Technical Aspects of Artificial Intelligence: An Understanding from an Intellectual Property Law Perspective
SSRN, ISSN: 1556-5068
2019
- 9Citations
- 15,952Usage
- 57Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
The present Q&A paper aims at providing an overview of artificial intelligence with a special focus on machine learning as a currently predominant subfield thereof. Machine learning-based applications have been discussed intensely in legal scholarship, including in the field of intellectual property law, while many technical aspects remain ambiguous and often cause confusion. This text was drafted by the Research Group on the Regulation of the Digital Economy of the Max Planck Institute for Innovation and Competition in the pursuit of understanding the fundamental characteristics of artificial intelligence, and machine learning in particular, that could potentially have an impact on intellectual property law. As a background paper, it provides the technological basis for the Group’s ongoing research relating thereto. The current version summarises insights gained from background literature research, interviews with practitioners and a workshop conducted in June 2019 in which experts in the field of artificial intelligence participated.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85112684310&origin=inward; http://dx.doi.org/10.2139/ssrn.3465577; https://www.ssrn.com/abstract=3465577; https://dx.doi.org/10.2139/ssrn.3465577; https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3465577; https://ssrn.com/abstract=3465577
Elsevier BV
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