社会计算(英文)最新文献

{"title":"How We Will Discover Sentience in AI","authors":"Marc M. Anderson","doi":"10.23919/JSC.2023.0019","DOIUrl":"https://doi.org/10.23919/JSC.2023.0019","url":null,"abstract":"This paper explores the question of how we can know if Artificial Intelligence (AI) systems have become or are becoming sentient. After an overview of some arguments regarding AI sentience, it proceeds to an outline of the notion of negation in the philosophy of Josiah Royce, which is then applied to the arguments already presented. Royce's notion of the primitive dyadic and symmetric negation relation is shown to bypass such arguments. The negation relation and its expansion into higher types of order are then considered with regard to how, in small variations of active negation, they would disclose sentience in AI systems. Finally, I argue that the much-hyped arguments and apocalyptic speculations regarding Artificial General Intelligence (AGI) takeover and similar scenarios, abetted by the notion of unlimited data, are based on a fundamental misunderstanding of how entities engage their experience. Namely, limitation, proceeding from the symmetric negation relation, expands outward into higher types of order in polyadic relations, wherein the entity self-limits and creatively moves toward uniqueness.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"4 3","pages":"181-192"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375935","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Collaborative Diffusion Model of Information and Behavior in Social Networks","authors":"Qingsong Sun;Yang Wang;Gang Sun;Haibo Hu","doi":"10.23919/JSC.2023.0016","DOIUrl":"https://doi.org/10.23919/JSC.2023.0016","url":null,"abstract":"Information diffusion may lead to behaviors related to information content. This paper considers the co-existence of information and behavior diffusion in social networks. The state of users is divided into six categories, and the rules and model of collaborative diffusion of information and behavior are established. The influence of different parameters and conditions on the proportions of behavior diffusion nodes and information diffusion ones is analyzed experimentally. The results show that the proportion of nodes taking action in uniform networks is higher than that in non-uniform networks. Although users are more likely to take actions related to information content after spreading or knowing information, the results show that it has little influence on the proportion of users taking action. The proportion is mainly affected by the probability that users who do not take action become ones who take. The greater the probability, the less the proportion of nodes who know information. In addition, compared with choosing the same node as the initial information and behavior diffusion node, choosing different nodes is more beneficial to the diffusion of behaviors related to information content.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"4 3","pages":"243-253"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375937","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI Sentience and Socioculture","authors":"AJ Alvero;Courtney Peña","doi":"10.23919/JSC.2023.0021","DOIUrl":"https://doi.org/10.23919/JSC.2023.0021","url":null,"abstract":"Artificial intelligence (AI) sentience has become an important topic of discourse and inquiry in light of the remarkable progress and capabilities of large language models (LLMs). While others have considered this issue from more philosophical and metaphysical perspectives, we present an alternative set of considerations grounded in sociocultural theory and analysis. Specifically, we focus on sociocultural perspectives on interpersonal relationships, sociolinguistics, and culture to consider whether LLMs are sentient. Using examples grounded in quotidian aspects of what it means to be sentient along with examples of AI in science fiction, we describe why LLMs are not sentient and are unlikely to ever be sentient. We present this as a framework to reimagine future AI not as impending forms of sentience but rather a potentially useful tool depending on how it is used and built.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"4 3","pages":"205-220"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375932","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of Artificial Intelligence on Social Trust in American Institutions","authors":"Andrew Collins;Jason Jeffrey Jones","doi":"10.23919/JSC.2023.0022","DOIUrl":"https://doi.org/10.23919/JSC.2023.0022","url":null,"abstract":"In recent decades, social scientists have debated declining levels of trust in American institutions. At the same time, many American institutions are coming under scrutiny for their use of artificial intelligence (AI) systems. This paper analyzes the results of a survey experiment over a nationally representative sample to gauge the effect that the use of AI has on the American public's trust in their social institutions, including government, private corporations, police precincts, and hospitals. We find that artificial intelligence systems were associated with significant trust penalties when used by American police precincts, companies, and hospitals. These penalties were especially strong for American police precincts and, in most cases, were notably stronger than the trust penalties associated with the use of smartphone apps, implicit bias training, machine learning, and mindfulness training. Americans' trust in institutions tends to be negatively impacted by the use of new tools. While there are significant variations in trust between different pairings of institutions and tools, generally speaking, institutions which use AI suffer the most significant loss of trust. American government agencies are a notable exception here, receiving a small but puzzling boost in trust when associated with the use of AI systems.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"4 3","pages":"221-231"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375933","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational History: Challenges and Opportunities of Formal Approaches","authors":"Jürgen Jost;Roberto Lalli;Manfred D. Laubichler;Eckehard Olbrich;Jürgen Renn;Guillermo Restrepo;Peter F. Stadler;Dirk Wintergrün","doi":"10.23919/JSC.2023.0017","DOIUrl":"https://doi.org/10.23919/JSC.2023.0017","url":null,"abstract":"We propose a program for a computational analysis, based on large scale datasets, of deep conceptual and formal structures, representing the mechanisms of historical transformations in different domains ranging from biological to social, cultural, and knowledge systems. We conceptualize such systems as consisting of complex multi-layer networks. Structural properties of such networks may explain the spreading of innovations. Temporal relations between the dynamics of interacting networks may help to identify causalities. Complex systems may show path and context dependencies. We illustrate our approach by case studies from all those types of systems.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"4 3","pages":"232-242"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375934","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unlearning Descartes: Sentient AI is a Political Problem","authors":"Gordon Hull","doi":"10.23919/JSC.2023.0020","DOIUrl":"https://doi.org/10.23919/JSC.2023.0020","url":null,"abstract":"The emergence of Large Language Models (LLMs) has renewed debate about whether Artificial Intelligence (AI) can be conscious or sentient. This paper identifies two approaches to the topic and argues: (1) A “Cartesian” approach treats consciousness, sentience, and personhood as very similar terms, and treats language use as evidence that an entity is conscious. This approach, which has been dominant in AI research, is primarily interested in what consciousness is, and whether an entity possesses it. (2) An alternative “Hobbesian” approach treats consciousness as a sociopolitical issue and is concerned with what the implications are for labeling something sentient or conscious. This both enables a political disambiguation of language, consciousness, and personhood and allows regulation to proceed in the face of intractable problems in deciding if something “really is” sentient. (3) AI systems should not be treated as conscious, for at least two reasons: (a) treating the system as an origin point tends to mask competing interests in creating it, at the expense of the most vulnerable people involved; and (b) it will tend to hinder efforts at holding someone accountable for the behavior of the systems. A major objective of this paper is accordingly to encourage a shift in thinking. In place of the Cartesian question—is AI sentient?—I propose that we confront the more Hobbesian one: Does it make sense to regulate developments in which AI systems behave as if they were sentient?","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"4 3","pages":"193-204"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375936","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Counterfactual Reasoning Over Community Detection: A Case Study of the Public Science Day Community","authors":"Wenkang Jiang;Hongbo He;Lei Lin;Qirui Tang;Runqiang Wang","doi":"10.23919/JSC.2023.0010","DOIUrl":"10.23919/JSC.2023.0010","url":null,"abstract":"With the rapid rise of new media platforms such as Weibo and Tiktok, communities with science communication characteristics have progressively grown on social networks. These communities pursue essential objectives such as increased visibility and influence. For the success of the public understanding of science in China, case studies of science communication communities on social media are becoming increasingly valuable as a point of reference. The authenticity of user influence plays an important role in the analysis of the final outcome during the process of community detection. By integrating counterfactual reasoning theory into a community detection algorithm, we present a novel paradigm for eliminating influence bias in online communities. We consider the community of Public Science Day of the Chinese Academy of Sciences as a case study to demonstrate the validity of the proposed paradigm. In addition, we examine data on science communication activities, analyze the key elements of activity communication, and provide references for not only augmenting the communication impact of similar types of popular science activities but also advancing science communication in China. Our main finding is that the propagation channel for the science communication experiment exhibits multi-point scattered propagation and lacks a continuous chain in the process of propagation.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"4 2","pages":"125-138"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8964404/10239701/10239704.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45640688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Negative Sentiment Shift on a Chinese Movie-Rating Website","authors":"Hongkai Mao","doi":"10.23919/JSC.2023.0014","DOIUrl":"10.23919/JSC.2023.0014","url":null,"abstract":"Shifting to negativity is more and more prevalent in online communities and may play a key role in group polarization. While current research indicates a close relationship between group polarization and negative sentiment, they often link negative sentiment shifts with echo chambers and misinformation within echo chambers. In this work, we explore the sentiment drift using over 4 million comments from a Chinese online movie-rating community that is less affected by misinformation than other mainstream online communities and has no echo chamber structures. We measure the sentiment shift of the community and users of different engagement levels. Our analysis reveals that while the community does not show a tendency toward negativity, users of higher engagement levels are generally more negative, considering factors like the different movies they consume. The results indicate a fitting-in process, suggesting the possible mechanism of group identity on sentiment shift on social media platforms. These findings also provide guidance on web design to tackle the negativity issue and expand sentiment shift analysis to non-English contexts.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"4 2","pages":"168-180"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8964404/10239701/10239703.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42762279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cover","authors":"","doi":"10.23919/JSCTUP.2023.10241354","DOIUrl":"10.23919/JSCTUP.2023.10241354","url":null,"abstract":"","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"4 2","pages":"1-1"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8964404/10239701/10241354.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46119700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing an Integrated IoT Cloud Based Predictive Conservation Model for Asset Management in Industry 4.0","authors":"Karnam Shanmugam;Kachhti Satyam;Thimma Reddy Sreenivasula Reddy","doi":"10.23919/JSC.2023.0011","DOIUrl":"10.23919/JSC.2023.0011","url":null,"abstract":"With the advent of Industry 4.0 (I4.0), predictive maintenance (PdM) methods have been widely adopted by businesses to deal with the condition of their machinery. With the help of I4.0, digital transformation, information techniques, computerised control, and communication networks, large amounts of data on operational and process conditions can be collected from multiple pieces of equipment and used to make an automated fault detection and diagnosis, all with the goal of reducing unscheduled maintenance, improving component utilisation, and lengthening the lifespan of the equipment. In this paper, we use smart approaches to create a PdM planning model. The five key steps of the created approach are as follows: (1) cleaning the data, (2) normalising the data, (3) selecting the best features, (4) making a decision about the prediction network, and (5) producing a prediction. At the outset, PdM-related data undergo data cleaning and normalisation to get everything in order and within some kind of bounds. The next step is to execute optimal feature selection in order to eliminate unnecessary data. This research presents the golden search optimization (GSO) algorithm, a powerful population-based optimization technique for efficient feature selection. The first phase of GSO is to produce a set of possible solutions or objects at random. These objects will then interact with one another using a straightforward mathematical model to find the best feasible answer. Due to the wide range over which the prediction values fall, machine learning and deep learning confront challenges in providing reliable predictions. This is why we recommend a multilayer hybrid convolution neural network (MLH-CNN). While conceptually similar to VGGNet, this approach uses fewer parameters while maintaining or improving classification correctness by adjusting the amount of network modules and channels. The projected perfect is evaluated on two datasets to show that it can accurately predict the future state of components for upkeep preparation.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"4 2","pages":"139-149"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8964404/10239701/10239702.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41332111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}