The Enactive and Interactive
Dimensions of AI: Ingenuity and
Imagination Through the Lens of
Art and Music
Maki Sato*
Universität Tokio
Graduate School of Arts and
Wissenschaften
The New Institute
maki.sato@aya.yale.edu
Abstract Dualisms are pervasive. The divisions between the
rational mind, the physical body, and the external natural world
have set the stage for the successes and failures of contemporary
cognitive science and artificial intelligence.1 Advanced machine
learning (ML) and artificial intelligence (AI) systems have been
developed to draw art and compose music. Many take these facts as
calls for a radical shift in our values and turn to questions about AI
ethics, rights, and personhood. While the discussion of agency and
rights is not wrong in principle, it is a form of misdirection in the
current circumstances. Questions about an artificial agency can only
come after a genuine reconciliation of human interactivity, Kreativität,
and embodiment. This kind of challenge has both moral and
theoretical force. In diesem Artikel, the authors intend to contribute to
embodied and enactive approaches to AI by exploring the
interactive and contingent dimensions of machines through the lens
of Japanese philosophy. One important takeaway from this project
is that AI/ML systems should be recognized as powerful tools or
instruments rather than as agents themselves.
Jonathan McKinney
University of Cincinnati
Schlüsselwörter
Japanese philosophy, enactivism, AI
ethics, interactivity, contingency,
embodiment
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1 Einführung
Foundational theories of artificial intelligence (AI), following cognitive scientists, assume that the
content and capacities of the machine and its programming are essential for intelligence. The fruits
of these assumptions are the modern computers and myriad algorithms that permeate our world.
Machine learning (ML) systems are powerful content generators that can compile astounding data
sets and generate output from the given data set through either supervised, reinforced, or unsuper-
vised learning processes. As technology advances and methods become more precise, these systems
will likely become easier to use, faster, and less prone to disconcerting errors.
While early machines could generate and respond to input, they were criticized for only be-
ing capable of basic forms of processing (Turing, 1950). Jedoch, noteworthy advancements have
been made through the combination of ML systems and embodied approaches to AI and robotics.
* Korrespondierender Autor.
1 This sentence briefly introduces the line from Cartesian mind–body dualism, “I am thinking therefore I exist” (Descartes, 1637/2006,
P. 28) and its relation to cognitive science and artificial intelligence. In Ecological Psychology this is referred to as a “trialism” that
divides the brain, der Körper, and the external world.
© 2022 Massachusetts Institute of Technology Artificial Life 28: 310–321 (2022) https://doi.org/10.1162/artl_a_00376
M. Sato and J. McKinney
The Enactive and Interactive Dimensions of AI
Although embodied and socially integrated AI systems may still fall short of human levels of cre-
ativity and intelligence, they represent radical improvements in the kinds of tools and instruments
humans have available to them. Instead of viewing the inability of ML and AI systems to replicate
human creativity as failures to be overcome, they should be viewed as monumental successes in the
expansion of human expression (Flowers, 2019). To trace the path away from increasingly com-
plex ML algorithms toward embodied and enactive forms of Artificial Life, we will focus on the
importance of interactivity and contingency for creating art and music through the lens of modern
Japanese philosophy.
1.1 From Embodied to Enacted AI
The enactive paradigm in cognitive science began as a response to the cognitivist and Eurocentric
dogmas in the fields of philosophy, cognitive science, and artificial intelligence. In the inaugural text
The Embodied Mind, Varela et al. (2017) challenged the cognitivist assumptions of mind–body and
body–world dualism by grounding cognition in experience through the lived body.2 Early enactivists
saw a discontinuity between the models of cognition, intelligence, and living things and sought to
build a path forward based on our phenomenological experiences. The hallmark of a living system
for early enactivists was autopoiesis, or self-making, where organisms create and maintain them-
selves against environmental forces, like a cell that generates a cell wall to survive (Beer, 2004; Aus
Paolo & Thompson, 2014). These systems are considered operationally closed in that they continue
to function even when the context changes. These ideas have been developed and now persist as
Autonomie, sense-making, and agency, where agents remain autonomous in the face of environ-
mental forces and actively participate in the construction and maintenance of both themselves and
their world (Di Paolo et al., 2017). Das ist, they enact their world through their exploration and
interactions with the world they are embedded within.
As a key player in the embodied turn in cognitive science, the enactive approach has also in-
fluenced the turn toward embodiment in AI (Aguilar et al., 2014; Froese & Taguchi, 2019; Froese
& Ziemke, 2009; Pfeifer & Bongard, 2006). Where classical approaches to AI (GOFAI) define
the challenges of Artificial Life in terms of computation and intelligence, embodied AI theo-
rists see these challenges in terms of action, perception, and interaction (Anderson, 2003; Brooks,
1991A,B; Brooks & Stein, 1994). While many predicted that the turn toward functional robots would
inevitably lead to a genuine AI system, Brooks and other roboticists argue that even the most
advanced robots have only captured the intelligence of insects or other simple organisms. Das
is at least partially due to the vast differences in the information available to robots and living
Dinge. Whereas living things have long evolutionary histories and access to a vibrant sensory world,
machines and algorithmic systems have limited access to sensory data and are developed over a
relatively short time span.
While it is a brilliant feat of engineering to create a machine that is as capable of navigating
and flying as a dragonfly, there is more to life than maneuvering. Enactivists agree with embodied
AI theorists like Brooks that intelligence depends on having access to a world but add that life
depends on the agency and the autonomy to do otherwise (Di Paolo, 2004; Di Paolo & Thompson,
2014). When a living system is pushed by a harsh wind or pulled by a grappling undertow, it can
attempt to resist them or shape them to its advantage. Unlike living systems, nonliving systems
like rocks and toasters must simply endure the forces that surround them. While we can interact
with a toaster and it can produce an output in response, it cannot choose to overcome an input
command. This is crucially important because it establishes autonomy as a clear line between even
the most complex machines and basic living systems. The enactive approach to AI is promising
because it builds from the successes of embodied AI and provides a novel path forward. Echoing
the enactive critique of AI, we argue that there are crucial distinctions between ML and AI systems.
Wichtig, ML systems often overlook the success of embodied AI and robotics and oversell their
2 Wichtig, this article will focus on the branch of enactivism that is founded on the works of Maurice Merleau-Ponty (sehen
McKinney et al., 2021) and not the radical enactivism of Hutto and Myin (2012).
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The Enactive and Interactive Dimensions of AI
computational capabilities. Despite being powerful and complex machines, it is not clear that they
ever truly understand or know anything.
If our goal is to create a genuine AI system, then we should follow the enactive path and focus
on how machines can act and interact autonomously with their world and with other agents. To that
end, we hope to contribute new perspectives on interactivity between agents and their world from
the works of the modern Japanese philosophers Nishida Kitar¯o, Kuki Shuz¯o, and Watsiju Tetsur¯o.
Our hope is that these perspectives will be tools that will help contextualize the shortcomings of
ML and robotic systems in terms of their abilities to act as genuine interlocutors, rather than merely
as systems capable of generating novel output. While novel and complex output may seem like
intelligence to us, we should be skeptical of it without accompanying evidence of agency.
2 Problems with AI and ML Systems
The enactive and embodied approaches to AI have shifted the burden of proof in AI theory to opti-
mists who argue that advanced AI is imminent. Brooks and Stein (1994) established the importance
of modeling AI systems based on the only known examples of intelligent beings: living things.
This work continues today through the work of AI and ML critics like Birhane (2021), who identify
the many dangers of the careless deployment of AI based on the hype of technological advancement
(Kaltheuner, 2021; Taylor et al., 2021).
While the embodied turn toward Artificial Life has produced many highly functional robots, Es
has also produced new forms of techno-optimism. Artificial neural networks, Zum Beispiel, resemble
neurons in name only, yet are described as if they are parts of an artificial brain (Birhane, 2021).3 Es
seems that techno-optimism has motivated ML theorists to insist that ML systems are approaching
human-level capabilities. Advanced language and artistic machines are described as being capable of
holding a conversation or composing music. These interpretations, like the techno-optimistic posi-
tions before them, misunderstand what living systems are doing when they use language or create
Kunst. They are not creating novel linguistic or symbolic content but are expressing their autonomy
through their relationship to other agents and their world. Without clear evidence that ML systems
are capable of autonomously interacting with others and their world, the burden of proof of their
intelligence remains unmet.
Generative models like generative adversarial networks (GANs) and generative pre-trained trans-
formers (GPTs) are possible counterexamples that create novel content that is difficult to trace back
to mere input. This means that these systems seem to be creative and to have some kind of agency.
Infolge, one could argue that the poetry, prose, and music produced by GANs or GPTs are
works of art. Für unsere Analyse, these systems also embody some form of contingency that is crucial
for living systems. Although these systems produce texts, Bilder, and music that are high enough
quality for it to be almost impossible to determine whether the work is generated by humans or
machines, we argue that differences remain between living and nonliving systems. Whereas living
systems fundamentally possess the abilities to relate and interact with others and their surrounding
Welt, algorithmic systems do not. Simply attempting to trick human beings into accepting that ma-
chines are intelligent by focusing on intelligent-seeming output is insufficient for meeting Dreyfus’
burden of proof (Dreyfus, 1992). This is because the farther algorithmic systems stray from embodi-
ment and robotics, the less capable they become. Human beings are not defined by their ability to
create enigmatic musical combinations, but by our ability to navigate and shape our world together.
2.1 Returning to Turing
Given the similarities in arguments regarding interactivity and autonomy (in behavior) zwischen
ML rhetoric and Turing’s approach to AI, we will explore them together. Taking inspiration from
3 For further information, see Shpurov and Froese (2021).
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Turing’s (1950) central question—“Can machines think?”—we hope to delve into the criticisms of
AI more directly with the question, “Can machines create art and music?” While our argument will
not unravel the many issues facing ML/AI systems from engineering aspects, it will help carve a
path toward more fruitful ways of thinking about intelligent machines and their roles in our lives.
2.1.1 Can Machines Create Art and Music?
If this is an empirical question, then the answer is surely “Yes.” Machines already produce things like
technically challenging music, abstract digital paintings, realistic digital landscapes, and humorous
dialogues. If art is the content of the piece, then anything capable of producing melodic sounds,
beautiful color pallets, or engaging prose would be capable of creating art. In der Tat, digital art- Und
music-creating machines are capable of creating art and music in some sense (Weinberg et al., 2020).
The force of the objections to the artistic abilities of machines is not that they struggle with the
creation of artistic content, but that they are not the genuine inventors of the work. Machines
may produce art, but they are not artists who instinctively communicate with their surroundings
and intuitively create sensational art and mind-blowing music. Unlike an artist, machines are not
autonomous, meaning that they are limited in the ways that they can respond to input from other
agents or the environment. They may be able to act and react, but they lack the ability to make sense
of their world through their interactions with other agents (De Jaegher & Di Paolo, 2007).4
Turing frames this kind of problem in terms of an argument from consciousness (which seems
to inspire contemporary defenders of AI), that this is a matter of the quality of the works of art
(Turing, 1950). The argument from consciousness challenges the idea that machines can be intel-
ligent because machines lack the essential abilities to be creative and experience emotions. Turing
responds by claiming that these kinds of problems will be solved by engineers in that they will be
able to create machines that are powerful enough to emulate this feeling, which is echoed by theo-
rists today. This is evidenced by a comprehensive meta-analysis of terms used in ML/AI research by
Birhane et al. (2021), where terms like productivity, efficiency, and output completely overshadow
discussions of ethics, Autonomie, and interactivity.
This leads ML/AI researchers to frame the challenge of creating art in terms of the differences
between a skilled painter and an inexperienced novice playing with paint. It is useful for ML and
AI theorists to approach issues in this way because this escapes the entangling web of theory and
becomes an engineering problem. As the training data increases, the quality and accuracy of the work
will increase, meaning that it is only a matter of time before machines create art and music or become
experienced artists and musicians. The results of this approach to AI have produced countless
innovations in programs capable of generating digital images, producing music, and completing
unfinished drafts of prose, usw. For Turing and his followers, the argument from consciousness fails
because asking for more than the high-quality output from an author is more than we require of
ourselves and other humans (Turing, 1950). After all, we also lack good evidence for the intelligence
of human playwrights beyond their works and their human-like behavior.
This remains useful for differentiating between skilled and unskilled artists, but it falls apart
because it fails to account for the embodied relationality of artists. Simply, there is more to a piece
of art and melody than what is on the canvas and the music sheet. The artists behind a work relate
to the world in a way that we can experience for ourselves. Artists change in real-time and through
myriad life experiences through exposure to otherness, and we can experience these changes in
some sense for ourselves. It may seem like a fine line between the evolution of an artists’ style
or shift of their perspective and the iterative updating of an ML system, but there are important
distinctions to be made between what artists are doing and how ML systems function. Whereas
artists have experiences and develop expertise, ML systems merely produce augmented copies of
their input data.
4 For further information, see also Dotov and Froese (2020) and Fletcher-Watson et al. (2018).
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The terms machine learning and artificial intelligence are misleading because even the most advanced
language programs do not learn or develop outside of the one medium they receive input from.5
This is evidenced by their inability to resist environmental input, choose to do otherwise, or es-
cape the highly specialized worlds they are designed for.6 For example, a music generation program
cannot do anything but process its input and generate an output. It cannot leave its data set and
explore another one. It lacks a body, a world to explore, and the freedom to augment its surround-
ings. Learning is one multimodal activity among many that comprise our lived experiences through
our bodies. There simply are no living things outside of science fiction that lack a body and access
to a world. Zusamenfassend, sensory inputs from the bodily movement are a necessity for a multimodal
perception to change the mode of perception crucial to interactivity.
Instead of claiming that machines create art or that machines could become artists, we should
think of machines as powerful tools that define an artistic medium.7 This is beneficial for several
Gründe dafür. The first is that it properly situates ML systems within the history of technological innova-
tions that have transformed the mediums of artistic expression. Much like the impacts of keyboards
and soundboards on musical composition, ML systems make new kinds of musical engagement
möglich. Simply put, machines are not built to live and have experiences, so it is unclear how they
could express anything. Daher, instead of machine painters and musicians, we have machines that
are akin to enigmatic digital tools.
3 The Lens of Japanese Philosophy
Two shortcomings of disembodied approaches to ML and AI systems become clear when viewed
through the lens of modern Japanese philosophy.8 We will begin with the works of Nishida Kitar¯o,
who founded the Kyoto School and influenced the works of Kuki and Watsuji, whose works we
will focus on in the next section. The world as pure experience (junsui keiken,
) und das
frame of action-intuition (k¯oiteki chokkan,
) in the works of Nishida Kitar¯o demonstrate
two ways to view the agent-world relationship that current approaches to AI lack.9 The first man-
ner of viewing the agent-world relationship views the sensory world as rich and unimpoverished,
which is in contrast with how the world is characterized by cognitivist cognitive scientists.10 The
second is the active interactivity of embodiment, or action-intuition (k¯oiteki chokkan). Whereas AI
systems with robotic bodies can perceive in order to act, for human beings, acting and perceiving
are mutually reinforcing processes.11 Without moving bodies, oscillating eyes, and postural sway,
the human vision would be fundamentally different (Käufer & Chemero, 2021). Mit anderen Worten,
constant movement enables human beings to act in resonance to the world that they are thrown
into. While these theoretical frames do not completely capture the limits of artificial systems alone,
they can act as initial steps to re-center human experience in AI.
3.1 Watsuji, Kuki, and Machine Spontaneity
In order to understand the challenges ML and AI systems currently face, we will draw from the
works of Watsuji Tetsur¯o and Kuki Sh¯uz¯o, which explore what it means to be human in terms of
interactivity, contingency, and spontaneity. Watsuji argues in his Ethics (Watsuji, 1996, P. 9) that “the
essential misconception prevalent in the modern world” is that it conceives “ethics as a problem of
individual consciousness only.” Watsuji argues that although individualism itself is “an achievement
5 For comprehensive accounts of how the terms artificial intelligence and machine learning are misleading given the current state of
their fields, see Fake AI (Kaltheuner, 2021).
6 For an enactive account of the challenge of meaning and learning in artificial systems, see Froese and Taguchi (2019).
7 For more about reframing the successes of ML systems in this way, see Flowers (2019).
8 For other perspectives on ML and AI systems using non-Western comparative philosophy, see Sato (2020).
9 Refer to McKinney et al. (2021) for details on Nishida’s philosophy.
10 For an introduction to the embodied critique of the assumed poverty of the stimulus in cognitivist cognitive science, see Käufer and
Chemero (2021, S. 181–222). See also Di Paolo et al. (2017) for an enactive response to the challenge of impoverished coupling.
11 Hier, the authors are differentiating an AI system that runs solely on a computer and an AI system attached to a robot that enables
surrounding environmental perception through its sensors.
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of the modern spirit,” such a notion only represents the “standpoint of the isolated ego” and that
it lacks the notion of the “totality of ningen (humans).” For Watsuji, the “totality of ningen,” first
and foremost, includes the importance of connections, interactions, and interplay that happen con-
sciously and unconsciously between beings. This mutual interaction is not merely between agents
but extends to the agent-environment relationship (Watsuji, 1935/1988). Watsuji argues that hu-
mans are inherently situated in the “in-betweenness of person and person”; daher, he points out,
“the locus of ethical problems lies not in the consciousness of the isolated individuals” (Watsuji,
1996, P. 10).
Watsuji’s initial interest in what it means to be human, inspired by Heidegger’s (1927/1962)
argument made in Being and Time, was concerned with the specific problem of how humans interact
with space.12 This interest culminated in Watsuji’s Climate and Culture (1935/1988). Jedoch, in his
later works, he expanded his argument to the dynamism of inherent human–human interactions.
The importance of such interactions is the subject of his Ethics. The challenges that ML/AI sys-
tems currently face is not surprising when considered through Watsuji’s philosophical works. Viele
AI/ML systems are designed to process a particular kind of input or experience, which limits their
capacities for dynamism and complex interactions with other agents.13
In der Tat, ML systems only have access to certain data sets and thus can only produce conditional
responses based on that input. Jedoch, as long as data sets are static, in the sense that those data
are acquired from past events, the interactions between ML/AI and humans lack in their dynamism.
This can be characterized using temporality and spatiality, where agents can both react to their spatial
surroundings and interact meaningfully with other agents in real-time.14 Living beings are not only
responding to their visual surroundings or the movements of others but are deeply immersed in
the multimodality of experience that is social, visuell, and contextual. One example of this is how
living beings are capable of immediately predicting how other beings are going to respond and
then act accordingly. Watsuji’s concept of in-betweenness captures this relationship in terms of the
spontaneous interplay between living things.
Although there might be machines that are capable of more complex interactions, like swarm
robots, these systems still lack access to an evolutionary history of information that shapes how
they perceive, Akt, and interact with their surroundings.15 Swarm robots represent a step in the right
direction toward developing machines that can relate to their immediate surroundings in lifelike
ways, Aber, as Brooks (1999) reiterates, until these machines can pull from a lifetime of experiences
or rely on a body that has co-evolved with its environment and other beings, these machines will
fall short of human-level capabilities (Brooks, 1999). As a result of the bodies we have and the
developmental and evolutionary histories, humans can do more than merely dynamically respond to
our environment.16
4 Interactive and Musical Machines
Despite the lack of spontaneity and interactivity in many machines, there are some machines that
seem more interactive than others. While digital art-generating machines may function as an artistic
medium or tool, musical systems seem to be more interactive. Visual art creation, in that sense, nur
12 This mirrors the approach taken by many embodied AI theorists, which focuses on how machines navigate their surroundings and
then attempt to scale them up to deal with challenges of coordination.
13 Heidegger (1954/1977) points out in his essay “The Question Concerning Technology” that “technology is a means to an end,”
and simultaneously “technology is a human activity” (P. 4). Jedoch, he argues that when the technology (techn¯e) is used appropri-
ately it reveals (Entbergen); “Techne belongs to bringing-forth, to poiesis; it is something poietic” (S. 5–13).
14 For arguments in regard to body and mind relationship from a philosophical viewpoint, see Yuasa (1987).
15 Swarm robots are an interesting case because they are systems with advanced coordination and interaction capabilities that resem-
ble the dynamics of living things. While these systems are especially good for tasks with clear targets or “prey,” such as foraging, Sie
struggle with more general tasks. This is what sets them apart from living systems, which often wander or act without a precise
target or goal. Spontaneity is an informative way to differentiate living and machine systems, where both act and interact to some
extent, only living systems act spontaneously as we define it. See Brambilla et al. (2013, S. 34–37).
16 For an account of how human beings interact with our environment using dynamical systems theory, see Beer (1995).
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touches upon spatiality, and thus could be the outcome of the static work of data sets that may
lack the dynamism of in-betweenness. Jedoch, musical performance is more dynamic and thus
requires beings to act spontaneously while being immersed in the spatiality and temporality of the
Erfahrung. There have been attempts to make musical robots that generate music in more reactive
and collaborative ways.17 This shift from disembodied ML systems that generate music to embodied
robotic musicians is crucial because it enables these systems to engage with others and their worlds
more spontaneously.
Human musicians do not generate music in a sensory deprivation chamber after memorizing the
wave patterns of thousands of songs. They listen to, and are moved by, Musik. From their place
in a musical world, immersed in musical cultures and experiences, they express themselves through
instruments and lyrics. For AI engineers, one challenge of overcoming the limitations of music
generators involves incorporating perception.
Listening to music in ways that humans listen, rather than as data sets, is crucial for understanding
both what music is and what it means to create it. Likewise, ML systems cannot simply be scaled
up to replicate human behaviors because humans are not a system of mere reflexes and responses.
Birhane (2021) rightly argues that it is misleading and harmful for AI researchers to claim that ML
systems can automate human behavior and simulate human becoming (Birhane, 2021). For Birhane
and van Dijk (2020), philosophical questions about the humanity of ML/AI systems are eclipsed
by concerns about the harm of misleading narratives in ML/AI research. One moral takeaway of
our project is that we should properly situate our expectations and arguments in our world, welche
then must center on notions of embodiment. This does not address the myriad problems of ML/AI
Forschung, but it is a nontrivial first step toward more humane ML/AI research.18
Weinberg et al. (2020) have made promising progress by incorporating principles of embodi-
ment, interactivity, and improvisation in their machine musician project at the Georgia Institute
der Technologie. One prime example is Shimon, an improvising marimba-playing ML/AI system
equipped with a robotic body. It is capable of music generation through perception (seeing and
listening to other players through a sensory system embedded in its robotic body) that enables per-
ceptive, expressive, and improvisational music playing with humans. With its robotic embodiment,
while generating reactionary music, it can search for social and environmental cues and create its
own cues for others to follow. While Shimon lacks the agency of a human musician, this approach
is far more advanced and qualified as a musical performer than mere music generator programs. Wenn
the goal of AI is to achieve human-like capabilities, then we should focus on creating robots that
are capable of social perception and assistance. This will require a return to embodied robotics with
a built-in sensory system in conjunction with an ML/AI system.
Weinberg et al. (2020) explain the importance of limits for embodied musical machines, welche
have downstream implications for how ML/AI systems will be developed, how they impact our
Leben, and our expectations of AI. There is a seemingly paradoxical relationship between the limita-
tions of bodies and how those limits enable action. While an arm is limited in its degrees of motion,
these limits provide the necessary structure and coherence to its movements. Its limits reduce the
cognitive load of moving, which makes it easier to move in precise and skillful ways. This is a core
takeaway from the embodied turn in cognitive science and artificial intelligence. The body shapes
thinking, and thinking is bound and realized through our embodiment (Pfeifer & Bongard, 2006).
When thinking is divorced from the body, then critical elements are lost. By focusing on the com-
putational power of the brain, we were able to create computers and calculators, but this also made
simple problems like building a robot that could lift a cup much more difficult.
Thinking of limits in this way is crucial for avoiding problematic futurist tendencies in the field,
which often associate improved performance as evidence of nearing genuine artificial general in-
telligence (AGI). Chalmers (2020) addresses this concern directly when considering the conscious-
ness of GPT-3, a state-of-the-art language generator with billions of parameters, by comparing its
17 For a comprehensive account of robotic musicianship see Weinberg et al. (2020).
18 The Humane AI project initiated at the East-West Center suggests that “humane AI” connotes an ethical dimension in some sense.
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Figur 1. The place of contingency (adapted from Kuki, 2012).
consciousness to that of a worm. It is possible that the worm and GPT-3 are conscious only in
some limited sense. In addition to theoretical concerns of consciousness and intelligence, es gibt
practical differences between disembodied language programs like GPT-3 and worms: the abilities
to relate and interact with others and their world. Whereas the worm and robots like Shimon can
explore and manipulate their environments and interact with other agents in some sense, GPT-3 is
completely cut off from the social, contextual, and perceptual dimensions of the world.
5 A Path Forward Through Contingency and Coincidence
When considering what it would take to make a machine that is more than a mere tool, Kuki Sh¯uz¯o’s
notions of contingency and coincidence paint a clear path forward. Kuki worked on the problem
of contingency throughout his life to tackle the issue of causality. Mit anderen Worten, his concern was
to overcome the temporal constraints of a given lineage or fate based on a rigid causality. To over-
come rigid causality, one must react spontaneously to one’s contingent circumstances. Wie gezeigt in
Figur 1, a modified version from Kuki’s own drawings, Kuki argues that the past is the inevitable
necessity (hituzensei,
) that has already happened as a consequential outcome of causality,
whereas the future is the possibility (kanousei,
) where nothing has yet unfolded thus no one
can know what could happen (Kuki, 2012). daher, the “present” is the only possible moment
where there can be an accidental or an unpredictable encounter. He argues that only with such
a contingent encounter could a choice be made between multiple paths or between various ways
of responding to one’s circumstances that could then create new possibilities of opportunities in
the future (Kuki, 2012). Zusamenfassend, we can only overcome contingencies right here and now in the
present.19
Kuki (2012) argues that contingency is not something that can be analyzed in the past, nor
that could be anticipated in the future, since contingency is contingent only in its “present-ness
(genzaisei,
).” Kuki further argues that “if we are to give meaning to the contingency
of the present, we need to understand the contingency through the possibility of the future”
and that “only in the futuristic moment yet to come, contingency would be given its meaning”
(S. 228–374). Mit anderen Worten, while the dynamism of human interactions is embedded in their
shared circumstances, it empowers them to overcome these circumstances together. Zum Beispiel,
one musician playing a riff in response to a melody performed by others is an act of spontaneous
creativity that is made possible through their mutual contingency.
For Kuki, arts in general (not limited to that of paintings, Musik, and performances) connote
contingency in their structural characteristics. Tatsächlich, when something is considered art, it subjugates
19 Kuki got his initial philosophical ideas on contingency and coincidences from Aristotle’s argument on contingency, while con-
) argued in Encyclopaedia (Hegel, 1830/1975). Although Kuki touches upon
sidering Hegel’s notion of necessity/inevitability (
several Buddhist texts (Kuki, 2012, S. 224, 282), his inspiration is not derived from early Buddhism.
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Figur 2. Emotional dimensions of Temporality (adapted from Kuki, 2012).
contingency. daher, Kuki (2012) argues that art embodies and fulfills freedom since it is freed
from all things inevitable that are in the realm of necessity. With art being freed from the linear
temporality where contingency is constantly happening, people view art with awe and surprise that
brings about the excitement to those who encounter art in its “present-ness,” whether that art is
statisch (such as paintings and poems) or dynamic (such as music or drama) (Kuki, 2012).20
Kuki describes the temporality of experience as a creative and emotional relationship (sehen
Figur 2). When the route of the causality is obvious or under static conditions where humans
can easily predict what is going to happen, humans tend to be relaxed and bored, knowing what is
going to happen next. Jedoch, in dynamic conditions, when things are non-predictable, und da
is a possibility that things may happen outside the route of the stringent causality, then humans feel
anxiety and tension. This is because there is a hidden expectation of what might happen. Infolge,
the mutual contingency between agents is a source of excitement and surprise.
Kuki’s argument suggests that the current embodied ML/AI lacks a fundamental contingency
that may coincidentally occur. Although one might argue that complex systems like artificial neural
networks are capable of learning by processing their data sets because it is difficult for human
beings to predict and understand what output will follow from a particular input, it is clear that the
output will be limited to a predetermined medium.21 Music and language bots compute input of a
particular kind and can only produce an output of that kind. A music bot cannot produce prose or
poetry regardless of the quality of its musical output. Even the simplest forms of life and expression
can choose between passively experiencing and actively changing their experiences. Mit anderen Worten,
there is a fundamental absence of the sheer contingency that Kuki argues is the major source of
humans being moved and struck with works of art.
) and its aim (mokuteki,
Being a student of Edmund Husserl at the University of Freiburg, Kuki also argues that “without
Bewusstsein, time does not exist,” and that flow of time can only exist in between one’s will
(ishi,
) (Kuki, 2016, P. 9). In Kuki’s view, AI/ML systems lack
both discernable aims and the will to act. Even though embodied ML/AI systems look as if they
are improvising music with humans, they do not have the instinctive joy of playing along with
their music partners, or the ability to perform, or compose on their own. Das ist, they play music
only contingently and never spontaneously. Unlike ML/AI systems that are restricted to producing
output based on limited input and being activated by a human engineer, humans are constantly in
the midst of dynamic creativity, Werden, and changing (Birhane, 2021). Mit anderen Worten, Kuki’s
20 If the art loses the ability to inspire awe in the viewer who was once inspired by it, then it is the viewer that changes not the art
selbst. This phenomenon happens because of the excessive encountering with the specific art and the encounter with the specific art
becomes habitual. In this case the once thought of awe should be described as “discovery” as argued in the Gallese (2021).
21 If given stringent rules as in Go games (z.B., AlphaGo developed by Google DeepMind (https://www.deepmind.com/)), where there
is a limitation for the contingency to sneak in, it is easy for the ML/AI to achieve, predict, and adjust its hand, das ist, more easily
than for humans.
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argument suggests that without such constant interaction (temporality) based on the dynamic input
sensory data collected from the surrounding environment (spatiality), there will be no will or aim.
As Watsuji argues, humans are open-ended, and part of what it means to be human is to aid
and affect others, and thus be aided and affected by others. This kind of interactivity is not merely
conditionally responding to sensory inputs but involves sharing and shaping each other’s aims.
Daher, ML/AI systems are less analogous to living beings and closer to powerful and complex tools.
Watsuji’s work makes this clear through his insistence on the importance of the in-betweenness of
the agency. Jedoch, Kuki’s philosophy requires more than that of Watsuji through his focus on
temporality. An ML/AI agent must be capable of actively shaping its contingent reactions spon-
taneously in the present moment. To be creative, an agent must be free to change how it engages
with its circumstances. This is crucial, because the circumstances before us are always changing and
rarely analogous to repetitive data sets. Tatsächlich, Kuki argues that our experiences only come together
accidentally and never happen again in just the same way. Daher, living beings must be prepared for
new experiences, even when their circumstances seem familiar.
6 Abschluss
Abschließend, ML/AI systems can neither act nor truly interact. The Turing test is not only about
fooling humans, which is an arbitrary and relatively easy thing to do. It is about successfully interact-
ing with human beings (Turing, 1951/2004). This cannot be solved with increasing computational
power but must be hashed out through exploring the shared spaces of language and expression. Das
is a multimodal problem that calls for embodied and enactive approaches to solve. While intelligent
robots lack the vast lexical database of advanced ML/AI systems, they are capable of navigating
some aspect of our shared world, which is fundamentally important for human lived experiences.
They are becoming intelligent to some extent when there is a skillful interaction with others and the
world in a narrow context.
Our hope is to use the tools of Japanese philosophy to shed light on how easy it is to misun-
derstand ML/AI issues, and thus fail to understand how to address these misunderstandings. Der
purpose of our argument is not that art-creating machines can never be artists or are inherently
immoral, but that visions of futuristic disembodied AI systems are harmful and should be situated
more responsibly through the embodied and enactive approaches, where interactions are made visi-
ble to enhance communicable in-betweenness between humans and ML/AI systems. Jedoch, Wir
should think carefully about the impacts of uncritically creating and promoting machine-generated
art to people already facing social, cultural, and economic hardship. If the problem is that we want
more music, we can fund music programs, hire musicians, and standardize higher pay for perform-
ers. We also hope to encourage AI/ML theorists to embrace the successes of embodied AI and
robotics, which have created many powerful tools that augment our capacities of expression.
Außerdem, we want to acknowledge that there are important hurdles such as agency and inter-
subjectivity that ML/AI systems may never overcome. Kuki argues that the beauty and awe of art
lie in the “contingency” that is a source of freedom where creativity flourishes that leads to unpre-
dictable and unknowable reactions, which occur in the very in-betweenness of the subjects. Nach
alle, if machine art and music lack the “contingency” and the authentic interplay that coincidentally
occurs in-between embodied subjects, then they are the mere products of the machine’s designers.
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