Introduction
This article analyzes the genealogy of artificial intelligence, rejecting the personification of machines in favor of a rigorous philosophical framework. It argues that AI is not an autonomous agent, but rather a condensation of human labor and the statistical regularities of language.
The reader will discover how the transition from rigid rules to probabilistics has altered our understanding of knowledge. The text presents AI as a tool for the reorganization of power, where it is crucial to establish the accountability of specific individuals for algorithmic decisions.
Narrative as a System of Rules and Functions
Vladimir Propp's analysis of fairy tales made it possible to treat narrative as a structure composed of repeatable functions. Rather than focusing on content, Propp isolated constant roles and sequences of events.
This approach allowed computers to model stories not as literature, but as systems of rules. An example is the TALE-SPIN program, which simulated a world and character goals instead of merely selecting words at random.
Consequently, moving from a single sentence to a coherent story required the creation of a representation of the world state. The machine had to understand causal relationships to avoid contradictions in the plot.
Structuralism as the Foundation for Systemic Approaches to Language and Narrative
Concepts of structuralism, such as the distinction between langue (the system) and parole (the usage), became the basis for content generation. Language began to be perceived as a system of relations and differences.
This influenced the development of Noam Chomsky's generative grammar, which sought to describe a finite set of rules capable of producing an infinite number of sentences. This approach relied on rigid syntactic rules.
Modern systems have moved away from these rules in favor of statistics. While explicit rules were programmed in the past, today's models learn the probability of an element's occurrence based on massive datasets.
The Gap Between Grammatical Rules and Linguistic Statistics
A fundamental shift occurred in the transition from deductive rule programming to the inductive study of regularities. Instead of teaching a machine definitions, researchers began analyzing the statistical proximity of words.
This approach originates from Markov chains and Claude Shannon's information theory. Here, information is defined as the reduction of uncertainty rather than a semantic understanding of the message's content.
Despite their proficiency in predicting subsequent tokens, contemporary models lack an embodied experience of the world. Their surface-level correctness results from data distribution, not from an epistemic justification of truth.
Conclusion
The problem of AI is ceasing to be a puzzle of the philosophy of mind and is becoming a dispute over the model of our civilization. Machines simulate intentionality, yet they possess neither moral autonomy nor consciousness.
The real challenge lies in preserving human agency in a world dominated by algorithmic management. We must distinguish between the functional efficiency of AI and the responsibility of human subjects to avoid the erosion of ethics.
Frequently Asked Questions
How did Vladimir Propp's analysis of fairy tales contribute to the creation of computer models for text generation?
Vladimir Propp's analysis allowed narrative to be treated as a structure with repeatable functions and rules, rather than just as a unique literary work. The separation of variable content from stable plot functions created the conditions for the subsequent algorithmization of stories and their representation in a form that enables computational operations.
Which concepts from linguistics and structuralism have influenced the understanding of content generation systems?
The relational approach to meaning by Ferdinand de Saussure and the concept of language as a system of differences, where an element's value results from its position relative to others, had an influence. The structuralists' pursuit of discovering a "grammar of culture" and systems of rules (narrative langue) that enable the creation of many specific content realizations was also significant.
How does text generation based on rigid grammatical rules differ from the modern statistical approach?
The rule-based approach involves searching for explicit generative principles and encoding grammar to create structures consistent with a specific description. The modern statistical approach is based on probabilistic methods, in which elements are predicted based on their statistical dependencies without the need to manually write rules.
What is the difference between generating single sentences and creating a coherent narrative in AI systems?
Generating single sentences can rely solely on syntactic rules without possessing a model of the world. Creating a coherent narrative, however, requires a representation of the state of the world and a theory of action to maintain logical relationships between events, characters, goals, and their consequences.
Why is modeling linguistic structures and syntactic rules alone insufficient for a machine to fully understand a text?
Modeling structures and syntactic rules alone is not enough because understanding text depends on a vast amount of unspoken knowledge that is not contained within the syntax itself. To correctly interpret an utterance and draw conclusions from it, a machine would have to reconstruct a broad real-world context.
What fundamental change occurred in the approach to language processing when moving from rule-based systems to statistical models?
The change consists of a shift away from manually describing language structures and rules toward studying statistical regularities present in texts. Instead of designing explicit theories of world knowledge, systems began to utilize the probability of subsequent sequence elements occurring based on data.
How did Markov's mathematical approach change the understanding of text structure in the context of probability?
Markov's approach changed the understanding of text by treating it as a sequence of dependent rather than independent events. He demonstrated that language can be modeled statistically by analyzing the probability of the next element occurring (e.g., a vowel or consonant) based on the previous state.
Does statistical text analysis (e.g., Markov chains) replace the understanding of the content of a literary work?
No, statistical analysis does not replace the understanding of a work's content because both methods construct different epistemic objects. While probability answers questions about character dependencies and regularities, hermeneutics focuses on meaning and historical-cultural interpretation.
What is information in the technical sense, and why does its transmission not imply an understanding of the content?
In the technical sense, information is a measure of the reduction of the receiver's uncertainty regarding which of the possible messages was chosen. The transmission of information does not imply an understanding of the content because, from an engineering perspective, the transmission of symbols has been separated from their meaning (semantics).
How do distributional linguistics and the philosophy of language explain the relationship between the statistical occurrence of words and their meaning?
Distributional linguistics assumes that words occurring in similar contexts exhibit semantic similarity, and that the statistical distributions of these co-occurrences encode the regularities of the physical and social world. This approach, similar to the philosophy of the later Wittgenstein, rejects essentialism in favor of analyzing linguistic relations and practices, although it differs from it in scope (textual co-occurrence versus human forms of life).
How have mathematics and statistics replaced traditional dictionaries in the process of defining the meaning of a text?
Traditional dictionaries have been replaced by vector space models, in which documents and queries are represented as points in a multidimensional space, and their meaning is determined through the geometric measurement of distance and direction. Additionally, a statistical interpretation of term specificity (e.g., TF-IDF) was introduced, shifting the definition of sense from dictionary meaning to the level of statistical usage within a collection.
Does the fact that AI correctly operates on relationships between words mean that it understands reality and speaks the truth?
No, because a probabilistic model merely predicts the likely continuation of text based on learned linguistic regularities, rather than the relationship between a statement and the world. High linguistic probability may correlate with truth, but it does not constitute a logical guarantee of it.
Does the fact that AI can predict subsequent words in a text with extreme precision mean that it understands the content and possesses knowledge of the world?
The high quality of predicting subsequent language elements does not establish an evidentiary relationship between the generated statement and reality. The system's proficiency in this dimension does not automatically determine its possession of other competencies, such as semantic reference or practical agency.
How has the understanding of knowledge representation in AI evolved – from simple statistical chains to modern language models?
The evolution of representation in AI has progressed from simple transition probabilities between characters and points in term space, through learned internal states in neural networks, to the dynamic reconstruction of meaning depending on context in the Transformer model. Currently, operational meaning does not derive from a classical dictionary of concepts but emerges as a property of a multidimensional architecture of relations.
How did technology move from dictionary definitions of words to modern neural representations of meaning?
This evolution involved a transition from essentialist definitions and explicit rules to relational representation, where the meaning of a word results from its distribution of use and 'company'. A key stage was the introduction of dense vectors (e.g., word2vec, GloVe), which replaced dictionary descriptions with geometric coordinates in a multidimensional space, followed by the development of contextual models that dynamically calculate a word's representation based on its surroundings.
How do transformer architecture and their training process allow a machine to simulate language understanding?
The simulation of language understanding is based on the attention mechanism, which dynamically recontextualizes the meaning of elements in relation to other parts of the sequence. This process is supported by self-supervised learning on vast data corpora, where the task of predicting the next token forces the creation of internal representations of syntactic, semantic, and pragmatic structures.
Does the ability of models to create internal representations of the world (e.g., in the game of Othello) mean that AI actually understands reality?
Creating internal representations does not imply the construction of a complete human ontology of the world or a dynamic, causal model of reality. It merely indicates that a predictive goal can lead to the emergence of structures representing environmental features relevant from a causality perspective.
Can language models possess actual knowledge of the world without having a body and direct experience?
LLM models can indirectly recover the structure of the world from linguistic traces of experience, as text corpora are records of what humans have seen and felt. However, this is not an independent discovery of the world by the machine, but rather the effect of utilizing knowledge embedded in language through human cognitive processes and a distributed socio-technical system.
Does the high accuracy of AI responses mean that the system possesses knowledge and reasons in a way similar to a human?
No, high response accuracy does not imply possessing knowledge or reasoning in a human manner. A model can generate true statements without a reliable justification mechanism, and effectiveness in reasoning tests does not determine whether this process corresponds to human symbolic reasoning.
How can AI be understood from the perspective of political economy and human labor?
From a political economy perspective, AI is a socio-technical system resulting from the accumulated collective labor of engineers, researchers, and users. Socially produced knowledge is objectified in this process and transformed into technical capital, allowing infrastructure owners to organize production on a massive scale.
Why is personifying AI and calling it autonomous problematic from the point of view of economics and labor?
Personifying AI and attributing autonomy to it obscures social labor relations and the multi-layered structure of human input necessary to create the system. This leads to a diffusion of responsibility and the systematic hiding of the work of people involved in, among other things, data labeling, which is often associated with low wages and a lack of social protection.
Will artificial intelligence lead to the complete disappearance of specific professions?
Artificial intelligence does not lead to the mass disappearance of entire professions, but rather transforms the structure of tasks within them. Automation covers specific activities, while most professions still require human involvement in areas resistant to technological replacement.
How do current legal regulations and economic structures in the EU address the issue of using human labor and data in training AI models?
The EU regulates platform work through Directive 2024/2831, introducing principles of transparency and human oversight in algorithmic management. Regarding training data, the law permits text and data mining for legal content unless the author has opted out, and AI model providers must publish summaries of the data used and comply with EU copyright law.
Who bears legal responsibility for errors made by AI systems, and can AI be recognized as a legal person?
Legal responsibility for errors in AI systems is borne by specific participants in the process, such as providers and deployers, rather than the algorithm itself. AI systems do not possess legal personality, as the law treats them as products or tools, assigning liability for damages to the entities managing them.
How does AI affect power relations, and who actually bears responsibility for the actions of distributed intelligence systems?
AI affects power relations by reorganizing relationships between entities, potentially serving both to democratize access to knowledge and to enable surveillance and limit worker autonomy. Responsibility for the actions of distributed intelligence systems is linked to the issue of delegating agency by humans, corporations, and states, as well as to whom the social system assigns the obligation to bear the consequences.
Why does AI's ability to generate ethically correct answers not make it a moral agent?
AI is not a moral agent because generating correct results is merely technical proficiency (techne), not practical wisdom (phronesis). Virtue and responsibility require having a character and the capacity to experience emotions and pain, which algorithms lack.
How does the technical pursuit of a goal by AI differ from human will and moral responsibility?
The technical pursuit of a goal by AI is based on a mathematical optimization criterion and a loss function assigned by an external cause. Human will and moral responsibility, however, result from rational self-determination, the ability to reflectively relate to motives, and the internal recognition and choice of the good.
Can artificial intelligence be considered a moral agent in light of classical philosophical concepts?
Recognizing AI as a moral agent depends on the adopted philosophical perspective: for Hume, it would require the capacity to experience emotions and empathy; for Kant, the possession of autonomous practical reason; and for Hegel, the possibility of entering into a relationship of mutual recognition with another subject. Current AI systems merely simulate these functions without being actual participants in a community of feelings, autonomy, or ethical life.
Does the ability of AI to explain its decisions and respond in a human-like manner mean that it is a morally responsible agent?
No, because the ability of AI to generate explanations may be merely retrospective rationalization rather than the actual cause of action. Moral responsibility requires that specific reasons actually guide the decision, and providing a narrative justification post factum is insufficient.
Why should errors in AI systems not lead to recognizing the machine as a moral agent?
The responsibility gap arises from the fact that AI systems can generate unpredictable behaviors; however, this is not evidence of the emergence of a synthetic moral person. Instead of personifying the machine, solutions should be sought in governance mechanisms, product liability, insurance, or treating AI as an element of a corporate agent.
Can AI be considered a moral agent, and is simulating consciousness equivalent to possessing it?
AI can be recognized as a moral agent in a technical and functional sense, based on autonomy and interactivity, without the need for free will. At the same time, behavioral linguistic competence is not evidence of consciousness, as fluency in dialogue must be distinguished from actual phenomenal experience.
Why is the distinction between different types of AI agency crucial for establishing responsibility for errors in technological systems?
Distinguishing between types of agency helps avoid erroneously attributing autonomous moral agency to AI systems, which could mask the responsibility of those designing and implementing the technology. It also prevents situations where organizations shift blame onto a human who serves only as a nominal safety guarantor.
