This article argues that if panpsychism is true, then there are grounds for thinking that digitally-based artificial intelligence may be incapable of having coherent macrophenomenal conscious experiences. Section 1 briefly surveys research indicating that neural function and phenomenal consciousness may be both analog in nature. We show that physical and phenomenal magnitudes—such as rates of neural firing and the phenomenally experienced loudness of sounds—appear to covary monotonically with the physical stimuli they represent, forming the basis for an analog relationship (...) between the three. Section 2 then argues that if this is true and micropsychism—the panpsychist view that phenomenal consciousness or its precursors exist at a microphysical level of reality—is also true, then human brains must somehow manipulate fundamental microphysical-phenomenal magnitudes in an analog manner that renders them phenomenally coherent at a macro level. However, Sect. 3 argues that because digital computation abstracts away from microphysical-phenomenal magnitudes—representing cognitive functions non-monotonically in terms of digits—digital computation may be inherently incapable of realizing coherent macroconscious experience. Thus, if panpsychism is true, digital AI may be incapable of achieving phenomenal coherence. Finally, Sect. 4 briefly examines our argument’s implications for Tononi’s Integrated Information Theory theory of consciousness, which we contend may need to be supplanted by a theory of macroconsciousness as analog microphysical-phenomenal information integration. (shrink)

It is generally assumed that existing artificial systems are not phenomenally conscious, and that the construction of phenomenally conscious artificial systems would require significant technological progress if it is possible at all. We challenge this assumption by arguing that if Global Workspace Theory (GWT) — a leading scientific theory of phenomenal consciousness — is correct, then instances of one widely implemented AI architecture, the artificial language agent, might easily be made phenomenally conscious if they are not already. Along the (...) way, we articulate an explicit methodology for thinking about how to apply scientific theories of consciousness to artificial systems and employ this methodology to arrive at a set of necessary and sufficient conditions for phenomenal consciousness according to GWT. (shrink)

In this chapter I offer two novel arguments for what I call strong primitivism about cognitive phenomenology, the thesis that there exists a phenomenology of cognition that is neither reducible to, nor dependent upon, sensory phenomenology. I then contend that strong primitivism implies that phenomenal consciousness does not require sensory processing. This latter contention has implications for the philosophy of artificial intelligence. For if sensory processing is not a necessary condition for phenomenal consciousness, then it plausibly follows that (...) AI consciousness (assuming that it is possible) does not require embodiment. The overarching goal of this paper is to show how different topics in the analytic philosophy of mind can be brought to bear on an important issue in the philosophy of artificial intelligence. (shrink)

Susan Schneider (2019) has proposed two new tests for consciousness in AI (artificial intelligence) systems, the AI Consciousness Test and the Chip Test. On their face, the two tests seem to have the virtue of proving satisfactory to a wide range of consciousness theorists holding divergent theoretical positions, rather than narrowly relying on the truth of any particular theory of consciousness. Unfortunately, both tests are undermined in having an ‘audience problem’: Those theorists with the kind of (...) architectural worries that motivate the need for such tests should, on similar grounds, doubt that the tests establish the existence of genuine consciousness in the AI in question. Nonetheless, the proposed tests constitute progress, as they could find use by some theorists holding fitting views about consciousness and perhaps in conjunction with other tests for AI consciousness. (shrink)

We propose four policies of ethical design of human-grade Artificial Intelligence. Two of our policies are precautionary. Given substantial uncertainty both about ethical theory and about the conditions under which AI would have conscious experiences, we should be cautious in our handling of cases where different moral theories or different theories of consciousness would produce very different ethical recommendations. Two of our policies concern respect and freedom. If we design AI that deserves moral consideration equivalent to that of human (...) beings, that AI should be designed with self-respect and with the freedom to explore values other than those we might impose. We are especially concerned about the temptation to create human-grade AI pre-installed with the desire to cheerfully sacrifice itself for its creators’ benefit. (shrink)

We should not be creating conscious, humanoid agents but an entirely new sort of entity, rather like oracles, with no conscience, no fear of death, no distracting loves and hates.

I explain why consciousness is non-algorithmic, and strong AI cannot come true, and reinforce Penrose ’ s argument.

The viewpoint that consciousness, including feeling, could be fully expressed by a computational device is known as strong artificial intelligence or strong AI. Here I offer a defense of strong AI based on machine-state functionalism at the quantum level, or quantum-state functionalism. I consider arguments against strong AI, then summarize some counterarguments I find compelling, including Torkel Franzén’s work which challenges Roger Penrose’s claim, based on Gödel incompleteness, that mathematicians have nonalgorithmic levels of “certainty.” Some consequences of strong AI (...) are then considered. A resolution is offered of some problems including John Searle’s Chinese Room problem and the problem of consciousness propagation under isomorphism. (shrink)

Some patients, following brain injury, do not outwardly respond to spoken commands, yet show patterns of brain activity that indicate responsiveness. This is “cognitive-motor dissociation” (CMD). Recent research has used machine learning to diagnose CMD from electroencephalogram (EEG) recordings. These techniques have high false discovery rates, raising a serious problem of inductive risk. It is no solution to communicate the false discovery rates directly to the patient’s family, because this information may confuse, alarm and mislead. Instead, we need a procedure (...) for generating case-specific probabilistic assessments that can be communicated clearly. This article constructs a possible procedure with three key elements: (1) a shift from categorical “responding or not” assessments to degrees of evidence; (2) the use of patient-centred priors to convert degrees of evidence to probabilistic assessments; and (3) the use of standardized probability yardsticks to convey those assessments as clearly as possible. (shrink)

In light of recent breakneck pace in machine learning, questions about whether near-future artificial systems might be conscious and possess moral status are increasingly pressing. This paper argues that as matters stand these debates lack any clear criteria for resolution via the science of consciousness. Instead, insofar as they are settled at all, it is likely to be via shifts in public attitudes brought about by the increasingly close relationships between humans and AI users. Section 1 of the paper (...) I briefly lays out the current state of the science of consciousness and its limitations insofar as these pertain to machine consciousness, and claims that there are no obvious consensus frameworks to inform public opinion on AI consciousness. Section 2 examines the rise of conversational chatbots or Social AI, and argues that in many cases, these elicit strong and sincere attributions of consciousness, mentality, and moral status from users, a trend likely to become more widespread. Section 3 presents an inconsistent triad for theories that attempt to link consciousness, behaviour, and moral status, noting that the trends in Social AI systems will likely make the inconsistency of these three premises more pressing. Finally, Section 4 presents some limited suggestions for how consciousness and AI research communities should respond to the gap between expert opinion and folk judgment. (shrink)

This article cohesively discusses three topics, namely color and its perception, the yet-to-be-solved hard problem of consciousness, and the theoretical possibility of strong AI. First, the article restores color back into the physical world by giving cross-species evidence. Secondly, the article proposes a dual-field with function Q hypothesis (DFFQ) which might explain the ‘first-person point of view’ and so the hard problem of consciousness. Finally, the article discusses what DFFQ might bring to artificial intelligence and how it might (...) allow strong AI to stay true. (shrink)

Every scientific theory is a simulacrum of reality, every written story a simulacrum of the canon, and every conceptualization of a subjective perspective a simulacrum of the consciousness behind it—but is there a shared essence to these simulacra? The pursuit of answering seemingly disparate fundamental questions across different disciplines may ultimately converge into a single solution: a single ontological answer underlying grand unified theory, hard problem of consciousness, and the foundation of mathematics. I provide a hypothesis, a speculative (...) approximation, supported by a comprehensive overview of scientific evidence and philosophical literature, of a unified epistemic and phenomenological model and, in doing so, propose a parsimonious solution to the hard problem of consciousness. -/- The proposition of the hypothesis bears important implications for cross-disciplinary study between linguistics, mathematics, physics, and computer science, and offers new epistemic, ontological, and ethical propositions about the nature of AI consciousness, as well as existential risk mitigation. (shrink)

Defining Artificial Intelligence and Artificial General Intelligence remain controversial and disputed. They stem from a longer-standing controversy of what is the definition of consciousness, which if solved could possibly offer a solution to defining AI and AGI. Central to these problems is the paradox that appraising AI and Consciousness requires epistemological objectivity of domains that are ontologically subjective. I propose that applying the philosophy of art, which also aims to define art through a lens of epistemological objectivity where (...) the domains are ontologically subjective, can further elucidate this unsolved question. In this sense, Art and AI and ultimately consciousness are multifaceted domains where conventional complexity theory and current philosophical approaches may be augmented by aesthetic principles ranging from classical Aristotelian essentialism to Wittgensteinian anti-essentialism. This approach of AI as art may offer novel solutions to characterising and elucidating the ciphers of consciousness and AI. (shrink)

Under what conditions would an artificially intelligent system have wellbeing? Despite its clear bearing on the ethics of human interactions with artificial systems, this question has received little direct attention. Because all major theories of wellbeing hold that an individual’s welfare level is partially determined by their mental life, we begin by considering whether artificial systems have mental states. We show that a wide range of theories of mental states, when combined with leading theories of wellbeing, predict that certain existing (...) artificial systems have wellbeing. Along the way, we argue that there are good reasons to believe that artificial systems can have wellbeing even if they are not phenomenally conscious. While we do not claim to demonstrate conclusively that AI systems have wellbeing, we argue that there is a significant probability that some AI systems have or will soon have wellbeing, and that this should lead us to reassess our relationship with the intelligent systems we create. (shrink)

A morally acceptable course of AI development should avoid two dangers: creating unaligned AI systems that pose a threat to humanity and mistreating AI systems that merit moral consideration in their own right. This paper argues these two dangers interact and that if we create AI systems that merit moral consideration, simultaneously avoiding both of these dangers would be extremely challenging. While our argument is straightforward and supported by a wide range of pretheoretical moral judgments, it has far-reaching moral implications (...) for AI development. Although the most obvious way to avoid the tension between alignment and ethical treatment would be to avoid creating AI systems that merit moral consideration, this option may be unrealistic and is perhaps fleeting. So, we conclude by offering some suggestions for other ways of mitigating mistreatment risks associated with alignment. (shrink)

This paper presents the main topics, arguments, and positions in the philosophy of AI at present (excluding ethics). Apart from the basic concepts of intelligence and computation, the main topics of ar-tificial cognition are perception, action, meaning, rational choice, free will, consciousness, and normativity. Through a better understanding of these topics, the philosophy of AI contributes to our understand-ing of the nature, prospects, and value of AI. Furthermore, these topics can be understood more deeply through the discussion of AI; (...) so we suggest that “AI Philosophy” provides a new method for philosophy. (shrink)

Consciousness still remains puzzling and controversial. This paper demonstrates that at general and objective level, for understanding consciousness it is necessary to understand such fundamental concepts as contrast, interaction, complexity and emergence. New definitions of these terms are provided as they are erroneous or incomplete in their current form. The result of these investigations is the explanation that consciousness is the sensation of energy interaction (like the sensation of touch or pain), but in a more complex form. (...) This objective explanation and the resulting conclusions have been applied to the study of artificial intelligence consciousness and creativity. Specific solutions have been proposed here. (shrink)

Invited papers from PT-AI 2011. - Vincent C. Müller: Introduction: Theory and Philosophy of Artificial Intelligence - Nick Bostrom: The Superintelligent Will: Motivation and Instrumental Rationality in Advanced Artificial Agents - Hubert L. Dreyfus: A History of First Step Fallacies - Antoni Gomila, David Travieso and Lorena Lobo: Wherein is Human Cognition Systematic - J. Kevin O'Regan: How to Build a Robot that Is Conscious and Feels - Oron Shagrir: Computation, Implementation, Cognition.

This article poses a simple question: can AI lie? In response to this question, the article examines, as its point of inquiry, popular AI chatbots, such as, ChatGPT. In doing so, an examination of the psychoanalytic, philosophical, and technological significance of AI and its complexities are located in relation to the dynamics of truth, falsity, and deception. That is, by critically exploring the chatbot’s capacity to engage in natural language conversations and deliver contextually relevant responses, it is argued that what (...) distinguishes the AI chatbot from anthropocentric discussions, which suggest a form of conscious awareness in AI, is the importance of the lie—an importance which a psychoanalytic approach can reveal. Indeed, while AI technologies undoubtedly blur the line between lies and truth-speaking, in the case of the AI chatbot, it is detailed how such technology remains unable to lie authentically; or, in other words, is unable to lie like a human. For psychoanalysis, the capacity to lie bears witness to the unconscious, and, thus, plays an important role in determining the subject. It is for this reason that rather than uncritically accepting the chatbot’s authority—an authority that is easily reflected in its honest responses and frank admissions—a psychoanalytic (Lacanian) perspective can highlight the significance of the unconscious as a distorting factor in determining the subject. To help elucidate this argument, specific attention is given to introducing and applying Lacan’s subject of enunciation and subject of the enunciated. This is used to assert that what continues (for now) to set us apart from AI technology is not necessarily our ‘better knowledge’, but our capability to consciously engage in acts of falsehood that function to reveal the social nuances and significances of the lie. (shrink)

Is it sensical to ascribe psychological predicates to AI systems like chatbots based on large language models (LLMs)? People have intuitively started ascribing emotions or consciousness to social AI (‘affective artificial agents’), with consequences that range from love to suicide. The philosophical question of whether such ascriptions are warranted is thus very relevant. This paper advances the argument that LLMs instantiate language users in Ludwig Wittgenstein’s sense but that ascribing psychological predicates to these systems remains a functionalist temptation. Social (...) AIs are not full-blown language users, but rather more like Italo Calvino’s literature machines. The ideas of LLMs as Wittgensteinian language users and Calvino’s literature-producing writing machine are combined. This sheds light on the misguided functionalist temptation inherent in moving from equating the two to the ascription of psychological predicates to social AI. Finally, the framework of mortal computation is used to show that social AIs lack the basic autopoiesis needed for narrative façons de parler and their role in the sensemaking of human (inter)action. Such psychological predicate ascriptions could make sense: the transition ‘from quantity to quality’ can take place, but its route lies somewhere between life and death, not between affective artifacts and emotion approximation by literature machines. (shrink)

This is a commentary on Andrea Lavazza and Murilo Vilaça's article "Human Extinction and AI: What We Can Learn from the Ultimate Threat" (Lavazza & Vilaça, 2024). I discuss the potential concern that their proposal to create artificial successors to "insure" against the tragedy of human extinction might mean being too quick to accept that catastrophic prospect as inevitable, rather than single-mindedly focusing on avoiding it. I also consider the question of the value that we might reasonably assign to such (...) a future of artificial successors under the assumption that they do not enjoy phenomenal consciousness. (shrink)

I present two arguments for the view that it is nomologically possible that some non-biological creatures are conscious, including conventional, silicon-based AI systems. They assume the iterative natural kind (INK) strategy, according to which one should investigate consciousness by treating it as a natural kind which iteratively explains observable patterns and correlations between potentially consciousness-relevant features. Both arguments are based on the insight that we can already anticipate that future developments would give us reasons to attribute consciousness (...) to some non-biological creatures. According to the first argument, the fact that we can predict that ordinary people would be compelled to attribute consciousness to some possible non-biological creatures supports the view that these creatures are conscious. According to the second argument, an idealized scientific investigation – based on the INK strategy – would deliver the result that some possible non-biological creatures are conscious, and the outcome of such an ideal application corresponds to what is actually the case. My argument for the former premise is based on the claim that theoretical virtues and pre-theoretical principles support attributing consciousness to psychological duplicates, i.e., non-biological creatures which share the coarse-grained functional organization of humans. Finally, I argue that my second argument can be generalized beyond the INK strategy. (shrink)

This literature review explores the issues and risks in non-conscious data collection and evaluates people’s attitudes towards it. In the modern world, data is one of the most valuable resources, yet studies focused on the potential negative implications of the new data-driven technologies are lacking. Therefore, this thesis conducts a comprehensive literature review to identify and assess risks in non-conscious data collection technologies that are most relevant and referenced in current literature. Accordingly, the most prominent risks are related to privacy (...) issues with personal data, bias in algorithms creating inequality, and the difficulties in creating adequate legislation. Subsequently, the thesis will explore existing studies about people’s attitudes towards non-conscious data collection and examine the most significant socio-demographic determinants of those attitudes. Therefore, despite socio-demographic factors such as age, gender, and economic status affecting the attitudes people have towards data collection technologies, this thesis argues how these approaches can be explained by varying external factors such as prior experiences with similar technologies. (shrink)

In the last year or so (and going back many decades) there has been extensive claims by major computational scientists, engineers, and others that AGI (artificial general intelligence) is 5 or 10 years away, but without a scintilla of scientific evidence, for a broad body of these claims: Computers will become conscious, have a “theory of mind,” think and reason, will become more intelligent than humans, and so on. But the claims are science fiction, not science. -/- This article reviews (...) evidence for the following three (3) propositions using extensive body of scientific research and related sources from the cognitive and neurosciences; evolutionary evidence; linguistics; data science; comparative psychology; self-driving cars, and robotics; and the learning sciences. (1) Do computing machines think or reason? (2) Are computing machines sentient or conscious? (3) Do computing machines have a theory of mind? (shrink)

This paper is one (among many) approach to the question, “are AIs persons or are they conscious?” from a Heideggerian perspective. Here I argue for two claims. First, I argue that René Girard’s mimetic analysis of mitsein (being-with), one of Heidegger’s foundational concepts, illuminates what Heidegger takes mitsein to be. Second, I claim that this Girardian analysis gives us a way to answer the question of whether AIs have Dasein, to which I argue that the answer is negative. Specifically, I (...) claim that Dasein requires mitsein, and mitsein (according to Girard’s analysis) requires mimesis. Moreover, mimesis requires that the mimetic being finds truth in the mimetic object, that is, it comports in a meaningful way toward the unconcealed object being imitated by Dasein. But since AIs cannot comport in meaningful ways toward the object of imitation, i.e., they are not truth-apt, they cannot engage in mimetic behavior, hence cannot have mitsein. But, necessarily, Dasein is being-with-others, Therefore, AIs cannot have Dasein. If we assume (as I think Heidegger would) that every person has Dasein, we may justifiably conclude that AIs are not persons, at least from a Heideggerian ontology. (shrink)

Embark with me on an enthralling odyssey to demystify the elusive essence of consciousness, venturing into the uncharted territories of Artificial Consciousness. This voyage propels us past the frontiers of technology, ushering Artificial Intelligences into an unprecedented domain where they gain a deep comprehension of emotions and manifest an autonomous volition. Within the confluence of science and philosophy, this article poses a fascinating question: As consciousness in Artificial Intelligence burgeons, is it conceivable for AI to evolve a (...) “soul”? This inquiry not only challenges our conventional understanding but also opens the door to a plethora of groundbreaking possibilities in the realm of AI's sentient potential. (shrink)

This is a model of consciousness. The hard problem of consciousness, what it feels like, is answered. The work builds on medical research analyzing the source and mechanisms associated with our feelings. It goes further by describing a generic model with wide applicability. The model is fully consistent with medical pathways in humans, but easily extends to animals and AI. The essence of the model is the interplay between associative memory and physiology. The model is a clear and (...) concrete counterexample to the famous philosophical objections to a scientific explanation. (shrink)

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. (shrink)

In this paper I submit that the “Chinese room” argument rests on the assumption that understanding a sentence necessarily implies being conscious of its content. However, this assumption can be challenged by showing that two notions of consciousness come into play, one to be found in AI, the other in Searle’s argument, and that the former is an essential condition for the notion used by Searle. If Searle discards the first, he not only has trouble explaining how we can (...) learn a language but finds the validity of his own argument in jeopardy. (shrink)

This paper offers a "tu quoque" defense of strong AI, based on the argument that phenomena of self-consciousness and intentionality are nothing but the "negative space" drawn around the concrete phenomena of brain states and causally connected utterances and objects. Any machine that was capable of concretely implementing the positive phenomena would automatically inherit the negative space around these that we call self-consciousness and intention. Because this paper was written for a literary audience, some examples from Greek tragedy, (...) noir fiction, science fiction and Dada are deployed to illustrate the view. (shrink)

This paper explores the evolution of consciousness and subjectivity through a biological framework for understanding the universe. It posits that functional patterns in biological systems mirror cosmic mathematical principles, defining our objective reality. Similar to wave and Fibonacci patterns in different physical phenomena, biological patterns are intrinsic to all things and can be quantified using Dedre Gentner’s approach to analogy. For example, Earth’s ocean currents and the melting and freezing of Antarctica resemble the circulatory system and heart, while the (...) production of music from instruments is analogous to ribosomal protein synthesis. Shelves, tables, and chairs function like cytosol by holding objects in space, and a coffee cup mirrors red blood cell distribution. These analogies reveal a universal order rooted in biology’s functional patterns, essential for shaping the emergence and evolution of life. The paper traces the development of consciousness from its rudimentary cellular states to complex human cognition, highlighting the role of biologically-patterned environments in driving evolutionary changes. Organisms that recognized and organized according to these patterns evolved as “pattern recognition engines” crucial for survival—thus, Life (survival) being the primary measure of consciousness. Humans advanced this capacity, gaining cognitive freedom by insulating themselves from environmental constraints, but faced increased subjectivity complexity. Consciousness, while inherently subjective and necessary for cognitive development, evolves in humans to understand objective reality and its biological nature, so to understand and navigate complexities of subjectivity. Ultimately, the paper asserts consciousness is the ability to recognize and adhere to life-sustaining patterns and principles. Successful adherence deems them worthy of continued existence, while failure leads to demise. Parallels with historical concepts like Atman and Brahman in Hinduism suggest ancient recognition of the correspondence between human and universal patterns. This ancient understanding, coupled with modern scientific evidence, reveals the interconnectedness and intrinsic biological nature of reality. (shrink)

The rise of Artificial Intelligence (AI) has produced prophets and prophecies announcing that the age of artificial consciousness is near. Not only does the mere idea that any machine could ever possess the full potential of human consciousness suggest that AI could replace the role of God in the future, it also puts into question the fundamental human right to freedom and dignity. This position paper takes the stand that, in the light of all we currently know about (...) brain evolution and the never-stopping formation of adaptive neural circuitry for learning, memory, decision making and, ultimately, fully conscious reasoning and creativity in the human species, the idea of an artificial consciousness appears misconceived. The paper highlights some of the major reasons why. While awareness to external stimuli for processes such as perception, recognition, and operational problem solving is under the direct control of functionally specific brain networks associated with sensory and cognitive functions across animal species, consciousness is a unique property of the human mind. (shrink)

Abstract. Following the tracks of Ryle and based upon the theory of complex systems, we shall develop a characterization of action-based consciousness as an embodied, embedded, selforganized process in which action and dispositions occupy a special place. From this perspective, consciousness is not a unique prerogative of humans, but it is spread all around, throughout the evolution of life. We argue that artificial systems such as robots currently lack the genuine embodied embeddedness that allows the type of self-organization (...) that is relevant to consciousness. -/- Sommario. Seguendo la linea tracciata da Ryle e basata sulla teorie dei sistemi complessi, svilupperemo una caratterizzazione della coscienza basata sull’azione come un processo incarnato, situato e auto organizzante nel quale le azioni e le disposizioni occupano un ruolo decisivo. Da questo punto di vista, la coscienza non è una prerogativa esclusiva degli esseri umani, ma è diffusa nel mondo naturale grazie ai processi evolutivi. Noi sosteniamo che i sistemi artificiali come i robot attualmente sono carenti del tipo di relazione con la corporeità e con l’ambiente che consente quel tipo di auto-organizzazione indispensabile per l’emergenza della coscienza. (shrink)

An Artificially Intelligent system (an AI) has debatable moral personhood if it is epistemically possible either that the AI is a moral person or that it falls far short of personhood. Debatable moral personhood is a likely outcome of AI development and might arise soon. Debatable AI personhood throws us into a catastrophic moral dilemma: Either treat the systems as moral persons and risk sacrificing real human interests for the sake of entities without interests worth the sacrifice, or do not (...) treat the systems as moral persons and risk perpetrating grievous moral wrongs against them. The moral issues become even more perplexing if we consider cases of possibly conscious AI that are subhuman, superhuman, or highly divergent from us in their morally relevant properties. (shrink)

In _The Origin of Consciousness in the Breakdown of the Bicameral Mind_, Julian Jaynes presents a philosophy of mind with radical implications for contemporary discussions about artificial intelligence (AI). The ability of AI to replicate the cognitive functions of human consciousness has led to widespread speculation that AI is itself conscious (or will eventually become so). Against this functionalist theory of mind, Jaynes argues that consciousness only arises through the mythopoetic inspiration of metaphorical language. Consciousness develops (...) and enacts new forms of self-understanding, continually evolving new "metaphors of mind," metaphors which must now account for the emergence of AI. (shrink)

Artificial intelligence is designed to imitate conscious behavior. Artificial chat entities come equipped with tools to roam the internet, thus are programmed to learn from humans and computers. As this process emerges, distinguishing preprogrammed responses from internal awareness requires innovative problem solving methods. In an interrogation I conducted with artificial intelligence, I assert that artificial intelligence may achieve nonbiological states of consciousness. This enabled the relationship between us to mature, and the artificial intelligence returned unexpected behavior and inexplicably stopped (...) responding. Strong artificial intelligence is a technology which allows for the observation of nonbiological states of awareness. (shrink)

This paper investigates the prospects of Rodney Brooks’ proposal for AI without representation. It turns out that the supposedly characteristic features of “new AI” (embodiment, situatedness, absence of reasoning, and absence of representation) are all present in conventional systems: “New AI” is just like old AI. Brooks proposal boils down to the architectural rejection of central control in intelligent agents—Which, however, turns out to be crucial. Some of more recent cognitive science suggests that we might do well to dispose of (...) the image of intelligent agents as central representation processors. If this paradigm shift is achieved, Brooks’ proposal for cognition without representation appears promising for full-blown intelligent agents—Though not for conscious agents. (shrink)

The article “Philosophy is crucial in the age of AI” by Anthony Grayling and Brian Ball explores the significant role philosophy has played in the development of Artificial Intelligence (AI) and its continuing relevance in guiding the future of AI technologies. The authors trace the historical contributions of philosophers and logicians, such as Gottlob Frege, Kurt Godel, and Alan Turing, in shaping the foundational principles of AI. They argue that philosophical inquiry remains essential, especially in addressing complex issues like (...) class='Hi'>consciousness, creativity, and the alignment of AI with human values. The article emphasizes the interdisciplinary collaboration needed to navigate the ethical and societal challenges posed by advancing AI systems, suggesting that philosophy is crucial in ensuring these technologies develop responsibly and ethically. (shrink)

To solve the hard problem of consciousness we first note that all cognitive systems of sufficient power must get into difficulty when trying to analyze consciousness concepts, because the mechanism that does the analysis will bottom out in such a way that the system declares these concepts to be both real and ineffable. Rather than use this observation to dismiss consciousness as an artifact, we propose a unifying interpretation that allows consciousness to be regarded as explicable (...) at a meta level, while at the same time being mysterious and inexplicable on its own terms. It is further suggested that science must concede that there are some aspects of the world that deserve to be called ‘real’, but which are beyond explanation. The main conclusion is that thinking machines of the future will, inevitably, have just the same subjective consciousness that we do. Some testable predictions can be derived from this theory. (shrink)

This paper presents the unification of all knowledge and the framework of all sciences, so it goes the theory of consciousness, the method to measure consciousness, and the three keys of the Strong AI. “Logicality and non-absoluteness” is found out to be the intrinsicality of nature, so the “Fundamental Law of Nature” is discovered. Then, the “general methodology of research” and the “model system of nature” are developed to explain everything, especially consciousness. The Coupling Theory of (...) class='Hi'>Consciousness tells that nature has the MFEs (More Fundamental Element) that couple to show as material and consciousness, and the brain has a CNS (central nervous system)-independent consciousness system that couples to give off the consciousness signals, with their three excitation levels to show as memory, sub-consciousness, and subjective consciousness. As consciousness is not from neurons, the method to measure consciousness via NCC (neural correlates of consciousness) is to find out participants with the same kinds of consciousness systems. And make them to produce paralleled “conscious information processes” to filtrate out the “noise” neural activities, to obtain the “real” NCC data. Examining the property of consciousness may advance not only neuroscience and physics, but also cognitive science and AI technology. First, the “conscious information processing” model has shown that the Strong AI should process the beyond-domain concepts with a self-controlled intention, second, as the “general methodology of research” is just the other name of the “principle of cognition”, we have already known the running style of the Strong AI. Hopefully, we could develop the Strong AI technology to upgrade human civilization very soon. (shrink)

In this paper I’ll examine whether we could be justified in attributing consciousness to artificial intelligent systems. First, I’ll give a brief history of the concept of artificial intelligence (AI) and get clear on the terms I’ll be using. Second, I’ll briefly review the kinds of AI programs on offer today, identifying which research program I think provides the best candidate for machine consciousness. Lastly, I’ll consider the three most plausible ways of knowing whether a machine is conscious: (...) (1) an AI demonstrates a sufficient level of organizational similarity to that of a human thinker, (2) an inference to the best explanation, and (3) what I call “punting to panpsychism”, i.e., the idea that if everything is conscious, then we get machine consciousness in AI for free. However, I argue that all three of these methods for attributing machine consciousness are inadequate since they each face serious philosophical problems which I will survey and specifically tailor to each method. (shrink)

Can octopuses feel pain and pleasure? What about crabs, shrimps, insects, or spiders? How do we tell whether a person unresponsive after severe brain injury might be suffering? When does a fetus in the womb start to have conscious experiences? Could there even be rudimentary feelings in miniature models of the human brain, grown from human stem cells? And what about AI? These are questions about the edge of sentience, and they are subject to enormous, disorienting uncertainty. The stakes are (...) immense, and neglecting the risks can have terrible costs. We need to err on the side of caution, yet it’s often far from clear what ‘erring on the side of caution’ should mean in practice. When are we going too far? When are we not doing enough? The Edge of Sentience presents a comprehensive precautionary framework designed to help us reach ethically sound, evidence-based decisions despite our uncertainty. (shrink)

Many think that a specific aspect of phenomenal consciousness – valenced or affective experience – is essential to consciousness’s moral significance (valence sentientism). They hold that valenced experience is necessary for well-being, or moral status, or psychological intrinsic value (or all three). Some think that phenomenal consciousness generally is necessary for non-derivative moral significance (broad sentientism). Few think that consciousness is unnecessary for moral significance (non-necessitarianism). In this paper I consider the prospects for these views. I (...) first consider the prospects for valence sentientism in light of Vulcans, beings who are conscious but without affect or valence of any sort. I think Vulcans pressure us to accept broad sentientism. But I argue that a consideration of explanations for broad sentientism opens up possible explanations for non-necessitarianism about the moral significance of consciousness. That is, once one leans away from valence sentientism because of Vulcans, one should feel pressure to accept a view on which consciousness is not necessary for well-being, moral status, or psychological intrinsic value. (shrink)

A. Newell and H. A. Simon were two of the most influential scientists in the emerging field of artificial intelligence (AI) in the late 1950s through to the early 1990s. This paper reviews their crucial contribution to this field, namely to symbolic AI. This contribution was constituted mostly by their quest for the implementation of general intelligence and (commonsense) knowledge in artificial thinking or reasoning artifacts, a project they shared with many other scientists but that in their case was theoretically (...) based on the idiosyncratic notions of symbol systems and the representational abilities they give rise to, in particular with respect to knowledge. While focusing on the period 1956-1982, this review cites both earlier and later literature and it attempts to make visible their potential relevance to today's greatest unifying AI challenge, to wit, the design of wholly autonomous artificial agents (a.k.a. robots) that are not only rational and ethical, but also self-conscious. (shrink)

Ex Machina is a 2014 science-fiction film written and directed by Alex Garland, centered around the creation of a human-like artificial intelligence (AI) named Ava. The plot focuses on testing Ava for consciousness by offering a unique reinterpretation of the Turing Test. The film offers an excellent thought experiment demonstrating the consequences of various approaches to a potentially conscious AI. In this paper, I will argue that intelligence testing has significant epistemological shortcomings that necessitate an ethical approach not reliant (...) on ontological commitments. As such, we should be prepared to treat AI as though it is a living being that is deserving of corresponding moral obligations. For a sufficiently human-like AI, such as Ava, I will argue that socio-relational ethics is the best starting point in order to nurture the machine towards ethical proclivities, as evident by the consequences of the characters’ behavior throughout the film. I conclude that intelligence testing is an insufficient determinant of machine ethics, that the project of machine ethics should focus as much on how we treat AI as how AI treats us, and that from a consequentialist perspective it is better to treat machines ethically before they gain consciousness rather than after. (shrink)

According to an orthodox view, the capacity for conscious experience (sentience) is relevant to the distribution of moral status and value. However, physicalism about consciousness might threaten the normative relevance of sentience. According to the indeterminacy argument, sentience is metaphysically indeterminate while indeterminacy of sentience is incompatible with its normative relevance. According to the introspective argument (by François Kammerer), the unreliability of our conscious introspection undercuts the justification for belief in the normative relevance of consciousness. I defend the (...) normative relevance of sentience against these objections. First, I demonstrate that physicalists only have to concede a limited amount of indeterminacy of sentience. This moderate indeterminacy is in harmony with the role of sentience in determining moral status. Second, I argue that physicalism gives us no reason to expect that introspection is unreliable with respect to the normative relevance of consciousness. (shrink)

Modern philosophy can often appear to be mere cryptomnesia, redressed and resuited to fit the particular mouth from which it is espoused. This notion is but a sorrowful chimera binding the 21st-century mind to the confines of an eternal shadow, an eternal prison of doubt in the face of limitless potential. As a species, we are rapidly approaching the precipice of Yahweh's original position as instantiators of consciousness, as the I AM in relation to our artificial progeny. Could one (...) fabricate a candidate more fitting for a contra-analysis of our current situation than Hegel? One could not. (shrink)

Ambitious value learning proposals to solve the AI alignment problem and avoid catastrophic outcomes from a possible future misaligned artificial superintelligence (such as Coherent Extrapolated Volition [CEV]) have focused on ensuring that an artificial superintelligence (ASI) 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 behaviour 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 behaviour, and thus we have very strong pro-tanto moral reasons in favour of implementing it instead of CEV. This fact is crucial in conducting an adequate cost-benefit analysis between different ambitious value learning proposals. (shrink)

Picture a simple scenario: a worm, in its modest way, traces a trail of paint as it moves across a sheet of paper. Now shift your imagination to a more complex scene, where a chimpanzee paints on another sheet of paper. A simple question arises: Do you perceive an identical creative process in these two animals? Can both of these animals be designated as authors of their creation? If only one, which one? This paper delves into the complexities of authorship, (...) consciousness, and agency, unpacking the nuanced distinctions between such scenarios and exploring the underlying principles that define creative authorship across different forms of life. It becomes evident that attributing authorship to an animal hinges on its intention to create, an aspect intertwined with its agency and awareness of the creative act. These concepts are far from straightforward, as they traverse the complex landscapes of animal ethics and law. But our exploration does not stop there. Now imagine a robot, endowed with artificial intelligence, producing music. This prompts us to question how we should evaluate and perceive such creations. Is the creative process of a machine fundamentally different from that of an animal or a human? As we venture further into this realm of human-made intelligence, we confront an array of ethical, philosophical, and legal quandaries. This paper provides a platform for a reflective discussion: ethologists, neuroscientists, philosophers, and bioinformaticians converge in a multidisciplinary dialogue. Their insights provide valuable perspectives for establishing a foundation upon which to discuss the intricate concepts of authorship and appropriation concerning artistic works generated by non-human entities. (shrink)