| Anderson, M. L. (2003). Embodied cognition: A field guide. Articificial Intelligence, 149, 91–130. |
 
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| Added by: Mark Grimshaw-Aagaard 2/17/11, 7:55 AM |
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| Anderson critiques the AI framework 'sense-model-plan-act' (SMPA) as being insufficiently dynamic and not taking account of relevance. SMPA depends upon modelling an explicit representation of the world. How can one model for every potential situation and how does one decide which situations are relevant enough to be modelled as representations for future use? |
| Nagel, T. (1974). What is it like to be a bat? The Philosophical Review, 83(4), 435–450. |
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| Added by: Mark Grimshaw-Aagaard Last edited by: Mark Grimshaw-Aagaard 5/12/25, 2:14 AM |
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| "A Martian scientist with no understanding of visual perception could understand the rainbow, or lightning, or clouds as physical phenomena, though he would never be able to understand the human concepts of rainbow, lightning, or cloud, or the place these things occupy in our phenomenal world. The objective nature of the things picked out by these concepts could be apprehended by him because, although the concepts themselves are connected with a particular point of view and a particular visual phenomenology, the things apprehended from that point of view are not: they are observable from the point of view but external to it; hence they can be comprehended from other points of view also, either by the same organisms or by others. Lightning has an objective character that is not exhausted by its visual appearance, and this can be investigated by a Martian without vision. To be precise, it has a more objective character than is revealed in its visual appearance. In speaking of the move from subjective to objective characterization, I wish to remain noncommittal about the existence of an end point, the completely objective intrinsic nature of the thing, which one might or might not be able to reach. It may be more accurate to think of objectivity as a direction in which the understanding can travel. And in understanding a phenomenon like lightning, it is legitimate to go as far away as one can from a strictly human viewpoint." |
| Wingström, R., Hautala, J., & Lundman, R. (2022). Redefining creativity in the era of AI? Perspectives of computer scientists and new media artists. Creativity Research Journal, 36(2), 177–193. |
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| Added by: alexb44 Last edited by: Mark Grimshaw-Aagaard 2/19/25, 12:28 AM |
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| "We further apply Hayles’s theory (2017, pp. 31–32) to demonstrate that AI is a cognizer (i.e., an actor that can autonomously pursue a goal). It differs from noncognizer (i.e., a non-autonomous artifact such as a pen). Thus, researching AI from these perspectives is critical because it is a novel technology that can make decisions and change the process it participates in (cf. Mazzone & Elgammal, 2019)."
Hayles, N. K. (2017). Unthought: The power of the cognitive nonconscious. University of Chicago Press. doi:10.7208/chi cago/9780226447919.001.0001
Mazzone, M., & Elgammal, A. (2019). Art, creativity, and the potential of artificial intelligence. Arts, 8(1), 26. doi:10.3390/ arts8010026 |
| "The second perspective of computational creativity focuses on developing AI that is co-creative with humans. Human–AI co-creativity aims to blend the creativity of humans and AI in an interactive process “on a shared task in real time” (Karimi et al., 2020, p. 22). Such AI is capable of interacting with humans, learning, and adapting its functions in real time, and this interaction is also known as “human in the loop” (Chung, 2021). Thus, some consider it “an equal creative partner” to humans (Berman & James, 2018, p. 257) or a tool that can support the creativity of a human (Kantosalo & Toivonen, 2016). Research has shown that AI is capable of generating new ideas and inspiration for humans, providing knowledge that enhances humans’ creative abilities, overcoming fixated thinking and “blank canvas paralysis,” and inspiring individuals by presenting sketches of varying similarity (Kantosalo & Toivonen, 2016; Karimi et al., 2020; Maher, 2012)."
Berman, A., & James, V. (2018). Learning as performance: Autoencoding and generating dance movements in real time. In A. Liapis, J. J. R. Cardalda, & A. Ekárt (Eds.), International Conference on Computational Intelligence in Music, Sound, Art and Design (pp. 256–266). Springer. doi:10.1007/978-3-319-77583-8
Chung, N. C. (2021). Human in the loop for machine creativity. In 9th AAAI Conference on Human Computationand Crowdsourcing (HCOMP 2021), Virtual conference.arXiv:2110.03569
Kantosalo, A., & Toivonen, H. (2016). Modes for creative human-computer collaboration: Alternating and task- divided co-creativity. In A. Cardoso, V. Corruble, & F. Ghedini (Eds.), Proceedings of the Seventh International Conference on Computational Creativity (pp. 77–84). Paris, France
Karimi, P., Rezwana, J., Siddiqui, S., Maher, M. L., & Dehbozorgi, N. (2020). Creative sketching partner: An ana
lysis of human-AI co-creativity. In Proceedings of the 25th International Conference on Intelligent User Interfaces, 221–230. Cagliary, Italy. doi:10.1145/3377325.3377522
Maher, M. L. (2012). Computational and collective creativity: Who’s being creative? In M. L. Maher, K. Hammond, A. Pease, R. Pérez y Pérez, D. Ventura & G. Wiggins (Eds.), Proceedings of the Third International Conference on Computational Creativity (pp. 67–71). Dublin, Ireland: University College Dublin |
| Zhou, E., & Lee, D. (2024). Generative artificial intelligence, human creativity, and art. PNAS Nexus, 3(3). |
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| Added by: alexb44 Last edited by: alexb44 2/24/25, 3:06 PM |
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| "While generative AI has demonstrated the capability to automatically create new digital artifacts, there remains a significant knowledge gap regarding its impact on productivity in artistic endeavors which lack welldefined objectives, and the long-run implications on human creativity more broadly. In particular, if humans increasingly rely on generative AI for content creation, creative fields may become saturated with generic content, potentially stifling exploration of new creative frontiers." |
| "A simplified view of human creative novelty with respect to art can be summarized via two main channels through which humans can inject creativity into an artifact: Contents and Visuals. These concepts are rooted in the classical philosophy of symbolism in art which suggests that the contents of an artwork is related to the meaning or subject matter, whereas visuals are simply the physical elements used to convey the content (7). In our setting, Contents concern the focal object(s) and relations depicted in an artifact, whereas Visuals consider the pixel-level stylistic elements of an artifact. Thus, Content and Visual Novelty are measured as the pairwise cosine distance between artifacts in the feature space (see Materials and methods for details on feature extraction and how novelty is measured)."
7: Huang S, Grady P, GPT-3. 2022. Generative AI: a creative new world. Sequoia Capital US/Europe. https://www.sequoiacap.com/article/generative-ai-a-creative-new-world/ |
| "We define creative productivity as the log of the number of artifacts that a user posts in a month. Figure 1a reveals that upon adoption, artists experience a 50% increase in productivity on average, which then doubles in the subsequent month. For the average user, this translates to approximately 7 additional artifacts published in the adoption month and 15 artifacts in the following month. Beyond the adoption month, user productivity gradually stabilizes to a level that still exceeds preadoption volume. By automating the execution stage of the creative process, adopters can experience prolonged productivity gains compared to their nonadopter counterparts." |
| "Figure 1b reveals an initial nonsignificant upward trend in the Value of artworks produced by AI adopters. But after 3 months, AI adopters consistently produce artworks judged significantly more valuable than those of nonadopters. This translates to a 50% increase in artwork favorability by the sixth month, jumping from the preadoption average of 2% to a steady 3% rate of earning a favorite per view" |
| "The result shown in Fig. 1e highlights that average Visual Novelty is decreasing over time among adopters when compared to nonadopters. The same result holds for the maximum Visual Novelty seen in Fig. 1f. This suggests that adopters may be gravitating toward a preferred visual style, with relatively minor deviations from it. This tendency could be influenced by the nature of text-to-image workflows, where prompt engineering tends to follow a formulaic approach to generate consistent, high-quality images with a specific style. As is the case with contents, publicly available fine-tuned checkpoints and adapters for these models may be designed to capture specific visual elements from which users can sample from to maintain a particular and consistent visual style. In effect, AI may be pushing artists toward visual homogeneity." |
