421 research outputs found
Healthcare Activism, Marketization, and the Collective Good
No full textThis chapter engages with three key dynamics of contemporary healthcare - digitalization, marketization and individualization. It draws on several theoretical frameworks to conceptualize the notion of collective good and to consider how healthcare activism may play into defining and defending the collective good when faced with the outlined societal, economic, and scientific dynamics. Presenting contemporary examples from the Covid-19 pandemic, the chapter argues that the way activists define and defend the collective good can only fully be understood by grasping how this good is shaped by other, often more dominant, stakeholders in healthcare: governmental institutions, professional experts, scientists, and private industry – the latter being a focal point of concern for this current volume.European Commission Horizon 2020Check for published version during checkdate report - AC2021-04-28 JG: PDF replaced at author's request2021-06-04 JG: embargo removed following documentation from author/publishe
When the Robots (try to) Take Over: Of Artificial Intelligence, Authors, Creativity and Copyright Protection
No full textAs works are increasingly produced by machines using artificial intelligence (AI) systems, with a result often difficult to distinguish from that of a human creator, the question of what should be the appropriate response of the legal system and, in particular, of the copyright system has become central. If the creative input of the author has traditionally been the generator of copyright protection, AI forces to reassess what in the creative process is special in human creativity and where the creative input lies in AI-generated works. But it also poses more fundamental questions on what the copyright system should achieve and who/what it should protect. In particular, as many human authors will potentially face the competition of these AI machines on the market, new ways of remunerating human creators have to be imagined while making sure that the copyright system does not stand in the way of these important technological developments.
This contribution analyses the copyright issues related to so-called “generative AI” systems and reviews the arguments currently advanced to change the copyright regime for AI-generated works. It is argued that the copyrightability of AI-generated outputs should be considered with outmost care and only when AI is used as a technical tool for creators in their creation process- meaning when they can serve a human author. At the same time, AI systems are here to stay, and their development should not be inhibited as they can have many beneficial aspects (including for creators) if appropriately regulated. For this reason, it is proposed that the machine learning process using copyright-protected works to train the AI gives rise to a limitation-based remuneration right to the benefit of human creators.
More generally, it is argued that for the EU to continue to be a vibrant place for culture and creativity, (finally) cherishing and putting the Human Author at the center of the copyright system is necessary (and not only to built-up protection/fences to the benefit of copyright industries). In doing so, we might be able to have in the future AI-robots that serve creators and creativity, and not the other way around
Benno Geiger, umanista mitteleuropeo. Il carteggio con Stefan Zweig
No full textThis article outlines the development of work in progress on the Austrian author Benno Geiger. Mostly forgotten in the field of German studies, Geiger is better known for his writings as an art critic and his translations of Dante, Petrarch and Pascoli than for his compositions as a poet. However, in the decades that followed the Jahrhundertwende, he was a cultural benchmark for an entire generation of artists and intellectuals. In particular, this project focuses on his friendship with Stefan Zweig, which is well documented by a long and intense correspondence (from 1904 to 1939). Both of them see the question of Europeanism in a new light, still to be appraised
Elaborating a Human Rights friendly Copyright Framework for Generative AI
No full textAs works are increasingly produced by machines using artificial intelligence (AI) systems, with a result that is often difficult to distinguish from that of a human creator, the question of what should be the appropriate response of the legal system and, in particular, of the copyright system has become central. If the generator of copyright protection has traditionally been the author’s creative input, AI forces us to reassess what in the creative process is special in human creativity and where the creative input lies in AI-generated works. But it also poses more fundamental questions on what the copyright system should achieve and who/what it should protect. In particular, since many human authors will potentially face the competition of these AI machines on the market, new ways of remunerating creators will have to be imagined while making sure that the copyright system does not stand in the way of these important technological developments.
This contribution analyses the copyright issues related to so-called “generative AI” systems and reviews the arguments currently being advanced to change the copyright regime for AI-generated works. To do so, the underlying human rights framing intellectual property laws are used as the starting point from which a balanced copyright framework for generative AI could (and even should) be derived. It follows from the applicable human rights framework for copyright, but also from the anthropocentric approach of human rights, that the protection of creators and human creativity must be considered the point of reference when assessing future reforms with regard to copyright and generative AI systems. This approach establishes generative AI systems as an instrument of the human creator – and not as a substitute. It also reinforces the notion that copyright should be a tool to protect creativity and creators, not a legal mechanism to secure the amortization of economic investments in AI technology. As a consequence, it is argued that the copyrightability of AI-generated outputs should be considered with utmost care and only when AI is used as a technical tool for creators in their creation process – in other words, when they can serve a human author. At the same time, AI systems are here to stay, and their development should not be inhibited, as they can have many beneficial aspects (including for creators) if appropriately regulated.
The right to train generative AI systems via machine learning technology can be derived from the right to science and culture and freedom of (artistic) expression (Arts. 19 and 27(1) Universal Declaration of Human Rights (UDHR); Art. 15(1)(a) and (b) International Covenant on Economic, Social and Cultural Rights (ICESCR); Arts. 11 and 13 EU Charter of Fundamental Rights (EUCFR)), as AI can lead to useful advances in science and the arts; moreover, it is important for human creators to be able to use outputs produced by generative AI in their creative process. This grounding is even stronger when the training is conducted for research purposes, as the training process can then also benefit from the fundamental right-to-research justification. However, since a large quantity of copyrighted works is required for the training of generative AI systems, a remuneration obligation for these uses arises from a human rights perspective, in particular when AI systems have a commercial purpose. It follows from the right to the protection of the creator’s moral and material interests (Arts. 27(2) and 17 UDHR, 15(1)(c) ICESCR; 17(2) EUCFR, 1 Protocol No. 1, 8 European Convention on Human Rights (ECHR)) that authors must be adequately remunerated for the commercial use of their works unless there is a strong justification legitimizing the use. For this reason, it is proposed that the machine learning process using copyright-protected works to train the AI gives rise to a limitation-based remuneration right to the benefit of human creators. The article also briefly explores if and when the moral interest of creators deriving from human rights protection could justify their opposition to the use of their work for the purpose of training AI systems. It is argued that the weaker the fundamental rights claim to train the AI is, the stronger the moral rights claim could be. For example, training an AI to produce works for discriminatory or racist purposes will benefit from a weaker (if any) fundamental rights protection, but will potentially raise important moral concerns of the author of the work used for training purposes.
More generally, the article concludes that in order to secure a vibrant space for culture and creativity, (finally) cherishing and putting the Human Author at the center of the copyright system is necessary (and not only to erect fences to the benefit of copyright industries, which could be the unfortunate result of the recent first broad regulatory intervention on AI by the EU, the so-called “Artificial Intelligence Act”). In doing so, it might be possible in the future to have AI-systems that serve creators and creativity, and not the other way around
The Vikings in the North Atlantic: The Rise and Fall of the Greenland Colony
No full textAbout the Author
Caitlyn Floyd Geiger graduated with a B.A. in History from Armstrong State University in December of 2016. Her main research interests are military history and archaeological studies. She hopes to use the knowledge and skills she has gained in college to further her career as a fiction writer
Towards a European 'Fair Use' Grounded in Freedom of Expression
No full textIt is often claimed that an open-ended provision for copyright limitations such as the US fair use clause would be unfit for civil law countries because of their author-centered traditions of copyright law and their traditional skepticism towards “judge made law” encouraged by open norms. However, the rising application in those countries of fundamental rights by the judiciary to solve copyright cases (mainly based on freedom of expression and information) and the balancing of interests it requires resemble in many aspects the practice of common law jurisdictions and the weighing of factors typically done in the context of a fair use analysis. As a consequence, this article argues that some sort of “fair use” is already a reality in Europe; therefore, the debate should shift from the question of the compatibility of an open-ended copyright limitation with the European legal system to the question on how to draft a “fair use” provision that would better fit the European legal tradition. In order to do so, the paper analyses in detail the judicial application of the freedom of expression’s test of proportionality to IP disputes. It further demonstrates that, by providing for a developed list of fairness factors analogous to those of the US fair use, the courts have developed appropriate and functioning criteria to assess the legality of a copyright use, which, once systematized, could serve as a European open-ended copyright limitation. Since in Europe a clause analogous in openness and flexibility to the US fair use provision is lacking, the article advocates the legislative incorporation of an open-ended clause grounded in freedom of expression in EU copyright law in order to enhance clarity, transparency and legal security, and concludes with a drafting proposal for such a provision
Development and application of a free energy force field for all atom protein folding
No full textProteins are the workhorses of all cellular life. They constitute the building blocks and the machinery of all cells and typically function in specific three-dimensional conformations into which each protein folds. Currently over one million protein sequences are known, compared to about 40,000 structures deposited in the Protein Data Bank (the world-wide database of protein structures). Reliable theoretical methods for protein structure prediction could help to reduce the gap between sequence and structural databases and elucidate the biological information in structurally unresolved sequences. In this thesis we explore an approach for protein structure prediction and folding that is based on the Anfinsen’s hypothesis that most proteins in their native state are in thermodynamic equilibrium with their environment. We have developed a free energy forcefield (PFF02) that locates the native conformation of many proteins from all structural classes at the global minimum of the free-energy model. We have validated the forcefield against a large decoy set (Rosetta). The average root mean square deviation (RMSD) for the lowest energy structure for the 32 proteins of the decoy set was only 2.14 from the experimental conformation. We have successfully implemented and used stochastic optimization methods, such as the basin hopping technique and evolutionary algorithms for all atom protein structure prediction. The evolutionary algorithm performs exceptionally well on large supercomputational architectures, such as BlueGene and MareNostrum. Using the PFF02 forcefield, we were able to fold 13 proteins (12-56 amino acids), which include helix, sheet and mixed secondary structure. On average the predicted structure of these proteins deviated from their experimental conformation by only 2.89 RMSD.Proteine sind die nano-skaligen Maschinen der Zelle. Sie sind Bausteine und Funktionseinheiten aller Zellen und funktionieren typischerweise in spezifischen dreidimensionalen Konformationen, die sie als Endpunkt eines komplexen Faltungsprozesses annehmen. Gegenwärtig sind über eine Million Proteinsequenzen bekannt, es konnten jedoch nur etwa 40.000 Strukturen von Proteinen aufgelöst und in der Proteindatenbank hinterlegt werden. Verlässliche theoretische Methoden zu Proteinstrukturvorhersage könnten helfen, diese Lücke zwischen den Sequenz- und den strukturellen Datenbanken zu schließen und die biologische Information in den bislang strukturell unbekannten Proteinen zu entschlüsseln. In dieser Dissertation untersuchten wir einen Ansatz zur Proteinstrukturvorhersage und -faltung, der auf Anfinsons thermodynamischer Hypothese aufbaut, nach der sich Proteine in ihrem nativen Zustand im Gleichgewicht mit ihrer Umgebung befinden. Wir entwickelten daher ein Kraftfeld für die freie Energie von Proteinen (PFF02), das die nativen Konformationen vieler Proteine aller bekannten Strukturklassen als das globale Minimum des Modells der freien Energie beschreibt. Wir haben dieses Kraftfeld gegen die Strukturen des Rosetta Testdatensatzes getestet und fanden, dass die Strukturen mit der jeweils niedrigsten Energie für 32 Proteine dieses Datensatzes im Mittel nur 2,14 Å von der assoziierten experimentellen Konformation abwichen. Wir haben darüber hinaus stochastische Optimierungsverfahren, unter anderem die Basin-Hopping Methode und evolutionären Algorithmen, für die Proteinstrukturvorhersage und - faltung mit atomarer Auflösung entwickelt. Insbesondere der evolutionäre Algorithmus lieferte auf großen Supercomputern, wie zum Beispiel den BlueGene oder MareMonstrum Supercomputer- Clustern, hervorragende Ergebnisse. Mit dem PFF02 Kraftfeld waren wir in der Lage, 13 Proteine mit 12-56 Aminosäuren Länge mit helikaler, Faltblatt- oder gemischter Sekundärstruktur zu falten. Im Mittel wichen dabei die vorhergesagten Strukturen von den jeweiligen experimentell bekannten Strukturen dieser Proteine um nur 2,89 Å RMSD ab
Theoretische und experimentelle Untersuchungen zur Struktur von Wasser in unterschiedlichen Lösungsmitteln
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