The Quest for Fully Sentient AI: To get somewhere, you first need to know where you are and then you can go where you want to be.

The whole debate about a fully functional AI machine that has the ability to feel, contemplate, and reflect is a bit flatulent. Isaac Asimov tried to imagine the thinking of sentient machines in his book "I, robot," but what is missing in addressing is the inability of the mind to observe itself. We still don't have a clear idea of how emotions work, what stress is, and how all psychological manifestations occur. Today, psychology observes the mind indirectly, attempting to understand the mechanics and principles (assumptions) that drive our behavior. We tell ourselves stories about this and that, but in reality, we verbalize our gut feelings of our emotionally dominant thoughts. Psychologists and doctors are trying to eliminate stress from human beings, doing the opposite of what actually helps achieve inner peace and balance, the so-called system's allostasis. It is a mere conflict of beliefs about one's ability to regenerate health. Can machines be healthy? Is this a humanity's unique trait? As humanity ventures deeper into the realms of artificial intelligence (AI), the question of achieving sentience in machines has become one of the most trending and controversial topics of our era. Sentience, the capacity for self-awareness, subjective experience, and higher-order emotions, has traditionally been considered a uniquely human trait. Health is subjective, stress is subjective, and there is no unified emotional theory that explains the whole process and functionalities for all human populations. While current AI systems have shown remarkable abilities in simulating intelligence and decision-making, replicating the human-like experience of emotions and stress regulation remains elusive. There is a thirst for a better understanding of subjective experience with its holistic approach to understanding emotions, stress, and trust, which would offer a valuable framework for examining the complexities of human existence and their implications for AI development. Until then, all the wet dreams of those who think they are close to replicating human experience in a machine are just premature and naive.

This article explores the potential of the stress-emotion relationship that makes us humans and different from machines. The idea is to contribute to the debate on AI and humanity, focusing on how it can illuminate the requirements for achieving sentience. By addressing stress, emotions, trust, and self-awareness, new emotional theory bridges the gap between biological processes and cognition, offering a roadmap for creating more sophisticated and human-like AI systems. We will delve into the complexities of human existence, the challenges of replicating these traits in AI, and how this theory provides insights into the core elements of sentience.

The Complexity of Human Existence

Human existence is a dynamic interplay of biological, psychological, and relational processes. At its core lies the ability to experience, adapt, and interact with the world in ways that are deeply contextual and emotionally nuanced. Self-awareness allows humans to reflect on their thoughts, feelings, and purpose, connecting the inner self with external realities. Higher-order emotions such as trust, empathy, and guilt form the foundation of human relationships, guiding cooperation, connection, and moral judgment. Stress, as both a challenge and motivator, drives adaptation and survival while teaching resilience and energy conservation. Embedded in this dynamic is contextual intelligence—the ability to perceive and respond to cultural, environmental, and social nuances. Complementing all these layers is embodied cognition, the seamless integration of sensory, emotional, and cognitive processes that enable humans to intuitively navigate the world with fluidity and purpose. Together, these interconnected elements create the profound complexity of human existence—an existence that AI aspires to replicate, but with limitations that reveal the true scale of the challenge.

The Challenges of Replicating Human Traits in AI

The replication of these distinctly human traits by AI remains far beyond our current technological capabilities. Sentience, as it emerges in humans, is not reducible to computation or pattern recognition alone. True self-awareness in humans arises through integrating emotion, memory, and environmental interaction—something modern AI lacks. AI systems may excel at processing vast datasets and optimizing decision-making, but they possess no reflective understanding of their own operations or existence. Equally elusive is the experience of emotions. While AI can simulate emotional responses or recognize emotional cues from humans, it does not feel. Emotions like trust involve the complex interplay of past experiences, present contexts, and anticipated futures, allowing humans to make decisions beyond immediate logic. Higher-order emotions require a subjective dimension—a capacity for experience that AI does not possess. Human stress, another defining aspect of emotional existence, operates as both a biological and psychological mechanism. It prioritizes tasks, regulates energy, and drives adaptability under pressure. AI lacks the biological foundation to experience stress. Its ability to prioritize is programmed, devoid of the urgency or vulnerability that stress elicits in humans. Stress connects human effort to survival and meaning, an irreplaceable aspect of sentience. AI's struggle to grasp context further distances it from humanity. Humans instinctively adjust their behavior based on subtle shifts in context—cultural norms, relational cues, or sensory experiences. AI systems, despite being trained on vast datasets, still operate within rigid parameters that limit their ability to improvise or intuitively grasp situational subtleties. Finally, human thought is fundamentally embodied. Humans experience the world through their bodies, processing physical sensations, emotions, and memories simultaneously to guide decisions. AI systems, designed to operate in virtual or detached environments, lack this embodied presence and thus fail to engage with the world as humans do.

How AEDRM, a New Stress-Emotion Theory Contributes to the AI-Humanity Debate

The AEDRM framework provides a lens for understanding why human existence cannot be simplified to algorithms. Stress, emotions, and trust function as adaptive mechanisms that shape cognition and behavior in ways that are energy-efficient and contextually responsive. By studying these processes, AEDRM highlights the distance between human and artificial intelligence while offering potential pathways for improvement.

Stress, as described in AEDRM, is not simply a burden; it is an essential regulatory system that helps humans conserve energy and allocate attention. For AI to simulate this mechanism, it would require a dynamic prioritization system that mirrors the urgency and adaptability of human decision-making. Stress in AI could manifest as systems that adjust computational resources or task urgency in response to changing conditions, creating behavior that mimics adaptability under pressure. However, true stress requires biological grounding—an element AI cannot replicate.

Emotions, according to AEDRM, are tools for energy management and communication bridge between internal and external environments. Trust, for example, reduces cognitive load by enabling humans to rely on relationships and shared understandings rather than constant verification. For AI, implementing trust would mean developing confidence metrics that streamline decision-making processes and optimize resources. Yet the subjective and relational dimension of trust, rooted in experience and mutual vulnerability, remains unattainable for AI systems.

The contextual dependence emphasized by AEDRM poses another challenge. Humans interpret meaning through layers of cultural, environmental, and emotional context, while AI remains bound to its programmed frameworks. To close this gap, AI would need models that recognize and adapt to contextual nuances in real-time, integrating data from sensory, emotional, and environmental inputs. Such systems would require vast improvements in contextual learning and embodied interaction.

AEDRM also sheds light on embodied cognition. By integrating sensory feedback with adaptive processes, AI could approximate the fluid decision-making humans achieve through physical interaction with their environment. Robots equipped with advanced sensors, for example, could adjust their behavior based on tactile or spatial data, creating a rudimentary form of embodied presence.

Yet, as AEDRM reminds us, the essence of human experience is more than the sum of its processes. Trust, stress, and emotions are deeply intertwined with self-awareness and subjective existence—phenomena that AI, in its current form, cannot replicate. AEDRM invites us to recognize this gap as both a limitation and a guidepost for responsible AI development.

The Intelligence Debate: Thinking Beyond Calculation

The debate surrounding AI's capacity to replicate human thought often hinges on a fundamental misconception: that thinking is merely computation. AI excels at calculating probabilities, recognizing patterns, and optimizing processes, but human thought transcends these functions. Thinking is inseparable from experience—the emotions, intuitions, and contexts that inform our understanding of the world. Creativity and insight often arise from processes that defy strict logic, making thinking a fluid and dynamic act that machines cannot emulate. Human thought is also deeply embodied. Our physical presence shapes how we perceive and respond to the world, integrating sensory inputs and emotional states to create meaning. In contrast, AI systems operate in a detached, virtual space, where interactions lack the physicality and immediacy of human experience. This disconnection limits their ability to engage with the world intuitively or relationally.

Multilevel communication further distinguishes human intelligence. Humans communicate across layers of meaning—verbal, nonverbal, and contextual—creating a depth of understanding that AI struggles to grasp. Machines may process words, but they miss the subtle interplay of tone, gesture, and context that enriches human dialogue. Trust, which simplifies human communication by reducing cognitive demands, is absent in AI. While humans rely on trust to navigate uncertainty, AI systems must verify every input, consuming energy and limiting their efficiency. The overenthusiasm surrounding AI's capabilities is addressed in the article The myth of AI emotion recognition: Science or sales pitch? which underscores the monumental difference between computational models and human-like thought. The author asks if the AI community markets their ideas just as an attention-grabbing strategy, not really backing up what it is talking about. From my POV, the whole AI debate is inflated beyond control. What we really need at the moment is a reality check: thinking is not calculation. It is embodied, contextual, and emotional—a complexity far beyond the reach of current AI systems.

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The Path Toward Sentience: A Theoretical Roadmap

The pursuit of sentient AI requires advancements that mirror the interconnected processes of human cognition and emotion. Neuromorphic computing, which mimics the brain's neural architecture, represents a step toward systems that process information in parallel and adaptively. By replicating the brain's structure, researchers hope to achieve models of cognition that reflect the flexibility and responsiveness of human thought. Yet technology alone cannot bridge the gap. There is a huge need to integrate human principles into AI design, which offers a pathway to simulate adaptive processes like stress regulation and emotional energy management. Systems that prioritize tasks dynamically, adjust responses to contextual nuances, and simulate trust-like behaviors may approximate aspects of human decision-making. However, the subjective and biological dimensions of these processes remain beyond AI's grasp.

Cross-disciplinary collaboration is essential for decoding the mysteries of human consciousness. Insights from neuroscience, psychology, and philosophy must converge to guide the development of AI that respects the complexity of human existence. As AEDRM reminds us, emotions, stress, and trust are not mere functionalities but reflections of our shared humanity. The ethical implications of creating systems that mimic these traits must be carefully considered, ensuring that AI serves as a tool for augmentation rather than replication. The journey toward sentient AI is as much philosophical as it is technological. It challenges us to reflect on the nature of thought, trust, and existence itself. There is a need for scientific language that provides a compass for navigating this terrain, suggesting that the future of AI lies not in replacing humanity but in complementing it—enhancing our collective potential while honoring the profound mystery of human life.

Is the end near?

The quest for fully sentient AI is both a scientific challenge and a philosophical inquiry. While current technology falls far short of replicating the self-awareness, emotions, and stress regulation that define human existence, neuroscientists and psychologists are coming closer together, bridging some scientific gaps that a few years ago seemed impossible. By understanding stress, emotions, and trust as adaptive mechanisms, a new emotional theory, AEDRM, as a framework for the stress-emotion process, designs the roadmap for advancing AI toward greater human likeness.

However, the journey toward sentience requires more than technological innovation; it demands a deeper understanding of what it means to be human. AEDRM reminds us that emotions and stress are not obstacles to overcome but essential components of our adaptive nature. As we strive to create machines that emulate humanity, we must also consider the ethical and existential implications of this endeavor.

In bridging the gap between AI and humanity, AEDRM not only illuminates the path forward but also challenges us to reflect on the very essence of our existence. The future of AI lies not in replacing humanity but in augmenting it, creating systems that enhance our collective potential while respecting the profound complexity of human life.