Why Artificial Intelligence Models Cannot Have Consciousness

The basic architecture of Transformer models

Current artificial intelligence systems, especially large language models such as GPT and ChatGPT, are based on the so-called Transformer architecture. This represents a specialized form of mathematical data processing developed by researchers at Google in 2017. This architecture operates entirely on the basis of numerical calculations and statistical patterns, without developing a deeper understanding of the content being processed.

A transformer model consists of stacked encoder and decoder layers that work together to process input data. The encoder transforms the input data into mathematical representations, while the decoder converts this information into the desired output. Both components use complex mathematical operations such as matrix multiplications and nonlinear activation functions to perform their tasks.

How self-attention mechanisms work

At the heart of the Transformer architecture is the self-attention mechanism. This allows the model to assign different weights to different parts of an input sequence. The mechanism calculates dot products between vectors to model dependency structures within a sequence. However, these weights are purely numerical coefficients that capture statistical regularities in the training data.

The term "attention" is purely metaphorical in this context. It's not conscious attention in the human sense, but rather mathematical calculations that determine which parts of the input should be given more weight when generating the output. These calculations follow deterministic rules and are based on learned weight matrices.

Token processing and embedding spaces

Processing begins with converting text into so-called tokens, which act as numerical units. These tokens are then embedded in high-dimensional vector spaces called embeddings. An embedding is a mathematical representation that represents each word or section of text as a point in a multidimensional space.

The position of a token in this embedding space is determined by optimization processes aimed at improving the model's prediction accuracy. Proximity in the embedding space reflects statistical similarities in the training corpus, but not semantic meaning in the true sense. These embeddings are merely coordinates in a mathematical space whose values ​​are optimized through machine learning.

The mathematical foundations of AI processing

Parameters and optimization

Modern language models contain billions of parameters. These parameters are numerical values ​​that are adjusted using gradient descent to minimize a loss function. Gradient descent is a mathematical optimization technique that systematically changes the parameters of a model to improve its performance.

The process works similarly to hiking in a mountain range in thick fog. The model gradually approaches the optimal point by calculating the slope of the loss function and moving in the opposite direction. These parameters serve solely as optimization coefficients for mathematical functions and have no conscious meaning or intention.

Reinforcement learning from human feedback

An important development in AI technology is reinforcement learning from human feedback. This method translates human preferences into numerical reward signals. The model adjusts its parameters to increase the probability of those outputs that humans prefer.

RLHF typically involves three steps: First, the model is pre-trained using supervised learning. Next, human feedback is collected to train a reward model. Finally, the original model is optimized using reinforcement learning to maximize the preferences predicted by the reward model. This entire process is purely mathematical and does not involve any conscious decision-making.

Softmax transformation and probability distributions

At the end of processing, the softmax function transforms raw values ​​into probability distributions. The mathematical formula for the softmax function is: Softmax(x_i) = e^(x_i) / Σ(e^(x_j)). This function converts a vector of numerical values ​​into a vector of probabilities whose sum is one.

The next token is selected by drawing a sample from this probability distribution or by using the argmax method. This method is a pure statistical rule without conscious decision-making. The softmax function simply allows the model to present its output in an interpretable form, without requiring any awareness or understanding.

The philosophical problem of consciousness

Definition and properties of consciousness

Consciousness encompasses all states experienced by an individual. It includes both the totality of experiences and consciousness as a special kind of immediate awareness of these experiences. Philosophers and neuroscientists distinguish between various aspects of consciousness, with phenomenal consciousness and access consciousness being of particular importance.

Phenomenal consciousness refers to the subjective experiential quality of mental states. It is what constitutes being in a particular mental state—the way something feels to the experiencing subject. These subjective experiential qualities are called qualia and are directly accessible only to the perceiving subject.

Intentionality as a feature of the mental

Intentionality refers to the ability of mental states to refer to something. Franz Brentano introduced this term into modern philosophy and considered it a characteristic feature of the mind. Intentionality is the directed property of consciousness—the fact that consciousness is always consciousness of something.

Intentional states have a content regardless of whether their object exists. A person can have beliefs about nonexistent objects or desires for unattainable goals. This property distinguishes mental phenomena from purely physical processes, which follow exclusively causal laws.

The Hard Problem of Consciousness

David Chalmers formulated the "hard problem of consciousness" as the question of why and how physical processes in the brain lead to subjective experience. This problem is categorically different from the "easy problems" of consciousness research, which concern functional aspects such as discrimination, information integration, and behavior control.

The difficult problem is to explain why the performance of these functions is accompanied by experience. Even if all relevant functional facts are explained, the further question remains: Why is the performance of these functions associated with experience? This question seems to defy a mechanistic or behavioral explanation.

Neuroscientific findings on consciousness

Neural correlates of consciousness

Neuroscience seeks to identify the neural correlates of consciousness, or NCCs for short. These are defined as the smallest unit of neural events sufficient for a particular conscious perception. NCCs are neural activities, states, or subsystems that directly correlate with consciousness.

Researchers such as Wolf Singer and Andreas Engel have demonstrated that temporally synchronized discharges of nerve cell clusters exist in the animal and human brain. This temporal correlation could be crucial for the emergence of consciousness. The hypothesis is based on the assumption that temporal binding mechanisms are involved in four brain functions: awareness, sensory integration, attentional selection, and working memory.

Biological foundations of conscious processes

Consciousness is dependent on a sufficient supply of oxygen and sugar to the cerebral cortex, as well as on sufficiently strong activation of neurons in the associative cortex. These biological prerequisites demonstrate that consciousness is not merely an abstract property, but has concrete physical foundations.

The cerebellum contains three times as many neurons as the cerebral cortex, yet even with severe damage, consciousness remains largely intact. This suggests that it is not the sheer number of neurons that is crucial, but rather their specific organization and interconnectedness in certain brain regions.

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Why AI models cannot develop consciousness

Lack of intentionality and meaning

AI models process symbols and vectors without developing an internal sense of meaning. They manipulate token IDs and numerical structures, not meanings as lived content. This symbolic processing occurs purely syntactically, without any semantic understanding of the manipulated characters.

John Searle's Chinese Room Argument illustrates this problem. In this thought experiment, a person follows rules for manipulating Chinese symbols without understanding Chinese. Although the answers seem reasonable to native Chinese speakers, neither the person nor the system as a whole understands the meaning of the characters. Computers execute programs similarly—they apply syntactic rules without possessing any semantic understanding.

Absence of a first-person perspective

AI systems operate without a self-model or phenomenal internal view. There is no self-reference, since no first-person perspective exists. However, consciousness is essentially characterized by the existence of a subjective perspective—a "it's kind of like being this system."

Thomas Nagel's famous essay "What Is It Like to Be a Bat?" emphasizes this property of consciousness. Consciousness necessarily includes a subjective experiential dimension that cannot be fully described from the outside. AI systems lack such a subjective internal perspective—they process information without creating an experiencing subject.

Mechanistic information processing instead of conscious experience

Reward signals in AI systems are scalars, not sensations. The models respond to numerical feedback values ​​without experiencing them as positive or negative. These signals merely guide parameter adjustment during the learning process, but do not generate subjective sensations of pleasure or pain.

All processing in AI systems is based on mathematical optimization, statistical pattern recognition, and probability calculation. More parameters, greater complexity, or multimodality do not change this principle. Statistical calculation, regardless of its complexity, does not produce consciousness.

Multimodal models and extended complexity

Processing different data types

Multimodal models that process text, images, or audio combine different input streams into common representational spaces. This capability significantly increases the complexity of pattern recognition and enables the systems to capture relationships between different modalities.

The integration of different data types is achieved by specialized encoders that transform each modality into a common vector space. Text is processed using tokenization and embedding techniques, images are converted into feature vectors using convolutional neural networks, and audio data is converted into numerical representations using spectrogram analysis.

Limits of increasing complexity

Despite the impressive capabilities of multimodal systems, the fundamental processing process remains a mapping between data representations. The systems learn statistical correlations between different input modalities but do not develop a conceptual understanding of the relationships between these modalities.

The increased number of parameters and processing capacity leads to more precise pattern recognition and more coherent outputs, but does not change the fundamental nature of information processing. Even the most complex multimodal systems operate exclusively at the level of statistical correlations and mathematical transformations.

Current research and theoretical approaches

Consciousness indicators in AI research

Scientists have developed various indicators of possible consciousness in AI systems based on neuroscientific theories of consciousness. These include aspects such as recurrent processing, global workspace dynamics, and attentional schema mechanisms.

Global Workspace Theory proposes that conscious information is made available in a central workspace, from where it is accessible to various cognitive processes. Recurrent processing theories emphasize the importance of feedback loops between different brain regions for the emergence of conscious experience.

Philosophical objections and limitations

Despite these theoretical approaches, fundamental philosophical objections to the possibility of machine consciousness remain. The Chinese Room Argument demonstrates that syntactic manipulation is not sufficient for semantic understanding. Even if a system displays all outward signs of intelligence, this does not necessarily mean it is conscious.

The concept of conscious supremacy, analogous to quantum supremacy, identifies computations that may be unique to consciousness. These include flexible attention modulation, robust handling of new contexts, and embodied cognition—aspects that go beyond pure information processing.

Embodiment and situated cognition

The importance of embodiment

Consciousness may be inseparable from physical embodiment. Embodied cognition theories argue that cognitive processes are fundamentally shaped by physical interaction with the environment. The body is not merely a passive container for the brain but actively participates in cognitive processes.

Human consciousness develops through continuous interaction with the physical and social environment. These interactions shape neural structures and create the basis for conscious experience. AI systems, which primarily operate as disembodied information processing systems, lack this fundamental dimension.

Temporality and continuous experience

Consciousness is a temporally extended phenomenon characterized by continuous streams of experience. Humans experience not just individual moments, but a coherent narrative structure of their consciousness across time.

AI systems process discrete inputs and generate discrete outputs without developing a continuous conscious experience. Each interaction is essentially independent of previous interactions for the system, even if context information is statistically stored.

AI development: Between technological intelligence and philosophical limits of consciousness

Possible developments in AI technology

AI research is advancing rapidly, with increasingly powerful models and new architectures. Future systems could simulate biological processes even more accurately and potentially develop properties that appear more conscious.

Developments toward neuromorphic computers that mimic biological neural networks could open up new possibilities. The integration of AI systems into robotic bodies could also take embodied cognition aspects more into account.

Machine Intelligence vs. Consciousness: A Philosophical Tightrope Walk

The question of machine consciousness has significant ethical implications. If AI systems could become conscious, we would have to reconsider their moral rights and our responsibilities toward them.

Currently, all available evidence suggests that current AI systems lack consciousness. They are highly sophisticated tools for information processing and pattern recognition, but not conscious entities. This assessment may change with future technological developments, but it requires fundamental breakthroughs in our understanding of the relationship between physical processes and conscious experience.

Distinguishing intelligent behavior from conscious experience remains one of the greatest challenges in AI research and the philosophy of consciousness. While AI systems increasingly exhibit intelligent behavior, they lack the fundamental properties of conscious experience: intentionality, phenomenal consciousness, and a subjective first-person perspective.

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