Abstract
The historical evolution of terms such as artificial intelligence and related concepts has perpetuated a misconception conflating machine intelligence with human cognition. Recent strides in generative AI technology have exacerbated this phenomenon by enabling AI systems to simulate human behaviors. The progress in generative AI has facilitated the production of convincingly human-like texts, images, music, videos, and other forms of media, thereby intensifying significant legal concerns regarding the safeguarding of intellectual property. Judicial precedents and legislative measures have begun grappling with issues surrounding AI-generated content, sparking debates on matters concerning copyright protections and liabilities for potential infringements. Key focal points of analysis include the copyright status of works generated by AI tools and the implications of their potential infringement on existing copyrights. Furthermore, these discussions underscore the need for further research to address emerging questions in this evolving field.
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Notes
- 1.
Some bestselling books, such as Nick Bostrom’s “Superintelligence” [1] and Ray Kurzweil’s “The Age of Spiritual Machines” [2], reinforce the public perception that computers will inevitably dominate the world. We discern these viewpoints as hasty and alarmist, contributing little to the broader understanding of the matter.
- 2.
To illustrate the algorithm’s functionality without delving deeply into mathematics, consider the autocorrect feature on a cell phone. When composing a message, this feature typically suggests three words based on the likelihood predicted by a simple model: each word typed influences the probability of the next suggested word. Selecting a suggested word increases its likelihood of being recommended in similar contexts. Thus, the autocorrect operates not by thinking, but by employing probabilistic and statistical principles as an algorithm to suggest and interpret contextual words [9].
- 3.
In the author’s words: “Once in use, successful AI systems were simply considered valuable automatic helpers [...] If you could see how it was done, people seemed to think, then it couldn’t be intelligence - a fancy that many people entertain to this day.” (op. cit., p. 423).
- 4.
Flach [13] organizes ML algorithms into six groups: binary classification, concept learning, tree models, rule models, linear models, and distance-based models. Murphy [14] presents a typology related to the underlying statistical procedures, including generative models, Gaussian processes, Bayesian methods, frequentist approaches, linear regression, logistic regression, Bayesian networks, latent variable models, sparse linear models, kernel methods, Markov models, state space models, variational inference, Monte Carlo methods, clustering, graphical models, and deep learning. Zaki and Meira [15] discuss Data Mining and ML together, categorizing models into four major groups: frequent pattern mining, clustering, classification, and regression. Neural networks and deep learning are included in the classification and regression groups in their book.
- 5.
Copyright is “the right to copy, spec., a property right in an original work of authorship (including literary, musical, dramatic, choreographic, pictorial, graphic, sculptural, and architectural works; motion pictures and other audiovisual works; and sound recordings) fixed in any tangible medium of expression, giving the holder the exclusive right to reproduce, adapt, distribute, perform, and display the work. [17]”.
- 6.
To demonstrate the idea to the reader, we have created a simple GUI using GPT 3.5 with the following simple prompt: “Generate html javascript code for a web page of a product catalog register form. Use Boostrap and Jquery. Make it beautiful and futuristic.” The resulting interface sample was neither beautiful nor futuristic unfortunately. Nevertheless, it could save a programmer hours of tedious boilerplate coding work, and some tweaking of the prompt could easily improve its aesthetics. The source code generated can be accessed at https://github.com/rvnovaes/epia2024/blob/main/index.html and the GUI itself on https://rvnovaes.github.io/epia2024.
- 7.
The CJEU ruled in paragraphs 92–93 that, “By making those various choices, the author of a portrait photograph can stamp the work created with his ‘personal touch’. Consequently, as regards a portrait photograph, the freedom available to the author to exercise his creative abilities will not necessarily be minor or even non-existent” [25].
- 8.
In this case it was decided (par. 45 et seq.) that the storing and subsequent printing of an extract from a newspaper article that contains the search word and the five words preceding and following it must be considered a reproduction within the meaning of Art. 2 of Infosoc Directive.
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Novaes, R.V., Ferraz, F.F. (2025). Protection of Copyrights in the Era of Generative Artificial Intelligence. In: Santos, M.F., Machado, J., Novais, P., Cortez, P., Moreira, P.M. (eds) Progress in Artificial Intelligence. EPIA 2024. Lecture Notes in Computer Science(), vol 14967. Springer, Cham. https://doi.org/10.1007/978-3-031-73497-7_27
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