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 narration to be treated as a structure with repeatable functions and rules, rather than just a unique literary work. Separating 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?
A key influence was Ferdinand de Saussure's relational approach to meaning, the concept of language as a system of differences, and the distinction between langue and parole. Also significant were Claude Lévi-Strauss's structuralist search for a "grammar of culture" and narratology, which aimed to describe the system of rules responsible for producing narrative structures.
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 relies on probabilistic methods, where 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. In contrast, creating a coherent narrative requires a representation of the state of the world and a theory of action to maintain logical relationships between events, characters, as well as their goals and consequences.
Why is the modeling of 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 syntax itself. To correctly interpret an utterance, a machine must possess knowledge about the world, context, and the purposefulness of actions.
What fundamental change occurred in the approach to language processing during the transition from rule-based systems to statistical models?
The change consists of moving away from manually describing language structures and world knowledge using rules toward studying statistical regularities present in texts. Instead of designing explicit principles that generate a correct structure, systems began to model the probability of subsequent elements appearing in a sequence.
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 (e.g., a vowel or consonant) occurring 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 examines dependencies between characters and regularities invisible during reading, hermeneutics focuses on the meaning of the text.
What is information in the technical sense, and why does its transmission not imply an understanding of the content?
In a 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 mean understanding 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 regularities of the physical and social world. This approach, much like the philosophy of the later Wittgenstein, rejects essentialism in favor of analyzing linguistic relations and practices, although they differ in methodology.
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 the dictionary level to the level of word frequency 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 predicts the most likely continuation of text based on data distribution, rather than the relationship between a statement and the world. High correlation with truth results from the fact that texts describe reality; however, probabilistic coherence does not constitute a logical guarantee of truthfulness nor an epistemic justification.
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 linguistic 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?
This evolution involved a transition from simple transition probabilities between characters and manually designed features to learned, multidimensional contextual representations. Modern models, including the Transformer, have replaced the classic dictionary of concepts with a dynamic architecture of relations, where operational meaning emerges as a property of the representational structure.
How did technology move from dictionary definitions of words to modern neural representations of meaning?
This evolution consisted of a shift from essentialist definitions to relational representations, in which the meaning of a word results from its "company" and position in vector space. A key stage was the introduction of dense vectors (e.g., word2vec, GloVe), which replaced explicit rules with the automatic reconstruction of language structure from usage distributions, followed by the development of contextual models where the representation of a word is dynamically calculated depending on its surroundings.
How do transformer architecture and their training process allow a machine to simulate language understanding?
The simulation of language understanding results from the application of 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 massive data corpora, where the task of predicting the next token forces the creation of internal representations of syntactic, semantic, and pragmatic structures. This effect is amplified by scaling model parameters, data volume, and computational power.
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 shows that the pursuit of effective prediction can lead to the emergence of structures representing environmental features that are relevant from a cause-and-effect 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 knowledge is not discovered independently by the machine; rather, it was introduced into the data 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?
AI can be understood as a socio-technical system that is the result of accumulated collective labor (engineers, data creators, and users), rather than an autonomous entity. From a political economy perspective, these models constitute a form of objectified social knowledge that is transformed into technical capital controlled by infrastructure owners.
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 build the system. Such a narrative serves to diffuse responsibility and systematically hide the work of people involved in, among other things, data labeling or moderation, 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 to a transformation of the task structure within them. Automation takes over specific activities, while most professions still require human involvement in areas resistant to automation.
How do current legal regulations and economic structures in the EU address the issue of using human labor and data in the training of AI models?
The EU regulates platform work through Directive 2024/2831, which introduces principles of transparency and human oversight in algorithmic management. Regarding training data, the DSM Directive applies (an exception for text and data mining provided there are no author objections), as does the AI Act, which obliges model providers to comply with copyright law and publish summaries of the content used.
Who bears legal responsibility for errors made by AI systems, and can AI be recognized as a legal person?
Legal liability 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 compensatory obligations to human or organizational entities.
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 democratizing access to knowledge while simultaneously strengthening organizational surveillance over employees and market concentration. 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), rather than virtue stemming from character and practical wisdom (phronesis). These systems lack biological condition and emotions, such as pain or love, which excludes them from the community of mutual vulnerability that forms the foundation of human morality.
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 conscious 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, possessing 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 traits or moral language, 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. A subject is responsible for an act only when specific reasons actually guided their decision, and providing a narrative justification post factum is not sufficient.
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 birth of a synthetic moral person. Instead of attributing agency to the machine, alternative strategies should be applied, such as institutional design responsibility, risk insurance, or treating AI as an element of a corporate agent.
Can AI be recognized as a moral subject, and is simulating consciousness equivalent to possessing it?
AI can be recognized as a moral agent in a technical and functional sense, based on interactivity, autonomy, and adaptivity, without the need for free will. At the same time, behavioral linguistic competence is not evidence of consciousness, as one must distinguish between simulating experiences and the actual existence of a phenomenal state.
Why is the distinction between different types of AI agency crucial for establishing responsibility for errors in technological systems?
This distinction prevents the erroneous attribution of autonomous moral agency to AI systems, which could mask the responsibility of those designing and implementing the technology. It helps avoid situations where the personification of the machine or a fictional 'human-in-the-loop' is used to unjustifiably shift blame for errors.
