From Pavlov to ChatGPT: how LLMs have revolutionized our understanding of thinking

"The real problem is not whether machines think like humans, but whether we know enough about human speech." - Claude Shannon

"The paradox of the digital 'Chinese': An LLM in the Chinese Room knows more than any real Chinese person, but has never tasted real oolong tea." (author)

Introduction

In recent years, the world has witnessed unprecedented progress in the field of artificial intelligence, particularly in the development of large language models (LLMs). Systems such as GPT-3, BERT, and the recently introduced ChatGPT demonstrate remarkable abilities in understanding and generating human speech, solving complex tasks, and even exhibiting creativity.

The success of LLMs raises fundamental questions that have long been discussed in the philosophy of mind and cognitive sciences. What does it mean to "understand" language? Can information processing based on statistical patterns be considered true understanding? How do the symbolic operations performed by LLMs relate to the thinking processes in the human brain?

In this article, we will attempt to trace the evolution of our understanding of thinking - from the classical works of Ivan Pavlov on signaling systems to modern achievements in the field of artificial intelligence. We will examine how the successes of LLMs force us to reconsider our notions of the nature of intelligence, and try to understand whether machines have really begun to "think," or if we are simply witnessing the creation of an incredibly complex information processing system.

Our reasoning will lead us to unexpected conclusions about the nature of human thinking and artificial intelligence, and may make us look at what we consider uniquely human in our thinking and understanding of the world in a new way.

Definitions and Basic Concepts

Before we delve into the analysis of modern language models and their impact on our understanding of thinking, it is necessary to define the key concepts that underlie our discussion.

First Signal System

The concept of the first signal system was introduced by Ivan Pavlov in the early 20th century. It refers to the immediate perception and reaction to environmental stimuli. This is the basic level of information processing common to humans and higher animals.

Definition: The first signal system is a set of conditioned and unconditioned reflexes based on the immediate perception of reality through the senses (sight, hearing, touch, smell, taste).

Second Signal System

The second signal system, also introduced by Pavlov, is unique to humans and is associated with the use of language and abstract thinking.

Definition: The second signal system is a specifically human system of conditioned reflexes to speech signals, allowing abstraction from immediate sensations and operating with concepts and ideas.

Qualia

Qualia is a philosophical term describing the subjective, qualitative aspects of conscious experience.

Definition: Qualia (singular: quale) are the subjective aspects of perception, such as how we experience color, sound, or pain. Each person experiences them in their own way, and they are difficult to describe through symbols such as words.

Subjective Experience

Subjective experience is closely related to the concept of qualia, but encompasses a broader range of internal states and experiences.

Definition: Subjective experience is the sum of an individual's personal, internal experiences, including sensations, emotions, thoughts, and memories that cannot be directly observed or measured from the outside.

These concepts form the basis for understanding the differences between human thinking and the functioning of artificial intelligence. The first and second signaling systems describe the mechanisms of information processing, while qualia and subjective experience refer to the phenomenological aspects of consciousness.

It is important to note that the given definitions of the first and second signaling systems are basic and reflect their initial understanding, formulated by Ivan Pavlov in the early 20th century. In the context of this article and in light of recent technological advances, such as the emergence of ChatGPT and other advanced language models, the reader will have to consider these concepts in a somewhat expanded and reinterpreted form.

Pavlov, of course, could not foresee the emergence of artificial intelligence and large language models. Therefore, in the course of our discussion, we will analyze how these classical concepts can be interpreted and applied in the context of modern AI technologies. This will allow us to use the rich legacy of Pavlov's ideas while adapting them to new realities.

Understanding these concepts and their modern interpretation is critically important for our further discussion, as they will help us analyze to what extent modern LLMs can emulate or represent various aspects of human thinking and understanding.

From Theory to Biology: The Uniqueness of the Human Brain

Having considered the basic concepts of signaling systems, qualia, and subjective experience, we have laid the theoretical foundation for our discussion. However, to fully appreciate the complexity of human thinking and understand how close or far modern AI systems are to it, we need to turn to the biological basis of our mind - the brain.

Advances in neurobiology and neuroimaging over the past decades have significantly expanded our understanding of how the brain works. These studies have not only confirmed many theoretical assumptions but also revealed an astonishing degree of individuality in each human brain. It is this uniqueness of neural structures and processes that underlies our subjective experience and individual perception of the world.

Let's consider what modern science tells us about the uniqueness of the human brain, and how this knowledge can affect our understanding of the nature of thinking and consciousness.

The uniqueness of the human brain: from neural connections to excitation patterns

Modern research in the field of neurobiology and neuroimaging has allowed us to look into the amazing world of the human brain, revealing its incredible complexity and uniqueness for each individual.

Connectome: a map of neural connections

The human brain consists of approximately 86 billion neurons forming trillions of synaptic connections. This complex network, known as the connectome, is unique to each person. Research shows that even identical twins, who have an identical genome, have different neural connection structures. This is because the formation of the connectome depends not only on genetic factors but also on individual experience, learning, and interaction with the environment.

Brain plasticity and the uniqueness of neural pathways

Neuroplasticity - the brain's ability to change its structure and functions in response to experience - plays a key role in shaping the uniqueness of each brain. As we learn, experience new things, and form memories, our brain constantly creates new neural connections and strengthens or weakens existing ones. This process leads to the formation of unique neural pathways that reflect the individual life experience of each person.

The uniqueness of neural activity patterns

Modern neuroimaging methods, such as functional magnetic resonance imaging (fMRI), allow observing brain activation patterns in real time. Research shows that even when performing the same tasks, brain activation patterns can vary significantly among different people. These unique "neural signatures" reflect individual information processing and problem-solving strategies.

Implications for understanding thinking and consciousness

The uniqueness of neural connections and activation patterns has profound implications for our understanding of thinking and consciousness. It emphasizes that each person perceives and interprets the world in a unique way, based on their unique neural architecture. This also raises interesting questions in the context of artificial intelligence: can an AI system, lacking such biological uniqueness, truly emulate human thinking in all its fullness?

Formation of experience in the first signaling system: more than just reflexes

Traditionally, the first signaling system (1SS) has been viewed primarily through the lens of simple conditioned and unconditioned reflexes. However, modern research in cognitive ethology and neurobiology allows us to expand this view, demonstrating that 1SS has much more complex information processing mechanisms than previously thought.

The process of experience formation in 1SS

The formation of experience within the 1SS begins with the direct perception of the environment through the senses. This process includes several stages:

• *Sensory perception: receiving information from the sense organs.

• Signal processing: primary processing of sensory information in the corresponding areas of the brain.

• Integration: combining information from different sensory modalities.

• Formation of associations: creating connections between different stimuli and responses.

• Memory: storing the acquired experience for future use.

Elements of logical thinking in the first signaling system

Although we usually associate logical thinking with the second signaling system, research shows that some of its elements are also present in the first signaling system. Let's consider a few examples:

• Decomposition: Animals demonstrate the ability to break down complex tasks into simpler components. For example, chimpanzees trying to get food with a stick can break this task into stages: find a suitable stick, position it correctly, use it to achieve the goal.

• Synthesis: Observed in the ability of animals to combine various elements of experience to solve new problems. Crows, for example, can combine several objects to create a tool for obtaining food.

• Induction: Manifested in the ability to generalize based on repeated experience. Dogs, having learned to open one door, often apply this skill to other doors, demonstrating a simple form of inductive thinking.

• Deduction: Elements of deductive thinking can be observed in the ability of animals to apply general rules to specific situations. For example, a predator tracking prey uses general knowledge about the behavior of the prey to predict its actions in a specific situation.

It is important to note that these processes in 1CC occur at a more primitive level compared to human abstract thinking. They are closely related to specific situations and direct experience, rather than abstract concepts.

Significance for understanding thinking

Recognition of the presence of elements of logical thinking in the 1SS has important implications for our understanding of the evolution of intelligence and the nature of thinking. It shows that the roots of complex cognitive processes lie deep in our biological history, and that the boundary between "instinctive" and "rational" thinking may be less clear than we are used to thinking.

This understanding is also important in the context of the development of artificial intelligence. It suggests that to create truly "thinking" systems, it may not be enough to focus only on symbolic operations of the second signaling system. Perhaps we need to delve deeper into and model the processes occurring at the level of the first signaling system.

Genesis of the second signaling system: from communication to abstraction

Pavlov's theory of signaling systems has given us a valuable foundation for understanding the mechanisms of information processing. However, the question of how and why the second signaling system (2SS) arose remains a subject of discussion. Let me present my vision of this process, based on modern data and logical reasoning.

A look at the process of the emergence of the second signaling system:

In my opinion, the second signaling system arose as a natural extension of the first, due to the need for more effective communication and exchange of experience in growing social groups. Here is how this process can be imagined:

• Need for communication: As social interactions became more complex, the need for more detailed information exchange arose.

• Symbolization: Gradually, specific sounds or gestures began to be associated with certain objects or actions, becoming their symbols.

• Formation of common concepts: A key moment in the development of 2CC was the formation of common concepts through the intersection of individual experiences. When different people encountered similar phenomena, they found common features in their subjective experiences (qualia). These common elements became the basis for creating shared concepts and symbols. This process of "averaging" individual experience allowed for the creation of a common knowledge base necessary for effective communication and exchange of ideas.

• Abstraction: With the development of symbolization, it became possible to operate not only with concrete objects but also with abstract concepts.

• Formation of language: Symbols were organized into a system, forming a primitive language.

• Development of thinking: The use of language allowed for the formulation and transmission of complex ideas, which in turn stimulated the development of abstract thinking.

Global development: evolution of language and writing

On a global scale, the development of 2CC can be traced through the evolution of language and writing:

• Proto-language: Presumably began to form about 100,000 years ago.

• Development of grammar: The complication of language structures allowed the transmission of more complex ideas.

• Emergence of writing: About 5,000 years ago, which significantly expanded the possibilities for storing and transmitting information.

• Standardization of languages: Formation of literary norms and rules.

• Development of science and philosophy: Language became a tool for formulating and discussing abstract concepts.

Individual development: mastering speech in children

At the individual level, the formation of 2SS can be observed in the process of child development:

• Preverbal stage: The child communicates through cries, gestures, facial expressions (1SS).

• Appearance of the first words: Usually around 1 year, the beginning of the formation of 2SS.

• Vocabulary development: Rapid accumulation of words and their meanings.

• Mastering grammar: Formation of the ability to build complex sentences.

• Development of abstract thinking: The ability to operate not only with concrete but also with abstract concepts.

Logicality of the proposed approach

This hypothesis about the development of 2SS logically explains several key aspects:

• Evolutionary continuity: 2SS develops based on 1SS, rather than arising "out of nothing".

• The social nature of language: Language develops as a tool for communication within a group.

• The connection between language and thinking: The development of language and abstract thinking go hand in hand.

• Individual development and evolution of language: In the process of a child mastering speech, some parallels with the historical development of language can be observed. For example, simple word-tags appear first, then grammar develops, and finally the ability for abstract thinking is formed. However, it is important to note that this similarity is not absolute, and the child masters an already existing complex language system, rather than going through the entire path of its evolution.

Significance for AI development

Understanding the process of forming 2SS is important for AI development. It suggests that to create truly "thinking" systems, it may not be enough to simply train them in language processing. We may need to model the entire process of development from primitive communication to complex symbolic thinking.

Limits of abstraction: limitations of the second signaling system

Despite the huge evolutionary leap that the second signaling system has provided humanity, it is important to understand its limitations. 2SS, with all its power, is primarily a methodology for operating information, rather than a source of new experience.

Methodological nature of 2SS

The second signaling system provides us with tools for processing, analyzing, and transmitting information. However, it does not create new experiences by itself. This can be illustrated by the old wisdom: "A person learns only from their mistakes." Although we can absorb information about someone else's experience through language (2SS), true understanding and lesson absorption often come only through personal experience (1SS).

Limitations in understanding abstract concepts

One of the main limitations of 2SS manifests when trying to operate with concepts beyond our immediate experience:

• Multidimensional spaces: The human brain, adapted to a three-dimensional world, experiences significant difficulties when trying to imagine and operate in higher-dimensional spaces. We can mathematically describe a five-dimensional space, but we cannot intuitively "feel" or visualize it.

• Quantum phenomena: The principles of quantum mechanics often contradict our everyday experience, making them extremely difficult to intuitively understand, despite the possibility of their mathematical description.

• Infinity: Although we can operate with the concept of infinity mathematically, our brain cannot truly "imagine" infinity.

Cultural limitations in understanding concepts

Studies of isolated tribes provide vivid examples of the limitations of 2SS:

• Piraha tribe: This Amazonian tribe has no concept of numbers and counting beyond "one", "two", and "many". Despite attempts at education, tribe members experience great difficulty in mastering basic arithmetic, as these concepts are absent in their daily experience.

• Tribes without the concept of time: Some tribes do not have a developed concept of linear time, which makes it difficult for them to understand historical events or plan for the future in the way we do.

• Concepts absent in experience: Some tribes living in equatorial regions do not have words for snow or ice. When these phenomena are explained to them, they experience significant difficulty in understanding, as they lack corresponding experience in the first signaling system. This demonstrates how the absence of direct experience can limit the capabilities of the second signaling system in forming and operating certain concepts.

Significance for understanding the nature of thinking and AI

Awareness of these limitations of the second signaling system has important implications:

• It emphasizes the inseparable connection between the first and second signaling systems. The 2SS cannot fully replace direct experience.

• This raises the question of the nature of "understanding" in AI systems. If AI is trained exclusively on textual data (analogous to 2SS), can it achieve true "understanding" of concepts without relying on direct sensory experience?

• It points to potential directions for AI development, including the need to integrate various types of data and model processes similar to the formation of primary experience in humans.

• Understanding the limitations of 2SS does not diminish its significance, but helps us better understand the complex nature of human thinking and raises interesting questions about the capabilities and limitations of artificial intelligence.

Symbols and Qualia: The True Nature of Human Thinking

When we talk about operating with symbols in the second signaling system (2SS), it may seem that our thinking is just the manipulation of abstract signs. However, reality is much more complex and deeper. Let's consider how the thinking process actually occurs in terms of the interaction between symbols and qualia.

Symbols as Pointers, Not Content

Symbols in our thinking are not self-sufficient units, but rather "pointers" or "triggers" that activate certain qualia or complexes of qualia. When we think of the word "apple," we do not operate with an abstract symbol, but activate a whole complex of subjective experiences associated with apples: their taste, smell, texture, visual image, and even related memories.

Individuality of Qualia

It is important to understand that the activated qualia are strictly individual. They are based on each person's personal experience and can vary significantly from person to person. For example, the word "home" can evoke completely different images and sensations in people from different cultures or with different life experiences.

Process of Operating Qualia

• Activation: A symbol (word or concept) activates the qualia associated with it.

• Interaction: Activated qualia interact with each other, creating new combinations and associations.

• Transformation: In the process of thinking, qualia can transform, creating new subjective experiences.

• Synthesis: The result of this process is a new complex of qualia, representing a new idea or concept.

Formalization of the Result

After the thinking process at the level of qualia is completed, the result needs to be "translated" back into symbolic form for communication or further analysis. This is the process of formalization:

• Generalization: Individual qualia are generalized to a level understandable to others.

• Structuring: The result is organized into a logical structure.

• Symbolization: Appropriate symbols (words, terms) are selected to express the idea.

Example: creative process:

Let's consider the process of writing a poem. The poet does not just manipulate words. He immerses himself in the world of his qualia, activated by a certain theme or emotion. In this inner world, a complex interaction of images, feelings, and memories takes place. The result of this interaction is a new complex of qualia, which the poet then tries to express through words, choosing the most appropriate symbols to convey his inner experience.

Significance for AI

Understanding that human thinking operates not with symbols, but with qualia, has important implications for the development of AI:

• Limitations of the symbolic approach: Systems that operate only with symbols without connection to "real" experience may be limited in their ability to achieve true understanding and creativity.

• Need to model qualia: To create AI that approximates human thinking, it may be necessary to develop ways to model qualia or their analogs.

• Individuality of thinking: A truly "thinking" AI would need to have an individual "experience" and a unique set of "qualia," which raises interesting philosophical and ethical questions.

Understanding the role of qualia in the thinking process emphasizes the deep connection between the first and second signaling systems. It also shows how complex and multifaceted the process of human thinking is, and what challenges lie ahead for AI creators striving to recreate such processes in machines.

LLM Paradox: Understanding without qualia?

At first glance, it may seem that large language models (LLM) are not capable of truly understanding and operating with symbols, as they lack qualia – the subjective experience underlying human understanding. However, the LLM phenomenon challenges this assumption, demonstrating surprising abilities in text processing and generation, solving complex tasks, and even showing signs of "understanding" context.

The LLM Phenomenon

Modern LLMs, such as GPT-3 or BERT, are capable of:

• Generating coherent and contextually appropriate text

• Answering complex questions that require reasoning

• Performing tasks related to text understanding and analysis

• Demonstrating some forms of "creative" thinking

How is this possible without qualia?

The key to understanding this phenomenon lies in two factors:

• Huge volumes of text data for training

• The use of embedding techniques

The Role of Big Data

LLMs are trained on colossal volumes of textual information, covering a wide range of human knowledge and experience. This allows the models to capture complex patterns and relationships in the language, which in some sense reflect the collective experience of humanity recorded in texts.

Embedding: The Key to LLM "Understanding"

Embedding is a method of representing words, phrases, or even entire documents as vectors in a multidimensional space. This is a fundamental technique underlying the operation of modern LLMs.

How embedding works:

• Vector representation: Each word or phrase is represented as a vector in a high-dimensional space (usually from 100 to 1000 dimensions).

• Semantic proximity: Words with similar meanings or used in similar contexts are located close to each other in this space.

• Preservation of relationships: The vector representation preserves semantic and syntactic relationships between words. For example, the vector "king" - "man" + "woman" will be close to the vector "queen".

• Contextuality: Modern models, such as BERT, create dynamic embeddings that take into account the context of the word's use in a specific sentence.

Embedding as a replacement for qualia?

Embeddings can be considered as a kind of "artificial qualia" for LLM. They provide the model with a rich, multidimensional representation of words and concepts, reflecting their use and relationships in a vast corpus of texts.

This allows LLM to "understand" and manipulate symbols in a way that is very similar to human thinking, although based on completely different principles.

Limitations of this approach

However, it is important to remember that embeddings, for all their power, are based solely on statistical patterns in textual data. They are not directly related to sensory experience or emotional experiences that form human qualia.

This raises interesting questions about the nature of "understanding" in LLMs and how close such "understanding" is to human understanding. Can a statistical model based on a huge amount of textual data really replace direct experience? Or do LLMs merely demonstrate a very complex form of information processing that only seems like understanding?

Embedding as a reflection of the collective experience of humanity

To better understand the nature of embedding and its role in the work of LLMs, let's look at it from a somewhat unexpected perspective – as a kind of mapping of the structures of the human brain into multidimensional space.

The brain as a complex graph

The human brain can be represented as an incredibly complex graph, where neurons are nodes and synaptic connections are edges. This graph is huge (about 86 billion neurons and trillions of connections), but finite. This graph encodes all of a person's experience, knowledge, associations, and understanding of the world.

From individual graph to collective experience

Now imagine not one brain, but many brains of all the people who have ever created texts. Each of these brains has its own unique graph structure, but they all have something in common – they generate texts that we can read and analyze.

Embedding as a mapping of the collective graph

Embedding in the context of LLMs can be seen as an attempt to map this vast diversity of neural graphs into n-dimensional Euclidean space. It is important to understand that:

• Specific brain is not translated: The embedding does not attempt to recreate the structure of a single brain.

• Collective representation: Instead, it aims to create a representation that reflects the collective experience and knowledge encoded in many individual brains.

• Artifacts as intermediaries: This translation does not occur directly from the brain, but through artifacts - texts created by people. These texts are the external manifestation of the internal structures of the brain.

• Correlations in symbols: In texts, symbols (words, phrases) and their relationships indirectly reflect the structures and connections in the neural graphs of their creators.

How does it work?

• Data collection: LLM is trained on huge arrays of texts created by many people.

• Pattern recognition: During training, the model identifies statistical patterns and relationships between words and concepts.

• Vector creation: These patterns are transformed into vector representations (embeddings) in multidimensional space.

• Reflection of collective experience: The resulting vector space can be seen as a kind of projection of the collective experience of humanity encoded in texts.

The result of this approach

Generalization of experience: LLM embeddings can potentially reflect a wider range of experience and knowledge than is available to a single person.

Lack of individuality: At the same time, they lack the individuality and personal experience characteristic of a single human brain.

Limitations of text representation: Embeddings are limited by the information that can be conveyed through text and do not include direct sensory or emotional experience.

New analysis opportunities: This approach opens up interesting opportunities for analyzing the collective knowledge and experience of humanity encoded in language.

Considering embedding as a kind of translation of the collective neural graph of humanity into a multidimensional space helps us better understand the nature of LLM "knowledge". This explains both their impressive capabilities and their limitations, and raises interesting questions about the nature of knowledge, understanding, and the possibilities of artificial intelligence.

LLM limitations in generating new knowledge: the problem of formalizing experience

Despite the impressive capabilities of modern language models (LLMs), there is a fundamental limitation in their ability to generate truly new knowledge. This limitation is closely related to the process of formalizing human experience and translating the rich world of qualia into the limited world of symbols.

The process of formalizing human experience

• Richness of qualia: Human experience includes a rich world of subjective experiences, sensations, emotions, and intuitive understandings.

• Need for communication: To convey this experience to other people, we have to translate it into a form accessible for communication – usually language.

• Compression of qualia into symbols: In the process of this translation, an inevitable "compression" occurs – the rich multidimensional experience is compressed into a limited set of symbols (words, phrases, concepts).

• Loss of information: In this compression, a significant part of the information is inevitably lost, especially related to the subjective aspects of experience.

How this affects LLM

• Training on formalized experience: LLMs are trained on texts that have already undergone the formalization process. They do not have access to the original, "uncompressed" experience.

• Limitations of input data: Models work only with the information that has managed to pass through the "bottleneck" of formalization.

• Lack of direct experience: LLMs do not have the ability to gain new experience directly, as humans do through interaction with the physical world.

Consequences for generating new knowledge

• Recombination of existing knowledge: LLMs are capable of very complex recombination and extrapolation of existing knowledge, but this is not the same as creating truly new knowledge.

• Lack of intuitive breakthroughs: Many scientific discoveries and creative breakthroughs in humans occur due to intuitive insights based on rich, informal experience. LLMs lack this source of insights.

• Limited abstractions: The abstractions that LLMs work with are limited to those that have already been formulated by humans and reflected in texts.

• The "new context" problem: LLMs may struggle in situations that require applying knowledge in a completely new context not reflected in the training data.

Examples of limitations

• Scientific discoveries: LLMs can help analyze existing data, but they are unlikely to independently make a fundamental scientific discovery that requires a new perspective on the nature of reality.

• Artistic creativity: While LLMs can generate impressive texts, they are limited by existing styles and ideas. Creating a fundamentally new artistic direction remains the prerogative of humans.

• Philosophical breakthroughs: Radically new philosophical ideas are often based on unique personal experiences and intuitive understanding of the world, which are inaccessible to LLMs.

The limitations of LLM in generating new knowledge are deeply rooted in the very nature of their training data – formalized human experience. This does not diminish their immense value as tools for processing and analyzing information, but it raises important questions about the boundaries of their capabilities.
Understanding these limitations is important not only for a realistic assessment of AI's potential but also for recognizing the unique role of human experience and intuition in the process of creating new knowledge. It also points to potential directions for AI development, possibly towards systems capable of somehow acquiring and integrating "non-formalized" experience.

Engineers and Scientists: Where is the Line in the Age of AI

In the context of our discussion on the nature of thinking, qualia, and the limitations of LLM, it is important to consider how these concepts manifest in various fields of human activity, especially in engineering and science.

The Essence of Differences

The line between a scientist and an engineer is drawn where formalized knowledge ends and the realm of intuition and new experience begins. An engineer works predominantly within established paradigms and methodologies, applying and combining known principles to solve specific problems. A scientist, on the other hand, often operates at the edge of the known, relying on their unique experience and intuition to form new hypotheses and theories.

The Role of AI and LLM

AI and LLM, trained on formalized knowledge, can effectively assist engineers by automating routine tasks and offering optimal solutions within known approaches. However, in the field of scientific discoveries, where the generation of fundamentally new ideas beyond existing paradigms is required, AI is still limited, as it lacks the informal part of experience from which revolutionary scientific breakthroughs are born.

Implications for the future of AI

This distinction raises important questions about the future development of AI:

1. Is it possible to create AI capable of true scientific creativity?

2. How can we integrate informal experience into AI systems?

3. What are the ethical implications of potentially replacing some human roles with AI systems?

Universal translator: LLM as a bridge between worlds

From science fiction to reality

The idea of a universal translator, capable of instantly translating between any languages, including hypothetical alien ones, has long remained in the realm of science fiction. However, modern advances in LLM bring us closer to realizing this concept.

LLMs have a unique ability to learn from vast amounts of textual data, identifying complex patterns and relationships without "understanding" in the human sense. This key property makes them potential candidates for the role of a universal translator, even for hypothetical alien languages.

Let's imagine that we have discovered an archive of texts from an alien civilization. An LLM could be trained on this data just as it is trained on human languages. By training on both sets of data - terrestrial and alien - the LLM could potentially become a bridge of understanding between two completely different species.

Mechanism of Operation and Potential

1. Identifying Common Patterns: The LLM is capable of detecting similar structures and patterns in both symbol systems, even if they appear completely different on the surface.

2. Contextual Connections: The model can establish contextual connections between concepts in both systems, finding analogies and correspondences.

3. Translating Concepts: Based on the identified patterns and contextual connections, the LLM can "translate" concepts from one symbol system to another.

Limitations and Challenges

Despite its enormous potential, such a universal translator would have a number of limitations:

1. Lack of Direct Experience: The LLM does not possess the sensory experience of either humans or aliens, which may limit the depth of "understanding".

2. Cultural Nuances: Some concepts may be so unique to each culture that their exact translation would be difficult or impossible.

3. Abstract Ideas: Translating complex abstract concepts, deeply rooted in the unique experience of each species, can be particularly challenging.

4. Data Limitations: The effectiveness of the translation will depend on the volume and quality of the available textual data from both cultures.

Fundamental Similarity as the Basis of Translation

Despite these limitations, there is a fundamental factor working in favor of the possibility of universal translation: the unity of the laws of physics in our universe. The basic experience of interacting with the physical world will be common to both humans and aliens (if they exist in the same physical reality).

This common ground means that a certain degree of comparability between languages will always exist. Even the most distant and different languages will have points of contact based on this common experience of interacting with the physical world.

An LLM trained on both datasets could identify these fundamental similarities and use them as a basis for translating more complex and abstract concepts. This could be the key to establishing a basic level of communication between species, even if full mutual understanding remains unattainable.

Thus, while the ideal universal translator may remain an unattainable goal, LLM has the potential to bring us significantly closer to this ideal, opening up new horizons in interspecies communication and deepening our understanding of the nature of language and thought.

Conclusion: The Universality of Mind in the Physical Universe

Our journey from the first and second signaling systems through qualia and embeddings to the capabilities of LLM as a universal translator has led us to an unexpected but deeply significant conclusion. This conclusion goes beyond linguistics and artificial intelligence, touching on fundamental questions about the nature of the mind and its place in the universe.

Reflecting on the possibility of LLM serving as a bridge between human and hypothetical alien language, we came to understand that the same fundamental laws lie at the core of any intelligence that has arisen in our physical universe. This discovery has far-reaching implications.

The fear of the alienness and incomprehensibility of alien intelligence or the potential hostility of strong AI may be exaggerated. As we have seen, even the most diverse forms of intelligence, whether biological or artificial, have a common foundation — the physical reality in which they arose and evolved.

Just as the biochemical basis of life on Earth makes it possible for different species to have common principles of nutrition, the fundamental laws of physics create a common ground for the development of intelligence. This does not mean that all forms of intelligence are identical or easily compatible, but it suggests the fundamental possibility of mutual understanding and communication.

In this context, LLMs become not just a tool for language processing, but a model of how different forms of intelligence can find a common language. They demonstrate that understanding can arise even where there is no common sensory experience, relying on statistical regularities and common patterns.

This understanding opens up new perspectives not only for the development of AI and intercultural communication, but also for our perception of humanity's place in the universe. It offers a more optimistic view of the possibility of contact with extraterrestrial civilizations and our coexistence with artificial intelligence.

Ultimately, our exploration of the nature of language, thought, and artificial intelligence leads us to a profound philosophical question: is the mind, in all its forms, a natural and inevitable consequence of the laws of our universe? And if so, does this not unite all forms of mind, making us part of something greater than we could have imagined?

These questions go beyond our current discussion, but they open up exciting prospects for further research and reflection. They invite us to continue exploring not only the nature of language and thought, but also the very essence of the mind and its place in the cosmos.