„Künstliche Intelligenz“ und Urheberrecht - Quo vadis?

2021 ◽  
Author(s):  
David Linke

"Artificial Intelligence“ (AI) is experiencing a boom which has undoubtedly impacted copyright law. “AI“-generated works such as “The Next Rembrandt" raise the question whether, against the conventional anthropocentric focus of copyright law, such works are eligible for protection de lege lata. Moreover, the question regarding the protectability of “AI“ has not yet been answered conclusively. Taking an interdisciplinary approach, this thesis examines these questions as well as how creative “AI“ is and what is understood by “AI“. This thesis also considers options for action and the need for adaptation whilst offering a comparative analysis by examining the legal situation in the UK and the US.

2006 ◽  
Vol 14 (3) ◽  
pp. 147-158 ◽  
Author(s):  
Ruth V. Aguilera ◽  
Cynthia A. Williams ◽  
John M. Conley ◽  
Deborah E. Rupp

2017 ◽  
Vol 8 (2) ◽  
pp. 244-255
Author(s):  
Apnizan Abdullah ◽  
Shahrul Mizan Ismail ◽  
Halila Faiza Zainal Abidin

2020 ◽  
Author(s):  
Amir Hussain ◽  
Ahsen Tahir ◽  
Zain Hussain ◽  
Zakariya Sheikh ◽  
Kia Dashtipour ◽  
...  

UNSTRUCTURED Background: Global efforts towards the development and deployment of a vaccine for SARS-CoV-2 are rapidly advancing. We developed and applied an artificial-intelligence (AI)-based approach to analyse social-media public sentiment in the UK and the US towards COVID-19 vaccinations, to understand public attitude and identify topics of concern. Methods: Over 300,000 social-media posts related to COVID-19 vaccinations were extracted, including 23,571 Facebook-posts from the UK and 144,864 from the US, along with 40,268 tweets from the UK and 98,385 from the US respectively, from 1st March - 22nd November 2020. We used natural-language processing and deep learning-based techniques to predict average sentiments, sentiment trends and topics of discussion. These were analysed longitudinally and geo-spatially, and a manual- eading of randomly selected posts around points of interest helped identify underlying themes and validated insights from the analysis. Results: We found overall averaged positive, negative and neutral sentiment in the UK to be 58%, 22% and 17%, compared to 56%, 24% and 18% in the US, respectively. Public optimism over vaccine development, effectiveness and trials as well as concerns over safety, economic viability and corporation control were identified. We compared our findings to national surveys in both countries and found them to correlate broadly. Conclusions: AI-enabled social-media analysis should be considered for adoption by institutions and governments, alongside surveys and other conventional methods of assessing public attitude. This could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccinations, help address concerns of vaccine-sceptics and develop more effective policies and communication strategies to maximise uptake.


2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Haotian Hu ◽  
Dongbo Wang ◽  
Sanhong Deng

AbstractPurposeThis study aims to explore the trend and status of international collaboration in the field of artificial intelligence (AI) and to understand the hot topics, core groups, and major collaboration patterns in global AI research.Design/methodology/approachWe selected 38,224 papers in the field of AI from 1985 to 2019 in the core collection database of Web of Science (WoS) and studied international collaboration from the perspectives of authors, institutions, and countries through bibliometric analysis and social network analysis.FindingsThe bibliometric results show that in the field of AI, the number of published papers is increasing every year, and 84.8% of them are cooperative papers. Collaboration with more than three authors, collaboration between two countries and collaboration within institutions are the three main levels of collaboration patterns. Through social network analysis, this study found that the US, the UK, France, and Spain led global collaboration research in the field of AI at the country level, while Vietnam, Saudi Arabia, and United Arab Emirates had a high degree of international participation. Collaboration at the institution level reflects obvious regional and economic characteristics. There are the Developing Countries Institution Collaboration Group led by Iran, China, and Vietnam, as well as the Developed Countries Institution Collaboration Group led by the US, Canada, the UK. Also, the Chinese Academy of Sciences (China) plays an important, pivotal role in connecting the these institutional collaboration groups.Research limitationsFirst, participant contributions in international collaboration may have varied, but in our research they are viewed equally when building collaboration networks. Second, although the edge weight in the collaboration network is considered, it is only used to help reduce the network and does not reflect the strength of collaboration.Practical implicationsThe findings fill the current shortage of research on international collaboration in AI. They will help inform scientists and policy makers about the future of AI research.Originality/valueThis work is the longest to date regarding international collaboration in the field of AI. This research explores the evolution, future trends, and major collaboration patterns of international collaboration in the field of AI over the past 35 years. It also reveals the leading countries, core groups, and characteristics of collaboration in the field of AI.


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