Prediction of emerging technologies based on analysis of the US patent citation network

Abstract The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (1) identifies actual clusters of patents: i.e., technological branches, an

Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network

The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (i) identifies actual clusters of patents: i.e. technological branches, and (ii) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the {citation vector}, is defined for characterizing technological development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which shows significant overlap of the class 442 formed at the beginning of 1997. These new tools of predictive analytics could support policy decision making processes in science and technology, and help formulate recommendations for action

Prediction of emerging technologies based on analysis of the US patent citation network

The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (1) identifies actual clusters of patents: i.e., technological branches, and (2) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the citation vector, is defined for characterizing technological development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which shows significant overlap of the class 442 formed at the beginning of 1997. These new tools of predictive analytics could support policy decision making processes in science and technology, and help formulate recommendations for action. © 2012 Akadémiai Kiadó, Budapest, Hungary

Perception, performance, and detectability of conversational artificial intelligence across 32 university courses

Abstract The emergence of large language models has led to the development of powerful tools such as ChatGPT that can produce text indistinguishable from human-generated work. With the increasing accessibility of such technology, students across the globe may utilize it to help with their school work—a possibility that has sparked ample discussion on the integrity of student evaluation processes in the age of artificial intelligence (AI). To date, it is unclear how such tools perform compared to students on university-level courses across various disciplines. Further, students’ perspectives regarding the use of such tools in school work, and educators’ perspectives on treating their use as plagiarism, remain unknown. Here, we compare the performance of the state-of-the-art tool, ChatGPT, against that of students on 32 university-level courses. We also assess the degree to which its use can be detected by two classifiers designed specifically for this purpose. Additionally, we conduct a global survey across five countries, as well as a more in-depth survey at the authors’ institution, to discern students’ and educators’ perceptions of ChatGPT’s use in school work. We find that ChatGPT’s performance is comparable, if not superior, to that of students in a multitude of courses. Moreover, current AI-text classifiers cannot reliably detect ChatGPT’s use in school work, due to both their propensity to classify human-written answers as AI-generated, as well as the relative ease with which AI-generated text can be edited to evade detection. Finally, there seems to be an emerging consensus among students to use the tool, and among educators to treat its use as plagiarism. Our findings offer insights that could guide policy discussions addressing the integration of artificial intelligence into educational frameworks

Wired into Each Other: Network Dynamics of Adolescents in Hungarian Secondary Schools: 2010-2013

The project “Wired into each other” is a three-year longitudinal social network study conducted by the Research Center for Educational and Network Studies (RECENS) of Corvinus University of Budapest and the Hungarian Academy of Sciences. The study involved the collection of a unique, large-scale survey dataset about the evolution of interpersonal relations and various individual behaviours and attitudes in more than 40 student communities from Hungary between 2010 and 2013. The project aimed at a) developing novel measures of informal social networks among students and b) gaining new insight into the social processes shaping adolescent communities. In scope of this, the RECENS team developed a multi-item network questionnaire about peer relations in more than 30 different aspects, including contact, affection, behavioural perceptions, role and status attributions, and bullying. Using this measurement tool, the team collected data of unprecedented depth about the multidimensional nature of social processes in school communities. The dataset allows researchers to study the social mechanisms behind status competition, group formation, ethnic integration (with focus on the Roma minority group), bullying and victimization, school performance, substance use, and other phenomena.The project “Wired into each other” is a three-year longitudinal social network study conducted by the Research Center for Educational and Network Studies (RECENS) of Corvinus University of Budapest and the Hungarian Academy of Sciences. The study involved the collection of a unique, large-scale survey dataset about the evolution of interpersonal relations and various individual behaviours and attitudes in more than 40 student communities from Hungary between 2010 and 2013. The project aimed at a) developing novel measures of informal social networks among students and b) gaining new insight into the social processes shaping adolescent communities. In scope of this, the RECENS team developed a multi-item network questionnaire about peer relations in more than 30 different aspects, including contact, affection, behavioural perceptions, role and status attributions, and bullying. Using this measurement tool, the team collected data of unprecedented depth about the multidimensional nature of social processes in school communities. The dataset allows researchers to study the social mechanisms behind status competition, group formation, ethnic integration (with focus on the Roma minority group), bullying and victimization, school performance, substance use, and other phenomena.</p