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Original paper available at: philarchive.org/rec/KUTIIA

Introduction

What is reasoning? What is logic? What is math? Philosophical perspectives vary greatly. Platonism, for instance, posits that mathematical and logical truths exist independently in an abstract realm, waiting to be discovered. On the other hand, formalism, nominalism, and intuitionism suggest that mathematics and logic are human constructs or mental frameworks, created to organize and describe our observations of the world. Common sense tells us that concepts such as numbers, relations, and logical structures feel inherently familiar—almost intuitive, but why? Just think a bit about it… They seem so obvious, but why? Do they have deeper origins? What is number? What is addition? Why they work in this way? If you pounder on basic axioms of math, their foundation seems to be very intuitive, but absolutely mysteriously appears to our mind out of nowhere. In a way their true essence magically slips away unnoticed from our consciousness, when we try to pinpoint exactly at their foundation. What the heck is happening? 

Here I want to tackle those deep questions using the latest achievements in machine learning and neural networks, and it will surprisingly lead to the reinterpretation of the role of consciousness in human cognition.

Long story short, what is each chapter about:

1. Intuition often occur in philosophy of reason, logic and math
2. There exist unreasonable effectiveness of mathematics problem
3. Deep neural networks explanation for philosophers. Introduce cognitive close of neural networks and rigidness. Necessary for further argumentation.
4. Uninteroperability of neural network is similar to conscious experience of unreasonable knowledge aka intuition. They are the same phenomena actually. Human intuition insights sometimes might be cognitively closed
5. Intuition is very powerful, more important that though before, but have limits
6. Logic, math and reasoning itself build on top of all mighty intuition as foundation
7. Consciousness is just a specialized tool for innovation, but it is essential for innovation outside of data, seen by intuition
8. Theory predictions
9. Conclusion

Feel free to skip anything you want!

1. Mathematics, logic and reason

Let's start by understanding the interconnection of these ideas. Math, logic and reasoning process can be seen as a structure within an abstraction ladder, where reasoning crystallizes logic, and logical principles lay the foundation for mathematics. Also, we can be certain that all these concepts have proven to be immensely useful for humanity. Let focus on mathematics for now, as a clear example of a mental tool used to explore, understand, and solve problems that would otherwise be beyond our grasp All of the theories acknowledge the utility and undeniable importance of mathematics in shaping our understanding of reality. However, this very importance brings forth a paradox. While these concepts seem intuitively clear and integral to human thought, they also appear unfathomable in their essence. 

No matter the philosophical position, it is certain, however, is that intuition plays a pivotal role in all approaches. Even within frameworks that emphasize the formal or symbolic nature of mathematics, intuition remains the cornerstone of how we build our theories and apply reasoning. Intuition is exactly how we call out ‘knowledge’ of basic operations, this knowledge of math seems to appear to our heads from nowhere, we know it's true, that’s it, and that is very intuitive. Thought intuition also allows us to recognize patterns, make judgments, and connect ideas in ways that might not be immediately apparent from the formal structures themselves. 

2. Unreasonable Effectiveness..

Another mystery is known as unreasonable effectiveness of mathematics. The extraordinary usefulness of mathematics in human endeavors raises profound philosophical questions. Mathematics allows us to solve problems beyond our mental capacity, and unlock insights into the workings of the universe.But why should abstract mathematical constructs, often developed with no practical application in mind, prove so indispensable in describing natural phenomena?

For instance, non-Euclidean geometry, originally a purely theoretical construct, became foundational for Einstein's theory of general relativity, which redefined our understanding of spacetime. Likewise, complex numbers, initially dismissed as "imaginary," are now indispensable in quantum mechanics and electrical engineering. These cases exemplify how seemingly abstract mathematical frameworks can later illuminate profound truths about the natural world, reinforcing the idea that mathematics bridges the gap between human abstraction and universal reality.

And as mathematics, logic, and reasoning occupy an essential place in our mental toolbox, yet their true nature remains elusive. Despite their extraordinary usefulness and centrality in human thought and universally regarded as indispensable tools for problem-solving and innovation, reconciling their nature with a coherent philosophical theory presents a challenge.

3. Lens of Machine Learning

Let us turn to the emerging boundaries of the machine learning (ML) field to approach the philosophical questions we have discussed. In a manner similar to the dilemmas surrounding the foundations of mathematics, ML methods often produce results that are effective, yet remain difficult to fully explain or comprehend. While the fundamental principles of AI and neural networks are well-understood, the intricate workings of these systems—how they process information and arrive at solutions—remain elusive. This presents a symmetrically opposite problem to the one faced in the foundations of mathematics. We understand the underlying mechanisms, but the interpretation of the complex circuitry that leads to insights is still largely opaque. This paradox lies at the heart of modern deep neural network approaches, where we achieve powerful results without fully grasping every detail of the system’s internal logic.

For a clear demonstration, let's consider a deep convolutional neural network (CNN) trained on the ImageNet classification dataset. ImageNet contains more than 14 million images, each hand-annotated into diverse classes. The CNN is trained to classify each image into a specific category, such as "balloon" or "strawberry." After training, the CNN's parameters are fine-tuned to take an image as input. Through a combination of highly parallelizable computations, including matrix multiplication (network width) and sequential data processing (layer-to-layer, or depth), the network ultimately produces a probability distribution. High values in this distribution indicate the most likely class for the image.

These network computations are rigid in the sense that the network takes an image of the same size as input, performs a fixed number of calculations, and outputs a result of the same size. This design ensures that for inputs of the same size, the time taken by the network remains predictable and consistent, reinforcing the notion of a "fast and automatic" process, where the network's response time is predetermined by its architecture. This means that such an intelligent machine cannot sit and ponder. This design works well in many architectures, where the number of parameters and the size of the data scale appropriately. A similar approach is seen in newer transformer architectures, like OpenAI's GPT series. By scaling transformers to billions of parameters and vast datasets, these models have demonstrated the ability to solve increasingly complex intelligent tasks. 

With each new challenging task solved by such neural networks, the interoperability gap between a single parameter, a single neuron activation, and its contribution to the overall objective—such as predicting the next token—becomes increasingly vague. This sounds similar to the way the fundamental essence of math, logic, and reasoning appears to become more elusive as we approach it more closely.

To explain why this happens, let's explore how CNN distinguishes between a cat and a dog in an image. Cat and dog images are represented in a computer as a bunch of numbers. To distinguish between a cat and a dog, the neural network must process all these numbers, or so called pixels simultaneously to identify key features. With wider and deeper neural networks, these pixels can be processed in parallel, enabling the network to perform enormous computations simultaneously to extract diverse features. As information flows between layers of the neural network, it ascends the abstraction ladder—from recognizing basic elements like corners and lines to more complex shapes and gradients, then to textures]. In the upper layers, the network can work with high-level abstract concepts, such as "paw," "eye," "hairy," "wrinkled," or “fluffy."

The transformation from concrete pixel data to these abstract concepts is profoundly complex. Each group of pixels is weighted, features are extracted, and then summarized layer by layer for billions of times. Consciously deconstructing and grasping all the computations happening at once can be daunting. This gradual ascent from the most granular, concrete elements to the highly abstract ones using billions and billions of simultaneous computations is what makes the process so difficult to understand. The exact mechanism by which simple pixels are transformed into abstract ideas remains elusive, far beyond our cognitive capacity to fully comprehend.

4. Elusive foundations

This process surprisingly mirrors the challenge we face when trying to explore the fundamental principles of math and logic. Just as neural networks move from concrete pixel data to abstract ideas, our understanding of basic mathematical and logical concepts becomes increasingly elusive as we attempt to peel back the layers of their foundations. The deeper we try to probe, the further we seem to be from truly grasping the essence of these principles. This gap between the concrete and the abstract, and our inability to fully bridge it, highlights the limitations of both our cognition and our understanding of the most fundamental aspects of reality.

In addition to this remarkable coincidence, we’ve also observed a second astounding similarity: both neural networks processing and human foundational thought processes seem to operate almost instinctively, performing complex tasks in a rigid, timely, and immediate manner (given enough computation). Even advanced models like GPT-4 still operate under the same rigid and “automatic” mechanism as CNNs. GPT-4 doesn’t pause to ponder or reflect on what it wants to write. Instead, it processes the input text, conducts N computations in time T and returns the next token, as well as the foundation of math and logic just seems to appear instantly out of nowhere to our consciousness.

This brings us to a fundamental idea that ties all the concepts together: intuition. Intuition, as we’ve explored, seems to be not just a human trait but a key component that enables both machines and humans to make quick and often accurate decisions, without consciously understanding all the underlying details. In this sense, Large Language Models (LLMs), like GPT, mirror the way intuition functions in our own brains. Just like our brains, which rapidly and automatically draw conclusions from vast amounts of data through what Daniel Kahneman calls System 1 in Thinking, Fast and Slow. LLMs process and predict the next token in a sequence based on learned patterns. These models, in their own way, are engaging in fast, automatic reasoning, without reflection or deeper conscious thought. This behavior, though it mirrors human intuition, remains elusive in its full explanation—just as the deeper mechanisms of mathematics and reasoning seem to slip further from our grasp as we try to understand them.

One more thing to note. Can we draw parallels between the brain and artificial neural networks so freely? Obviously, natural neurons are vastly more complex than artificial ones, and this holds true for each complex mechanism in both artificial and biological neural networks. However, despite these differences, artificial neurons were developed specifically to model the computational processes of real neurons. The efficiency and success of artificial neural networks suggest that we have indeed captured some key features of their natural counterparts. Historically, our understanding of the brain has evolved alongside technological advancements. Early on, the brain was conceptualized as a simple stem mechanical system, then later as an analog circuit, and eventually as a computational machine akin to a digital computer. This shift in thinking reflects the changing ways we’ve interpreted the brain’s functions in relation to emerging technologies. But even with such anecdotes I want to pinpoint the striking similarities between artificial and natural neural networks that make it hard to dismiss as coincidence. They bowth have neuronal-like computations, with many inputs and outputs. They both form networks with signal communications and processing. And given the efficiency and success of artificial networks in solving intelligent tasks, along with their ability to perform tasks similar to human cognition, it seems increasingly likely that both artificial and natural neural networks share underlying principles. While the details of their differences are still being explored, their functional similarities suggest they represent two variants of the single class of computational machines.

5. Limits of Intuition

Now lets try to explore the limits of intuition. Intuition is often celebrated as a mysterious tool of the human mind—an ability to make quick judgments and decisions without the need for conscious reasoning However, as we explore increasingly sophisticated intellectual tasks—whether in mathematics, abstract reasoning, or complex problem-solving—intuition seems to reach its limits. While intuitive thinking can help us process patterns and make sense of known information, it falls short when faced with tasks that require deep, multi-step reasoning or the manipulation of abstract concepts far beyond our immediate experience. If intuition in humans is the same intellectual problem-solving mechanism as LLMs, then let's also explore the limits of LLMs. Can we see another intersection in the philosophy of mind and the emerging field of machine learning?

Despite their impressive capabilities in text generation, pattern recognition, and even some problem-solving tasks, LLMs are far from perfect and still struggle with complex, multi-step intellectual tasks that require deeper reasoning. While LLMs like GPT-3 and GPT-4 can process vast amounts of data and generate human-like responses, research has highlighted several areas where they still fall short. These limitations expose the weaknesses inherent in their design and functioning, shedding light on the intellectual tasks that they cannot fully solve or struggle with (Brown et al., 2020)[18].

1. Multi-Step Reasoning and Complex Problem Solving: One of the most prominent weaknesses of LLMs is their struggle with multi-step reasoning. While they excel at surface-level tasks, such as answering factual questions or generating coherent text, they often falter when asked to perform tasks that require multi-step logical reasoning or maintaining context over a long sequence of steps. For instance, they may fail to solve problems involving intricate mathematical proofs or multi-step arithmetic. Research on the "chain-of-thought" approach, aimed at improving LLMs' ability to perform logical reasoning, shows that while LLMs can follow simple, structured reasoning paths, they still struggle with complex problem-solving when multiple logical steps must be integrated. 
2. Abstract and Symbolic Reasoning: Another significant challenge for LLMs lies in abstract reasoning and handling symbolic representations of knowledge. While LLMs can generate syntactically correct sentences and perform pattern recognition, they struggle when asked to reason abstractly or work with symbols that require logical manipulation outside the scope of training data. Tasks like proving theorems, solving high-level mathematical problems, or even dealing with abstract puzzles often expose LLMs’ limitations and they struggle with tasks that require the construction of new knowledge or systematic reasoning in abstract spaces.
3. Understanding and Generalizing to Unseen Problems: LLMs are, at their core, highly dependent on the data they have been trained on. While they excel at generalizing from seen patterns, they struggle to generalize to new, unseen problems that deviate from their training data. Yuan LeCun argues that LLMs cannot get out of the scope of their training data. They have seen an enormous amount of data and, therefore, can solve tasks in a superhuman manner. But they seem to fall back with multi-step, complex problems. This lack of true adaptability is evident in tasks that require the model to handle novel situations that differ from the examples it has been exposed to. A 2023 study by Brown et al. examined this issue and concluded that LLMs, despite their impressive performance on a wide array of tasks, still exhibit poor transfer learning abilities when faced with problems that involve significant deviations from the training data.
4. Long-Term Dependency and Memory: LLMs have limited memory and are often unable to maintain long-term dependencies over a series of interactions or a lengthy sequence of information. This limitation becomes particularly problematic in tasks that require tracking complex, evolving states or maintaining consistency over time. For example, in tasks like story generation or conversation, LLMs may lose track of prior context and introduce contradictions or incoherence. The inability to remember past interactions over long periods highlights a critical gap in their ability to perform tasks that require dynamic memory and ongoing problem-solving

Here, we can draw a parallel with mathematics and explore how it can unlock the limits of our mind and enable us to solve tasks that were once deemed impossible. For instance, can we grasp the Pythagorean Theorem? Can we intuitively calculate the volume of a seven-dimensional sphere? We can, with the aid of mathematics. One reason for this, as Searle and Hidalgo argue, is that we can only operate with a small number of abstract ideas at a time—fewer than ten (Searle, 1992)(Hidalgo, 2015). Comprehending the entire proof of a complex mathematical theorem at once is beyond our cognitive grasp. Sometimes, even with intense effort, our intuition cannot fully grasp it. However, by breaking it into manageable chunks, we can employ basic logic and mathematical principles to solve it piece by piece. When intuition falls short, reason takes over and paves the way. Yet, it seems strange that our powerful intuition, capable of processing thousands of details to form a coherent picture, cannot compete with mathematical tools. If, as Hidalgo posits, we can only process a few abstract ideas at a time, how does intuition fail so profoundly when tackling basic mathematical tasks?

6. Abstraction exploration mechanism

The answer may lie in the limitations of our computational resources and how efficiently we use them. Intuition, like large language models (LLMs), is a very powerful tool for processing familiar data and recognizing patterns. However, how can these systems—human intuition and LLMs alike—solve novel tasks and innovate? This is where the concept of abstract space becomes crucial. Intuition helps us create an abstract representation of the world, extracting patterns to make sense of it. However, it is not an all-powerful mechanism. Some patterns remain elusive even for intuition, necessitating new mechanisms, such as mathematical reasoning, to tackle more complex problems.

Similarly, LLMs exhibit limitations akin to human intuition. Ultimately, the gap between intuition and mathematical tools illustrates the necessity of augmenting human intuitive cognition with external mechanisms. As Kant argued, mathematics provides the structured framework needed to transcend the limits of human understanding. By leveraging these tools, we can kinda push beyond the boundaries of our intelligent capabilities to solve increasingly intricate problems.

What if, instead of trying to search for solutions in a highly complex world with an unimaginable degree of freedom, we could reduce it to essential aspects? Abstraction is such a tool. As discussed earlier, the abstraction mechanism in the brain (or an LLM) can extract patterns from patterns and climb high up the abstraction ladder. In this space of high abstractions, created by our intuition, the basic principles governing the universe can be crystallize. Logical principles and rational reasoning become the intuitive foundation constructed by the brain while extracting the essence of all the diverse data it encounters. These principles, later formalized as mathematics or logic, are actually the map of a real world. Intuition arises when the brain takes the complex world and creates an abstract, hierarchical, and structured representation of it, it is the purified, essential part of it—a distilled model of the universe as we perceive it. Only then, basic and intuitive logical and mathematical principles emerge. At this point simple scaling of computation power to gain more patterns and insight is not enough, there emerges a new more efficient way of problem-solving from which reason, logic and math appear.

When we explore the entire abstract space and systematize it through reasoning, we uncover corners of reality represented by logical and mathematical principles. This helps explain the "unreasonable effectiveness" of mathematics. No wonder it is so useful in the real world, and surprisingly, even unintentional mathematical exploration becomes widely applicable. These axioms and basic principles, manipulations themselves represent essential patterns seen in the universe, patterns that intuition has brought to our consciousness. Due to some kind of computational limitations or other limitations of intuition of our brains, it is impossible to gain intuitive insight into complex theorems. However, these theorems can be discovered through mathematics and, once discovered, can often be reapplied in the real world. This process can be seen as a top-down approach, where conscious and rigorous exploration of abstract space—governed and  grounded by mathematical principles—yields insights that can be applied in the real world. These newly discovered abstract concepts are in fact rooted in and deeply connected to reality, though the connection is so hard to spot that it cannot be grasped, even the intuition mechanism was not able to see it. 

7. Reinterpreting of consciousness

The journey from intuition to logic and mathematics invites us to reinterpret the role of consciousness as the bridge between the automatic, pattern-driven processes of the mind and the deliberate, structured exploration of abstract spaces. Latest LLMs achievement clearly show the power of intuition alone, that does not require resigning to solve very complex intelligent tasks.

Consciousness is not merely a mechanism for integrating information or organizing patterns into higher-order structures—that is well within the realm of intuition. Intuition, as a deeply powerful cognitive tool, excels at recognizing patterns, modeling the world, and even navigating complex scenarios with breathtaking speed and efficiency. It can uncover hidden connections in data often better and generalize effectively from experience. However, intuition, for all its sophistication, has its limits: it struggles to venture beyond what is already implicit in the data it processes. It is here, in the domain of exploring abstract spaces and innovating far beyond existing patterns where new emergent mechanisms become crucial, that consciousness reveals its indispensable role.

At the heart of this role lies the idea of agency. Consciousness doesn't just explore abstract spaces passively—it creates agents capable of acting within these spaces. These agents, guided by reason-based mechanisms, can pursue long-term goals, test possibilities, and construct frameworks far beyond the capabilities of automatic intuitive processes. This aligns with Dennett’s notion of consciousness as an agent of intentionality and purpose in cognition. Agency allows consciousness to explore the landscape of abstract thought intentionally, laying the groundwork for creativity and innovation. This capacity to act within and upon abstract spaces is what sets consciousness apart as a unique and transformative force in cognition.

Unlike intuition, which works through automatic and often subconscious generalization, consciousness enables the deliberate, systematic exploration of possibilities that lie outside the reach of automatic processes. This capacity is particularly evident in the realm of mathematics and abstract reasoning, where intuition can guide but cannot fully grasp or innovate without conscious effort. Mathematics, with its highly abstract principles and counterintuitive results, requires consciousness to explore the boundaries of what intuition cannot immediately "see." In this sense, consciousness is a specialized tool for exploring the unknown, discovering new possibilities, and therefore forging connections that intuition cannot infer directly from the data.

Philosophical frameworks like Integrated Information Theory (IIT) can be adapted to resonate with this view. While IIT emphasizes the integration of information across networks, such new perspective would argue that integration is already the forte of intuition. Consciousness, in contrast, is not merely integrative—it is exploratory. It allows us to transcend the automatic processes of intuition and deliberately engage with abstract structures, creating new knowledge that would otherwise remain inaccessible. The power of consciousness lies not in refining or organizing information but in stepping into uncharted territories of abstract space.

Similarly, Predictive Processing Theories, which describe consciousness as emerging when the brain's predictive models face uncertainty or ambiguity, can align with this perspective when reinterpreted. Where intuition builds models based on the data it encounters, consciousness intervenes when those models fall short, opening the door to innovations that intuition cannot directly derive. Consciousness is the mechanism that allows us to work in the abstract, experimental space where logic and reasoning create new frameworks, independent of data-driven generalizations.

Other theories, such as Global Workspace Theory (GWT) and Higher-Order Thought Theories, may emphasize consciousness as the unifying stage for subsystems or the reflective process over intuitive thoughts, but again, powerful intuition perspective shifts the focus. Consciousness is not simply about unifying or generalize—it is about transcending. It is the mechanism that allows us to "see" beyond the patterns intuition presents, exploring and creating within abstract spaces that intuition alone cannot navigate.

Agency completes this picture. It is through agency that consciousness operationalizes its discoveries, bringing abstract reasoning to life by generating actions, plans, and make innovations possible. Intuitive processes alone, while brilliant at handling familiar patterns, are reactive and tethered to the data they process. Agency, powered by consciousness, introduces a proactive, goal-oriented mechanism that can conceive and pursue entirely new trajectories. This capacity for long-term planning, self-direction, and creative problem-solving is a part of what elevates consciousness from intuition and allows for efficient exploration.

In this way, consciousness is not a general-purpose cognitive tool like intuition but a highly specialized mechanism for innovation and agency. It plays a relatively small role in the broader context of intelligence, yet its importance is outsized because it enables the exploration of ideas and the execution of actions far beyond the reach of intuitive generalization. Consciousness, then, is the spark that transforms the merely "smart" into the truly groundbreaking, and agency is the engine that ensures its discoveries shape the world.

8. Predictive Power of the Theory

This theory makes several key predictions regarding cognitive processes, consciousness, and the nature of innovation. These predictions can be categorized into three main areas:

1. Predicting the Role of Consciousness in Innovation:

The theory posits that high cognitive abilities, like abstract reasoning in mathematics, philosophy, and science, are uniquely tied to conscious thought. Innovation in these fields requires deliberate, reflective processing to create models and frameworks beyond immediate experiences. This capacity, central to human culture and technological advancement, eliminates the possibility of philosophical zombies—unconscious beings—as they would lack the ability to solve such complex tasks, given the same computational resource as the human brain.

1. Predicting the Limitations of Intuition:

In contrast, the theory also predicts the limitations of intuition. Intuition excels in solving context-specific problems—such as those encountered in everyday survival, navigation, and routine tasks—where prior knowledge and pattern recognition are most useful. However, intuition’s capacity to generate novel ideas or innovate in highly abstract or complex domains, such as advanced mathematics, theoretical physics, or the development of futuristic technologies, is limited. In this sense, intuition is a powerful but ultimately insufficient tool for the kinds of abstract thinking and innovation necessary for transformative breakthroughs in science, philosophy, and technology.

1. The Path to AGI: Integrating Consciousness and Abstract Exploration

There is one more crucial implication of the developed theory: it provides a pathway for the creation of Artificial General Intelligence (AGI), particularly by emphasizing the importance of consciousness, abstract exploration, and non-intuitive mechanisms in cognitive processes. Current AI models, especially transformer architectures, excel in pattern recognition and leveraging vast amounts of data for tasks such as language processing and predictive modeling. However, these systems still fall short in their ability to innovate and rigorously navigate the high-dimensional spaces required for creative problem-solving. The theory predicts that achieving AGI and ultimately superintelligence requires the incorporation of mechanisms that mimic conscious reasoning and the ability to engage with complex abstract concepts that intuition alone cannot grasp. 

The theory suggests that the key to developing AGI lies in the integration of some kind of a recurrent, or other adaptive computation time mechanism on top of current architectures. This could involve augmenting transformer-based models with the capacity to perform more sophisticated abstract reasoning, akin to the conscious, deliberative processes found in human cognition. By enabling AI systems to continually explore high abstract spaces and to reason beyond simple pattern matching, it becomes possible to move towards systems that can not only solve problems based on existing knowledge but also generate entirely new, innovative solutions—something that current systems struggle with

9. Conclusion

This paper has explored the essence of mathematics, logic, and reasoning, focusing on the core mechanisms that enable them. We began by examining how these cognitive abilities emerge and concentrating on their elusive fundamentals, ultimately concluding that intuition plays a central role in this process. However, these mechanisms also allow us to push the boundaries of what intuition alone can accomplish, offering a structured framework to approach complex problems and generate new possibilities.

We have seen that intuition is a much more powerful cognitive tool than previously thought, enabling us to make sense of patterns in large datasets and to reason within established frameworks. However, its limitations become clear when scaled to larger tasks—those that require a departure from automatic, intuitive reasoning and the creation of new concepts and structures. In these instances, mathematics and logic provide the crucial mechanisms to explore abstract spaces, offering a way to formalize and manipulate ideas beyond the reach of immediate, intuitive understanding.

Finally, our exploration has led to the idea that consciousness plays a crucial role in facilitating non-intuitive reasoning and abstract exploration. While intuition is necessary for processing information quickly and effectively, consciousness allows us to step back, reason abstractly, and consider long-term implications, thereby creating the foundation for innovation and creativity. This is a crucial step for the future of AGI development. Our theory predicts that consciousness-like mechanisms—which engage abstract reasoning and non-intuitive exploration—should be integrated into AI systems, ultimately enabling machines to innovate, reason, and adapt in ways that mirror or even surpass human capabilities.

It is unsettling how frequently we encounter individuals rejoicing at the death of someone who did not harm them personally. This contradiction- professing universal moral values while cheering the demise of a distant figure-demands philosophical scrutiny. From ancient to modern thinkers, many philosophers habe grappled with the tension between universal morality and the inconsistent application of ethical principles. The question at hand: Why do we find it permissible to celebrate death when it occurs outside our immediate moral sphere, and how should we respond if that death touches us, or someone we know, directly?

Stating the probkem (Inspired by Kant and Smith):
Immanuel Kant’s insistence on the categorical imperative demands a universal moral law applicable to all rational beings, yet individuals often behave as though moral principles apply selectively. Adam Smith, in The Theory of Moral Sentiments, reminds us that our capacity for empathy diminishes as the person in question grows more distant from our social circle. Cheering a stranger’s death, then, exploits the emotional gap noted by Smith, circumventing Kant’s notion that ethical duties must hold regardless of personal involvement. Instead of treating human life as an absolute end, the death of a distant “enemy” is met as if it were a permissible exception-something Kant would have considered a clear violation of moral law.

The thesis (taken from Levinas and Butler):
The thesis here is that a morally coherent stance forbids celebrating any death, regardless of how far removed the person may be. Emmanuel Levinas argues that encountering the Other’s face imposes an infinite ethical responsibility upon us. Judith Butler, in Frames of War, contends that societies designate certain lives as “ungrievable,” implicitly giving permission to disregard their inherent value. Together, they warn that the moral duty to acknowledge the humanity of another must not wane simply because that person is distant or despised. The thesis thus contends: moral consistency demands recognizing the worth of all life, close or distant, and refraining from triumphal joy at its end.

How this thesis contributes (from Mill and Hume):
By holding fast to the idea that moral standards do not fluctuate with proximity, we affirm John Stuart Mill’s principle of impartial consideration of interests, central to a truly ethical society. David Hume’s exploration of moral sentiments emphasizes that while our sympathy is naturally stronger for those near us, genuine virtue is shown by extending moral concern universally. Embracing this thesis contributes to our moral development, elevating us beyond parochial loyalties and the fickle winds of emotional convenience.

Examining alternativrs (Nietzsche, Girard, and Hobbes):

1. Moral relativism and tribal justification: Friedrich Nietzsche’s On the Genealogy of Morality suggests that revenge and ressentiment often masquerade as righteousness. One might claim that celebrating the death of a distant wrongdoer is morally sound because it seems to restore balance. Yet Nietzsche’s insight warns us that what appears as moral victory can be a disguised outlet for vengeance. Similarly, René Girard’s scapegoat mechanism shows how societies unite by sacrificing a single victim, turning that victim’s demise into a communal “good.” This reveals that the celebration is not a pure moral judgment but rather a convenient ritual of exclusion, something thar Thomas Hobbes might say cultivates an unstable peace built upon fear and the construction of enemies.
2. Utilitarian relief: Those inspired by Mill’s utilitarianism might argue that removing a harmful agent prevents future pain. But Mill would also insist that a moral stance must consider long-term implications. Once we accept cheering for one death, we risk normalizing indifference to human life, eroding the stable moral fabric that Mill’s philosophy seeks to preserve for all. In short, while acknowledging relief, a rigorous utilitarian approach would reject the uncritical celebration of mortality, given the damage it does to moral sympathy over time.

Anticipating the role of personal loss (Arendt and Nagel):
When a death affects us personally-touching a loved one or a friend-moral attitude shifts dramatically. Hannah Arendt’s exploration of the “banality of evil” in Eichmann in Jerusalem cautions that moral failings arise not from monstrous hatred alone but also from everyday thoughtlessness. If we allow ourselves to celebrate distant deaths thoughtlessly, what prevents others from trivializing our own losses should circumstances reverse? Thomas Nagel’s discussions of subjective experience emphasize that the more intimately we know a person’s internal life, the harder it is to dismiss their suffering or demise. This closeness forces us to see what Levinas describes as the face of the Other-and to recognize our moral duties more clearly. Thus, the personal encounter corrects our moral blindness: when death strikes near, we remember that each life is profoundly singular and cannot be casually replaced.

Why the stated thesis is preferablr (Kant, Butler):
By upholding a universal prohibition against celebrating death, we fulfill Kant’s demand for moral consistency-no exceptions allowed. We also counter the process Butler describes, where societies create categories of lives that seem unworthy of grief. Insisting on a uniform moral standard means acknowledging the innate worth of all individuals, resisting the temptation to treat distant persons as disposable symbols. This uniformity, drawn from rational ethical principles, safeguards the moral community from descending into selective compassion and cyclical hatred.

Anticipating objections and counterarguments (Levinas and Hume):
One objection might be that it is “natural” to feel relief-even joy-at the removal of a malevolent figure. Yet, as Levinas emphasizes, ethics call us beyond our natural inclinations. Another objection asserts that empathizing with someone monstrous dishonors their victims. But as Hume’s sentimentalist approach reminds us, compassion does not equal endorsement. We can condemn evil vigorously while still recognizing the inherent moral weight of a human life. To do otherwise encourages moral hypocrisy and a readiness to deny empathy under the slightest provocation.

My position (me, inspired by all the philosophers above):
From Kant’s call for moral universality, to Levinas’s emphasis on the primacy of the Other’s humanity, to Butler’s critique of socially devalued lives-I find it ethically untenable to celebrate a distant death. The weight of these philosophical traditions suggests that giving in to such celebrations corrodes our moral framework. If we accept that some lives are less worthy of respect, we risk normalizing indifference and future cruelty. My stance, guided by these thinkers, is that we must resist the seductive simplicity of moral convenience. We must instead uphold a consistent ethical standard that values all human beings, no matter how distant or disagreeable they may seem. While stating this, I do recognize my own weakness; sometimes when someone, who has destroyed so so many innocent lives passes away, peacefully or by someone's hand, I do find it to be a moment of enjoyment for myself. I do believe it to be natural, but again, just like Levinas wrote, ethics are something we must work beyond our natural capabilities. For me that is recognizing my own weakness and not spreading the feeling I previously mentioned.

I think this view may be unpopular, but in my opinion, philosophical rigor demands it.