As AI algorithms advance and produce surprising outputs, the question of imagination arises. Can we classify their output as imaginative? And what is their effect on human imagination? Apparently, algorithms follow Kant’s explanations on human imagination, thereby pushing us to update our understanding of imagination by taking into account the co-shaping between humans and their technologies. Such a new understating is offered in this article based on the theories of Don Ihde and Bernard Stiegler. With Ihde, imagination is conceived as (...) tightly linked to perception. Imagination evolves from the modernist task of seeking new points-of-view to a new mode in which technologies present imaginative layers of information on top of reality. With Stiegler, imagination is regarded as tightly linked to memory. He demonstrates how imagination complements memory, serves as a condition to memory/technology and at the same time how imagination itself is conditioned by memory/technology. In this article I develop a new synthesis: imagination as composed of perception and memory. This is the foundation of my layered model of digital imagination. In this model, the task of AI algorithms is the filling in of the layers with data. By producing endless possibilities, these technologies “automate” the Kantian “free play” of imagination, allowing us to examine more options and focus on the best of them. The model reserves the production of meaning to humans. Our role as humans is to generate meaning to the link between layers and establish new layers. (shrink)

Everyday Aesthetics, radicalizing Dewey’s notion of the continuity between art and experience, aims to find aesthetic qualities in ordinary experience. The problem is that it reduces the aesthetic significance that Dewey attributed to artistic production. Analyzing Dewey’s work and its interpreters, I will demonstrate that the continuity of ordinary experience and art is what lends art its vital and distinctive character. The work of art contributes to developing other ways of seeing and acting in the world, reinforcing life in common, (...) the basis of democracy. (shrink)

To be intrinsically valuable means to be valuable for its own sake. Moral philosophy is often ethically anthropocentric, meaning that it locates intrinsic value within humans. This paper rejects ethical anthropocentrism and asks, in what ways might nonhumans be intrinsically valuable? The paper answers this question with a wide-ranging survey of theories of nonhuman intrinsic value. The survey includes both moral subjects and moral objects, and both natural and artificial nonhumans. Literatures from environmental ethics, philosophy of technology, philosophy of art, (...) moral psychology, and related fields are reviewed, and gaps in these literatures are identified. Although the gaps are significant and much work remains to be done, the survey nonetheless demonstrates that those who reject ethical anthropocentrism have considerable resources available to develop their moral views. Given the many very high-stakes issues involving both natural and artificial nonhumans, and the sensitivity of these issues to how nonhumans are intrinsically valued, this is a vital project to pursue. (shrink)

In this paper, I explore Derrida’s concept of exteriorization in relation to texts generated by machine learning. I first discuss Heidegger’s view of machine creation and then present Derrida’s criticism of Heidegger. I explain the concept of iterability, which is the central notion on which Derrida’s criticism is based. The thesis defended in the paper is that Derrida’s account of iterability provides a helpful framework for understanding the phenomenon of machine learning–generated literature. His account of textuality highlights the incalculability and (...) mechanical elements characteristic of all texts, including machine-generated texts. By applying Derrida’s concept to the phenomenon of machine creation, we can deconstruct the distinction between human and non-human creation. As I propose in the conclusion to this paper, this provides a basis on which to consider potential positive uses of machine learning. (shrink)

In two experiments (total N=693) we explored whether people are willing to consider paintings made by AI-driven robots as art, and robots as artists. Across the two experiments, we manipulated three factors: (i) agent type (AI-driven robot v. human agent), (ii) behavior type (intentional creation of a painting v. accidental creation), and (iii) object type (abstract v. representational painting). We found that people judge robot paintings and human painting as art to roughly the same extent. However, people are much less (...) willing to consider robots as artists than humans, which is partially explained by the fact that they are less disposed to attribute artistic intentions to robots. (shrink)

There is a well-documented Pre-Reflective Hostility against Machine Art (PRHMA), exemplified by the sentiments of fear and anxiety. How can it be explained? The present paper attempts to find the answer to this question by surveying a considerable amount of research on machine art. It is found that explanations of PRHMA based on the (alleged) fact that machine art lacks an element that is (allegedly) found in human art (for example, autonomy) do not work. Such explanations cannot account for the (...) sentiments of fear and anxiety present in PRHMA, because the art receiver could simply turn to human art for finding the element she is looking for. By contrast, an explanation based on the idea that machine art is “symbolically” a threat to human survival can be successful, since the art receiver’s turning from machine art to human art does not eliminate the (alleged) “symbolic” threat machine art poses for human survival. If there is a pre-reflective belief or feeling that machine art is such a threat, then it is perfectly understandable why humans exhibit a pre-reflective hostility against machine art. (shrink)