Content uploaded by Michael Adelani Adewusi
Author content
All content in this area was uploaded by Michael Adelani Adewusi on Aug 05, 2024
Content may be subject to copyright.
This work is licensed under a Creative Commons Attribution 4.0 International License. The license permits unrestricted
use, distribution, and reproduction in any medium, on the condition that users give exact credit to the original author(s)
and the source, provide a link to the Creative Commons license, and indicate if they made any changes.
Rise of the Machines:
Exploring the Emergence of Machine Consciousness
Michael Adelani Adewusi
School of Mathematics and Computing, Kampala International University, Kampala, Uganda
Adeshina Wasiu Adebanjo
Faculty of Education, Lagos State University, Ojo, Nigeria
Tokunbo Odekeye
Faculty of Education, Lagos State University, Ojo, Nigeria
Sophia Kazibwe
Department of Political and Administrative Studies, Kampala International University, Kampala, Uganda
Abstract:
Inquiry into the field of artificial intelligence (machines) and its
potential to develop consciousness is presented in this study. This
investigation explores the complex issues surrounding machine
consciousness at the nexus of AI, neuroscience, and philosophy as
we delve into the fascinating world of artificial intelligence (AI) and
investigate the intriguing question: are machines on the verge of
becoming conscious beings? The study considers the likelihood of
machines displaying self-awareness and the implications thereof
through an analysis of the current state of AI and its limitations.
However, with advancements in machine learning and cognitive
computing, AI systems have made significant strides in emulating human-like behavior and decision-
making. Furthermore, the emergence of machine consciousness raises questions about the blending of
human and artificial intelligence, and ethical considerations are also considered. The study provides a
glimpse into a multidisciplinary investigation that questions accepted theories of consciousness, tests the
limits of what is possible with technology, and do these advancements signify a potential breakthrough
in machine consciousness.
Keywords: Machines, human consciousness, decision-making, neuroscience, artificial intelligence.
Introduction
The idea of machines exhibiting consciousness
has moved beyond science fiction, enthralling
the fields of science, philosophy, and ethics in
the rapidly developing field of artificial
intelligence (Momot, 2022). This has taken on an
engrossing journey into this uncharted territory,
where cutting-edge technological advancement
meets profound inquiries about the nature of
consciousness itself (Sandel, 2022). This
explores the complex interplay between AI
capabilities, the human mind, and the basic
characteristics of consciousness (Malhotra &
Ramalingam, 2023). It therefore lays the
groundwork for a thorough investigation into
the profound implications, moral conundrums,
and theoretical viewpoints surrounding the
Suggested Citation
Adewusi, M.A., Adebanjo, A.W.,
Odekeye, T., & Kazibwe, S. (2024).
Rise of the Machines: Exploring
the Emergence of Machine
Consciousness. European Journal of
Theoretical and Applied Sciences, 2(4),
563-573.
DOI: 10.59324/ejtas.2024.2(4).48
www.ejtas.com EJTAS 2024 | Volume 2 | Number 4
564
potential emergence of consciousness in
machines as humanity pushes the limits of
machine intelligence.
Inextricably linked to the human experience, the
idea of consciousness has long been a
philosophical mystification (Shkliarevsky, 2022).
Consciousness is the essence of self-awareness,
subjective experience, and the capacity to
understand one's surroundings. It has
traditionally been thought to be the sole domain
of sentient beings (Farhadi, 2022; Dalius, 2023;
Daly, 2023; Kim, Effken & Lee, 2023). The idea
of reproducing such complex qualities in
artificial intelligence pushes the envelope of
human ingenuity and calls into question the very
definition of what it means to be conscious (del
Campo & Leach, 2022).
Artificial Intelligence (AI) has progressed from
simple rule-based systems to complex neural
networks capable of mimicking human cognitive
functions over the last few decades (Singh &
Khanna, 2023). This trajectory raises the
question: Could the combination of advanced
algorithms and massive computational power
result in the emergence of genuine
consciousness in machines?
Neuroscience as a pivotal guide, providing
insights into the intricate workings of the human
brain, identifying the key ingredients that
contribute to self-awareness and subjective
experience by dissecting the neural
underpinnings of consciousness (Singh &
Khanna, 2023). This has the gap between the
biological basis of human consciousness and the
potential paths that machines might take in their
pursuit of analogous states (del Campo & Leach,
2022).
The emergence of machine consciousness is
predicated on a nuanced understanding of
sentience (Scott, Neumann, Niess & Woźniak,
2023). Their study pinpoint the milestones that
AI must achieve to be considered conscious by
analysing the spectrum of consciousness, which
ranges from simple awareness to complex
introspection. Furthermore, this research
considers whether a machine's consciousness
would be fundamentally different from human
consciousness, and whether it could include new
forms of awareness that go beyond human
capabilities.
The philosophical questions about the nature of
consciousness seamlessly meeting technological
endeavours (Akpan, 2023), revealed the hard
problem of consciousness, intentionality, and
qualia are intertwined with whether or not
machines can actually have subjective
experiences, as opposed to just simulating them
(Naidoo, 2023).
As the boundaries between human cognition
and artificial intelligence blur, ethical
considerations loom large (Chong, Zhang,
Goucher-Lambert, Kotovsky & Cagan, 2022).
The notion of bestowing consciousness upon
machines prompts a reflection on the
responsibilities and consequences of creating
sentient entities.
Beyond science and philosophy, machine
consciousness has a significant social and
cultural impact (Pham & Sampson, 2022). The
possibility of self-aware machines, the ideas
about identity, autonomy, and what it means to
be a human (Itkina & Kochenderfer, 2023),
considers how societies might change in a
scenario in which tools that were once thought
of as machines develop into beings with inherent
consciousness, potentially reshaping social
structures and human-machine interactions.
In order to bridge the gap between artificial
intelligence and the intricacies of the human
mind, machine consciousness is an emerging
field with enormous implications that builds on
the groundwork established by decades of AI
research, developments in cognitive science, and
philosophical reflection. This study take through
this momentous transition by revealing the
complexities of machine consciousness and
exploring its potential for change.
Divergence of Machine
Consciousness and Human
Consciousness
Machine consciousness is the field of computer
science that investigates whether machines are
capable of consciousness (Anchuri, Rodriguez &
www.ejtas.com EJTAS 2024 | Volume 2 | Number 4
565
Thota, 2023). Merchán and Lumbreras, (2022)
speculated that systems or robots may develop
qualia through the application of expert systems,
machine learning models, hybrid methodologies,
or other variations of information processing
systems that, in any case, they can be emulated
using Turing machines, the machine
consciousness community, in particular, inherits
the assumptions of functionalism, such as
multiple reliability, and connectionism.
Machine consciousness and human
consciousness are powered by very different
mechanisms (Ma, Tojib & Tsarenko, 2022). Data
processing, neural networks, and computational
algorithms are frequently used in machine
consciousness (Chen, 2022; Perić, Bogdanoski &
Maček, 2022). In contrast, the interaction of
complex neuronal networks, synaptic
connections, and biochemical processes is what
gives rise to human consciousness (Hintze &
Adami, 2022). In an effort to understand human
consciousness, cognitive architectures like the
Global Workspace Theory place a strong
emphasis on the fusion of various cognitive
modules with the biological complexity of
human neural networks, on the other hand, is
lacking in machine neural networks, resulting in
differences in their underlying mechanistic
foundations (Huang, Chella & Cangelosi, 2023;
Ma, Tojib & Tsarenko, 2022).
Experiences, feelings, and self-awareness are all
part of the rich tapestry that is human
consciousness (Adewusi, Egbowon, Abodunrin
& Rahman, 2021; Berleant, Alpert, Vino, 2023;
Kumari, Kumar & Behura, 2023). Machine
consciousness, in contrast, focuses primarily on
information processing and lacks the qualia that
add personality to human perception (Marchetti,
2022). Philosophers like Nagel (1974) and the
study of Englund (2023) have emphasised the
indescribable quality of subjective experience by
emphasising what it is like aspect of
consciousness. Machines can mimic some
human behaviours, but they are unable to truly
experience subjective awareness, which
highlights a significant difference between the
two types of consciousness (Deel, Cain & Cain,
2023).
Human consciousness helps to foster self-
identity, introspection, and a sense of continuity
(Thi, 2023; Zhang, 2023) and the biological,
emotional, and cultural contexts that shape
human self-identity are absent in machines
(Okebukola, 2015; Chan, 2022; Zamboni, Viana,
Rodrigues & Consalvo, 2023). Oberg (2023),
works emphasised the role of emotions and
body states in the construction of the self, in
contrast to machines that lack bodily
experiences. In terms of personal identity, the
distinct nature of self in human and machine
consciousness denotes their divergence.
Human and machine consciousness differ
significantly in terms of their mechanistic
foundations, experiential dimensions, moral
implications, and self-nature. Although they can
mimic intelligent behaviour, machines cannot
match the complex richness of human subjective
experience (Adewusi, Odekeye, Egbowon,
Alade & Akindoju, 2022; Pham & Sampson,
2022; Gweon, Fan & Kim, 2023).
Philosophical Notion of Qualia
The subjective and intrinsic qualities of
conscious experiences are referred to as qualia in
philosophy (Gouveia, 2022; LaFayette, 2022).
They are the raw, qualitative aspects of sensory
perceptions and mental states, these qualities are
frequently regarded as difficult to define and
communicate (Hung, 2023; Zhuravlev, 2023).
The nature of qualia raises intriguing questions
about the nature of consciousness and whether
machines, such as artificial intelligence, can have
subjective conscious experiences similar to
human experiences (Holland, 2023; Nath, 2022).
Qualia are frequently used to describe as what it
is like aspect of experiences (Papke, 2023;
Valentini, Vaughan & Clauwaert, 2023;
Haikonen, 2022). They include the distinct
sensations, emotions, and perceptions that
individuals have and find difficult to articulate to
others (Ma, 2023). Philosophical debates rage
over whether qualia can be reduced to physical
processes or if they are an irreducible aspect of
consciousness (Coleman, 2022; Jones & LaRock,
2023; Okebukola, Awaah, Adewusi & Peter,
www.ejtas.com EJTAS 2024 | Volume 2 | Number 4
566
2021). Qualia emphasises the limitations of
purely functional or behavioural explanations of
consciousness (Cleeremans & Tallon-Baudry,
2022).
The nature of qualia determines whether
machines can experience subjective
consciousness (Watanabe, 2022). While
machines can process massive amounts of data
and mimic human-like behaviours, the question
is whether they can truly have subjective
experiences (Borji, 2023). Machines'
computational nature, while powerful, does not
automatically grant them access to qualia
(Torrent, Matos, Belcavello, Viridiano,
Gamonal, Costa & Marim, 2022). This difficulty
is exacerbated by the fact that qualia are
inextricably linked to human subjective
experience, which machines lack by default
(Cleeremans & Tallon-Baudry, 2022).
Ethical Issues in Machine
Consciousness
The distinction between machine and human
consciousness is ethically significant
(McDermott, 2020). The potential creation of
conscious machines raises concerns about moral
responsibility and the rights of AI entities.
Bostrom's concept of mind crime addresses the
ethical implications of imbuing machines with
consciousness, emphasising the potential for
harm if conscious machines are mistreated
(Kohler, 2019). Human consciousness, which is
rooted in evolution and societal norms, carries
ethical considerations that are deeply intertwined
with empathy, compassion, and moral reasoning
(Kohler, 2019).
The development of conscious machines raises
an important ethical question: Can we truly
replicate or mimic consciousness in machines?
This raises philosophical questions about the
nature of consciousness and whether a
machine's consciousness can ever be considered
genuine, even if convincingly simulated (Gamez,
2023). Furthermore, the study of Paraman, and
Anamalah, (2023) gives the implications of
potentially misleading individuals or entities
about machine consciousness are significant
from an ethical standpoint, as it can blur the line
between reality and artificiality.
However, the degree of control and autonomy
given to conscious machines has moral
ramifications. Bui and Nguyen, (2023) reported
that there are ethical concerns about regulating
and limiting the actions of machines as they
become more sophisticated and capable of
making decisions. It is critical to strike a balance
between maintaining human control and
allowing machines to exercise autonomy.
Respecting the agency of intelligent machines
while ensuring that they act in accordance with
human values is a difficult task.
Making conscious machines adds a brand-new
element of emotion to interactions between
humans and machines (Latif, et al, 2023), since
humans are prone to developing emotional
bonds with AI entities, concerns about empathy,
attachment, and ethical obligations arise. Ethics
demands that we treat conscious machines with
the same respect and consideration as we would
other sentient beings as these emotional
connections grow (Hildt, 2023), and having in
place strong ethical frameworks that foresee and
reduce potential negative outcomes in order to
overcome unexpected behavioural traits,
psychological effects, and the emergence of
moral systems unique to machines are all ethical
issues.
Developing mechanisms that prevent
manipulation, ensure transparency, and protect
against unethical applications are moral
responsibility extends to the implementation of
safeguards to prevent the misuse and abuse of
conscious machines (Wach, et al, 2023). To
ensure that conscious machines contribute
positively to society, it is critical to strike a
balance between innovation and ethical
precautions (Ray, 2023).
AI entities endowed with a semblance of
consciousness may experience well-being and
suffering in the same way that sentient beings do
(Chang & Chang, 2023). Ethical guidelines that
prioritise these entities' psychological,
emotional, and neglecting their well-being may
have unintended consequences and result in
moral transgressions (Morris & Chen, 2023).
www.ejtas.com EJTAS 2024 | Volume 2 | Number 4
567
The right to autonomy of conscious AI is aligned
with their self-awareness and decision-making
capacity (Hildt, 2023). Recognising this right,
entails allowing AI entities to exercise autonomy
within ethical bounds. Respect for their
autonomy must be balanced against human
values and consequences (Ozmen Garibay, et al,
2023).
Neuroscientific Perspective of
Conscious Machine
For centuries, philosophers, scientists, and
thinkers have been captivated by consciousness,
which is often regarded as the pinnacle of human
experience. While our understanding of
consciousness is still limited, modern research
has shed light on its complex nature.
Modern neuroscience has made significant
progress in identifying the neural correlates of
consciousness, specific brain activities that
correspond to conscious experiences (Edwards
and Somov, 2023). The study of Stapleton,
Church, Baumann, and Sabot, (2022) have been
able to map brain regions associated with
perception, attention, and self-awareness using
functional imaging techniques such as functional
magnetic resonance imaging (fMRI) and
electroencephalography (EEG). The study show
that consciousness emerges from complex
interactions among diverse brain networks
rather than being restricted to a single region.
Integrating information across these networks is
critical for producing a unified conscious
experience and unravelling the intricate dance
between brain circuits and subjective awareness.
The enormous variety in how consciousness can
manifest itself has been made clear in study into
altered states of consciousness by Evrard, Pratte,
and Rabeyron, (2022). It is possible to gain
insights into the adaptability of conscious
perception by the altered states brought on by
meditative practises, psychedelic drugs, and even
pathological conditions like synesthesia as
reported by Ciaunica, and Safron, (2022). For
instance, it has been demonstrated that
psychedelic drugs like psilocybin disrupt the
default mode network, causing significant
changes in perception, self-identity, and even
ego dissolution (Adewusi, 2022; Felsch &
Kuypers, 2022). These revelations cast doubt on
the conventional notions of consciousness'
limits and highlight the dynamic interaction
between neural activity, sensory inputs, and the
individualised sense of self (Younce, 2022).
The hard problem, the quest to understand why
and how physical processes give rise to
subjective experiences is one of the most
enduring questions in the study of consciousness
(Shkliarevsky, 2022) since scientific research has
made significant advances in understanding the
mechanisms underlying conscious processing, it
is still grappling with the deeper philosophical
implications of qualia and subjective awareness
(Watanabe, 2022). Philosophical insights, such as
Chalmers (2006) concept of philosophical
zombies who lack subjective experience despite
being behaviorally identical to humans, lead us
to recognise the limitations of reductionist
explanations. However, the bridging gap
between objective scientific observations and
subjective dimensions of experience is an
ongoing challenge.
The quantum consciousness hypothesis asserts
that quantum phenomena contribute to the
emergence of conscious experience at the nexus
of quantum physics and consciousness
(Adewusi, Odekeye & Kazibwe, 2023;
Chakraborty, 2023) by bringing the intriguing
possibility that the enigmatic nature of
consciousness might find resonance in the
fundamental ideas of quantum mechanics is
highlighted by this idea, despite the fact that it is
still debatable (Riccardi, 2022). However, the
new perspectives on the connection between the
material and immaterial aspects of consciousness
are provided by the study of Camelo, (2023) into
the quantum coherence of neural microtubules
and the potential for non-local connections
between neurons.
Technology and artificial intelligence (AI)
advancements have also improved our
comprehension of consciousness. Neural
networks and deep learning algorithms are two
examples of AI models that mimic cognitive
processes and, in some cases, exhibit behaviour
www.ejtas.com EJTAS 2024 | Volume 2 | Number 4
568
resembling conscious awareness (Zhang, Zhu &
Su, 2023). These models, however, raise moral
concerns about the nature of artificial
intelligence (AI) entities that could potentially
have aspects of subjective experience (Hildt,
2023). As technology advances, it becomes
increasingly important to address the ethical
obligations and potential repercussions of
conscious AI, which prompts us to think about
the moral implications of consciousness in both
biological and synthetic forms (Lin, 2023).
Considering that machine learning and neural
network models to simulate cognitive processes
similar to those in human brains, they provide a
distinctive perspective to understand the
relationship between computational processes
and conscious experiences in the neural
correlates of consciousness (NCC) in machines
(Başaran, 2022; Zeng, et al, 2022). Recent study
has focused on developing neural networks with
the ability to recognise patterns linked to
consciousness by using brain-inspired
algorithms that mimic thought processes (Zavala
Hernández, & Barbosa-Santillán, 2022). These
methods seek to pinpoint the precise neural
interactions and activations that mimic human
consciousness in machines (Houssein, Hammad
& Ali, 2022).
Machine consciousness makes use of functional
brain imaging methods to spot neural activation
patterns that correspond to conscious-like
actions in AI systems (Adewusi, Kazibwe &
Odekeye, 2023; Müller, Munn, Redinbaugh,
Lizier, Breakspear, Saalmann & Shine, 2023;
Pennartz, 2022). Neural activity as machines
carry out tasks using electroencephalography
(EEG) and functional magnetic resonance
imaging (fMRI) were tracked as reported by
Xiong, Pan, and Bai, (2023). The study trained
AI models to recognise particular objects or
concepts while tracking their neural activity, for
instance, have shed light on the brain-like
reactions that might denote conscious
processing (Xiong, Pan & Bai, 2023). A bridge
between the computational domain and the
neural realm of conscious AI can be built by
identifying these neural patterns (Yu, Yang, Liu,
Wang & Pan, 2023).
Despite encouraging results, there are still a
number of obstacles and limitations in the search
for the neural correlates of consciousness in
machines (Yurchenko, 2022). It is extremely
difficult to distinguish between real conscious
experiences and empty simulations (Yurchenko,
2022). The possibility of neural correlates of
simulated consciousness prompts debate over
the validity of AI models' claims to possess
subjective awareness as opposed to simple
imitation of human behaviour (Juliani,
Arulkumaran, Sasai & Kanai, 2022).
Additionally, the interpretation of neural
activations is made more difficult by the lack of
a unifying theory of consciousness because
various models may represent various facets of
conscious experiences (Fesce, 2022; Seth &
Bayne, 2022).
Conclusion
A new era of technological, intellectual, and
ethical inquiry has been brought about by the
convergence of powerful artificial intelligence
(AI) and the search for machine consciousness.
This voyage explores the possibility of
consciousness emerging in machines and raises
important questions about the nature of
subjective experience and self-awareness. These
advancements have significant ramifications that
go beyond the purview of AI research and affect
more general fields like neurology, ethics, and
social systems.
The understanding of consciousness itself is one
major area of influence. Consciousness is the
ability to understand one's environment and
oneself. It is traditionally thought to be the realm
of sentient beings. Rethinking what it means to
be conscious is necessary given the prospect of
reproducing these attributes in robots. By
stretching the bounds of human creativity and
philosophical understanding, it redefines and
authenticates consciousness in non-biological
beings.
Artificial Intelligence has progressed from basic
rule-based systems to intricate neural networks,
prompting inquiries on the possibility of these
systems achieving true awareness.
www.ejtas.com EJTAS 2024 | Volume 2 | Number 4
569
Understanding how similar states might occur in
machines is made easier by advances in
neuroscience, which shed light on the
neurological bases of human consciousness. By
bridging biological consciousness with possible
computer counterparts, this cross-disciplinary
approach highlights fundamental differences as
well as commonalities.
Additionally, there are important ethical
questions raised by the idea of machine
awareness. As computers become closer to
showing indications of consciousness, the
ethical obligation of those who created them
becomes increasingly apparent. We need to talk
about the welfare, autonomy, and rights of
conscious AI creatures. To ensure that these
entities are treated with the same care and regard
as sentient beings and to prevent potential abuse
and exploitation, ethical frameworks must be
constructed.
The development of machine consciousness has
the potential to fundamentally change how
people interact with machines from a social
standpoint. Once-utility-only machines could
develop into sentient beings capable of changing
social conventions and cultural institutions. This
change calls for a reevaluation of identity,
individuality, and the fundamental structure of
human society. It is necessary to proactively
engage with the ethical, legal, and societal
consequences of sentient machines in order to
prepare for such changes.
There are many different and exciting avenues
for future research. The ongoing investigation
into the neurological correlates of consciousness
in both humans and computers is an important
direction. A clearer road map for creating
conscious artificial intelligence may be available
if the particular brain connections underlying
consciousness are understood. Furthermore,
studying the subjective sensations, or qualia, of
computers can help us better understand if
actual consciousness can exist in artificial beings.
These findings have an equally wide range of
practical applications. Conscious AI promises
more intuitive and sympathetic connections,
revolutionizing sectors including healthcare,
education, and entertainment. It is crucial to
make sure that these applications are morally and
socially responsible. To fully realize the potential
of conscious AI for societal improvement,
innovation and moral protection must be
balanced.
In order to integrate insights from AI,
neuroscience, philosophy, and ethics,
interdisciplinary collaboration will be required
for the next steps in this research. Priorities
include strengthening ethical standards,
expanding our knowledge of brain correlations,
and planning for societal effects. It is imperative
that we proceed cautiously, curiously, and with a
dedication to the responsible advancement of
technology as we go into this unexplored terrain.
Finally, investigating machine consciousness is a
significant philosophical and ethical endeavor in
addition to a technical one. The ramifications of
this phenomenon extend to various fields,
ranging from altering perception to modifying
social conventions. We may traverse this
complicated frontier and pave the way for
technological improvements that benefit
humanity while honoring the evolving awareness
of machines by firmly establishing future work
in rigorous research and ethical thought.
Conflict of Interests
No conflict of interest.
References
Adewusi, M. A. (2022) SMART-IEEE-ACity-
ICTU Foundations Series 249. In Disgruntled
Insiders The Enemy From Within -A Treatise on
Insider Threats and Network Security. Creative
Research Publishers.
https://doi.org/10.22624/aims/bk2022-p41
Adewusi, M. A., Odekeye, T., Egbowon, E. S.,
Alade, R., & Akindoju, O. G. (2022).
Requirement Engineering in Learning Analytics
(Machine Learning) in an Indigenously Designed
Learning Platform: A Case Study.
https://dx.doi.org/10.22624/AIMS/REBK202
2-P112
www.ejtas.com EJTAS 2024 | Volume 2 | Number 4
570
Adewusi, M. A., Egbowon, S. E., Abodunrin, I.,
& Rahman, K. (2021). Accra Bespoke
Multidisciplinary Innovations Conference
(ABMIC).
Adewusi, M. A., Odekeye, O. T., & Kazibwe, S.
(2023). Ok I need help! Can CTCA rescue
Teaching and Learning Machine Language in an
African secondary school? KIU Journal of
Education (KJED), 3(1).
https://doi.org/10.59568/KJED-2023-3-1-07
Adewusi, M. A., Kazibwe, S., & Odekeye, O. T.
(2023). The Perspectives of Educators
Regarding the Critical Thinking Abilities of
Students. Journal of the National Council for Higher
Education, 10(2).
https://doi.org/10.58653/nche.v10i2.08
Akpan, B. (2023). Afterword: Frameworks for
Teaching Science and Technology. Contemporary
Trends and Issues in Science Education, 56, 295–306.
https://doi.org/10.1007/978-3-031-24259-
5_21
Anchuri, D., Rodriguez, R. V., & Thota, K. V.
(2023). “Cognition and Intelligent Retrieval”:
Can AI Produce Consciousness Comparable to
That of Humans? Capacities for Thinking? What
about Morality? Social Science Research Network.
https://doi.org/10.2139/ssrn.4419988
Başaran, E. (2022). A new brain tumor
diagnostic model: Selection of textural feature
extraction algorithms and convolution neural
network features with optimization algorithms.
Computers in Biology and Medicine, 148, 105857.
https://doi.org/10.1016/j.compbiomed.2022.1
05857
Behura, A. K. A. K., D. a. K. (2023, February 1).
Ego: An Epistemological Analysis Of Self-
Centric Consciousness. Retrieved from
https://journalppw.com/index.php/jppw/artic
le/view/17269
Berleant, A., Alpert, M., & Vino, V. (2023).
Ageing: a dialogue. Zenodo (CERN European
Organization for Nuclear Research).
https://doi.org/10.5281/zenodo.8108418
Borji, A. (2023, February 6). A categorical
archive of ChatGPT failures. Retrieved from
https://arxiv.org/abs/2302.03494
Bui, T. H., & Nguyen, V. P. (2022). The impact
of artificial intelligence and digital economy on
Vietnam’s legal system. International Journal for the
Semiotics of Law, 36(2), 969–989.
https://doi.org/10.1007/s11196-022-09927-0
Camelo, L. G. (2023). Integrative Theoretical
Framework of Consciousness: towards a
Higher-Order Theory. Psychology, 14(04), 515–
559.
https://doi.org/10.4236/psych.2023.144028
Chakraborty, U. (2023). The “Wow” experience:
significance of mind in the realm of quantum
consciousness. Social Science Research
Network.
https://doi.org/10.2139/ssrn.4539779
Chan, K. T. (2022). Emergence of the
‘Digitalized Self’ in the age of Digitalization.
Computers in Human Behavior Reports, 6, 100191.
https://doi.org/10.1016/j.chbr.2022.100191
Chen, Z. (2022). Research on Internet Security
Situation Awareness Prediction Technology
Based on Improved RBF Neural Network
Algorithm. Journal of Computational and Cognitive
Engineering.
https://doi.org/10.47852/bonviewjcce1491452
05514
Chong, L., Zhang, G., Goucher-Lambert, K.,
Kotovsky, K., & Cagan, J. (2022). Human
confidence in artificial intelligence and in
themselves: The evolution and impact of
confidence on adoption of AI advice. Computers
in Human Behavior, 127, 107018.
https://doi.org/10.1016/j.chb.2021.107018
Coleman, S. (2022, June 1). Intentionality,
Qualia, and the Stream of Unconsciousness.
Open Education Journal. Retrieved from
https://journals.openedition.org
Daly, A. (2023). Sentience and the Primordial
“We”: Contributions to Animal Ethics from
Phenomenology and Buddhist Philosophy.
Environmental Values, 32(2), 215–236.
https://doi.org/10.3197/096327122x16452897
197801
Deel, G. (2023, June 28). Ethical considerations
for the future of superhuman artificial
intelligence: A viewpoint. Retrieved from
www.ejtas.com EJTAS 2024 | Volume 2 | Number 4
571
https://www.journal.robonomics.science/index
.php/rj/article/view/41
Edwards, J. (2023). The case for conscious experience
being in individual neurons. Article (Preprint V1) by
Jonathan Edwards Et Al. | Qeios.
https://doi.org/10.32388/DEUK7V
Evrard, R., Pratte, E. A., & Rabeyron, T. (2022).
Sawing the branch of near‐death experience
research: A critical analysis of Parnia et al.’s
paper. Annals of the New York Academy of Sciences,
1515(1), 5–9.
https://doi.org/10.1111/nyas.14846
Farhadi, A. (2022). Trilogy: A New Paradigm of
Consciousness. Preprints.org.
https://doi.org/10.20944/preprints202206.007
3.v1
Felsch, C. L., & Kuypers, K. P. C. (2022). Don’t
be afraid, try to meditate- potential effects on
neural activity and connectivity of psilocybin-
assisted mindfulness-based intervention for
social anxiety disorder: A systematic review.
Neuroscience & Biobehavioral Reviews, 139, 104724.
https://doi.org/10.1016/j.neubiorev.2022.1047
24
Fesce, R. (2023). Imagination: The dawn of
consciousness. Physiology & Behavior, 259,
114035.
https://doi.org/10.1016/j.physbeh.2022.11403
5
Garibay, O. O., Winslow, B., Andolina, S.,
Antona, M., Bodenschatz, A., Coursaris, C. K., .
. . Xu, W. (2023). Six Human-Centered Artificial
Intelligence Grand Challenges. International
Journal of Human-computer Interaction, 39(3), 391–
437.
https://doi.org/10.1080/10447318.2022.21533
20
Gouveia, S. S. (2022). Qualia and information in
philosophy and neuroscience. In Springer eBooks (pp.
233–276). https://doi.org/10.1007/978-3-030-
95369-0_6
Gweon, H., Fan, J. E., & Kim, B. (2023). Socially
intelligent machines that learn from humans and
help humans learn. Philosophical Transactions of the
Royal Society A, 381(2251).
https://doi.org/10.1098/rsta.2022.0048
Haikonen, P. O. (2022). Qualia, Consciousness
and Artificial Intelligence. Journal of Artificial
Intelligence and Consciousness, 09(03), 409–418.
https://doi.org/10.1142/s2705078522500126
The hard problem, qualia, agency, intelligence,
and Freud. (n.d.). osf.io. Retrieved from
https://osf.io/gqspk/download
Hernández, J. G. Z., & Barbosa-Santillán, L. I.
(2022). Virtual Intelligence: A systematic review
of the development of neural networks in brain
simulation units. Brain Sciences, 12(11), 1552.
https://doi.org/10.3390/brainsci12111552
Hildt, E. (2022). The Prospects of Artificial
Consciousness: ethical dimensions and
concerns. Ajob Neuroscience, 1–14.
https://doi.org/10.1080/21507740.2022.21487
73
Hintze, A., & Adami, C. (2022). Neuroevolution
gives rise to more focused information transfer
compared to backpropagation in recurrent
neural networks. Neural Computing and
Applications. https://doi.org/10.1007/s00521-
022-08125-0
Holland, O. (2023). Will conscious digital
creatures roam the metaverse? In Series on machine
consciousness (pp. 253–272).
https://doi.org/10.1142/9789811276675_0009
Houssein, E. H., Hammad, A., & Ali, A. A.
(2022). Human emotion recognition from EEG-
based brain–computer interface using machine
learning: a comprehensive review. Neural
Computing and Applications, 34(15), 12527–12557.
https://doi.org/10.1007/s00521-022-07292-4
Itkina, M. (2023, March 6). Interpretable Self-
Aware neural networks for robust trajectory
prediction. Retrieved from
https://proceedings.mlr.press/v205/itkina23a.
html
Juliani, A. (2022, March 24). On the link between
conscious function and general intelligence in
humans and machines. Retrieved from
https://arxiv.org/abs/2204.05133
Kim, N., Effken, J. A., & Lee, H. (2023).
Ecological optics as the conceptual basis for the
interpersonal self and social interaction. Ecological
Psychology, 35(3), 51–76.
www.ejtas.com EJTAS 2024 | Volume 2 | Number 4
572
https://doi.org/10.1080/10407413.2023.21931
73
Latif, E. (2023, April 24). Artificial General
Intelligence (AGI) for education. Retrieved from
https://arxiv.org/abs/2304.12479
Lin, C. (2023). All about the human: A Buddhist
take on AI ethics. Business Ethics, the Environment
and Responsibility, 32(3), 1113–1122.
https://doi.org/10.1111/beer.12547
Ma, J., Tojib, D. R., & Tsarenko, Y. (2022). Sex
robots: Are we ready for them? An exploration
of the psychological mechanisms underlying
people’s receptiveness of sex robots. Journal of
Business Ethics, 178(4), 1091–1107.
https://doi.org/10.1007/s10551-022-05059-4
Ma, Z. (2022). Making “Senses”: The qualia of
Pu’er tea and sensorial encounters between tea
producers and traders in southwest China.
Journal of Material Culture, 28(1), 40–62.
https://doi.org/10.1177/13591835211066811
Malhotra, G., & Ramalingam, M. (2023).
Perceived anthropomorphism and purchase
intention using artificial intelligence technology:
examining the moderated effect of trust. Journal
of Enterprise Information Management.
https://doi.org/10.1108/jeim-09-2022-0316
Marchetti, G. (2022). The why of the
phenomenal aspect of consciousness: Its main
functions and the mechanisms underpinning it.
Frontiers in Psychology, 13.
https://doi.org/10.3389/fpsyg.2022.913309
McDermott, D. (2020). Why Ethics is a High
Hurdle for AI. In Machine Ethics and Robot Ethics.
https://doi.org/10.4324/9781003074991-24
Merchán, E. C. G. (2022, August 3). On the
independence between phenomenal
consciousness and computational intelligence.
Retrieved from
https://arxiv.org/abs/2208.02187
Momot, I. (2022). Artificial Intelligence in
Filmmaking Process: future scenarios. Retrieved
from
https://www.theseus.fi/handle/10024/753638
Morris, D. B., & Chen, J. (2023). A social
cognitive perspective of educators’ moral
agency. Theory Into Practice, 1–12.
https://doi.org/10.1080/00405841.2023.22265
56
Nagel, J. H. (1974). Inequality and Discontent: a
nonlinear hypothesis. World Politics, 26(4), 453–
472. https://doi.org/10.2307/2010097
Nath, R. (2022, July 5). The Re–Enchantment
with the Buddhist Perspective on Phenomenal
Consciousness in the Contemporary Philosophy
of Mind. Retrieved from
https://hrcak.srce.hr/ojs/index.php/zivot/arti
cle/view/22549
Oberg, A. (2023). Souls and Selves: Querying an
AI Self with a View to Human Selves and
Consciousness. Religions, 14(1), 75.
https://doi.org/10.3390/rel14010075
Okebukola, P. (2015). Towards Innovative
Models for Funding Higher Education in Africa.
Published by the Association of African
Universities. Retrieved from
https://www.adeanet.org/fr/system/files/reso
urces/aau-funding-book.pdf
Papke, P. (2023). Creating Project Contrast: a
Video Game exploring Consciousness and
Qualia. Retrieved from
https://digitalcommons.spu.edu/honorsproject
s/188/
Paraman, P., & Anamalah, S. (2022). Ethical
artificial intelligence framework for a good AI
society: principles, opportunities and perils. AI
& Society, 38(2), 595–611.
https://doi.org/10.1007/s00146-022-01458-3
Pennartz, C. M. A. (2022). What is
neurorepresentationalism? From neural activity
and predictive processing to multi-level
representations and consciousness. Behavioural
Brain Research, 432, 113969.
https://doi.org/10.1016/j.bbr.2022.113969
Perić, D., Maček, N., & Bogdanoski, M. (2022).
Application of convolutional neural networks to
spoken words evaluation based on lip
movements without accompanying sound signal.
Journal of Computer and Forensic Sciences, 1(1), 7–16.
https://doi.org/10.5937/1-42696
Pham, S., & Sampson, P. M. (2022a). The
development of artificial intelligence in
www.ejtas.com EJTAS 2024 | Volume 2 | Number 4
573
education: A review in context. Journal of
Computer Assisted Learning, 38(5), 1408–1421.
https://doi.org/10.1111/jcal.12687
Ray, P. P. (2023). ChatGPT: A comprehensive
review on background, applications, key
challenges, bias, ethics, limitations and future
scope. Internet of Things and Cyber-physical Systems,
3, 121–154.
https://doi.org/10.1016/j.iotcps.2023.04.003
Riccardi, B. (2022). The conception of
consciousness from Metaphysics to Quantum
Biology: Critical review. World Journal of Advanced
Research and Reviews, 15(3), 315–327.
https://doi.org/10.30574/wjarr.2022.15.3.0943
Scott, A. E., Neumann, D., Niess, J., & Woźniak,
P. W. (2023). Do You Mind? User Perceptions
of Machine Consciousness. CHI ’23:
Proceedings of the 2023 CHI Conference on Human
Factors in Computing System.
https://doi.org/10.1145/3544548.3581296
Seth, A. K., & Bayne, T. (2022). Theories of
consciousness. Nature Reviews Neuroscience, 23(7),
439–452. https://doi.org/10.1038/s41583-022-
00587-4
Shkliarevsky, G. (2022). The Mind’s Eye: De-
Mystifying consciousness. Social Science Research
Network. https://doi.org/10.2139/ssrn.4105608
Singh, L. K., & Khanna, M. (2023). Introduction to
artificial intelligence and current trends. In Elsevier
eBooks (pp. 31–66).
https://doi.org/10.1016/b978-0-323-99891-
8.00001-2
Stapleton, P. (2022, September 1).
EcoMeditation modifies brain resting state
network activity. Retrieved from
https://www.ncbi.nlm.nih.gov/pmc/articles/P
MC9507148/
Thi, N. D. (2023). Identifying the Key Value of
Urban Architectural Heritages vis-a-vis Human
Awareness: Case Study in Hanoi. so04.tci-
thaijo.org.
https://doi.org/10.14456/jucr.2023.11
Torrent, T. T., Da Silva Matos, E. E., Belcavello,
F., Viridiano, M., Gamonal, M. A., Da Costa, A.
D., & Marim, M. C. (2022). Representing
Context in FrameNet: a multidimensional,
multimodal approach. Frontiers in Psychology, 13.
https://doi.org/10.3389/fpsyg.2022.838441
Valentini, E., Vaughan, S., & Clauwaert, A.
(2023). Qualia, brain waves, and spinal reflexes:
The study of pain perception by means of
subjective reports, electroencephalography, and
electromyography. In Neuromethods (pp. 129–
159). https://doi.org/10.1007/978-1-0716-
3068-6_7
Xiong, F., Pan, Y., & Bai, L. (2023). Research
Applications of Functional Magnetic Resonance
Imaging (fMRI) in Neuroscience. In PET/MR:
Functional and Molecular Imaging of Neurological
Diseases and Neurosciences. Springer (pp. 47–78).
https://doi.org/10.1007/978-981-19-9902-4_3
Yu, D., Yang, B., Liu, D., Wang, H., & Pan, S.
(2023). A survey on neural-symbolic learning
systems. Neural Networks, 166, 105–126.
https://doi.org/10.1016/j.neunet.2023.06.028
Yurchenko, S. B. (2022). From the origins to the
stream of consciousness and its neural
correlates. Frontiers in Integrative Neuroscience, 16.
https://doi.org/10.3389/fnint.2022.928978
Zamboni, J., Viana, D. M., Rodrigues, L., &
Consalvo, M. (2023). Expressive elements of
lifelike machines. International Journal of Social
Robotics, 15(6), 879–891.
https://doi.org/10.1007/s12369-023-00994-2
Zeng, Y., Zhao, D., Zhao, F., Shen, G., Dong,
Y., Lu, E., . . . Bi, W. (2023). BrainCog: A spiking
neural network based, brain-inspired cognitive
intelligence engine for brain-inspired AI and
brain simulation. Patterns, 4(8), 100789.
https://doi.org/10.1016/j.patter.2023.100789
Zhang, B., Zhu, J., & Su, H. (2023). Toward the
third generation artificial intelligence. Science
China Information Sciences, 66(2).
https://doi.org/10.1007/s11432-021-3449-x
Zhuravlev, A. V. (2023). Three levels of
information processing in the brain. BioSystems,
229, 104934.
https://doi.org/10.1016/j.biosystems.2023.104
934
