机制。[结果/结论]研究发现,人机交互失误时,高拟人化的交互式人工智能会加剧用户对其的厌恶感和负面态度。验证了算法可解释性能够缓解厌恶感和负面态度。综合上述分析,从人机交互健康长远发展角度,提出了人工智能应采用有限拟人化的设计框架和具有可解释性的算法架构等建议。

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。

迭代、升维与信任建构:ChatGPT对广告传播互动性的变革

广告传播互动性是一个关于广告与受众之间相互作用和参与程度的概念。这种互动性可以通过个性化、循环性和情境感三个维度来衡量。个性化互动指的是广告能够根据受众的特定需求和偏好进行定制化。循环性互动指的是广告与受众之间的连续互动和反馈,以形成持续的交流关系。情境感互动则强调广告与受众在特定场景或环境中的相互关联和互动。ChatGPT的出现促进了广告传播互动性在个性化、循环性和情境感方面的不断迭代发展。ChatGPT具备强大的内容生成能力和人机交互能力,可以通过高效生产适配性内容、及时响应消费者需求、创新广告传播互动形式等手段,推动广告的互动性在个性化、循环性、情境感三个维度进行迭代。同时,ChatGPT利用GPT模型结构、上下文记忆、大规模训练和Fine-tuning机制,能够进行自由对话和交互,并生成连贯、合理的回应,推动广告传播的互动性呈现出“情理交融、有求必应、身临其境”的特征,从而实现广告的互动性升维。然而,ChatGPT也带来了一些广告伦理失范、隐蔽操纵和隐私侵犯等风险。为了应对这些风险,需要增强人机协作能力,平衡商业利益与社会责任。此外,还需要进行行业、技术和法律等多方面的共同治理,以建立广告传播互动的信任基础。这样才能构建一个以消费者为中心的广告传播生态系统。

智能人机交互在有人太空探索中的应用与展望

近年来,空间机器人技术发展迅速,国内外已有诸多空间站机器人、深空探测机器人的应用实例。未来有人太空探索过程当中,人与众多智能机器人协同开展任务将成为必然趋势。对国内外空间协作机器人应用实例进行了总结,在分析人机协作机制的基础上,提出了空间人机交互的关键技术,包含人体检测与跟踪、手势识别与分类、动作识别与意图预测、低重力环境下的人机协同控制等,对上述关键技术的难点与研究进展进行了分析综述,并对后续的发展方向进行了展望。

人工智能驱动新型界面分离材料的创制

计算机技术与人工智能的发展给新材料的开发带来了新的研究范式(第四代),也为新型界面分离材料的创制带来新的发展契机。然而,受界面分离过程机理研究和材料、基团的理化数据的局限性,人工智能在新型界面分离材料的创制中的应用仍处于初期阶段。本文为解决特定体系数据缺失的问题,构建了百万级(10^(6))的样板数据库,为大预测模型与生成模型提供了可靠、规范的示范数据基础;通过从大数据中挖掘分子结构与性质的隐藏关联,并借助分子模拟计算剖析其分子间相互作用机理,解析分子间相互作用机制和重组过程规律,进而实现高性能界面分离材料的构筑。最后,本文浅析了介尺度样板大数据、机器学习势理论、国产软件开发、有机合成、智能算法等多学科交叉在界面分离材料创制中的重要影响与发展机遇,以期推动虚拟实验室前沿发展。

感受性视角下人智交互研究:主题挖掘、方法识别、未来发展

[目的/意义]人工智能研究正经历从人工智能主义到人本人工智能主义的视角转变。将两视角看作两“桥墩”,感受性因强调人和人工智能代理之间的关系过程,成为架起通向更准确、高效人工智能理解的“桥梁”。以感受性为视角,系统梳理以人智交互为对象的相关研究主题、方法与发展具有重要意义。[方法/过程]以现有国内外文献为基础,借助开放式编码从人智需求识别和人智交互体验两方面以及研究方式、研究方法论、研究方法三方面进行研究主题和方法回顾,继而对感受性视角下人智交互的未来研究方向进行展望。[结果/结论]一方面,以人智能力和信息需求识别为研究主题,利用定性研究方法探索人和人工智能代理双方的认知感受性;另一方面,以人智能力差异和人智体验评估为研究主题,借助定量研究方法量化人和以用户为中介的人工智能代理的感知感受性。[创新/局限]从研究工具升级、研究主题扩展、研究要素嵌套三维度来提升现有研究框架;以定性描述为主,未来需进一步加强定量分析。

智能机械臂在沙画创作中的辅助创新实践

目的随着智能技术在人机交互领域的发展,设计学科创作领域的智能协同更加多样化,沙画需要依靠智能技术的力量突破创作技法上的困境。方法本文结合人工智能的发展和在设计方面的应用技术,分析了和沙画相结合的技术基础,梳理了沙画创作的流程,并确定了沙画领域中的应用需求及创作主体与人工智能的关系,结合机械臂辅助沙画师创作,以人机协同《沙画颂祖国》项目对可行性进行论证。结果本研究将形成人机认知共生、智能技术支撑、创意主体协作、智能互动创作的体系,不仅切实解决了沙画行业辅助创作的难题,而且增加了设计学科交互的多样性,提供了未来融合的新方向。

重识与建构:智能时代学生在课程变革中的主体地位

随着人工智能时代的来临,学校课程发展的方向不再侧重于课程资源的网络化和获取的便捷性,而是通过强调体验和生成来更加凸显学生在课程中的主体地位。但人工智能介入的课程对“体验”的重视并不意味着学生自主性的彰显,对生成的强调也容易导致知识的系统性受损。在人工智能时代更要加强教师对课堂的主导作用,帮助学生在技术带来的感官刺激基础上挖掘知识的内涵,从而激发并维持学习的动机;同时引导学生在生成的基础之上完善知识结构、形成知识系统,让学生真正成为知识的占有者,成为课程的主人。

生成式人工智能技术在博物馆叙事中的应用研究

目的本文旨在探索生成式人工智能技术(AIGC)在博物馆叙事设计中的具体应用及其对增强参观者互动体验的作用。结合用叙事学理论,本研究将分析AIGC如何通过动态与沉浸式叙事来转化展览叙事,提升用户的认知沉浸和情感联结。方法首先,运用文献研究法分析AIGC在博物馆叙事中的理论基础;其次,通过案例分析法挖掘并探析实际运用AIGC技术于博物馆的案例;最后,研究了博物馆叙事传播中的隐患与挑战。结果生成式人工智能技术能够有效地识别展览数据模式并生成吸引参观者的新内容,为用户带来个性化且情境化的叙事体验,并提供个性化学习路径促进观众的认知、情感发展。结论生成式人工智能技术的融合为博物馆叙事设计提供了新的交互需求和个性化体验,多感官设计策略让参观者更好地理解和参与,AIGC会在博物馆叙事设计中形成新的体验模式。

人工智能背景下的大学英语生态化教学模式研究

人工智能的集成可以优化教学内容,提升教学效率,促进个性化学习,为学生提供定制化的学习体验。智能评估工具的应用、教学活动的智能规划与组织,以及智能互动机制的实施,均对提高教学质量和学生的学习体验产生了显著的正面效果。这些工具和策略通过精确分析学生的学习需求和进度,使教学过程更加高效和有针对性。然而,人工智能技术在提升教育资源的可获取性、获得教师与学生的广泛接受,以及与传统教学方法的有效结合方面,仍面临着挑战,需要通过持续的创新来应对。