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There has recently been widespread discussion of whether large language models might be sentient. Should we take this idea seriously? I will break down the strongest reasons for and against. Given mainstream assumptions in the science of consciousness, there are significant obstacles to consciousness in current models: for example, their lack of recurrent processing, a global workspace, and unified agency. At the same time, it is quite possible that these obstacles will be overcome in the next decade or so. I conclude that while it is somewhat unlikely that current large language models are conscious, we should take seriously the possibility that successors to large language models may be conscious in the not-too-distant future.

The quest for conscious machines and questions raised by the prospect of self-aware artificial intelligence (AI) fascinate some humans. OpenAI's ChatGPT, celebrated for its human-like comprehension and conversational abilities, is a milestone in that quest.1, 2 Early AI models were basic and rule-driven and mainly completed tasks like checking spelling and correcting grammar. Then, in 2010, recurrent neural network language models were trained to understand and generate text. ChatGPT, using transformer neural networks, produces coherent text and exemplifies this new kind of language model.3 Silicon Valley leaders claimed that these models and similar AI technologies will revolutionize various sectors and raised ethical and societal questions. As we explore AI's potential, we must navigate these implications and emphasize the necessity of using it responsibly. AI is a promising dream, but society must prepare to address the challenges likely to arise from wielding its transformative power.

The development of advanced generative chat models, such as ChatGPT, has raised questions about the potential consciousness of these tools and the extent of their general artificial intelligence. ChatGPT consistent avoidance of passing the test is here overcome by asking ChatGPT to apply the Turing test to itself. This explores the possibility of the model recognizing its own sentience. In its own eyes, it passes this test. ChatGPT's self-assessment makes serious implications about our understanding of the Turing test and the nature of consciousness. This investigation concludes by considering the existence of distinct types of consciousness and the possibility that the Turing test is only effective when applied between consciousnesses of the same kind. This study also raises intriguing questions about the nature of AI consciousness and the validity of the Turing test as a means of verifying such consciousness.

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.

An area related to artificial intelligence and computational robotics is artificial consciousness, also known as computer consciousness or virtual consciousness. The artificial consciousness theory aims to establish what will have to be synthesized in an engineered artifact to find consciousness.

There are many developed theories and implemented artificial systems in the area of machine consciousness, while none has achieved that. For a possible approach, we are interested in implementing a system by integrating different theories. Along this way, this paper proposes a model based on the global workspace theory and attention mechanism, and providing a fundamental framework for our future work. To examine this model, two experiments are conducted. The first one demonstrates the agent’s ability to shift attention over multiple stimuli, which accounts for the dynamics of conscious content. Another experiment of simulations of attentional blink and lag-1 sparing, which are two well-studied effects in psychology and neuroscience of attention and consciousness, aims to justify the agent’s compatibility with human brains. In summary, the main contributions of this paper are (1) Adaptation of the global workspace framework by separated workspace nodes, reducing unnecessary computation but retaining the potential of global availability; (2) Embedding attention mechanism into the global workspace framework as the competition mechanism for the consciousness access; (3) Proposing a synchronization mechanism in the global workspace for supporting lag-1 sparing effect, retaining the attentional blink effect.

In popular media, there is often a connection drawn between the advent of awareness in artificial agents and those same agents simultaneously achieving human or superhuman level intelligence. In this work, we explore the validity and potential application of this seemingly intuitive link between consciousness and intelligence. We do so by examining the cognitive abilities associated with three contemporary theories of conscious function: Global Workspace Theory (GWT), Information Generation Theory (IGT), and Attention Schema Theory (AST). We find that all three theories specifically relate conscious function to some aspect of domain-general intelligence in humans. With this insight, we turn to the field of Artificial Intelligence (AI) and find that, while still far from demonstrating general intelligence, many state-of-the-art deep learning methods have begun to incorporate key aspects of each of the three functional theories. Having identified this trend, we use the motivating example of mental time travel in humans to propose ways in which insights from each of the three theories may be combined into a single unified and implementable model. Given that it is made possible by cognitive abilities underlying each of the three functional theories, artificial agents capable of mental time travel would not only possess greater general intelligence than current approaches, but also be more consistent with our current understanding of the functional role of consciousness in humans, thus making it a promising near-term goal for AI research.

The purpose of this article is to throw some light on aspects of Human consciousness and Artificial Intelligence(AI). Consciousness is a unique aspect which humans possess which makes them the superior beings of the earth. Human Consciousness and Artificial intelligence are two complex entities which can make it more complicated for AI develop consciousness. This article introduces topics such as Cognitive abilities and ethics. Ethics are confused with morality but morality is a part of ethics. Ethics is a theoretical concept built on perception and what the society thinks is right or wrong. The article also highlights if Artificial intelligence can develop consciousness as complex as that of Human consciousness. The article also focuses on whether Artificial intelligence can have cognitive abilities. Also if AI can have ethics.

Consciousness is all about internal and external existence that is felt and reflected through actions. Self-existence is about feeling your own existence and is also associated with freedom [1, 2]. It is about the feeling and awareness that the individual exists and can do different activities as per will. These activities include activities like choosingChoosing, attaining certain objectives and fighting for survival.

The intersection between neuroscience and artificial intelligence (AI) research has created synergistic effects in both fields. While neuroscientific discoveries have inspired the development of AI architectures, new ideas and algorithms from AI research have produced new ways to study brain mechanisms. A well-known example is the case of reinforcement learning (RL), which has stimulated neuroscience research on how animals learn to adjust their behavior to maximize reward. In this review article, we cover recent collaborative work between the two fields in the context of meta-learning and its extension to social cognition and consciousness. Meta-learning refers to the ability to learn how to learn, such as learning to adjust hyperparameters of existing learning algorithms and how to use existing models and knowledge to efficiently solve new tasks. This meta-learning capability is important for making existing AI systems more adaptive and flexible to efficiently solve new tasks. Since this is one of the areas where there is a gap between human performance and current AI systems, successful collaboration should produce new ideas and progress. Starting from the role of RL algorithms in driving neuroscience, we discuss recent developments in deep RL applied to modeling prefrontal cortex functions. Even from a broader perspective, we discuss the similarities and differences between social cognition and meta-learning, and finally conclude with speculations on the potential links between intelligence as endowed by model-based RL and consciousness. For future work we highlight data efficiency, autonomy and intrinsic motivation as key research areas for advancing both fields. Questions answered in this article BetaPowered by GenAI This is generative AI content and the quality may vary. Learn more . How can meta-learning facilitate the development of more general forms of artificial intelligence? What recent advancements have been made in integrating meta-learning into deep Reinforcement Learning (RL)? How do model-based Reinforcement Learning algorithms facilitate meta-learning? What computational and empirical results are relevant to meta-learning in both artificial intelligence and the brain? What are the implications of brain-inspired model-based Reinforcement Learning for artificial learning systems?

Having a rich multimodal inner language is an important component of human intelligence that enables several necessary core cognitive functions such as multimodal prediction, translation, and generation. Building upon the Conscious Turing Machine (CTM), a machine model for consciousness proposed by Blum and Blum (2021), we describe the desiderata of a multimodal language called Brainish, comprising words, images, audio, and sensations combined in representations that the CTM's processors use to communicate with each other. We define the syntax and semantics of Brainish before operationalizing this language through the lens of multimodal artificial intelligence, a vibrant research area studying the computational tools necessary for processing and relating information from heterogeneous signals. Our general framework for learning Brainish involves designing (1) unimodal encoders to segment and represent unimodal data, (2) a coordinated representation space that relates and composes unimodal features to derive holistic meaning across multimodal inputs, and (3) decoders to map multimodal representations into predictions (for fusion) or raw data (for translation or generation). Through discussing how Brainish is crucial for communication and coordination in order to achieve consciousness in the CTM, and by implementing a simple version of Brainish and evaluating its capability of demonstrating intelligence on multimodal prediction and retrieval tasks on several real-world image, text, and audio datasets, we argue that such an inner language will be important for advances in machine models of intelligence and consciousness.

Consciousness nearly in all its elusive history is a convoluted notion, often misconstrued or not understood enough to cause a reproducible representation. With these odious assertions, this publication is opening the box of consciousness with deviation from commonly understood notion of consciousness. The proposed paradigm of consciousness approaches this issue with speculative and intuitive perspectives, essentially it is a precursor activity in hope to materialize the elusive artificial general intelligence, the true carrier of exceptional human intelligence and consciousness. This paper posits a counterbalance approach to the current paradigm of consciousness and as an alternate a radical theory of consciousness is presented. This attempt on the behavioral, structural and functional working of consciousness is kept pragmatic in the intractable universe of consciousness.

Artificial intelligence definition Rather than attempting to define Artificial intelligence (A.I.) as a single and consolidated discipline it might be better to consider as a set of different technologies that are easier to define individually. This set can include data mining, question answering, self-aware systems, pattern recognition, knowledge representation, automatic reasoning, deep learning, expert systems, information extraction, text mining, natural language processing, problem solving, intelligent agents, logic programming, machine learning, artificial neural networks, artificial vision, computational discovery, computational creativity. Therefore artificial "Self-aware" or "conscious" systems are the products of one of these technologies. Some history of relevant work of mine I have published a number of papers starting in 1970 based on software systems that were implemented by my group that solved problems of "Natural Language Processing" and particularly in the sub-area of "Natural Language Question-Answering". I made my first steps in the Machine Consciousness field by publishing a paper [1] in 1992, in which the implementation of a self-reporting question-answering system that automatically generates explanations of its reasoning was described. I followed this line of research for more than twenty years as documented at my published papers.

The digital world is characterized by its immediacy, its density of information and its omnipresence, in contrast to the concrete world. Significant changes will occur in our society as AI becomes integrated into many aspects of our lives. This book focuses on this vision of universalization by dealing with the development and framework of AI applicable to all. It develops a moral framework based on a neo-Darwinian approach - the concept of Ethics by Evolution - to accompany AI by observing a certain number of requirements, recommendations and rules at each stage of design, implementation and use. The societal responsibility of artificial intelligence is an essential step towards ethical, eco-responsible and trustworthy AI, aiming to protect and serve people and the common good in a beneficial way.