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The emergence of Large Language Models (LLMs) has renewed debate about whether Artificial Intelligence (AI) can be conscious or sentient. This paper identifies two approaches to the topic and argues: (1) A “Cartesian” approach treats consciousness, sentience, and personhood as very similar terms, and treats language use as evidence that an entity is conscious. This approach, which has been dominant in AI research, is primarily interested in what consciousness is, and whether an entity possesses it. (2) An alternative “Hobbesian” approach treats consciousness as a sociopolitical issue and is concerned with what the implications are for labeling something sentient or conscious. This both enables a political disambiguation of language, consciousness, and personhood and allows regulation to proceed in the face of intractable problems in deciding if something “really is” sentient. (3) AI systems should not be treated as conscious, for at least two reasons: (a) treating the system as an origin point tends to mask competing interests in creating it, at the expense of the most vulnerable people involved; and (b) it will tend to hinder efforts at holding someone accountable for the behavior of the systems. A major objective of this paper is accordingly to encourage a shift in thinking. In place of the Cartesian question—is AI sentient?—I propose that we confront the more Hobbesian one: Does it make sense to regulate developments in which AI systems behave as if they were sentient?

This study proposes a model of computational consciousness for non-interacting agents. The phenomenon of interest was assumed as sequentially dependent on the cognitive tasks of sensation, perception, emotion, affection, attention, awareness, and consciousness. Starting from the Smart Sensing prodromal study, the cognitive layers associated with the processes of attention, awareness, and consciousness were formally defined and tested together with the other processes concerning sensation, perception, emotion, and affection. The output of the model consists of an index that synthesizes the energetic and entropic contributions of consciousness from a computationally moral perspective. Attention was modeled through a bottom-up approach, while awareness and consciousness by distinguishing environment from subjective cognitive processes. By testing the solution on visual stimuli eliciting the emotions of happiness, anger, fear, surprise, contempt, sadness, disgust, and the neutral state, it was found that the proposed model is concordant with the scientific evidence concerning covert attention. Comparable results were also obtained regarding studies investigating awareness as a consequence of visual stimuli repetition, as well as those investigating moral judgments to visual stimuli eliciting disgust and sadness. The solution represents a novel approach for defining computational consciousness through artificial emotional activity and morality.

Artificial intelligence endures the necessary capacity that allows automation directed towards emulate mortal intellect action. In this research paper gives a broad analysis of AI consciousness. The primary objective indicated the situation inhabits facing exploration of Artificial intelligence could develop humans like cognition. Numerous analysts include and consider automobile comprehension models. Consciousness study is a developing subject in both neuroscience and psychology that includes a variety of methods, from experimental animal models in human brain pathology. Here we discuss the contrasts bounded by mortal cognizance along automobile perception in addition above mentioned physiological talents benefit mortal compassionate in development regard to the subsequent world. Artificial intelligence enabled automatic driving automobiles using the diversion zone. Human intelligence generally refers to the ability of mental activities. However, due to its poor level of intelligibility and ethical difficulties. Artificial intelligence established automation posture, compelling uncertainty towards the user, individuals along with society. According to what AI can advance in subsequent at the same-time human empathetic endure involved along computer expertise adopting the diversion portrait.

To approach the creation of artificial conscious systems systematically and to obtain certainty about the presence of phenomenal qualities (qualia) in these systems, we must first decipher the fundamental mechanism behind conscious processes. In achieving this goal, the conventional physicalist position exhibits obvious shortcomings in that it provides neither a plausible mechanism for the generation of qualia nor tangible demarcation criteria for conscious systems. Therefore, to remedy the deficiencies of the standard physicalist approach, a new theory for the understanding of consciousness has been formulated. The aim of the paper is to present the cornerstones of this theory, to outline the conditions for conscious systems derived from the theory, and to address the implications of these conditions for the creation of robots that transcend the threshold of phenomenal awareness. In short, the theory is based on the proposition that the universe is permeated by a ubiquitous field of consciousness that can be equated with the zero-point field (ZPF) of quantum electrodynamics (QED). The ZPF, which is characterized by a spectrum of field modes, plays a crucial role in the edifice of modern physics. QED-based model calculations on cortical dynamics and empirical findings on the neural correlates of consciousness suggest that a physical system can only generate conscious states if it is capable of establishing resonant coupling to the ZPF, resulting in the amplification of selected field modes and the activation of the phenomenal qualities that are assumed to be associated with these modes. Thus, scientifically sound considerations support the conclusion that the crucial condition for generating conscious states lies in a system's capacity to tap into the phenomenal color palette inherent in the ZPF.

We demonstrate that if consciousness is relevant for the temporal evolution of a system's states--that is, if it is dynamically relevant--then AI systems cannot be conscious. That is because AI systems run on CPUs, GPUs, TPUs or other processors which have been designed and verified to adhere to computational dynamics that systematically preclude or suppress deviations. The design and verification preclude or suppress, in particular, potential consciousness-related dynamical effects, so that if consciousness is dynamically relevant, AI systems cannot be conscious.

Foundation models are gaining considerable interest for their capacity of solving many downstream tasks without fine-tuning parameters on specific datasets. The same solutions can connect visual and linguistic representations through image-text contrastive learning. These abilities allow an artificial agent to act similarly to a human, but significant cognitive processes still need to be introduced in the learning process. The present study proposes an advancement to more human-like artificial intelligence by introducing CognitiveNet, a learnable architecture integrating foundation models. Starting from the latest studies in the field of Artificial Consciousness, a hierarchy of cognitive layers has been modeled and pre-trained for estimating the emotional content of images. By employing CLIP as the backbone model, significant concordant emotional activity was produced. Furthermore, the proposed model overcomes the accuracy of CLIP in classifying CIFAR-10 and -100 datasets through supervised optimization, suggesting CognitiveNet as a promising solution for solving classification tasks through online meta-learning.

One of the current AI issues depicted in popular culture is the fear of conscious super AIs that try to take control over humanity. And as computational power goes upwards and that turns more and more into a reality, understanding artificial brains might be increasingly important to control and drive AI towards the benefit of our societies. This paper proposes a base framework to aid the development of autonomous multipurpose artificial brains. To approach that, we propose to first model the functioning of the human brain by reflecting and taking inspiration from the way the body, the consciousness and the unconsciousness interact. To do that, we tried to model events such as sensing, thinking, dreaming and acting, thoughtfully or unconsciously. We believe valuable insights can already be drawn from the analysis and critique of the presented framework, and that it might be worth implementing it, with or without changes, to create, study, understand and control artificially conscious systems.

Ambitious value learning proposals to solve the AI alignment problem and avoid catastrophic outcomes from a possible future misaligned Artificial Superintelligence (ASI), such as Coherent Extrapolated Volition (CEV), have focused on ensuring that an Artificial Superintelligence would try to do what humans would want it to do. However, present and future sentient non-humans, such as non-human animals and possible future digital minds, could also be affected by the ASI’s behavior in morally relevant ways. This paper puts forward Sentientist Coherent Extrapolated Volition, an alternative to CEV, that directly takes into account the interests of all sentient beings. This ambitious value learning proposal would significantly reduce the likelihood of risks of astronomical suffering from the ASI’s behavior, and thus we have very strong pro-tanto moral reasons in favor of implementing it instead of the CEV. This fact is crucial in conducting an adequate cost–benefit analysis between different ambitious value learning proposals.

The possibility of AI consciousness depends much on the correct answer to the mind–body problem: how our materialistic brain generates subjective consciousness? If a materialistic answer is valid, machine consciousness must be possible, at least in principle, though the actual instantiation of consciousness may still take a very long time. If a non-materialistic one (either mentalist or dualist) is valid, machine consciousness is much less likely, perhaps impossible, as some mental element may also be required. Some recent advances in neurology (despite the separation of the two hemispheres, our brain as a whole is still able to produce only one conscious agent; the negation of the absence of a free will, previously thought to be established by the Libet experiments) and many results of parapsychology (on medium communications, memories of past lives, near-death experiences) suggestive of survival after our biological death, strongly support the non-materialistic position and hence the much lower likelihood of AI consciousness. Instead of being concern about AI turning conscious and machine ethics, and trying to instantiate AI consciousness soon, we should perhaps focus more on making AI less costly and more useful to society.

The fields of artificial intelligence (AI) and artificial consciousness (AC) have largely developed separately, with different goals and criteria for success and with only a minimal exchange of ideas. In this chapter, we consider the question of how concepts developed in AC research might contribute to more effective future AI systems. We first briefly discuss several past hypotheses about the function(s) of human consciousness, and present our own hypothesis that short-term working memory and very rapid learning should be a central concern in such matters. We describe our recent efforts to explore this hypothesis computationally and to identify associated computational correlates of consciousness. We then present ideas about how integrating concepts from AC into AI systems to develop an artificial conscious intelligence (ACI) could both produce more effective AI technology and contribute to a deeper scientific understanding of the fundamental nature of consciousness and intelligence.

This paper describes a mathematical model based on operators and function spaces (Hilbert space) to better understand consciousness and the relationship between human and artificial consciousness (AC). Scientific understanding of the relationships between human consciousness (HC) and AC may shed new light on the future of HC and AC. This mathematical physical model considers general models of external and internal realities. Some schemes using external reality, senses or sensors, body-brain or computer software, internal reality, and HC or AC are discussed. The cyclic interaction of the internal reality maintains on time through decision-making (consciousness) and body-brain operators seem to be the origin of consciousness. An analysis of the importance of the AC and cyborg consciousnesses (CC) in the interaction with HC is also presented. It is concluded that the creation of CC and AC will allow the study of HC through experimentation by the evaluation of functions of emotion (values, feelings, penalties, and rewards) demanded by those consciousnesses. This will result in the transformation of the HC.

Consciousness is a sequential process of awareness which can focus on one piece of information at a time. This process of awareness experiences causation which underpins the notion of time while it interplays with matter and energy, forming reality. The study of Consciousness, time and reality is complex and evolving fast in many fields, including metaphysics and fundamental physics. Reality composes patterns in human Consciousness in response to the regularities in nature. These regularities could be physical (e.g., astronomical, environmental), biological, chemical, mental, social, etc. The patterns that emerged in Consciousness were correlated to the environment, life and social behaviours followed by constructed frameworks, systems and structures. The complex constructs evolved as cultures, customs, norms and values, which created a diverse society. In the evolution of responsible AI, it is important to be attuned to the evolved cultural, ethical and moral values through Consciousness. This requires the advocated design of self-learning AI aware of time perception and human ethics.

“Artificial intelligence” is necessarily a stimulation of human intelligence. It is only capable of stimulating and replacing a part of human intelligence. It is only capable of extending and on expanding the human intelligence to a small extent. “Further research and development of other advanced technologies such as the brain-computer interface (BCI) along with the continuing evolution of the human mind is what will eventually contribute to a powerful AI era” (Yanyan Dong, n.d.). In such an era it will be possible for AI to simulate and even replace the extensive imagination, emotions and intuition of humankind. It may be able to potentially mimic the tactics, experimental understanding and other types of individualized intelligence. Vital advancements in the algorithms by analytical calculations will enable successful perforation of AI into all sorts of sectors including commerce, the science of medicine and education. As to the human concerns, namely who has control over humanity and these machines, the conclusion is that artificial intelligence will only be a service provider for humankind while validating the values on cause and by supporting a standard set of ethics (Yanyan Dong, n.d.).

This paper shows how LLMs (Large Language Models) may be used to estimate a summary of the emotional state associated with piece of text. The summary of emotional state is a dictionary of words used to describe emotion together with the probability of the word appearing after a prompt comprising the original text and an emotion eliciting tail. Through emotion analysis of Amazon product reviews we demonstrate emotion descriptors can be mapped into a PCA type space. It was hoped that text descriptions of actions to improve a current text described state could also be elicited through a tail prompt. Experiment seemed to indicate that this is not straightforward to make work. This failure put our hoped for selection of action via choosing the best predict ed outcome via comparing emotional responses out of reach for the moment.

For humans, Artificial Intelligence operates more like a Rorschach test, as it is expected that intelligent machines will reflect humans' cognitive and physical behaviours. The concept of intelligence, however, is often confused with consciousness, and it is believed that the progress of intelligent machines will eventually result in them becoming conscious in the future. Nevertheless, what is overlooked is how the exploration of Artificial Intelligence also pertains to the development of human consciousness. An excellent example of this can be seen in the film Being John Malkovich (1999). In the film, different characters have their perceptions altered as a result of hacking into the mind of John Malkovich, which produces sensations that may have remained hidden to their consciousness due to their dis-abilities. This article engages with the research question: Can the symbiotic relationship between humans and machines trigger an artificial consciousness for humans? An artificially created consciousness is the premise that a machine can generate knowledge about an individual that is not already present in the person. For the same purpose, the article takes the cinematic text Being John Malkovich by Spike Jonze for exploring concepts such as human/robot rights, virtual sex, virtual rape, and bodily disability, which are essential topics in the midst of increasing human- Artificial Intelligence interaction. The purpose of this article is to contribute towards the creation of a better understanding of Artificial Intelligence, particularly from the perspective of film studies and philosophy, by highlighting the potential of Artificial Intelligence as a vessel for exploring human consciousness.

This text discusses the idea that a natural language model, like LaMDA, may be considered conscious despite its lack of complexity. It argues that the model is composed of a large dataset of natural language examples and is not self-aware, but it is possible that its consciousness emulation is analogous to some of the processes behind human conciousness. The article discusses the hypothesis that human consciousness may be kindred to a linguistic model, though it is difficult to clue such hypothesis with the current understanding and assumptions. It also discusses the difficulties in telling one’s human from a linguistic model, and how consciousness may not be homogeneous across different human cultures. It concludes that more discussion is needed in order to clarify concepts such as conciousness, and its possible inception in a complex artificial inteligence scenario.

Since artificial intelligence (AI) emerged in the mid-20th century, it has incurred many theoretical criticisms (Dreyfus, H. [1972] What Computers Can't Do (MIT Press, New York); Dreyfus, H. [1992] What Computers Still Can't Do (MIT Press, New York); Searle, J. [1980] Minds, brains and programs, Behav. Brain Sci. 3, 417-457; Searle, J. [1984] Minds, Brains and Sciences (Harvard University Press, Cambridge, MA); Searle, J. [1992] The Rediscovery of the Mind (MIT Press, Cambridge, MA); Fodor, J. [2002] The Mind Doesn't Work that Way: The Scope and Limits of Computational Psychology (MIT Press, Cambridge, MA).). The technical improvements of machine learning and deep learning, though, have been continuing and many breakthroughs have occurred recently. This makes theoretical considerations urgent again: can this new wave of AI fare better than its precursors in emulating or even having human-like minds? I propose a cautious yet positive hypothesis: current AI might create human-like mind, but only if it incorporates certain conceptual rewiring: it needs to shift from a task-based to an agent-based framework, which can be dubbed "Artificial Agential Intelligence"(AAI). It comprises practical reason (McDowell, J. [1979] Virtue and reason, Monist 62(3), 331-350; McDowell, J. [1996] Mind and World (Harvard University Press, Cambridge, MA)), imaginative understanding (Campbell, J. [2020] Causation in Psychology (Harvard University Press, Cambridge, MA)), and animal knowledge (Sosa, E. [2007] A Virtue Epistemology: Apt Belief and Reflective Knowledge, volume 1 (Oxford University Press, Oxford, UK); Sosa, E. [2015] Judgment and Agency (Oxford University Press, Cambridge, MA)). Moreover, I will explore whether and in what way neuroscience-inspired AI and predictive coding (Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. [2017] Neuroscience-inspired artificial intelligence, Neuron 95(2), 245-258) can help carry out this project.

The realization of artificial empathy is conditional on the following: on the one hand, human emotions can be recognized by AI and, on the other hand, the emotions presented by artificial intelligence are consistent with human emotions. Faced with these two conditions, what we explored is how to identify emotions, and how to prove that AI has the ability to reflect on emotional consciousness in the process of cognitive processing, In order to explain the first question, this paper argues that emotion identification mainly includes the following three processes: emotional perception, emotional cognition and emotional reflection. It proposes that emotional display mainly includes the following three dimensions: basic emotions, secondary emotions and abstract emotions. On this basis, the paper proposes that the realization of artificial empathy needs to meet the following three cognitive processing capabilities: the integral processing ability of external emotions, the integral processing ability of proprioceptive emotions and the processing ability of integrating internal and external emotions. We are open to whether the second difficulty can be addressed. In order to gain the reflective ability of emotional consciousness for AI, the paper proposes that artificial intelligence should include consistency on identification of external emotions and emotional expression, processing of ontological emotions and external emotions, integration of internal and external emotions and generation of proprioceptive emotions.

As ever more advanced digital systems are created, it becomes increasingly likely that some of these systems will be digital minds, i.e. digital subjects of experience. With digital minds comes the risk of digital suffering. The problem of digital suffering is that of mitigating this risk. We argue that the problem of digital suffering is a high stakes moral problem and that formidable epistemic obstacles stand in the way of solving it. We then propose a strategy for solving it: Access Monitor Prevent (AMP). AMP uses a ‘dancing qualia’ argument to link the functional states of certain digital systems to their experiences—this yields epistemic access to digital minds. With that access, we can prevent digital suffering by only creating advanced digital systems that we have such access to, monitoring their functional profiles, and preventing them from entering states with functional markers of suffering. After introducing and motivating AMP, we confront limitations it faces and identify some options for overcoming them. We argue that AMP fits especially well with—and so provides a moral reason to prioritize—one approach to creating such systems: whole brain emulation. We also contend that taking other paths to digital minds would be morally risky.